ACS Publications. Most Trusted. Most Cited. Most Read
Molecular Mechanism of pH-Induced Protrusion Configuration Switching in Piscine Betanodavirus Implies a Novel Antiviral Strategy
More

OR SEARCH CITATIONS

My Activity
Publications

Figure 1Loading Img
Download Hi-Res ImageDownload to MS-PowerPointCite This:ACS Infect. Dis. 2024, 10, 9, 3304-3319
  • Open Access
Article

Molecular Mechanism of pH-Induced Protrusion Configuration Switching in Piscine Betanodavirus Implies a Novel Antiviral Strategy
Click to copy article linkArticle link copied!

  • Petra Štěrbová
    Petra Štěrbová
    Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
    College of Life Science, National Tsing Hua University, Hsinchu 30044, Taiwan
    Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan
    More by Petra Štěrbová
  • Chun-Hsiung Wang
    Chun-Hsiung Wang
    Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan
    More by Chun-Hsiung Wang
  • Kathleen J. D. Carillo
    Kathleen J. D. Carillo
    Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan
    More by Kathleen J. D. Carillo
  • Yuan-Chao Lou
    Yuan-Chao Lou
    Biomedical Translation Research Center, Academia Sinica, Taipei 11529, Taiwan
    More by Yuan-Chao Lou
  • Takayuki Kato
    Takayuki Kato
    Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japan
    More by Takayuki Kato
  • Keiichi Namba
    Keiichi Namba
    Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japan
    More by Keiichi Namba
    Orcidhttps://orcid.org/0000-0003-2911-5875
  • Der-Lii M. Tzou
    Der-Lii M. Tzou
    Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan
    More by Der-Lii M. Tzou
    Orcidhttps://orcid.org/0000-0002-3755-8230
  • Wei-Hau Chang*
    Wei-Hau Chang
    Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
    Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan
    Genomics Research Center, Academia Sinica, Taipei 11529, Taiwan
    Institute of Physics, Academia Sinica, Taipei 11529, Taiwan
    *Email: [email protected]
    More by Wei-Hau Chang
    Orcidhttps://orcid.org/0000-0002-2612-850X
Open PDFSupporting Information (4)

ACS Infectious Diseases

Cite this: ACS Infect. Dis. 2024, 10, 9, 3304–3319
Click to copy citationCitation copied!
https://doi.org/10.1021/acsinfecdis.4c00407
Published August 1, 2024

Copyright © 2024 The Authors. Published by American Chemical Society. This publication is licensed under

CC-BY 4.0 .

Abstract

Click to copy section linkSection link copied!
High Resolution Image
Download MS PowerPoint Slide

Many viruses contain surface spikes or protrusions that are essential for virus entry. These surface structures can thereby be targeted by antiviral drugs to treat viral infections. Nervous necrosis virus (NNV), a simple nonenveloped virus in the genus of betanodavirus, infects fish and damages aquaculture worldwide. NNV has 60 conspicuous surface protrusions, each comprising three protrusion domains (P-domain) of its capsid protein. NNV uses protrusions to bind to common receptors of sialic acids on the host cell surface to initiate its entry via the endocytic pathway. However, structural alterations of NNV in response to acidic conditions encountered during this pathway remain unknown, while detailed interactions of protrusions with receptors are unclear. Here, we used cryo-EM to discover that Grouper NNV protrusions undergo low-pH-induced compaction and resting. NMR and molecular dynamics (MD) simulations were employed to probe the atomic details. A solution structure of the P-domain at pH 7.0 revealed a long flexible loop (amino acids 311–330) and a pocket outlined by this loop. Molecular docking analysis showed that the N-terminal moiety of sialic acid inserted into this pocket to interact with conserved residues inside. MD simulations demonstrated that part of this loop converted to a β-strand under acidic conditions, allowing for P-domain trimerization and compaction. Additionally, a low-pH-favored conformation is attained for the linker connecting the P-domain to the NNV shell, conferring resting protrusions. Our findings uncover novel pH-dependent conformational switching mechanisms underlying NNV protrusion dynamics potentially utilized for facilitating NNV entry, providing new structural insights into complex NNV-host interactions with the identification of putative druggable hotspots on the protrusion.

This publication is licensed under

CC-BY 4.0 .
  • cc licence
  • by licence
Copyright © 2024 The Authors. Published by American Chemical Society

Subjects

what are subjects

Keywords

what are keywords
Viral infections remain a major global health and economic threat. (1,2) Viruses are virtually nanoscale organic materials that can infect and replicate themselves inside host cells by using their own or host cell’s replication apparatus. (3) During an infection cycle, the viral genome encapsulated inside a protein capsid is delivered to a designated site in the host cell where it can be replicated, prior to which the viral genome must be released from the protein capsid. This process is usually enabled by profound structural alterations of the viral capsid induced by external stimuli, such as low pH, ligand binding, or interaction with the cell receptor. (4−7) Investigating structures of a virus capsid and their induced changes is crucial for understanding the pathological process and may offer clues to design antiviral drugs or vaccines.
Nervous necrosis virus (NNV) has emerged as one of the most widespread fish viruses to threat fish farming worldwide. (2,8) Outbreaks of the viral neuropathies and retinopathies caused by NNV result in almost 100% mortality of infected stock, with larval and juvenile fish being the most susceptible to NNV infections. Since the first characterization of the virus from larval striped jack (Pseudocaranx dentex) in the 1990s, (9) NNV infections have been reported in more than 120 marine and freshwater fish. (10) Due to the lack of an effective vaccine, NNV infections continue to severely damage the aquacultural economy.
NNV is a small nonenveloped RNA virus in the Genus Betanodavirus and Family Nodaviridae. In Nodaviridae, there are two genera: alphanodaviruses primarily infecting insects and betanodaviruses whose natural hosts are fish. (11,12) An NNV particle encapsulates two positive-sense single-stranded RNA molecules: RNA1 (3.1 kilobases, kb) encodes an RNA-dependent RNA polymerase, whereas RNA2 (1.4 kb) encodes a single structural capsid protein (CP). (11) A subgenomic RNA (RNA3), comprising part of the 3′ end of RNA1, has been identified as encoding a nonstructural protein B2 acting as an antagonist to host RNA interference. (13)
NNV has been demonstrated to enter host cells primarily via endocytic pathway. (14−16) During this process, viruses are internalized into host cells via small vesicles, eventually arriving at late endosomes or endolysosomes for their destruction. The decrease of the pH inside an endosome caused by endosome acidification may serve as a trigger for the release of the virus genome into the host cytoplasma, (17−19) as exemplified by Flock House Virus (FHV), a nonenveloped virus in Genus Alphanodavirus. Exposure of FHV to low endocytic pH induces structural changes of the FHV particle and release of its membrane-interacting γ peptide (4.4 kDa) for disrupting the endosomal membrane. (20,21) Although details of low-pH-induced changes during endocytosis are well-described for FHV, those mechanisms are unlikely to be applicable to betanodaviruses since not only the CP sequences of alphanodaviruses and betanodaviruses share low similarity but also the capsid structures of the two differ markedly─unlike betanodavirus, alphanodaviruses do not have conspicuous surface protrusions. (12) These protrusions are evidently powerful molecular structures used by betanodavirus to invade host cells.
A high-resolution structure of NNV capsid was obtained from crystals of Grouper NNV (GNNV) virus-like particles (VLPs) grown in a neutral condition (pH 7.2) that diffracted to near atomic resolution (3.6 Å, PDB 4WIZ). (22) The conditions used for GNNV VLP crystal growth are similar to those when the virus attaches to the host cell. The structure of GNNV VLP unveils GNNV capsid is a T = 3 icosahedral particle with a diameter of ∼35 nm and delineates the CP as consisting of three domains: an N-terminal arm, a shell domain (S-domain), and a protrusion domain (P-domain). The S-domain comprises an eight-stranded β-sheet to form a canonical “jelly-roll” structure as the building unit of virus capsid; three P-domains from contiguous CPs in one asymmetric unit are located outside of the capsid, presenting a protrusion at the quasi-3-fold axis. As the P-domain is connected to the S-domain via a flexible linker and thereby not well-resolved, it has been separately expressed and crystallized in a weakly acidic condition (pH 6.5) with an overwhelming amount of calcium ions (0.2 M). The structure of the isolated P-domain was resolved to an atomic resolution (1.2 Å, PDB 4RFU). In the crystal, three P-domain molecules assemble into a compact trimer. Notably, the trimer in the crystal is stabilized by two calcium ions coordinated by three sets of 273DxD275 motifs from neighboring subunits. As NNV protrusions play a central role in NNV infectivity (23,24) and determining NNV host specificity, (25−27) this crystal structure (22) of the P-domain has become the structural basis for understanding NNV infections. Accordingly, the configuration of the protrusion is believed to be compact and static when NNV engages with a host receptor. (28) However, comparison of a high-quality cryo-EM map of GNNV VLP generated previously (29) against the respective crystal structure (22) shows that the protrusions may gain dynamics as the virus particles are in solution.
As cryo-EM has advantages over X-ray crystallography in easily accessing to solution conformations (30) and aqueous conditions mimicking physiological environments with changes in pH, ions, (31) or temperature, (32) we herein used cryo-EM to investigate the pH dependence of GNNV structures. The aqueous conditions mimicking those during the route of endocytosis were used to embed GNNV particles prior to their freezing. Specifically, we examined three pH points: weakly alkaline (pH 8.0) for host cell docking, weakly acidic (pH 6.5) in the early endosome, and acidic (pH 5.0) in the late endosome. Our cryo-EM results show the protrusions on GNNV adopt erect/loose configuration at pH 8.0 and 6.5 but become prone/compact at pH 5.0. To delve into the atomic details underlying such dynamics, we separately expressed the GNNV P-domain for solution analysis. Strikingly, we found the P-domain adopted a monomeric form at pH 7.0 but could convert to a trimer at pH 5.0. Further NMR structural determinations of the P-domain (pH 7.0) yielded the first solution structure of the P-domain, identifying a region of 20 amino acids (aa; 311–330) that adopts a long flexible loop, contrasting with it being interrupted by a short β-strand (aa 323–326) in the crystal structure. (22) We used this solution structure as the basis for molecular dynamics (MD) simulations and discovered that this loop region can undergo acid-induced disorder-to-order transitions, inducing the P-domain to self-assemble into a compact trimer. In this study, we demonstrate that the NNV capsid acts as a pH-responsive nanoscale machine and unravels the molecular mechanism underlying the pH-dependent dynamics of GNNV protrusions. These findings provide new structural insights into the operation of NNV during its infection and imply druggable hotspots on it for guiding structure-based design of vaccines or inhibitors against NNVs.

Results

Click to copy section linkSection link copied!

Cryo-EM Reveals That GNNV Protrusions Are Dynamic Entities

Close inspection of a high-quality cryo-EM map of a GNNV VLP (29) against the crystal structure (22) indicates that the protrusions adopt a morphologically less compact form in solution. It is unclear whether this subtle discrepancy is attributable to crystal packing or due to differences in the buffer conditions employed in these two studies (pH 7.2 for the crystal (22) and pH 8.0 for cryo-EM (29)). We were thus prompted to investigate potential GNNV structural changes under different pH conditions with cryo-EM (Table S1). We immersed native GNNV virions or respective VLPs in buffers of different pH values corresponding to the weakly alkaline condition (8.0), weakly acidic condition (6.5), and acidic condition in late endosomes (5.0). Cryo-EM images (Figure S1) revealed a spikier appearance of the GNNV VLPs at pH 8.0 and 6.5 than at pH 5.0. By using three-dimensional (3D) reconstruction with icosahedral symmetry, we determined cryo-EM structures of GNNV VLPs at pH 8.0, 6.5, and 5.0 (Figure 1), as well as virions at pH 6.5 and 5.0 (Figure S2), with overall resolutions in the range of 2.82–4.36 Å (Figures S3, S4 and Table S1). These results indicate that lowering the pH prompted GNNV particles to undergo gross structural alteration (Figures 1 and S2), with VLP and virion structures at the same pH being virtually indistinguishable (Figure S2), affirming the use of VLPs as surrogates of virions for vaccine development. (33)

Figure 1

Figure 1. GNNV atomic model and cryo-EM structures in three different pH environments. (A) Atomic models of GNNV S-domain (PDB 4WIZ) and P-domain (PDB 4RFU). (B) Surface views of GNNV virus-like particles at pH 8.0 (left panel), 6.5 (middle panel), and 5.0 (right panel). The views are colored with a heatmap according to the local radius. (C) Conformational changes of the protrusion in response to the change in pH: 8.0 (left panel), 6.5 (middle panel), and 5.0 (right panel). The protrusions rotate clockwise and shift ∼5 Å toward the capsid shell as the pH decreases. (D) Enlarged views of pores (at 5- and 3-fold axis) in the capsid shell surface for different pH environments. The size of a pore at each pH condition is indicated. To highlight the pore at the 3-fold axis, the densities underneath are colored in light purple.

High Resolution Image
Download MS PowerPoint Slide
Notably, the diameter of the GNNV particle under acidic conditions (pH 5.0) was smaller (370 Å) than under weakly alkaline (pH 8.0) or neutral (pH 6.5) conditions (380 Å) (Figure 1A). This reduced size is due to an ≈5 Å displacement of the protrusions toward the capsid shell (Figure 1B), with radial density and cross-sectional analyses showing that the shell size remained virtually unchanged (Figures S5 and S6). Moreover, the shell morphology was relatively unperturbed by the acidic conditions (Figure 1C). Closer examination of the protrusions revealed that not only were they positioned closer to the shell in the acidic pH but they also became more compact and were rotated clockwise by ∼20° (Figure 1B, Movies S1 and S2). This repositioning prompted our hypothesis that the linker between the P-domain and S-domain is malleable (Movie S3), prompting further investigation of the detailed mechanism (Figures S3 and S4). Thus, our cryo-EM imaging has revealed that GNNV structures are pH-responsive for the first time, with protrusions on GNNV adopting an erect position under weakly alkaline and neutral conditions but a prone position under acidic conditions.

NMR Spectra Indicate That the P-Domain of GNNV Contains Highly pH-Sensitive Regions

To investigate the behavior in solution of the protrusions as isolated entities instead of on the GNNV capsid, we cloned and expressed the P-domain (aa 221–338) plus the flexible linker (aa 214–220, linking the P-domain and S-domain) from GNNV (GNNV-P) for structural analysis using NMR spectroscopy and sedimentation velocity analytical ultracentrifugation (AUC). GNNV-P had a molecular weight of 13.8 kDa. Notably, neither the N-terminal linker (aa 214–219) nor the C-terminal region (aa 337–338) were observed in a previously generated X-ray structure of GNNV-P (PDB 4RFU), likely due to their high flexibility. (22) In the buffer we used for solution analysis, we excluded divalent ions or small molecules used to induce the crystal formation. (22)
We previously collected two-dimensional (2D) 1H–15N heteronuclear single quantum coherence (HSQC) spectra for GNNV-P at pH 7.0. (34) To explore the pH dependence of GNNV-P, we conducted a pH titration experiment to cover a wide range of pH values (7.0, 6.0, 5.8, 5.5, 5.2, and 5.0; see Figure 2A for pH 7.0 and 5.0). Our pH titration experiment revealed signatures of pH-sensitive residues, the peaks of which disappeared as the pH decreased (Figure 2B). Moreover, we observed a significant line broadening with the reduction of SNR for the 1H–15N HSQC spectra at pH 5.0 (Figure 2A), indicating that GNNV-P may undergo a low-pH-induced conformational change or oligomerization, where the latter would induce issues of molecular tumbling and spin relaxation to degrade the spectral quality. To test if this pH-dependent property is reversible, we monitored the 2D HSQC spectra by titrating at increasing pH from 5.0 to 7.0, which showed that the pattern for each pH value could be reproduced, indicating that the pH-dependent changes are reversible (data not shown).

Figure 2

Figure 2. Effects of pH on GNNV-P in solution. (A) 1H–15N HSQC spectra of GNNV-P recorded at pH 7.0 (blue) and 5.0 (red). (B) Small box in panel (A) is enlarged. The six peaks, in green, at pH 7.0 are from Y279, L263, I222, I323, Q322, and R321, and the peaks, in red, at pH 5.0 are from E217 and I222. Y279, L263, I323, and Q322 disappear in the 1H–15N HSQC as the pH decreases. (C) Residue chemical shift perturbations (CSPs) by comparing 15N-labeled (pH 7.0) and 13C-, 15N-, and D-labeled (pH 5.0) HSQC spectra. The pH-sensitive Regions I (L263–Y279), II (W301–N303), and III (V312–V327) are highlighted in orange, yellow, and green, respectively. Residues with CSPs >2 standard deviations in pH-sensitive Regions I and III are highlighted in red and dark green, respectively. (D) pH-sensitive residues presented in panel (C) are mapped onto a surface representation of the GNNV-P crystal structure, (22) with the same color scheme as in panel (B).

High Resolution Image
Download MS PowerPoint Slide
To improve the quality of the pH 5.0 spectra, we utilized deuterium (D) labeling since replacing the 1H nuclei with D (2H) can suppress strong 1H–1H dipolar interactions to mitigate spin diffusion. The resulting uniformly 13C-, 15N-, and D-labeled GNNV-P yielded a well-resolved 2D HSQC pattern at pH 5.0 (Figure S7) as those peaks missing in the 1H–15N HSQC spectra at pH 5.0 reappeared, allowing us to assign backbone resonances for further chemical shift perturbations (CSPs) analysis. We identified 98.3% of the expected amide 1HN and 15NH resonances (115 out of 117 nonproline residues), with the exception of residues T214 and L325 (both of which precede prolines in the sequence). Moreover, backbone 13Cα, 13Cβ, and 13C′ resonances were assigned with 98.2–98.4% completeness (see Notes for Data Availability information).
Chemical shift perturbations (CSPs) reflect changes in the chemical environment of the atomic nuclei. These changes can arise from protein interactions or conformational changes. Mapping residues displaying CSPs onto a protein structure can help identify interaction sites. (35) In principle, CSPs can be analyzed for each residue at each pH titration point (Figure 2B), but we opted to analyze GNNV-P CSPs by comparing the chemical shifts recorded in the 15N-labeled HSQC spectra at pH 7.0 and those in the deuterium (D)-labeled HSQC spectra at pH 5.0 (Figures S7), representing the initial and final points of our pH titration experiment (see Figure S8). With nearly all of the chemical shifts assigned for the 1HN and 15NH data at pH 7.0 (34) and 5.0 (Figure S8), we applied CSPs analysis (36) to identify residues displaying significantly perturbed chemical shifts relative to pH 7.0, i.e., CSPs greater than one standard deviation from the mean. In Figure 2C, we show the backbone CSPs per residue based on amide nitrogen and proton chemical shifts. Intriguingly, residues displaying pronounced CSPs are clustered into three regions: Region I (L263–Y279), Region II (W301–N303), and Region III (V312–V327). Regions I and III are spatially proximal to each other, as determined by mapping the pH-sensitive residues onto the GNNV-P crystal structure (22) (Figure 2D). Since residues with pronounced CSPs are likely involved in protein interactions and/or conformational changes, (35) we speculate that these three regions undergo low-pH-induced interactions or conformational changes. Notably, these regions in GNNV-P that confer pH sensitivity coincide with three β-strands in the crystal structure. (22)

Determination of the Structure of GNNV-P in Solution at pH 7.0 Reveals That aa 311–330 Form a Long Flexible Loop

Based on the X-ray crystal structure determined under neutral conditions, (22) GNNV-P was assumed to form a trimer in solution at pH 7.0. Surprisingly, our AUC experiments showed that GNNV-P molecules predominantly adopted a monomeric form at pH 7.0 and assumed only the trimeric form in an acidic condition (pH 5.0) (Figures 3A and S9). Accordingly, we set out to determine the solution structure of GNNV-P based on the HSQC spectra at pH 7.0 (Figure 2A), which exhibited well-dispersed cross-peaks with sharp resonances, indicative of a well-folded GNNV-P structure in solution.

Figure 3

Figure 3. Structure of GNNV-P determined in solution at neutral pH. (A) Sedimentation velocity analytical ultracentrifugation (SV AUC) data of GNNV-P obtained at pH 7.0 (black), 6.0 (blue), and 5.0 (red) fitted to a continuous sedimentation coefficient distribution c(s) model. Sedimentation coefficients and the molecular weight determined by SEDFIT are denoted above each peak. (B) Backbone (N, Cα, and C′) superimposition of the ensemble of 20 low-energy conformations. The N-terminal is colored blue, and the C-terminal is colored red. (C) Cartoon representation of the GNNV-P solution structure with β-strands and α-helices highlighted in red and blue, respectively. (D) Superimposition of the GNNV-P structure determined by NMR at neutral pH (pink) and by the X-ray crystal (PDB 4RFU, blue). (E) Schematic representation of GNNV-P secondary structures, as determined by NMR (pH 7.0) and X-ray crystallography (pH 6.5). β-strands are displayed as black arrows and α-helices as gray barrels, with bordering residues indicated.

High Resolution Image
Download MS PowerPoint Slide
From the 1H, 13C, and 15N backbone and side-chain chemical shift assignments for 122 GNNV-P residues (except for Thr214, Leu228, and Gly270) at pH 7.0, (34) we calculated the solution structure of GNNV-P using nuclear Overhauser effect (NOE)-derived 1H–1H distance restraints, chemical shift-derived dihedral angle restraints, and hydrogen bonds inferred from hydrogen–deuterium exchange (Figure S10) (see the Supporting Information for NMR structural determination details). The respective calculation statistics are summarized in Table S2.
In Figure 3B, we present an ensemble of 20 low-energy solution structures, with a representative structure illustrated in Figure 3C. This solution structure of GNNV-P is cone-shaped, similar to the crystal structure (PDB entry 4RFU; see superimposed structures in Figure 3D). The folded GNNV-P tertiary structure comprises a number of secondary structures including antiparallel β-strands and α-helixes, with the order βA-α1-α2-βB-βC-βD-βE-βF-βG (Figure 3E). However, close comparison of the solution and crystal structures revealed a subtle difference; i.e., GNNV-P in solution exhibits a long flexible F′–G′ loop for aa 311–330 (Figure 3E), but this loop is interrupted by a short β-strand (aa 323–326) in the crystal structure (Figure 3E). Interestingly, this loop lies in the trimeric interface of the crystal structure (Figure S11).

MD Simulations Predict That GNNV-P Forms a Trimer at pH 5.0 with Critical Subunit Interactions Perturbed by Site-Directed Mutagenesis

Our AUC experiments clearly showed that GNNV-P molecules adopt a monomer at pH 7.0 and a trimer at pH 5.0 (Figure 3A). Note that the molecular weight for the pH 6.0 profile (Figure 3A) could not be readily assigned as it likely represents species of heterogeneity stemming from monomer–oligomer exchange (see Figure S9). Identification of subunit interactions for stabilizing the trimer using traditional NMR structural determination is faced with challenges for homooligomers. Thus, we employed an in silico approach to the subunit interactions in the GNNV-P trimer. We used molecular dynamics (MD) simulations, a well-established method for understanding protein dynamics, to track GNNV-P structural transitions from the monomer to the trimer. To perform this experiment, we utilized our GNNV-P solution structure determined at pH 7.0 and adjusted the protonation state of amino acid side-chain groups to pH 5.0. Three separate GNNV-P molecules without any symmetry imposed on their spatial arrangement were used as an initial point for our MD simulations, which resulted in a stable trimer (Figure 4A,B), as evidenced by the overall root mean square deviation (RMSD) quickly reaching a stationary phase along the MD time trajectory (Figure S12).

Figure 4

Figure 4. MD-predicted structure of the GNNV-P oligomer at low pH. (A) Top and side view cartoon representations of the GNNV-P trimer formed under acidic pH conditions, as determined by molecular dynamics (MD) simulations. Chain A (blue), chain B (pink), and chain C (green). (B) Intermolecular interactions between neighboring GNNV-P units in the trimer. Residues at the trimeric interface are depicted as yellow sticks, and interactions are shown as dashed magenta lines. (C–E) Details of trimeric interface interactions between GNNV-P chains A and B, as boxed in panel (B).

High Resolution Image
Download MS PowerPoint Slide
In this complex, the three GNNV-P molecules tightly associated with one another via identical interfaces between neighboring A/C, A/B, and B/C subunits, with the total buried interfacial area estimated by PDBePISA as 1560 Å2. (37) Moreover, 3-fold rotational symmetry emerged for the arrangement of the three GNNV-P molecules, largely matching the structural arrangement of GNNV molecules in the crystal model (Figure S13). (22) In addition to predicting trimer formation, our MD simulations revealed specific interactions at the inter-GNNV-P level that could be readily verified against the NMR experimental data or tested by using mutagenesis.
Therefore, we searched the MD-predicted structure for residues in the trimer interface, guided by the pH-sensitive regions we had already identified (Figure 2C,D). We detected three pairs of contacts between the side-chains of one subunit and those of a neighboring subunit: Y315 with D266/R276/Q322 (Figure 4C); Q225 with W301/N303 (Figure 4D); and W280 with L324/P326 (Figure 4E). Notably, some of these contacts are disfavored at neutral pH as they are sterically hindered by the F′–G′ loop. Among the three interacting pairs, W280 with the L324/P326 pair seems to be relatively strong as the interactions are dominated by hydrophobic and CH–π interactions, whereas the other two pairs represent either polar interactions or primarily hydrogen bonds. To establish if these three MD-predicted inter-GNNV-P interactions are plausible, we generated three sets of single alanine mutants and assayed their impacts on GNNV-P oligomerization by means of AUC. The first set of mutations encompassed L324A, P326A, W280A, and I323A. As shown in Figure S14, the L324A, P326A, or W280A mutations abolished oligomer formation at low pH (5.0), indicating that this MD-predicted pair of interactions is indeed critical to stabilizing the trimer. As a control, we also mutated I323, a residue in the same pH-sensitive region, but that does not exhibit intermolecular interactions in the MD-predicted structure nor exhibited significant CSPs. As expected, the I323A mutation did not interfere with the formation of GNNV-P oligomers at low pH (5.0). AUC data for the second set of mutations showed that Q322A impaired oligomerization at pH 5.0, whereas R276A still permitted trimer formation, implying that residue Q322 is critical, whereas R276 is not. Regrettably, we were not able to perform AUC experiments for the third set of mutations as the W301A mutant protein failed to fold correctly. Nevertheless, our mutagenesis study verified the importance of the inter-GNNV-P interactions predicted by MD simulations, validating the MD simulation results. We also noticed a conserved histidine at position 281 of GNNV-P (Figure S15) and postulated that it might serve as a histidine switch through pH-induced protonation/deprotonation, as utilized for enveloped virus activation under acidic conditions. (38) However, the AUC of a H281Y mutant ruled out that this histidine is involved in low-pH-induced GNNV-P oligomerization (Figure S14). The overview of GNNV-P mutants and their effect on GNNV-P low-pH-induced oligomerization is summarized in Table 1.
Table 1. List of GNNV-P Mutants
residueintermolecular interactions at pH 5.0mutationAUC results (pH 5.0)
Region I
R276H-bond (Y315)R276Aoligomer
W280CH–π (P326)W280Amonomer
C–π (P326)
hydrophobic (L324)
H281 H281Yoligomer
Region II
W301aH-bond (Q225)W301AN/A
Region III
Q322H-bond (Y315)Q322Amonomer
hydrophobic (I300)
I323 I323Aoligomer
L324hydrophobic (W280)L324Amonomer
P326CH–π (W280)P326Amonomer
C–π (W280)
a

Mutation resulted in a misfolded protein.

NMR Signatures of GNNV-P Conformational Change at Low pH

Apart from capturing GNNV-P trimerization under acidic conditions, our MD simulations indicated a change in the GNNV-P secondary structure at pH 5.0. This conformational change can also be verified against the NMR experimental data. Compared to backbone CSPs, Cα and Cβ CSPs are more sensitive to a secondary structure change. In searching for residues with pronounced Cα and Cβ CSPs induced by low pH (Figure 5A), we identified a subregion within Region III (aa 312–327) (Figure 5A), exhibiting an increased β-strand propensity at pH 5.0. This subregion is composed of residues P320-L324, and it coincides precisely with an MD-predicted region that converted to a β-strand at pH 5.0. This region in the MD-predicted structure formed intramolecular hydrogen bonds with residues S264 and D266 (Figure 5B), two residues that belong to β-strand C (aa 263–267) (Figure 3E). The impact of these contacts is reflected by the pronounced CSPs of Region I (amino acids 263 and 279) (Figure 3A). Thus, details from our MD simulations could be further validated by CSP data extracted from NMR experiments. Of note, the β-strand of P320-L324 is stabilized by its association with β-strand C; these two strands together with β-strand D form a three-stranded antiparallel β-sheet in the GNNV-P structure at pH 5.0 obtained by MD simulations.

Figure 5

Figure 5. GNNV-P undergoes a low-pH-induced conformational change. (A) Secondary structure propensities of GNNV-P at pH 7.0 and 5.0, calculated using secondary chemical ΔCα–ΔCβ shifts and plotted against the amino acid sequence. The Q320–P324 region showing an increased β-strand propensity at pH 5.0 is highlighted in gray. The secondary structures are shown above each chart, with arrows and cylinders representing β-strands and α-helices, respectively. (B) MD simulations revealed the formation of a short β-strand (comprising residues Q322–L324) within the F′–G′ loop at low pH. Hydrogen bonds between R321–L324 and D266–S264 were identified as stabilizing this region. (C) Superimposition of GNNV-P at pH 7.0 (NMR structure, blue) and 5.0 (MD model, red), showing the open and closed conformations of the F′–G′ loop, respectively. (D) 1H–15N HSQC spectra of uniform U–D-, 13C-, and 15N-labeled GNNV-P at pH 5.0. The regions with duplicate signals for residues 216–220 in the linker region are marked with black boxes. (E–G) Details of the uniform U–D-, 13C-, and 15N-labeled GNNV-P 1H–15N HSQC spectra highlighted by black boxes in panel (D). (H) Sequence of the N-terminal linker T214-P221 and a stick representation of the linker region with proline residues colored red and residues presenting duplicate NMR signals colored blue. (I) Schematic representation of cis–trans isomerization of an Xaa–Pro peptide bond. Proline residues can switch between the trans (blue) and cis (red) conformations.

High Resolution Image
Download MS PowerPoint Slide
Given the aforementioned conformational change in the F′–G′ loop at low pH (Figure 3E), the space between the F′–G′ and C′–D′ loops collapsed (Figure 5C). Consequently, a pocket in GNNV-P open at neutral pH becomes closed at acidic pH, rendering individual GNNV-P units more compact, as evidenced by a decrease in the surface area from 14053 Å2 (pH 7.0) to 12160 Å2 (pH 5.0) calculated using Pymol. (39) Since the β-strand of P320–L324 is situated in the center of the trimer (Figure S13), its switching from the loop configuration mitigates inter-GNNV-P steric hindrances that, together with neutralization of the interfacial repulsing electrostatic potential at pH 5.0 (Figure S16), results in a compact trimer. This detailed mechanism nicely explains the low-pH-induced compaction of protrusions on GNNV particles observed by cryo-EM (Figure 1A,B).
To understand the linker malleability proposed by our cryo-EM observations, we searched the NMR data for residues in the linker region (214–220 aa). During our assignments of NMR spectra for GNNV-P at pH 5.0, we observed duplicate NMR signals (Figure 5D–G) for residues preceding the proline residue (P221) situated at the junction between the linker and the P-domain. This result indicates that the linker gradually shifts among different conformations, possibly due to the peptide bond of P221 undergoing cis-trans isomerization at pH 5.0 (Figure 5H,I). Our MD simulations support this conformational change of the linker around P221 (Figure S17). However, due to peptidyl bond isomerization being a slow process, directly capturing the switching from a trans to cis conformation was not possible with our MD simulations. Although the trans configuration is favored for peptide bonds, interconversion between the cis and trans configurations for a Xaa–Pro peptide bond has been previously demonstrated. (40) In addition, an increased population of P221 in cis conformation at low pH has also been inferred by analyzing our 13C chemical shifts data using the PROMEGA Web server. (41) An additional NMR signature for the malleability of the linker is evidenced by the signal for residue A220 in 2D HSQC spectra at pH 5.0, which exhibited various Cα and Cβ chemical shifts in the HNCACB spectra (Figure S18). Taken together, our NMR results provide spectral evidence to support that the linker between the P-domain and S-domain of GNNV is malleable.

Interactions of GNNV-P with Host Surface Glycan Receptors

Upon encountering a host cell, the protrusions of GNNV-P represent an immediate viral structural motif for the virus to engage with receptors on the host cell surface. Cell surface glycans such as sialic acids have been reported as common cell receptors for distinct NNVs. (42) By using molecular docking analysis, Nishizawa et al. identified interactions between the terminal moiety of sialic acid and a GNNV protrusion. (28) That analysis was based on the then available three-dimensional structure of the GNNV P-domain as a compact trimer obtained from X-ray crystallography. (22) However, our NMR solution structure indicates that the GNNV P-domain adopts a monomeric form under the pH conditions when GNNV engages with host cell surfaces.
Thus, using our P-domain structure at pH 7.0, we could reevaluate potential modes of interaction between sialic acids and GNNV-P. To do so, we conducted a molecular docking analysis with our solution structure of GNNV-P (pH 7.0) against two sialoside isomers, i.e., Neu5Ac-(α2,3)-Lac and Neu5Ac-(α2,6)-Lac. Remarkably, as shown in Figure 6A, the terminal Neu5Ac of both ligands could be inserted deep into the pocket formed by the F′–G′ loop (Figure 5C). The N-acetyl chain of Neu5AC was located inside the binding pocket, and the N-acetyl methyl group was oriented toward the hydrophobic part of this pocket (Figure 6B,C), with Neu5Ac binding being further stabilized by a number of interactions with the conserved residues lining the pocket. Those contacts include hydrogen bonds between R261 and the sialic acid carboxylate and hydroxyl groups of the penultimate galactose. The N-acetyl group of Neu5Ac-(α2,3)-Lac was stabilized by hydrogen bonds with S264 and Q319, and another hydrogen bond formed between the hydroxyl group of the penultimate galactose and Q322 (Figure 6B). For Neu5Ac-(α2,6)-Lac, sialic acid formed additional interactions with S264, Q322, and I323 (Figure 6C).

Figure 6

Figure 6. Model of Neu5Ac-Lac binding to GNNV-P at neutral pH. (A) Surface representation of the GNNV-P monomer in complex with Neu5Ac-(α2,3)-Lac (green) and Neu5Ac-(α2,3)-Lac (yellow). (B) Details of Neu5Ac-(α2,3)-Lac binding to GNNV-P in the open pocket conformation. (C) Details of Neu5Ac-(α2,6)-Lac binding to GNNV-P in the open pocket conformation. In panels (B) and (C), residues interacting with Neu5Ac-Lac are shown as sticks, and selected contacts between GNNV-P and Neu5Ac-Lac are represented by pink dashed lines.

High Resolution Image
Download MS PowerPoint Slide
Together, these specific interactions endow a strong binding affinity between GNNV-P and Neu5Ac-(α2,3)-Lac or Neu5Ac-(α2,6)-Lac, with free energies of −6.42 and −6.48 kcal/mol, respectively. Significantly, those interactions could be augmented multivalently by the three GNNV P-domains that form a viral capsid protrusion. As a control, we also performed a molecular docking analysis using our trimeric structure (pH 5.0) against the same two sialoside isomers. Since the pockets in the trimeric structure are closed at this pH due to a conformational switching in the F′–G′ loop, Neu5Ac-(α2,3)-Lac or Neu5Ac-(α2,6)-Lac could no longer insert into the pocket. Instead, they attached to a cavity at the apex of the GNNV-P trimer (Figure S19), in agreement with the docking results of Nishizawa et al. (28) It is noticed that this cavity was referred to as a “pocket” by Nishizawa et al., (28) which differs from the pocket uncovered herein.

Discussion

Click to copy section linkSection link copied!
Here, we provide the first glimpse of the structural changes of GNNV particles and reveal that the changes are programmable by external pH conditions. The drastic changes reside with the protrusion, and they are of physiological relevance as the sampled pH values mimic the stages in the journey of virus entry via the endocytic pathway. We next report the first solution structure of the GNNV P-domain (GNNV-P), in which an unexpected feature of structural nuance in the GNNV P-domain is revealed: a long and continuous flexible loop, the F′–G′ loop, outlining a previously unknown deep pocket. By means of MD simulations, we identify a region in this loop that can undergo a disorder-to-order transition triggered by an acidic pH, which, in turn, elicits P-domains to form a compact trimer. There are a number of significant outcomes from this work. First, this work shows that GNNV particles are dynamic biological nanomaterials, and challenge the notion that the crystal structure represents “the” form for host cell docking. (22) Second, since the pH condition in which each GNNV structure was investigated corresponds to a defined stage along the route of entry, our structural findings represent high-resolution snapshots in a video that describes NNV configurations along the journey of virus entry, which can be obtained by live imaging techniques but with much poorer resolution. Third, our results represent a rare study that connects the structural transitions observed at the coarse level─the loose protrusion configuration on the GNNV capsid at near-neutral conditions (pH 6.5–8.0) to the compact configuration at the acidic condition (pH 5.0), with those at the atomic level─the GNNV-P monomer structure at a neutral condition (pH 7.0) and the acid-induced GNNV-P trimer structure (pH 5.0). Lastly and most importantly, this study may lead to identification of putative druggable sites on NNV.

Advancement from the Crystal Structure─GNNV Structure Configuration for Cell Attachment

Regarding virus entry, docking of the virion on a host cell surface represents the first step during the journey. GNNVs dock on their host cells, e.g., Grouper fin cells, under weakly alkaline conditions as wild Groupers live in relatively warm oceanic regions that exhibit weakly alkaline conditions (pH 8.1) or under neutral conditions (pH 7.0–7.5) for farmed Grouper. A GNNV that has succeeded in entering a host cell is being trafficked through the endosomal system; it would encounter a wide range of pH variations (from pH 8.1 during cell attachment to pH 5.0 in late endosome). Prior to the present study, it has been accepted that the structure utilized for host cell docking is akin to the crystal structure (22) since it was obtained at a neutral condition (pH 7.2). In this crystal structure, GNNV protrusions adopt the resting/prone and compact configuration, and this configuration is thus believed to be the form interacting with the host cell receptor. Seeing the limitations of using the crystal approach on sampling aqueous conditions, we used cryo-EM to explore GNNV structures in various pH conditions as this method is effective for high-resolution structure determination of noncrystal specimens in a wide range of aqueous conditions. By mimicking those conditions encountered en route of host cell entry via the endocytic pathway, we discovered that the protrusions on the GNNV capsid adopts an erect/rising position with loose configuration at the docking condition (pH 8.0) and early endosome condition (pH 6.5), but compact and rest on the capsid shell at the late endosome condition (pH 5.0). Structural comparison shows that the crystal structure of GNNV (22) resembles the cryo-EM structure at pH 5.0, suggesting that the crystal structure probably best represents an intermediate structure that a GNNV particle adopts in the late stages of virus entry, but less likely to be the form of GNNV interacting with host cell receptors as has been implicated. (28)

Low-pH-Induced P-Domain Structural Transitions and Self-Assembly

As our cryo-EM analysis could not resolve the densities in the protrusion region of the GNNV virion (Figures S3 and S4), this analysis failed to offer a detailed mechanism to explain the GNNV protrusion dynamics. We therefore expressed the P-domain alone (GNNV-P) in order to obtain its atomic structure by solution NMR. The analytical ultracentrifugation results surprisingly show this GNNV-P protein is largely monomeric in solution at neutral pH but assembles into a trimer at acidic pH. We then determined the NMR structure for this monomer to yield the first atomic structure of the P-domain (pH 7.0), revealing that a region of 20 amino acids (aa 311–330) in the P-domain adopts a long flexible loop, contrasting to it being interrupted by a short β-strand (aa 323–326). (22) By using MD simulations, we showed that this long flexible loop could undergo unusual pH-dependent secondary structure changes. At pH 7.0 this F′–G′ loop seems to be disordered, but at pH 5.0 a subregion in it acquires a β-strand (aa 320–324). It is noted that the subregion of β-strand is not exactly the same as the counterpart in the crystal structure. (22) This structural transition enables this F′–G′ loop to switch from a continuous loop to an interrupted loop. Such conformational change at the level of the secondary structure has a number of significant consequences at the level of tertiary and quaternary structures. First, it causes the pocket outlined by the F′–G′ and C′–D′ loops to close (Figure 5C): at neutral pH, the pocket is open as the F′–G′ and C′–D′ loops lie far apart, so the pocket is open, whereas at acidic pH it becomes closed as the β-strand emerging from the F′–G′ loop forms a three-stranded β-sheet with C′ and D′ β-strands. Second, this conformational switching apparently ameliorates loop-elicited steric hindrance between neighboring P-domains that has prevented the P-domain from self-assembling into a trimer (Figure 4). This trimer is further stabilized by inter-GNNV-P interactions, of which critical contacts between subunits such as P326 and W280 have been verified by site-directed mutagenesis. Notably, our study demonstrates that at acidic conditions, the trimer of P-domains can be stabilized merely by protein–protein interactions in the absence of calcium ions as reported in the crystal structure, (22) since our study has omitted divalent ions used for crystallization. (22) This apparent discrepancy is understandable: calcium ions have the power to neutralize the electrostatic repulsion conferred by aspartate acids on the trimer interfaces, but the role of calcium ions is fulfilled by protons in acidic conditions.

Deep Pocket in the P-Domain and Its Implications

Since the protrusions on the GNNV capsid adopt a loose configuration at neutral aqueous conditions for host cell docking, the NMR structure of the P-domain (monomer) obtained at neutral conditions would represent a more appropriate system relative to the crystal structure (trimer) for evaluating how NNVs interact with host receptors. Strikingly, our molecular docking analysis of the interactions between the GNNV-P monomer and short sialylated oligosaccharides revealed the deep pocket in the P-domain outlined by the F′–G′ loop is the site to interact with the terminal sialic acid moiety. The sialic acid moiety is found on glycoproteins on host cell surfaces. Because the tested sialic acids interact with highly conserved residues deep in the pocket, our results support the notion that sialic acids are used as common cell receptors across all NNV genotypes. (42) It is envisioned that a small molecule targeting this pocket may inhibit the receptor binding. (43) It is noted that this pocket in the trimeric structure is no longer available, and the receptors of sialic acids are then withdrawn to the apex of the trimer.
Since a common cellular receptor cannot differentiate between NNV types, a protein coreceptor is required to determine the host specificity. (44−46) Such protein coreceptors include 90ab1 (45) and nectin, (46) which bind to amino acids 213–230 and 221–238 of the P-domain, respectively. These host-determining regions of NNVs have good overlap with a highly variable sequence (aa 223–244). (47) As this variable sequence lies close to the linker (aa 214–220), yet distant from the sialic acid binding pocket, we speculate that the pH-associated malleability may play a role in dynamically controlling the accessibility of those host-determining regions. Based on the interactions between the protrusion pocket and sialic acid receptors, as well as those between the protrusion host-determining region and a protein coreceptor, we propose a pH-dependent NNV-host engagement model (Figure 7) crucial for virus entry, where its connection to endosomal escape (48) warrants further study.

Figure 7

Figure 7. Model of GNNV interactions with host cell surface receptors. During infection, GNNV attaches to cells by interacting with the sialic acid moiety on the host cell surface and with cellular receptors (HSP70, HSP90ab1, or nectin-4). At weakly alkaline or neutral pH for host cell entry, protrusions on the GNNV surface are in extended and loose configuration, with the sialic acid binding pocket open, and linker region (red) and host-determining region (blue) accessible for interactions with cellular receptors. Acidification in late endosomes induces conformational change of the GNNV P-domain to result in resting/prone and compact protrusions. In this configuration, the sialic acid binding pocket is closed so that sialic acids are withdrawn to the tip of the protrusions. In addition, the accessibility of the linker region to a cellular receptor is also reprogrammed, leading to its potential GNNV detachment for endosomal escape. Note that the potential of the protrusion tip in directly interacting with the endosomal membrane may be altered by the pH-induced modification of the protrusion surface hydrophobicity distribution (Figure S20).

High Resolution Image
Download MS PowerPoint Slide

Malleable Linker That Connects the P-Domain to the S-Domain

Compared with X-ray crystallography or cryo-EM, NMR has a superior advantage of detecting signals from highly mobile regions. The ability to detect these signals revealed pH-associated malleability of the linker that connects the P-domain and the S-domain. The NMR spectra of this linker indicated that it could adopt preferred configurations in acidic conditions, which is made possible by the peptide bond of a conserved proline (P221), situated at the junction of the linker and P-domain, preferring the cis configuration (Figure 5). This linker malleability explains the pH-dependent positioning of protrusions on GNNV particles, as observed by cryo-EM.
While we were delving into the atomic details of GNNV-P using NMR analysis, Song et al. investigated mouse and human noroviruses by cryo-EM under various pH conditions (49) in order to resolve the mystery of classifying caliciviruses based on different protrusion positions in capsid structures. In the “rising” type as shown by human norovirus GII.10 and rabbit hemorrhagic disease virus (RHDV), the protrusions are in the rising position, whereas in the “resting” type as shown by human norovirus GI.1, sapovirus, and San Miguel sea lion virus (SMSV), the protrusions rest upon the virus shell. Like betanodavirus, norovirus is a nonenveloped virus exhibiting pronounced surface protrusions, with each norovirus protrusion composed of two copies of the P-domain. Like GNNV, the norovirus P-domain is connected to an S-domain via a flexible hinge. (50) This pH-dependent cryo-EM investigation of noroviruses (49) has revealed that norovirus protrusions rest on the virus capsid in neutral pH (<8.0) but become erect at alkaline pH (>8.0). This ability of dynamic switching of protrusion positions is apparently crucial for viral infection as the resting/prone protrusions are adopted in aqueous conditions favoring virus entry and seem more accessible to cellular receptors. (49) Comparison between GNNV and noroviruses indicates that both pH thresholds for triggering the conformational change and the physiology context are significantly different. For noroviruses, protrusion configuration switching occurs at approximately pH 8.0, while that for GNNV occurs at pH 5.0. For noroviruses, the resting/prone form is adopted at the neutral condition encountered at the host cell surface, whereas for GNNV the same form is adopted at acidic conditions in late endosomes. In other words, the infectious form of GNNV is more likely with rising protrusions, whereas that of the norovirus is with the resting protrusions. Those differences may be ascribed to the different natures of these viruses and the circumstances of the host infection as well. Nevertheless, they may use similar molecular features to respond to environment changes. First, the protrusion domains of both of these viruses are connected to the virus shell by a flexible linker/hinge. Second, the linkers and hinges of both viruses undergo conformational changes. Third, norovirus does bear a number of prolines in its hinge presumably pivotal for its protrusion malleability, e.g., the PPT (Pro–Pro–Thr) sequence. (50) Our study shows that the distribution of conformations in the GNNV’s linker might be altered by the change in the pH. In the case of the norovirus, the role played by pH in tuning the hinge’s conformation distribution has not been established. Thus, the pH-associated linker’s malleability learned from our study may offer an insight into the malleability of the norovirus linker. We speculate that the similarity in linker malleability between NNV and the norovirus, a phylogenetically distant virus, might have been acquired through convergent evolution.
Taken together, our discovery of GNNV pH-dependent structural changes by cryo-EM followed by combined analysis using NMR, MD simulations, and mutagenesis has unraveled the inner-working of GNNV protrusion dynamics. Specifically, our findings reveal a unique pH-induced conformational switching mechanism (51) that involves coupling of the local protein fold change and protein oligomerization. (52,53) This mechanism might be utilized by NNV as a means of host cell entry and perhaps subsequent survival. Considering anti-NNV strategies, conventional approaches focus on vaccine development, (23,54−56) for which our finding of GNNV conformational changes can be of benefit with regard to the structure-based vaccine design (57) of new NNV-neutralizing antibody that has the capacity to access conformation-sensitive epitopes. Nevertheless, most fish vaccines are administrated by injection, which is practically labor-intensive and may induce temporary immunosuppression. Thus, alternative anti-NNV strategies other than vaccines are desired. A pocket in a protein structure usually represents an ideal druggable site. (58,59) Our finding of the common receptor-binding pocket in the GNNV P-domain may suggest a brand new anti-NNV strategy that uses small molecules or short peptides, which can be easily delivered to farmed fish using fish feed. A small molecule that targets this pocket can disrupt interactions between NNV and a common cellular receptor to broadly inhibit the entry of NNV from host cell entry. In addition, since loops (60) or other protein motifs (61) critical for protein–protein interactions (62) are also suitable targets of small molecules, candidate druggable sites may include the F′–G′ loop and other protein motifs on the trimer interfaces. In conclusion, this work provides new structural insights into NNV with the identification of a number of putative druggable sites on the capsid protrusion in this environmentally and economically important virus and suggests that virus–host interactions can be potentially complex even for a simple virus.

Methods

Click to copy section linkSection link copied!

Sample Preparation of GNNV and VLPs for Cryo-EM

GNNV and VLPs were purified using a 10–40% (w/w) sucrose density gradient, as previously described. (14,29) To prepare GNNV and VLPs in different pH conditions, the purified particles were pelleted down by ultracentrifugation at 30,000 rpm for 3.5 h at 4 °C (Beckman Coulter, OptimaTM L-90K Ultracentrifuge, rotor: SW 41 Ti). The particle pellets were then resuspended overnight in 100 μL of TN buffer (50 mM NaCl, 50 mM Tris-HCl, pH 8.0), MES buffer (50 mM NaCl, 50 mM MES, pH 6.5), or acetate buffer (50 mM NaCl, 50 mM sodium acetate, pH 5.0).
To prepare the cryo-EM samples of GNNV and VLPs, approximately 3.5 μL of protein solution was deposited onto a Quantifoil R1.2/1.3 holey carbon grid (Quantifoil Micro Tools GmbH, Jena, Germany) coated with a thin carbon film. The grid was then rapidly plunged into liquid-nitrogen-cooled liquid ethane and stored in liquid nitrogen until imaging. The cryo-EM grids of GNNV at pH 6.5 and 5.0 were prepared using a Vitrobot Mark IV system (Thermo Fisher Scientific, Hillsboro, OR, USA) at 4 °C and 100% humidity, with a blotting time of 3.5 s. For VLPs at pH 8.0, 6.5, and 5.0, the cryo-EM grids were prepared using a Leica EM GP system (Leica Biosystems, Deer Park, IL, USA) with the sensor off option─the cryo-EM grids of VLPs at pH 8.0 and 5.0 were prepared at 15 °C and 80% humidity, with a blotting time of 1.2 s; whereas the cryo-EM grids of VLPs at pH 6.5 were prepared at 22 °C and 95% humidity, with a blotting time of 0.5 s. All subsequent steps were conducted at the liquid-nitrogen temperature to prevent devitrification.

Cryo-EM Data Acquisition

The cryo-EM grids containing GNNV virions at pH 6.5 and 5.0 were examined using cryo-ARM (JEOL Ltd., Akishima, Tokyo, Japan) at magnifications of 40 000× and 50 000×, respectively, with pixel sizes of 1.36 and 1.09 Å/pixel. Cryo-EM images of GNNV at pH 6.5 and 5.0 were recorded using a K2 camera (Gatan Inc., Pleasanton, CA, USA) in counting mode, with an exposure time of 8 s for 40 frames. The dose rate was approximately 6.5 electrons/Å2 per second, resulting in a total accumulated dose of around 52 electrons/Å2 (equivalent to approximately 1.3 electrons/Å2 per frame).
The cryo-EM grids containing VLPs at pH 6.5 were examined using Technai F20 (FEI, Hillsboro, OR, USA) at a nominal magnification of 29,000×, resulting in a pixel size of 1.24 Å/pixel. Cryo-EM images of the VLPs at pH 6.5 were recorded using a K2 camera (Gatan Inc., Pleasanton, CA, USA) in counting mode, with an exposure time of 10 s for 50 frames. The dose rate was approximately 5 electrons/Å2 per second, resulting in a total accumulated dose of around 50 electrons/Å2 (equivalent to approximately 1.0 electron/Å2 per frame).
The cryo-EM grids containing VLPs at pH 8.0 and 5.0 were examined using JEM-2100F with a high-contrast pole piece (JEOL Ltd., Akishima, Tokyo, Japan) at a magnification of 50,000×, with a pixel size of 1.16 Å/pixel. Cryo-EM images of the VLPs at pH 8.0 and 5.0 were recorded using a DE-20 camera (Direct Electron LP, San Diego, CA, USA) in linear mode, with an exposure time of 1.5 s for 38 frames. The dose rate was approximately 20 electrons/Å2 per second, resulting in a total accumulated dose of around 30 electrons/Å2 (equivalent to approximately 0.8 electrons/Å2 per frame). The parameters for cryo-EM data acquisition are summarized in Table S1.

Single-Particle Image Processing and 3D Reconstruction

All cryo-EM image stacks underwent motion correction and dose weighting using MotionCor2 (63) with a 5 × 5 patch. The contrast transfer function (CTF) was determined using CTFFIND4 (64) from the motion-corrected and dose-weighted images. Particle picking was conducted in cryoSPARC (5) using 2D templates generated from a previously determined VLP cryo-EM map. (29) After particle extraction and removal of bad particles through 2D classification, the remaining particles were used for further ab initio reconstruction and heterogeneous refinement with icosahedral symmetry (I). A subset of particles from a good 3D class with more particles and better resolution was chosen for homogeneous refinement with icosahedral symmetry (I). Overall resolution was assessed using the Fourier Shell Correlation (FSC) = 0.143 criterion, and local resolution was calculated within cryoSPARC. (65) The final cryo-EM maps achieved overall resolutions of 3.12 Å (GNNV pH 6.5), 4.36 Å (GNNV pH 5.0), 3.23 Å (VLP pH 8.0), 2.82 Å (GNNV pH 6.5), and 3.52 Å (GNNV pH 5.0) (Figures S3, S4 and Table S1). Visualization of the resulting 3D density maps was performed using the UCSF Chimera. (66) The details of single-particle image reconstructions of GNNV and VLPs can be found in the flowcharts in Figures S21 and S22, and the cryo-EM reconstruction details are summarized in Figures S3 and S4. Additional information regarding cryo-EM reconstruction statistics is available in Table S1.

Protein Construct and Site-Directed Mutagenesis

The cDNA encoding the Dragon grouper NNV P-domain (aa 214–338, GNNV-P) was tagged with His6-yeast SUMO (Smt3) at the N-terminus and was cloned into the pETDuet-1 vector. GNNV-P mutant constructs─namely, R276A, W280A, H281Y, W301A, Q322A, I323A, L324A, and L325A─were created using plasmids harboring the wild-type GNNV-P sequence and a QuickChange Lightning site-directed mutagenesis kit (Agilent Technologies, CA, USA). Mutations were confirmed by PCR sequencing (Genomics Inc., Taiwan). Primers used for mutagenesis were synthesized by Tri-I Biotech (NTC, Taiwan).

Protein Expression and Purification

Wild-type and mutant GNNV-P proteins were expressed using a transformed Escherichia coli BL21 (DE3) strain according to expression and purification protocols reported previously. (34) Uniform U-[2H, 13C, 15N] triple-labeled proteins for NMR assignments at pH 5.0 were expressed in M9 minimal medium prepared using 100% D2O (Sigma-Aldrich) as the solvent (M9 D2O medium), with 1 g/L 15NH4Cl (Sigma-Aldrich) and 2 g/L U-13C6-Glucose (Cambridge Isotope Laboratories) as the sole nitrogen and carbon sources, respectively, according to a modified expression protocol. In brief, overnight culture of transformed E. coli was first transferred to 1 L of LB medium supplemented with 100 μg/mL ampicillin and grown until the OD600 reached a value of 1.0. The cells were then collected by centrifugation and excess LB medium was removed before resuspending the cells in M9 D2O medium. Protein expression was induced by the addition of IPTG (final concentration of 0.3 mM) dissolved in 100% D2O. Then, the cells were grown for an additional 4 h at 37 °C with shaking at 150 rpm, before being harvested by centrifugation at 8000 rpm for 25 min at 4 °C.

Sedimentation Velocity Analytical Ultracentrifugation (SV AUC)

The sedimentation velocity experiments were performed in a Beckman Coulter ProteomeLab XL-I analytical ultracentrifuge equipped with a 190–800 nm absorbance optical system. The concentration of protein samples used for SV AUC was in the 0.25–0.4 mg/mL range. The SV experiments were carried out at a rotor speed of 60,000 rpm at 20 °C and absorbance was monitored at λ = 280 nm. The protein sample buffer was used as a reference. Sedimentation coefficients were determined using the SEDFIT v16-1c software. (67) The sedimentation velocity data was fitted into a continuous c(s) distribution model based on solving the Lamm equation by the least-squares technique. (67) Buffer density (ρ), viscosity (η), and molecule partial specific volume were estimated using SEDNTERP software. (68)

NMR Spectroscopy and Structure Determination

All NMR experiments were performed at 298 K on Bruker AVANCE 600, 800, and 850 MHz spectrometers equipped with 5 mm triple resonance TXI cryogenic probes, including a shielded Z-gradient. Samples containing 10% D2O were loaded into 5 mm Shigemi NMR tubes for NMR experiments.
The sequence-specific backbone resonance assignments at pH 5.0 were achieved using 1.0 mM U-[2H, 13C, 15N]-labeled GNNV-P protein in 20 mM sodium acetate (pH 5.0), 50 mM NaCl, 0.5 mM ethylenediaminetetraacetic acid (EDTA), 0.02% sodium azide, and 90% H2O/10% D2O. NMR spectra were processed using Bruker Topspin 3.6 and analyzed using NMRviewJ 9.2.0. (69) The sequence-specific backbone assignments have been determined by independent connectivity analysis of HNCACB, HNCO, and HN(CA)CO experiments. We completed backbone assignments for 123 of 125 residues (98.3%), with the exceptions of Thr214 and Leu325. These resonance assignments have been deposited into the Biological Magnetic Resonance Data bank with accession code 52218.
GNNV-P structural calculations at neutral pH were carried out in XPLOR-NIH software version 3.8 (70) in NMRbox (71) using experimentally determined distance restraints, hydrogen bonds, and predicted dihedral angles. Backbone and side-chain NMR assignments of the NNV P-domain at neutral pH were reported previously. (34) NOE distance restraints were derived from an 15N-edited NOESY-HSQC spectrum and they were analyzed using NMRviewJ software. (69) Hydrogen bonds were identified based on hydrogen/deuterium exchange experiments. The backbone dihedral angle restraints Φ and Ψ were predicted from chemical shifts using the TALOS+ Web server. (72) For the GNNV-P structural determination, 100 structures were generated according to a standard simulated annealing protocol. Twenty structures with the lowest energy were selected for refinement using the implicit solvation potential and effective energy function (EFFx) in XPLOR-NIH. (73) The 20 structures with the lowest energy and without reported violations were selected for assessment and quality checking using the protein structure validation suite in the wwPDB Validation server. (74) The final ensemble of 20 structural conformations of GNNV-P has been deposited in the Protein Data Bank (PDB entry 8XID) and the respective structural statistics are summarized in Table S2.

Amide Hydrogen–Deuterium Exchange Rate (HXD)

Hydrogen–deuterium exchange between the amide NH signal and D2O solvent was monitored using 1H, 15N HSQC spectra. First, an initial 15N HSQC spectrum was collected before freezing GNNV-P in liquid nitrogen and lyophilizing it. The progress of amide hydrogen–deuterium exchange was monitored by collecting 15N HSQC spectra for GNNV-P samples at pH 7.0 after redissolving them in 100% D2O for 4 h. The hydrogen–deuterium exchange rate was calculated by fitting peak intensities into the exponential decay function I(t) = I_0·e∧(−t/x) using NMRviewJ. (69)

Chemical Shift Perturbation and Secondary Chemical Shifts Calculation

2D 1H–15N HSQC spectra of GNNV-P at pH 7.0 and 5.0 were used for the chemical shift perturbation analysis. The chemical shift between pH 7.0 and 5.0 was calculated using the equation Δδ=ΔδH2+(α·ΔδN)2, where ΔδH and ΔδN are the 1H and 15N chemical shift changes, respectively. A scaling factor of α = 0.1 was used to account for the larger 15N chemical shift. (36) Secondary structure propensities were predicted from 13Cα and 13Cβ chemical shifts based on their deviations from random coil values. (75)

Molecular Dynamics (MD) Simulation

Simulations for GNNV-P trimer formation were performed using the GROMACS package. (76) Initially, the protein protonation state was adjusted to pH 5.0 using PDB 2PQR software. (77) To prepare the initial point for the simulation, three protein molecules were initially packed in a ∼300 Å cubic box using the software PACKMOL. (78) This step was done to ensure that no repulsive interactions would disrupt or cause errors during the simulations. Using a V-rescale thermostat, the overall temperature of the water and protein was kept constant by coupling each group of molecules independently at 300 K. A Parrinello–Rahman barostat was used to separately couple the pressure to 1 atm in every dimension. (79) The time constants for the temperature and pressure couplings were set to 0.1 and 2 ps, respectively. A time step of 2 fs was applied by using the leapfrog algorithm to integrate the equations of motion for the system. Periodic boundary conditions were set for the whole system. For the Lennard–Jones and the Ewald sum Coulombic interactions, we set a 1 nm cutoff. The Fourier space part of the Ewald splitting was calculated using the particle-mesh-Ewald method by applying cubic spline interpolation and 0.16 nm grid length on the side. (80) The TIP3P water model was used and the protein parameters were obtained from the AMBERff99SB-ILDN force field. (81,82) MD simulations were performed for a total scan length of 100 ns.

Molecular Docking Analysis

Molecular docking analysis on GNNV-P with two sialoside isomers, Neu5Ac-(α2,3)-Lac and Neu5Ac-(α2,6)-Lac, was carried out using the HADDOCK 2.4 Web server and protein–glycan default settings. (83) The three-dimensional structures of Neu5Ac-(α2,3)-Lac and Neu5Ac-(α2,6)-Lac were extracted from PDB entries 6TLZ and 6TM0, respectively. The binding energy between GNNV-P and the docked sialosides was estimated using the PRODIGY Web server. (84)

SV-AUC Data Interpretation and Protrusion Hydrophobicity Analysis

Interpretations of pH 6.0 and 5.5 SV-AUC profiles were performed with cautions according to Schuck and Zhao. (85) Hydrophobicity patches on P-domain (pH 7.0) and protrusion as trimer of P-domains (pH 5.0) were analyzed using Kyte–Dolittle analysis. (86)

Data Availability

Click to copy section linkSection link copied!

Cryo-EM maps of GNNV VLP at pH 8.0, 6.5, and 5.0 are available from the EMDB data bank with accession numbers EMDB-39212, 39213, and 39214, and the corresponding atomic models are with PDB entries of 8YF6, 8YF7, and 8YF8; Cryo-EM maps of the GNNV virion at pH 6.5 and 5.0 have EMDB accession numbers 39215 and 39217, and the atomic model of the GNNV virion at pH 6.5 is with PDB entry 8YF9. 1H, 13C, and 15N chemical shift assignments of GNNV-P at pH 5.0 have been deposited to the Biological Magnetic Resonance Bank (BMRB) with Accession No. 52218. The NMR structure of the GNNV P-domain has been deposited to the Protein Data Bank (PDB entry 8XID).

Supporting Information

Click to copy section linkSection link copied!

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsinfecdis.4c00407.

  • Experimental methods; Figures S1–S21; Tables S1 and S2 (PDF)

  • Conformational change of GNNV VLP from pH 8.0 to 5.0 (Movie S1) (MP4)

  • Enlarged views of GNNV protrusion conformational change from pH 8.0 to 5.0 (Movie S2) (MP4)

  • Hypothetical atomic model of GNNV-P conformational change from pH 8.0 to 5.0 (Movie S3) (MP4)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

Click to copy section linkSection link copied!
  • Corresponding Author
    • Wei-Hau Chang - Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan;  Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan;  Genomics Research Center, Academia Sinica, Taipei 11529, Taiwan;  Institute of Physics, Academia Sinica, Taipei 11529, Taiwan;  Orcidhttps://orcid.org/0000-0002-2612-850X;  Email: [email protected]
  • Authors
    • Petra Štěrbová - Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan;  College of Life Science, National Tsing Hua University, Hsinchu 30044, Taiwan;  Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan
    • Chun-Hsiung Wang - Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan;  Present Address: Academia Sinica Cryo-EM Facility, Academia Sinica, Taipei 115, Taiwan
    • Kathleen J. D. Carillo - Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan;  Present Address: Institute for Bioscience and Biotechnology Research, University of Maryland, College Park, Maryland 20742, United States
    • Yuan-Chao Lou - Biomedical Translation Research Center, Academia Sinica, Taipei 11529, Taiwan
    • Takayuki Kato - Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japan;  Present Address: Institute of Protein Research, Osaka University, Suita, Osaka, 565-0871, Japan
    • Keiichi Namba - Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japan;  Orcidhttps://orcid.org/0000-0003-2911-5875
    • Der-Lii M. Tzou - Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan;  Orcidhttps://orcid.org/0000-0002-3755-8230
  • Author Contributions

    P.Š. and C.-H.W. contributed equally to this work. W.-H.C. conceived this project. C.-H.W. cultured and isolated the native GNNV virus, expressed and purified GNNV virus-like particles (VLPs), performed negative-stain electron microscopy as quality control; performed cryo-EM imaging on GNNV VLPs using JEM-2100F (JEOL Ltd., Akishima, Tokyo, Japan) and Technai F20 (FEI, Hillsboro, OR, USA); and performed all cryo-EM image analysis. C.-H.W., T.K., and K.N. performed cryo-EM imaging on GNNV virus using cryo-ARM (JEOL Ltd., Akishima, Tokyo, Japan). P.Š. and C.-H.W. performed cloning of the GNNV P-domain and performed P-domain mutagenesis. P.Š. performed expression and purification of the P-domain, followed by analytical ultracentrifugation (AUC) analysis; conducted AUC tests for the mutants; and performed molecular docking analysis. P.Š., Y.-C.L., and D.-L.M.T. performed NMR experiments and respective analysis. K.J.D.C. performed molecular dynamic simulations. P.Š., C.-H.W., D.-L.M.T., and W.-H.C. prepared the manuscript.

  • Funding

    This project was supported by MOST Grants [103-2321-B-001-048], [104-2321-B-001-019], and [105-2321-B-001-009] and AS-SUMMIT Projects [AS-SUMMIT-106] and [AS-SUMMIT-107], and AS Infectious Disease Research Grant to W.-H.C., and MOST Grant [107-2113-M-001-017] to D.-L.M.T.

  • Notes
    The authors declare no competing financial interest.

Acknowledgments

Click to copy section linkSection link copied!

The authors are indebted to Yeukuang Hwu and Yi-Yun Chen at the Institute of Physics, Academia Sinica, for supporting the use of JEM-2100F (JEOL Ltd., Akishima, Tokyo, Japan), as well as to Dong-Hua Chen, David Bushnell, and Roger Kornberg at the Stanford University for supporting the use of Technai F20 (FEI, Hillsboro, OR, USA). The operation of cryo-ARM at the Osaka University was supported by a Japan Joint Research Grant to K.N. Note that all of the cryo-EM data for this work were obtained in the period of 2013–2017 prior to installation of the Academia Sinica Cryo-EM Core Facility (ASCEM) in 2019 supported by [AS-CFII-108-110]. The authors thank the Academia Sinica High-Field NMR Center (HFNMRC; funded by Academia Sinica Core Facility and Innovative Instrument Project AS-CFII-111-214) for technical support. The authors thank the Medicinal Chemistry and Analytical Core Facilities, funded by Academia Sinica Core Facility and Innovative Instrument Project AS-NBRPCF-111-201, for technical support with NMR data. We thank Mr. Kun Hung Chen in the Biophysics Core Facility, funded by Academia Sinica Core Facility and Innovative Instrument Project AS-CFII-111-201, for performing the sedimentation velocity analytical ultracentrifugation experiments. The authors thank the DNA Sequencing Core Facility of the Institute of Biomedical Sciences, Academia Sinica (funded by Academia Sinica Core Facility and Innovative Instrument Project AS-CFII-111-211), for DNA sequencing analysis. This study made use of NMRbox: National Center for Biomolecular NMR Data Processing and Analysis, a Biomedical Technology Research Resource (BTRR), supported by NIH Grant P41GM111135 (NIGMS).

References

Click to copy section linkSection link copied!

This article references 86 other publications.

  1. 1
    Baker, R. E.; Mahmud, A. S.; Miller, I. F.; Rajeev, M.; Rasambainarivo, F.; Rice, B. L.; Takahashi, S.; Tatem, A. J.; Wagner, C. E.; Wang, L.-F. Infectious disease in an era of global change. Nat. Rev. Microbiol. 2022, 20 (4), 193– 205,  DOI: 10.1038/s41579-021-00639-z
    Google Scholar
    1
    Infectious disease in an era of global change
    Baker, Rachel E.; Mahmud, Ayesha S.; Miller, Ian F.; Rajeev, Malavika; Rasambainarivo, Fidisoa; Rice, Benjamin L.; Takahashi, Saki; Tatem, Andrew J.; Wagner, Caroline E.; Wang, Lin-Fa; Wesolowski, Amy; Metcalf, C. Jessica E.
    Nature Reviews Microbiology (2022), 20 (4), 193-205CODEN: NRMACK; ISSN:1740-1526. (Nature Portfolio)
    A review on. The twenty-first century has witnessed a wave of severe infectious disease outbreaks, not least the COVID-19 pandemic, which has had a devastating impact on lives and livelihoods around the globe. The 2003 severe acute respiratory syndrome coronavirus outbreak, the 2009 swine flu pandemic, the 2012 Middle East respiratory syndrome coronavirus outbreak, the 2013-2016 Ebola virus disease epidemic in West Africa and the 2015 Zika virus disease epidemic all resulted in substantial morbidity and mortality while spreading across borders to infect people in multiple countries. At the same time, the past few decades have ushered in an unprecedented era of technol., demog. and climatic change: airline flights have doubled since 2000, since 2007 more people live in urban areas than rural areas, population nos. continue to climb and climate change presents an escalating threat to society. In this Review, we consider the extent to which these recent global changes have increased the risk of infectious disease outbreaks, even as improved sanitation and access to health care have resulted in considerable progress worldwide.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXit1Ciu77L&md5=e91900ca47ac5d7cf14a480ce6182ef9
  2. 2
    Doan, Q. K.; Vandeputte, M.; Chatain, B.; Morin, T.; Allal, F. Viral encephalopathy and retinopathy in aquaculture: a review. J. Fish Dis. 2017, 40 (5), 717– 742,  DOI: 10.1111/jfd.12541
    Google Scholar
    There is no corresponding record for this reference.
  3. 3
    Cann, A. J. Replication of Viruses. In Encyclopedia of Virology (Third ed.); Mahy, B. W. J.; Van Regenmortel, M. H. V., Eds.; Academic Press, 2008; pp 406– 412.
    Google Scholar
    There is no corresponding record for this reference.
  4. 4
    Bruinsma, R. F.; Wuite, G. J. L.; Roos, W. H. Physics of viral dynamics. Nat. Rev. Phys. 2021, 3 (2), 76– 91,  DOI: 10.1038/s42254-020-00267-1
    Google Scholar
    There is no corresponding record for this reference.
  5. 5
    Sherman, M. B.; Smith, H. Q.; Smith, T. J. The Dynamic Life of Virus Capsids. Viruses 2020, 12 (6), 618,  DOI: 10.3390/v12060618
    Google Scholar
    There is no corresponding record for this reference.
  6. 6
    Zhang, Y.; Cui, Y.; Sun, J.; Zhou, Z. H. Multiple conformations of trimeric spikes visualized on a non-enveloped virus. Nat. Commun. 2022, 13 (1), 550  DOI: 10.1038/s41467-022-28114-0
    Google Scholar
    There is no corresponding record for this reference.
  7. 7
    Lynch, D. L.; Pavlova, A.; Fan, Z.; Gumbart, J. C. Understanding Virus Structure and Dynamics through Molecular Simulations. J. Chem. Theory Comput. 2023, 19 (11), 3025– 3036,  DOI: 10.1021/acs.jctc.3c00116
    Google Scholar
    There is no corresponding record for this reference.
  8. 8
    Crane, M.; Hyatt, A. Viruses of fish: an overview of significant pathogens. Viruses 2011, 3 (11), 2025– 2046,  DOI: 10.3390/v3112025
    Google Scholar
    There is no corresponding record for this reference.
  9. 9
    Mori, K.-I.; Nakai, T.; Muroga, K.; Arimoto, M.; Mushiake, K.; Furusawa, I. Properties of a new virus belonging to nodaviridae found in larval striped jack (Pseudocaranx dentex) with nervous necrosis. Virology 1992, 187 (1), 368– 371,  DOI: 10.1016/0042-6822(92)90329-N
    Google Scholar
    9
    Properties of a new virus belonging to nodaviridae found in larval striped jack (Pseudocaranx dentex) with nervous necrosis
    Mori K; Nakai T; Muroga K; Arimoto M; Mushiake K; Furusawa I
    Virology (1992), 187 (1), 368-71 ISSN:0042-6822.
    Spherical virus particles were purified from larval striped jack (Pseudocaranx dentex) with nervous necrosis. The virus consists of nonenveloped particles, about 25 nm in diameter, and contains two single-stranded, positive-sense RNA molecules with molecular weights of 1.01 x 10(6)Da (RNA 1) and 0.49 x 10(6)Da (RNA 2), respectively. The RNAs do not have poly(A) sequences at the 3' terminus. Virus structural proteins consist of two proteins with molecular weights of 42 and 40 kDa. When translated into cell-free extracts of rabbit reticulocytes, RNA 1 directed the synthesis of the 1a protein (100 kDa), whereas RNA 2 synthesized the 2a protein (42 kDa), which is probably the coat protein of the virus, and a polypeptide of 40 kDa which appears to be the processed form of the 42-kDa protein. Under electron microscopic observation, the virus particles were found in the tissues of the central nervous system of the affected larval striped jack. From morphological and biochemical properties of the virus, we identified this virus as a new member of the family of Nodaviridae and designated it striped jack nervous necrosis virus.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADyaK387kt1Ghug%253D%253D&md5=2e07e4427b2baf88353ad92b9de7012d
  10. 10
    Bandín, I.; Souto, S. Betanodavirus and VER Disease: A 30-year Research Review. Pathogens 2020, 9 (2), 106,  DOI: 10.3390/pathogens9020106
    Google Scholar
    There is no corresponding record for this reference.
  11. 11
    Venter, P. A.; Schneemann, A. Nodaviruses. In Encyclopedia of Virology (Third ed.); Mahy, B. W. J.; Van Regenmortel, M. H. V., Eds.; Academic Press, 2008; pp 430– 438.
    Google Scholar
    There is no corresponding record for this reference.
  12. 12
    Tang, L.; Lin, C.-S.; Krishna, N. K.; Yeager, M.; Schneemann, A.; Johnson, J. E. Virus-like particles of a fish nodavirus display a capsid subunit domain organization different from that of insect nodaviruses. J. Virol. 2002, 76 (12), 6370– 6375,  DOI: 10.1128/JVI.76.12.6370-6375.2002
    Google Scholar
    There is no corresponding record for this reference.
  13. 13
    Iwamoto, T.; Mise, K.; Takeda, A.; Okinaka, Y.; Mori, K.-I.; Arimoto, M.; Okuno, T.; Nakai, T. Characterization of Striped jack nervous necrosis virus subgenomic RNA3 and biological activities of its encoded protein B2. J. Gen. Virol. 2005, 86, 2807– 2816,  DOI: 10.1099/vir.0.80902-0
    Google Scholar
    There is no corresponding record for this reference.
  14. 14
    Liu, W.; Hsu, C.-H.; Hong, Y.-R.; Wu, S.-C.; Wang, C.-H.; Wu, Y.-M.; Chao, C.-B.; Lin, C.-S. Early endocytosis pathways in SSN-1 cells infected by dragon grouper nervous necrosis virus. J. Gen. Virol. 2005, 86 (9), 2553– 2561,  DOI: 10.1099/vir.0.81021-0
    Google Scholar
    There is no corresponding record for this reference.
  15. 15
    Adachi, K.; Ichinose, T.; Takizawa, N.; Watanabe, K.; Kitazato, K.; Kobayashi, N. Inhibition of betanodavirus infection by inhibitors of endosomal acidification. Arch. Virol. 2007, 152 (12), 2217– 2224,  DOI: 10.1007/s00705-007-1061-7
    Google Scholar
    There is no corresponding record for this reference.
  16. 16
    Huang, R.; Zhu, G.; Zhang, J.; Lai, Y.; Xu, Y.; He, J.; Xie, J. Betanodavirus-like particles enter host cells via clathrin-mediated endocytosis in a cholesterol-, pH- and cytoskeleton-dependent manner. Vet. Res. 2017, 48 (1), 8  DOI: 10.1186/s13567-017-0412-y
    Google Scholar
    There is no corresponding record for this reference.
  17. 17
    Kumar, C. S.; Dey, D.; Ghosh, S.; Banerjee, M. Breach: Host Membrane Penetration and Entry by Nonenveloped Viruses. Trends Microbiol. 2018, 26 (6), 525– 537,  DOI: 10.1016/j.tim.2017.09.010
    Google Scholar
    There is no corresponding record for this reference.
  18. 18
    Mercer, J.; Schelhaas, M.; Helenius, A. Virus Entry by Endocytosis. Annu. Rev. Biochem. 2010, 79 (1), 803– 833,  DOI: 10.1146/annurev-biochem-060208-104626
    Google Scholar
    18
    Virus entry by endocytosis
    Mercer, Jason; Schelhaas, Mario; Helenius, Ari
    Annual Review of Biochemistry (2010), 79 (), 803-833CODEN: ARBOAW; ISSN:0066-4154. (Annual Reviews Inc.)
    A review. Although viruses are simple in structure and compn., their interactions with host cells are complex. Merely to gain entry, animal viruses make use of a repertoire of cellular processes that involve hundreds of cellular proteins. Although some viruses have the capacity to penetrate into the cytosol directly through the plasma membrane, most depend on endocytic uptake, vesicular transport through the cytoplasm, and delivery to endosomes and other intracellular organelles. The internalization may involve clathrin-mediated endocytosis (CME), macropinocytosis, caveolar/lipid raft-mediated endocytosis, or a variety of other still poorly characterized mechanisms. This review focuses on the cell biol. of virus entry and the different strategies and endocytic mechanisms used by animal viruses.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXpslSht7c%253D&md5=e083b9661ae42c15824b304f13c8e6fc
  19. 19
    Pletan, M. L.; Tsai, B. Non-enveloped virus membrane penetration: New advances leading to new insights. PLoS Pathog. 2022, 18 (12), e1010948  DOI: 10.1371/journal.ppat.1010948
    Google Scholar
    There is no corresponding record for this reference.
  20. 20
    Odegard, A.; Banerjee, M.; Johnson, J. E. Flock House Virus: A Model System for Understanding Non-Enveloped Virus Entry and Membrane Penetration. In Cell Entry by Non-Enveloped Viruses; Johnson, J. E., Ed.; Springer Berlin Heidelberg, 2010; pp 1– 22.
    Google Scholar
    There is no corresponding record for this reference.
  21. 21
    Odegard, A. L.; Kwan, M. H.; Walukiewicz, H. E.; Banerjee, M.; Schneemann, A.; Johnson, J. E. Low endocytic pH and capsid protein autocleavage are critical components of Flock House virus cell entry. J. Virol. 2009, 83 (17), 8628– 8637,  DOI: 10.1128/JVI.00873-09
    Google Scholar
    21
    Low endocytic pH and capsid protein autocleavage are critical components of flock house virus cell entry
    Odegard, Amy L.; Kwan, Maggie H.; Walukiewicz, Hanna E.; Banerjee, Manidipa; Schneemann, Anette; Johnson, John E.
    Journal of Virology (2009), 83 (17), 8628-8637CODEN: JOVIAM; ISSN:0022-538X. (American Society for Microbiology)
    The process by which nonenveloped viruses cross cell membranes during host cell entry remains poorly defined; however, common themes are emerging. Here, we use correlated in vivo and in vitro studies to understand the mechanism of Flock House virus (FHV) entry and membrane penetration. We demonstrate that low endocytic pH is required for FHV infection, that exposure to acidic pH promotes FHV-mediated disruption of model membranes (liposomes), and particles exposed to low pH in vitro exhibit increased hydrophobicity. In addn., FHV particles perturbed by heating displayed a marked increase in liposome disruption, indicating that membrane-active regions of the capsid are exposed or released under these conditions. We also provide evidence that autoproteolytic cleavage, to generate the lipophilic γ peptide (4.4 kDa), is required for membrane penetration. Mutant, cleavage-defective particles failed to mediate liposome lysis, regardless of pH or heat treatment, suggesting that these particles are not able to expose or release the requisite membrane-active regions of the capsid, namely, the γ peptides. Based on these results, we propose an updated model for FHV entry in which (i) the virus enters the host cell by endocytosis, (ii) low pH within the endocytic pathway triggers the irreversible exposure or release of γ peptides from the virus particle, and (iii) the exposed/released γ peptides disrupt the endosomal membrane, facilitating translocation of viral RNA into the cytoplasm.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtFahurrJ&md5=ca9a32cff587b467ac1302d995eff723
  22. 22
    Chen, N.-C.; Yoshimura, M.; Guan, H.-H.; Wang, T.-Y.; Misumi, Y.; Lin, C.-C.; Chuankhayan, P.; Nakagawa, A.; Chan, S. I.; Tsukihara, T. Crystal Structures of a Piscine Betanodavirus: Mechanisms of Capsid Assembly and Viral Infection. PLoS Pathog. 2015, 11 (10), e1005203,  DOI: 10.1371/journal.ppat.1005203
    Google Scholar
    There is no corresponding record for this reference.
  23. 23
    Gye, H. J.; Nishizawa, T. Sites responsible for infectivity and antigenicity on nervous necrosis virus (NNV) appear to be distinct. Sci. Rep. 2021, 11 (1), 3608  DOI: 10.1038/s41598-021-83078-3
    Google Scholar
    There is no corresponding record for this reference.
  24. 24
    Gye, H. J.; Park, M.-J.; Kim, W.-S.; Oh, M.-J.; Nishizawa, T. Heat-denaturation of conformational structures on nervous necrosis virus for generating neutralization antibodies. Aquaculture 2018, 484, 65– 70,  DOI: 10.1016/j.aquaculture.2017.10.034
    Google Scholar
    There is no corresponding record for this reference.
  25. 25
    Nishizawa, T.; Mori, K.-i.; Furuhashi, M.; Nakai, T.; Furusawa, I.; Muroga, K. Comparison of the coat protein genes of five fish nodaviruses, the causative agents of viral nervous necrosis in marine fish. J. Gen. Virol. 1995, 76 (7), 1563– 1569,  DOI: 10.1099/0022-1317-76-7-1563
    Google Scholar
    There is no corresponding record for this reference.
  26. 26
    Iwamoto, T.; Okinaka, Y.; Mise, K.; Mori, K.-I.; Arimoto, M.; Okuno, T.; Nakai, T. Identification of Host-Specificity Determinants in Betanodaviruses by Using Reassortants between Striped Jack Nervous Necrosis Virus and Sevenband Grouper Nervous Necrosis Virus. J. Virol. 2004, 78 (3), 1256– 1262,  DOI: 10.1128/JVI.78.3.1256-1262.2004
    Google Scholar
    There is no corresponding record for this reference.
  27. 27
    Moreno, P.; Souto, S.; Leiva-Rebollo, R.; Borrego, J. J.; Bandín, I.; Alonso, M. C. Capsid amino acids at positions 247 and 270 are involved in the virulence of betanodaviruses to European sea bass. Sci. Rep. 2019, 9 (1), 14068  DOI: 10.1038/s41598-019-50622-1
    Google Scholar
    There is no corresponding record for this reference.
  28. 28
    Nishizawa, T.; Lee, H. S.; Gye, H. J. Pocket structures of surface protrusions shared among serologically distinct nervous necrosis viruses (NNVs) were predicted in silico to bind to sialylated N-glycans, a host cellular receptor. Aquaculture 2023, 565, 739157,  DOI: 10.1016/j.aquaculture.2022.739157
    Google Scholar
    There is no corresponding record for this reference.
  29. 29
    Wang, C. H.; Chen, D. H.; Huang, S. H.; Wu, Y. M.; Chen, Y. Y.; Hwu, Y.; Bushnell, D.; Kornberg, R.; Chang, W. H. Sub-3 Å Cryo-EM Structures of Necrosis Virus Particles via the Use of Multipurpose TEM with Electron Counting Camera. Int. J. Mol. Sci. 2021, 22 (13), 6859,  DOI: 10.3390/ijms22136859
    Google Scholar
    There is no corresponding record for this reference.
  30. 30
    Chang, W. H.; Huang, S. H.; Lin, H. H.; Chung, S. C.; Tu, I. P. Cryo-EM Analyses Permit Visualization of Structural Polymorphism of Biological Macromolecules. Front. Bioinform. 2021, 1, 788308,  DOI: 10.3389/fbinf.2021.788308
    Google Scholar
    There is no corresponding record for this reference.
  31. 31
    Zhang, K.; Julius, D.; Cheng, Y. Structural snapshots of TRPV1 reveal mechanism of polymodal functionality. Cell 2021, 184 (20), 5138– 5150,  DOI: 10.1016/j.cell.2021.08.012
    Google Scholar
    There is no corresponding record for this reference.
  32. 32
    Chen, C.-Y.; Chang, Y.-C.; Lin, B.-L.; Huang, C.-H.; Tsai, M.-D. Temperature-Resolved Cryo-EM Uncovers Structural Bases of Temperature-Dependent Enzyme Functions. J. Am. Chem. Soc. 2019, 141 (51), 19983– 19987,  DOI: 10.1021/jacs.9b10687
    Google Scholar
    32
    Temperature-Resolved Cryo-EM Uncovers Structural Bases of Temperature-Dependent Enzyme Functions
    Chen, Chin-Yu; Chang, Yuan-Chih; Lin, Bo-Lin; Huang, Chun-Hsiang; Tsai, Ming-Daw
    Journal of the American Chemical Society (2019), 141 (51), 19983-19987CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
    Protein functions are temp.-dependent, but protein structures are usually solved at a single (often low) temp. because of limitations on the conditions of crystal growth or protein vitrification. Here we demonstrate the feasibility of solving cryo-EM structures of proteins vitrified at high temps., solve 12 structures of an archaeal ketol-acid reductoisomerase (KARI) vitrified at 4-70 °C, and show that structures of both the Mg2+ form (KARI:2Mg2+) and its ternary complex (KARI:2Mg2+:NADH:inhibitor) are temp.-dependent in correlation with the temp. dependence of enzyme activity. Furthermore, structural analyses led to dissection of the induced-fit mechanism into ligand-induced and temp.-induced effects and to capture of temp.-resolved intermediates of the temp.-induced conformational change. The results also suggest that it is preferable to solve cryo-EM structures of protein complexes at functional temps. These studies should greatly expand the landscapes of protein structure-function relationships and enhance the mechanistic anal. of enzymic functions.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitlygt7zO&md5=3866b861e15dbebe8e5657828643cbda
  33. 33
    Xie, J.; Li, K.; Gao, Y.; Huang, R.; Lai, Y.; Shi, Y.; Yang, S.; Zhu, G.; Zhang, Q.; He, J. Structural analysis and insertion study reveal the ideal sites for surface displaying foreign peptides on a betanodavirus-like particle. Vet. Res. 2016, 47 (1), 16,  DOI: 10.1186/s13567-015-0294-9
    Google Scholar
    There is no corresponding record for this reference.
  34. 34
    Štěrbová, P.; Wu, D.; Lou, Y.-C.; Wang, C.-H.; Chang, W.-H.; Tzou, D.-L. M. NMR assignments of protrusion domain of capsid protein from dragon grouper nervous necrosis virus. Biomol. NMR Assignments 2020, 14 (1), 63– 66,  DOI: 10.1007/s12104-019-09921-x
    Google Scholar
    There is no corresponding record for this reference.
  35. 35
    Walters, K. J.; Ferentz, A. E.; Hare, B. J.; Hidalgo, P.; Jasanoff, A.; Matsuo, H.; Wagner, G. Characterizing protein-protein complexes and oligomers by nuclear magnetic resonance spectroscopy. In Methods in Enzymology; Elsevier, 2001; Vol. 339, pp 238– 258.  DOI: 10.1016/S0076-6879(01)39316-3 .
    Google Scholar
    There is no corresponding record for this reference.
  36. 36
    Williamson, M. P. Using chemical shift perturbation to characterise ligand binding. Prog. Nucl. Magn. Reson. Spectrosc. 2013, 73, 1– 16,  DOI: 10.1016/j.pnmrs.2013.02.001
    Google Scholar
    36
    Using chemical shift perturbation to characterise ligand binding
    Williamson, Mike P.
    Progress in Nuclear Magnetic Resonance Spectroscopy (2013), 73 (), 1-16CODEN: PNMRAT; ISSN:0079-6565. (Elsevier B.V.)
    A review. Chem. shift perturbation (CSP, chem. shift mapping or complexation-induced changes in chem. shift, CIS) follows changes in the chem. shifts of a protein when a ligand is added, and uses these to det. the location of the binding site, the affinity of the ligand, and/or possibly the structure of the complex. A key factor in detg. the appearance of spectra during a titrn. is the exchange rate between free and bound, or more specifically the off-rate koff. When koff is greater than the chem. shift difference between free and bound, which typically equates to an affinity Kd weaker than about 3 μM, then exchange is fast on the chem. shift timescale. Under these circumstances, the obsd. shift is the population-weighted av. of free and bound, which allows Kd to be detd. from measurement of peak positions, provided the measurements are made appropriately. 1H shifts are influenced to a large extent by through-space interactions, whereas 13Cα and 13Cβ shifts are influenced more by through-bond effects. 15N and 13C' shifts are influenced both by through-bond and by through-space (hydrogen bonding) interactions. For detg. the location of a bound ligand on the basis of shift change, the most appropriate method is therefore usually to measure 15N HSQC spectra, calc. the geometrical distance moved by the peak, weighting 15N shifts by a factor of about 0.14 compared to 1H shifts, and select those residues for which the weighted shift change is larger than the std. deviation of the shift for all residues. Other methods are discussed, in particular the measurement of 13CH3 signals. Slow to intermediate exchange rates lead to line broadening, and make Kd values very difficult to obtain. There is no good way to distinguish changes in chem. shift due to direct binding of the ligand from changes in chem. shift due to allosteric change. Ligand binding at multiple sites can often be characterized, by simultaneous fitting of many measured shift changes, or more simply by adding substoichiometric amts. of ligand. The chem. shift changes can be used as restraints for docking ligand onto protein. By use of quant. calcns. of ligand-induced chem. shift changes, it is becoming possible to det. not just the position but also the orientation of ligands.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXht1yrsbzE&md5=74fc06bb4f4b3df369619bbf730016ce
  37. 37
    Krissinel, E.; Henrick, K. Inference of Macromolecular Assemblies from Crystalline State. J. Mol. Biol. 2007, 372 (3), 774– 797,  DOI: 10.1016/j.jmb.2007.05.022
    Google Scholar
    37
    Inference of Macromolecular Assemblies from Crystalline State
    Krissinel, Evgeny; Henrick, Kim
    Journal of Molecular Biology (2007), 372 (3), 774-797CODEN: JMOBAK; ISSN:0022-2836. (Elsevier Ltd.)
    The authors discuss basic phys.-chem. principles underlying the formation of stable macromol. complexes, which in many cases are likely to be the biol. units performing a certain physiol. function. The authors also consider available theor. approaches to the calcn. of macromol. affinity and entropy of complexation. The latter is shown to play an important role and make a major effect on complex size and symmetry. The authors develop a new method, based on chem. thermodn., for automatic detection of macromol. assemblies in the Protein Data Bank (PDB) entries that are the results of x-ray diffraction expts. As found, biol. units may be recovered at 80-90% success rate, which makes x-ray crystallog. an important source of exptl. data on macromol. complexes and protein-protein interactions. The method is implemented as a public WWW service (http://www.ebi.ac.uk/msd-srv/prot_int/pistart.html).
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXpvFGktb8%253D&md5=a5c764cfc7dc129f53ddc31ef9d475fa
  38. 38
    Kampmann, T.; Mueller, D. S.; Mark, A. E.; Young, P. R.; Kobe, B. The Role of histidine residues in low-pH-mediated viral membrane fusion. Structure 2006, 14 (10), 1481– 1487,  DOI: 10.1016/j.str.2006.07.011
    Google Scholar
    38
    The Role of Histidine Residues in Low-pH-Mediated Viral Membrane Fusion
    Kampmann, Thorsten; Mueller, Daniela S.; Mark, Alan E.; Young, Paul R.; Kobe, Bostjan
    Structure (Cambridge, MA, United States) (2006), 14 (10), 1481-1487CODEN: STRUE6; ISSN:0969-2126. (Cell Press)
    A central event in the invasion of a host cell by an enveloped virus is the fusion of viral and cell membranes. For many viruses, membrane fusion is driven by specific viral surface proteins that undergo large-scale conformational rearrangements, triggered by exposure to low pH in the endosome upon internalization. Here, we present evidence suggesting that in both class I (helical hairpin proteins) and class II (β-structure-rich proteins) pH-dependent fusion proteins the protonation of specific histidine residues triggers fusion via an analogous mol. mechanism. These histidines are located in the vicinity of pos. charged residues in the prefusion conformation, and they subsequently form salt bridges with neg. charged residues in the postfusion conformation. The mol. surfaces involved in the corresponding structural rearrangements leading to fusion are highly conserved and thus might provide a suitable common target for the design of antivirals, which could be active against a diverse range of pathogenic viruses.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XhtVCrtrnN&md5=ffb8d25828217e82747ef2838f53f028
  39. 39
    The PyMOL Molecular Graphics System, Version 1.8, Schrodinger, LLC, 2015.
    Google Scholar
    There is no corresponding record for this reference.
  40. 40
    Wedemeyer, W. J.; Welker, E.; Scheraga, H. A. Proline cis-trans isomerization and protein folding. Biochemistry 2002, 41 (50), 14637– 14644,  DOI: 10.1021/bi020574b
    Google Scholar
    40
    Proline Cis-Trans Isomerization and Protein Folding
    Wedemeyer, William J.; Welker, Ervin; Scheraga, Harold A.
    Biochemistry (2002), 41 (50), 14637-14644CODEN: BICHAW; ISSN:0006-2960. (American Chemical Society)
    A review. Proline cis-trans isomerization plays a key role in the rate-detg. steps of protein folding. The energetic origin of this isomerization process is summarized, and the folding and unfolding of disulfide-intact bovine pancreatic RNase A is used as an example to illustrate the kinetics and structural features of conformational changes from the heterogeneous unfolded state (consisting of cis and trans isomers of X-Pro peptide groups) to the native structure in which only one set of proline isomers is present.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38XotlOmsLw%253D&md5=f05d94502166b940f92d215d49352b6f
  41. 41
    Shen, Y.; Bax, A. Prediction of Xaa-Pro peptide bond conformation from sequence and chemical shifts. J. Biomol. NMR 2010, 46 (3), 199– 204,  DOI: 10.1007/s10858-009-9395-y
    Google Scholar
    41
    Prediction of Xaa-Pro peptide bond conformation from sequence and chemical shifts
    Shen, Yang; Bax, Ad
    Journal of Biomolecular NMR (2010), 46 (3), 199-204CODEN: JBNME9; ISSN:0925-2738. (Springer)
    The authors present a program, named Promega, to predict the Xaa-Pro peptide bond conformation on the basis of backbone chem. shifts and the amino acid sequence. Using a chem. shift database of proteins of known structure together with the PDB-extd. amino acid preference of cis Xaa-Pro peptide bonds, a cis/trans probability score is calcd. from the backbone and 13Cβ chem. shifts of the proline and its neighboring residues. For an arbitrary no. of input chem. shifts, which may include Pro-13Cγ, Promega calcs. the statistical probability that a Xaa-Pro peptide bond is cis. Besides its potential as a validation tool, Promega is particularly useful for studies of larger proteins where Pro-13Cγ assignments can be challenging, and for on-going efforts to det. protein structures exclusively on the basis of backbone and 13Cβ chem. shifts.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXivV2ku70%253D&md5=30d471955095ff1da608e18b1d3321d4
  42. 42
    Gye, H. J.; Nishizawa, T. Analysis of sialylated N-linked glycans on fish cell lines permissive to nervous necrosis virus for predicting cellular receptors of the virus. Aquaculture 2022, 555, 738198  DOI: 10.1016/j.aquaculture.2022.738198
    Google Scholar
    There is no corresponding record for this reference.
  43. 43
    Rodríguez, Y.; Cardoze, S. M.; Obineche, O. W.; Melo, C.; Persaud, A.; Fernández Romero, J. A. Small Molecules Targeting SARS-CoV-2 Spike Glycoprotein Receptor-Binding Domain. ACS Omega 2022, 7 (33), 28779– 28789,  DOI: 10.1021/acsomega.2c00844
    Google Scholar
    43
    Small Molecules Targeting SARS-CoV-2 Spike Glycoprotein Receptor-Binding Domain
    Rodriguez, Yoel; Cardoze, Scarlet Martinez; Obineche, Onyinyechi W.; Melo, Claudia; Persaud, Ashanna; Fernandez Romero, Jose A.
    ACS Omega (2022), 7 (33), 28779-28789CODEN: ACSODF; ISSN:2470-1343. (American Chemical Society)
    The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the coronavirus disease 2019 (COVID-19) pandemic. Several variants of SARS-CoV-2 have emerged worldwide. These variants show different transmissibility infectivity due to mutations in the viral spike (S) glycoprotein that interacts with the human angiotensin-converting enzyme 2 (hACE2) receptor and facilitates viral entry into target cells. Despite the effective SARS-CoV-2 vaccines, we still need to identify selective antivirals, and the S glycoprotein is a key target to neutralize the virus. We hypothesize that small mols. could disrupt the interaction of S glycoprotein with hACE2 and inhibit viral entry. We analyzed the S glycoprotein-hACE2 complex structure (PDB: 7DF4) and created models for different viral variants using visual mol. dynamics (VMD) and mol. operating environment (MOE) programs. Moreover, we started the hits search by performing structure-based mol. docking virtual screening of com. available small mols. against S glycoprotein models using OEDocking FRED-4.0.0.0 software. The FRED-4.0.0.0 Chemguass4 scoring function was used to rank the small mols. based on their affinities. The best candidate compds. were purchased and tested using a std. SARS-CoV-2 pseudotyped cell-based bioassay to investigate their antiviral activity. Three of these compds., alone or in combination, showed antiviral selectivity. These small mols. may lead to an effective antiviral treatment or serve as probes to better understand the biol. of SARS-CoV-2.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XitVKktrbO&md5=4c573b63f6c38170fbe0bff8533dd57b
  44. 44
    Chang, J.-S.; Chi, S.-C. GHSC70 is involved in the cellular entry of nervous necrosis virus. J. Virol. 2015, 89 (1), 61– 70,  DOI: 10.1128/JVI.02523-14
    Google Scholar
    There is no corresponding record for this reference.
  45. 45
    Zhang, W.; Jia, K.; Jia, P.; Xiang, Y.; Lu, X.; Liu, W.; Yi, M. Marine medaka heat shock protein 90ab1 is a receptor for red-spotted grouper nervous necrosis virus and promotes virus internalization through clathrin-mediated endocytosis. PLoS Pathog. 2020, 16 (7), e1008668  DOI: 10.1371/journal.ppat.1008668
    Google Scholar
    There is no corresponding record for this reference.
  46. 46
    Krishnan, R.; Qadiri, S. S. N.; Oh, M.-J. Functional characterization of seven-band grouper immunoglobulin like cell adhesion molecule, Nectin4 as a cellular receptor for nervous necrosis virus. Fish Shellfish Immunol. 2019, 93, 720– 725,  DOI: 10.1016/j.fsi.2019.08.019
    Google Scholar
    There is no corresponding record for this reference.
  47. 47
    Ito, Y.; Okinaka, Y.; Mori, K. I.; Sugaya, T.; Nishioka, T.; Oka, M.; Nakai, T. Variable region of betanodavirus RNA2 is sufficient to determine host specificity. Dis. Aquat. Org. 2008, 79 (3), 199– 205,  DOI: 10.3354/dao01906
    Google Scholar
    There is no corresponding record for this reference.
  48. 48
    Staring, J.; Raaben, M.; Brummelkamp, T. R. Viral escape from endosomes and host detection at a glance. J. Cell Sci. 2018, 131 (15), jcs216259  DOI: 10.1242/jcs.216259
    Google Scholar
    There is no corresponding record for this reference.
  49. 49
    Song, C.; Takai-Todaka, R.; Miki, M.; Haga, K.; Fujimoto, A.; Ishiyama, R.; Oikawa, K.; Yokoyama, M.; Miyazaki, N.; Iwasaki, K. Dynamic rotation of the protruding domain enhances the infectivity of norovirus. PLoS Pathog. 2020, 16 (7), e1008619  DOI: 10.1371/journal.ppat.1008619
    Google Scholar
    There is no corresponding record for this reference.
  50. 50
    Hu, L.; Salmen, W.; Chen, R.; Zhou, Y.; Neill, F.; Crowe, J. E.; Atmar, R. L.; Estes, M. K.; Prasad, B. V. V. Atomic structure of the predominant GII.4 human norovirus capsid reveals novel stability and plasticity. Nat. Commun. 2022, 13 (1), 1241  DOI: 10.1038/s41467-022-28757-z
    Google Scholar
    There is no corresponding record for this reference.
  51. 51
    Goto, Y.; Takahashi, N.; Fink, A. L. (1990). Mechanism of acid-induced folding of proteins. Biochemistry 1990, 29 (14), 3480– 3488,  DOI: 10.1021/bi00466a009
    Google Scholar
    51
    Mechanism of acid-induced folding of proteins
    Goto, Yuji; Takahashi, Nobuaki; Fink, Anthony L.
    Biochemistry (1990), 29 (14), 3480-8CODEN: BICHAW; ISSN:0006-2960.
    It was previously shown that β-lactamase, cytochrome c (I), and apomyoglobin (II) are maximally unfolded at pH 2 under conditions of low ionic strength, but that a further decrease in pH, by increasing the concn. of HCl, refolds the proteins to the A state with properties similar to those of a molten globule state. To understand the mechanism of acid-induced refolding of protein structure, the effects of various strong acids and their neutral salts on the acid-unfolded states of ferri-I and II were studied. The conformational transition of I was monitored at 20° by using changes in the far-UV CD and in the Soret absorption at 394 nm, and that of II was monitored by changes in the far-UV CD. Various strong acids (i.e., H2SO4, HClO4, HNO3, TCA, and trifluoroacetic acid) refolded acid-unfolded I and II to the A states as was the case with HCl. For both proteins, neutral salts of these acids caused similar conformational transitions, confirming that the anions are responsible for bringing about the transition. The order of effectiveness of anions was ferricyanide > ferrocyanide > SO42- > TCA- > SCN- > ClO4- > I- > NO3- > trifluoroacetate > Br- > Cl-. This series was similar to the electroselectivity series of anions toward the anion-exchange resins, showing that preferential binding of anions to the A states causes the conformational transitions.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3cXhsFeqs7w%253D&md5=48e21a0012913f7c73f4748b2c62a142
  52. 52
    Güthe, S.; Kapinos, L.; Möglich, A.; Meier, S.; Grzesiek, S.; Kiefhaber, T. Very fast folding and association of a trimerization domain from bacteriophage T4 fibritin. J. Mol. Biol. 2004, 337 (4), 905– 915,  DOI: 10.1016/j.jmb.2004.02.020
    Google Scholar
    There is no corresponding record for this reference.
  53. 53
    So, M.; Hall, D.; Goto, Y. Revisiting supersaturation as a factor determining amyloid fibrillation. Curr. Opin. Struct. Biol. 2016, 36, 32– 39,  DOI: 10.1016/j.sbi.2015.11.009
    Google Scholar
    53
    Revisiting supersaturation as a factor determining amyloid fibrillation
    So, Masatomo; Hall, Damien; Goto, Yuji
    Current Opinion in Structural Biology (2016), 36 (), 32-39CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)
    Amyloid fibrils involved in various diseases are formed by a nucleation-growth mechanism, similar to the crystn. of solutes from soln. Soly. and supersatn. are two of the most important factors detg. crystn. of solutes. Moreover, crystn. competes with glass formation in which solutes collapse into amorphous aggregates. Recent studies on the formation of amyloid fibrils and amorphous aggregates indicate that the partition between distinct types of aggregates can be rationally explained by a kinetic and thermodn. competition between them. Understanding the role of supersatn. in detg. aggregation-based phase transitions of denatured proteins provides an important complementary point of view to structural studies of protein aggregates.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXitVSkurjP&md5=01c17457e922fbefc12715f0bf000e1d
  54. 54
    Shih, T. C.; Ho, L. P.; Chou, H. Y.; Wu, J. L.; Pai, T. W. Comprehensive Linear Epitope Prediction System for Host Specificity in Nodaviridae. Viruses 2022, 14 (7), 1357,  DOI: 10.3390/v14071357
    Google Scholar
    There is no corresponding record for this reference.
  55. 55
    Zhang, Z.; Xing, J.; Tang, X.; Sheng, X.; Chi, H.; Zhan, W. Development and characterization of monoclonal antibodies against red-spotted grouper nervous necrosis virus and their neutralizing potency in vitro. Aquaculture 2022, 560, 738562  DOI: 10.1016/j.aquaculture.2022.738562
    Google Scholar
    There is no corresponding record for this reference.
  56. 56
    Huang, S.; Wu, Y.; Su, L.; Su, T.; Zhou, Q.; Zhang, J.; Zhao, Z.; Weng, S.; He, J.; Xie, J. A single-chain variable fragment antibody exerts anti-nervous necrosis virus activity by irreversible binding. Aquaculture 2022, 552, 738001  DOI: 10.1016/j.aquaculture.2022.738001
    Google Scholar
    There is no corresponding record for this reference.
  57. 57
    Graham, B. S.; Gilman, M. S. A.; McLellan, J. S. Structure-Based Vaccine Antigen Design. Annu. Rev. Med. 2019, 70, 91– 104,  DOI: 10.1146/annurev-med-121217-094234
    Google Scholar
    57
    Structure-Based Vaccine Antigen Design
    Graham, Barney S.; Gilman, Morgan S. A.; McLellan, Jason S.
    Annual Review of Medicine (2019), 70 (), 91-104CODEN: ARMCAH; ISSN:0066-4219. (Annual Reviews)
    A review. Enabled by new approaches for rapid identification and selection of human monoclonal antibodies, at.-level structural information for viral surface proteins, and capacity for precision engineering of protein immunogens and self-assembling nanoparticles, a new era of antigen design and display options has evolved. While HIV-1 vaccine development has been a driving force behind these technologies and concepts, clin. proof-of-concept for structure-based vaccine design may first be achieved for respiratory syncytial virus (RSV), where conformation-dependent access to neutralization-sensitive epitopes on the fusion glycoprotein dets. the capacity to induce potent neutralizing activity. Success with RSV has motivated structure-based stabilization of other class I viral fusion proteins for use as immunogens and demonstrated the importance of structural information for developing vaccines against other viral pathogens, particularly difficult targets that have resisted prior vaccine development efforts. Solving viral surface protein structures also supports rapid vaccine antigen design and application of platform manufg. approaches for emerging pathogens.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhvF2ltrw%253D&md5=ccbef186198ab5f51fe6aa228b440734
  58. 58
    Liang, J.; Edelsbrunner, H.; Woodward, C. Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design. Protein Sci. 1998, 7 (9), 1884– 1897,  DOI: 10.1002/pro.5560070905
    Google Scholar
    58
    Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design
    Liang, Jie; Edelsbrunner, Herbert; Woodward, Clare
    Protein Science (1998), 7 (9), 1884-1897CODEN: PRCIEI; ISSN:0961-8368. (Cambridge University Press)
    Identification and size characterization of surface pockets and occluded cavities are initial steps in protein structure-based ligand design. A new program, CAST, for automatically locating and measuring protein pockets and cavities, is based on precise computational geometry methods, including alpha shape and discrete flow theory. CAST identifies and measures pockets and pocket mouth openings, as well as cavities. The program specifies the atoms lining pockets, pocket openings, and buried cavities; the vol. and area of pockets and cavities; and the area and circumference of mouth openings. CAST anal. of over 100 proteins has been carried out; proteins examd. include a set of 51 monomeric enzyme-ligand structures, several elastase-inhibitor complexes, the FK506 binding protein, 30 HIV-1 protease-inhibitor complexes, and a no. of small and large protein inhibitors. Medium-sized globular proteins typically have 10-20 pockets/cavities. Most often, binding sites are pockets with 1-2 mouth openings; much less frequently they are cavities. Ligand binding pockets vary widely in size, most within the range 102-103 Å3. Statistical anal. reveals that the no. of pockets and cavities is correlated with protein size, but there is no correlation between the size of the protein and the size of binding sites. Most frequently, the largest pocket/cavity is the active site, but there are a no. of instructive exceptions. Ligand vol. and binding site vol. are somewhat correlated when binding site vol. is ≤700 Å3, but the ligand seldom occupies the entire site. Auxiliary pockets near the active site have been suggested as addnl. binding surface for designed ligands (Mattos C et al., 1994, Nat Struct Biol 1:55-58). Anal. of elastase-inhibitor complexes suggests that CAST can identify ancillary pockets suitable for recruitment in ligand design strategies. Anal. of the FK506 binding protein, and of compds. developed in SAR by NMR (Shuker SB et al., 1996, Science 274:1531-1534), indicates that CAST pocket computation may provide a priori identification of target proteins for linked-fragment design. CAST anal. of 30 HIV-1 protease-inhibitor complexes shows that the flexible active site pocket can vary over a range of 853-1,566 Å3, and that there are two pockets near or adjoining the active site that may be recruited for ligand design.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1cXmtFGjsLo%253D&md5=a6f62e03de03da0a6e51361de7a08765
  59. 59
    Naceri, S.; Marc, D.; Blot, R.; Flatters, D.; Camproux, A. C. Druggable Pockets at the RNA Interface Region of Influenza A Virus NS1 Protein Are Conserved across Sequence Variants from Distinct Subtypes. Biomolecules 2023, 13 (1), 64,  DOI: 10.3390/biom13010064
    Google Scholar
    There is no corresponding record for this reference.
  60. 60
    Gavenonis, J.; Sheneman, B. A.; Siegert, T. R.; Eshelman, M. R.; Kritzer, J. A. Comprehensive analysis of loops at protein-protein interfaces for macrocycle design. Nat. Chem. Biol. 2014, 10 (9), 716– 722,  DOI: 10.1038/nchembio.1580
    Google Scholar
    60
    Comprehensive analysis of loops at protein-protein interfaces for macrocycle design
    Gavenonis, Jason; Sheneman, Bradley A.; Siegert, Timothy R.; Eshelman, Matthew R.; Kritzer, Joshua A.
    Nature Chemical Biology (2014), 10 (9), 716-722CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
    Inhibiting protein-protein interactions (PPIs) with synthetic mols. remains a frontier of chem. biol. Many PPIs have been successfully targeted by mimicking α-helixes at interfaces, but most PPIs are mediated by nonhelical, nonstrand peptide loops. We sought to comprehensively identify and analyze these loop-mediated PPIs by writing and implementing LoopFinder, a customizable program that can identify loop-mediated PPIs within all of the protein-protein complexes in the Protein Data Bank. Comprehensive anal. of the entire set of 25,005 interface loops revealed common structural motifs and unique features that distinguish loop-mediated PPIs from other PPIs. 'Hot loops', named in analogy to protein hot spots, were identified as loops with favorable properties for mimicry using synthetic mols. The hot loops and their binding partners represent new and promising PPIs for the development of macrocycle and constrained peptide inhibitors.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhtFygs7jE&md5=bf07bd724f173ea74467ab7cd5aedb6a
  61. 61
    Corbi-Verge, C.; Kim, P. M. Motif mediated protein-protein interactions as drug targets. Cell Commun. Signal 2016, 14, 8  DOI: 10.1186/s12964-016-0131-4
    Google Scholar
    There is no corresponding record for this reference.
  62. 62
    Marković, V.; Szczepańska, A.; Berlicki, Ł. Antiviral Protein-Protein Interaction Inhibitors. J. Med. Chem. 2024, 67 (5), 3205– 3231,  DOI: 10.1021/acs.jmedchem.3c01543
    Google Scholar
    There is no corresponding record for this reference.
  63. 63
    Zheng, S. Q.; Palovcak, E.; Armache, J. P.; Verba, K. A.; Cheng, Y.; Agard, D. A. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 2017, 14 (4), 331– 332,  DOI: 10.1038/nmeth.4193
    Google Scholar
    63
    MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy
    Zheng, Shawn Q.; Palovcak, Eugene; Armache, Jean-Paul; Verba, Kliment A.; Cheng, Yifan; Agard, David A.
    Nature Methods (2017), 14 (4), 331-332CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)
    A review on anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Here we describe MotionCor2, a software tool for anisotropic correction of beam-induced motion. Overall, MotionCor2 is extremely robust and sufficiently accurate at correcting local motions so that the very time-consuming and computationally intensive particle polishing in RELION can be skipped, importantly, it also works on a wide range of data sets, including cryo tomog. tilt series.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXjt1ags7g%253D&md5=5f4e225ef8123dacd8475d526175e1d2
  64. 64
    Rohou, A.; Grigorieff, N. CTFFIND4: Fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 2015, 192 (2), 216– 221,  DOI: 10.1016/j.jsb.2015.08.008
    Google Scholar
    64
    CTFFIND4: Fast and accurate defocus estimation from electron micrographs
    Rohou Alexis; Grigorieff Nikolaus
    Journal of structural biology (2015), 192 (2), 216-21 ISSN:.
    CTFFIND is a widely-used program for the estimation of objective lens defocus parameters from transmission electron micrographs. Defocus parameters are estimated by fitting a model of the microscope's contrast transfer function (CTF) to an image's amplitude spectrum. Here we describe modifications to the algorithm which make it significantly faster and more suitable for use with images collected using modern technologies such as dose fractionation and phase plates. We show that this new version preserves the accuracy of the original algorithm while allowing for higher throughput. We also describe a measure of the quality of the fit as a function of spatial frequency and suggest this can be used to define the highest resolution at which CTF oscillations were successfully modeled.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC287js1Whsg%253D%253D&md5=8500953ad4898ae82de6f8cdc95832cf
  65. 65
    Punjani, A.; Rubinstein, J. L.; Fleet, D. J.; Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 2017, 14 (3), 290– 296,  DOI: 10.1038/nmeth.4169
    Google Scholar
    65
    cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination
    Punjani, Ali; Rubinstein, John L.; Fleet, David J.; Brubaker, Marcus A.
    Nature Methods (2017), 14 (3), 290-296CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)
    Single-particle electron cryomicroscopy (cryo-EM) is a powerful method for detg. the structures of biol. macromols. With automated microscopes, cryo-EM data can often be obtained in a few days. However, processing cryo-EM image data to reveal heterogeneity in the protein structure and to refine 3D maps to high resoln. frequently becomes a severe bottleneck, requiring expert intervention, prior structural knowledge, and weeks of calcns. on expensive computer clusters. Here we show that stochastic gradient descent (SGD) and branch-and-bound max. likelihood optimization algorithms permit the major steps in cryo-EM structure detn. to be performed in hours or minutes on an inexpensive desktop computer. Furthermore, SGD with Bayesian marginalization allows ab initio 3D classification, enabling automated anal. and discovery of unexpected structures without bias from a ref. map. These algorithms are combined in a user-friendly computer program named cryoSPARC (http://www.cryosparc.com).
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXitlGisbs%253D&md5=95d468147707707e70ac0ad38dd6ebf6
  66. 66
    Yang, Z.; Lasker, K.; Schneidman-Duhovny, D.; Webb, B.; Huang, C. C.; Pettersen, E. F.; Goddard, T. D.; Meng, E. C.; Sali, A.; Ferrin, T. E. UCSF Chimera, MODELLER, and IMP: an integrated modeling system. J. Struct Biol. 2012, 179 (3), 269– 278,  DOI: 10.1016/j.jsb.2011.09.006
    Google Scholar
    There is no corresponding record for this reference.
  67. 67
    Dam, J.; Schuck, P. Calculating Sedimentation Coefficient Distributions by Direct Modeling of Sedimentation Velocity Concentration Profiles. In Methods in Enzymology; Academic Press, 2004; Vol. 384, pp 185– 212.
    Google Scholar
    There is no corresponding record for this reference.
  68. 68
    Ribaric, S.; Peterec, D.; Sketelj, J. Computer aided data acquisition and analysis of acetlycholinesterase velocity sedimentation profiles. Comput. Methods Program. Biomed. 1996, 49 (2), 149– 156,  DOI: 10.1016/0169-2607(96)01719-1
    Google Scholar
    There is no corresponding record for this reference.
  69. 69
    Johnson, B. A.; Blevins, R. A. NMR View: A computer program for the visualization and analysis of NMR data. J. Biomol. NMR 1994, 4 (5), 603– 614,  DOI: 10.1007/BF00404272
    Google Scholar
    69
    NMRView: a computer program for the visualization and analysis of NMR data
    Johnson, Bruce A.; Blevins, Richard A.
    Journal of Biomolecular NMR (1994), 4 (5), 603-14CODEN: JBNME9; ISSN:0925-2738.
    NMRView is a computer program designed for the visualization and anal. of NMR data. It allows the user to interact with a practically unlimited no. of 2D, 3D and 4D NMR data files. Any no. of spectral windows can be displayed on the screen in any size and location. Automatic peak picking and facilitated peak anal. features are included to aid in the assignment of complex NMR spectra. NMR View provides structure anal. features and data transfer to and from structural generation programs, allowing for a tight coupling between the spectral anal. and structural generation.,. Visual correlation between structures and spectra can be done with the Mol. Data Viewer, a mol. graphics program with bidirectional communication to NMR View. The used interface can be customized and a command language is provided to allow for the automation of various tasks.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2cXmt1Gkurw%253D&md5=6a93a0d9704fbbb654abc192203c68ae
  70. 70
    Schwieters, C. D.; Kuszewski, J. J.; Marius Clore, G. Using Xplor–NIH for NMR molecular structure determination. Prog. Nucl. Magn. Reson. Spectrosc. 2006, 48 (1), 47– 62,  DOI: 10.1016/j.pnmrs.2005.10.001
    Google Scholar
    70
    Using Xplor-NIH for NMR molecular structure determination
    Schwieters, Charles D.; Kuszewski, John J.; Clore, G. Marius
    Progress in Nuclear Magnetic Resonance Spectroscopy (2006), 48 (1), 47-62CODEN: PNMRAT; ISSN:0079-6565. (Elsevier B.V.)
    A review of the title program for computerized structure detn.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XjtVOiurY%253D&md5=56257dc51ddafdf8c467d30aef191ee2
  71. 71
    Maciejewski, M. W.; Schuyler, A. D.; Gryk, M. R.; Moraru, I. I.; Romero, P. R.; Ulrich, E. L.; Eghbalnia, H. R.; Livny, M.; Delaglio, F.; Hoch, J. C. NMRbox: A Resource for Biomolecular NMR Computation. Biophys. J. 2017, 112 (8), 1529– 1534,  DOI: 10.1016/j.bpj.2017.03.011
    Google Scholar
    71
    NMRbox: A Resource for Biomolecular NMR Computation
    Maciejewski, Mark W.; Schuyler, Adam D.; Gryk, Michael R.; Moraru, Ion I.; Romero, Pedro R.; Ulrich, Eldon L.; Eghbalnia, Hamid R.; Livny, Miron; Delaglio, Frank; Hoch, Jeffrey C.
    Biophysical Journal (2017), 112 (8), 1529-1534CODEN: BIOJAU; ISSN:0006-3495. (Cell Press)
    Advances in computation have been enabling many recent advances in biomol. applications of NMR. Due to the wide diversity of applications of NMR, the no. and variety of software packages for processing and analyzing NMR data is quite large, with labs relying on dozens, if not hundreds of software packages. Discovery, acquisition, installation, and maintenance of all these packages is a burdensome task. Because the majority of software packages originate in academic labs, persistence of the software is compromised when developers graduate, funding ceases, or investigators turn to other projects. To simplify access to and use of biomol. NMR software, foster persistence, and enhance reproducibility of computational workflows, the authors have developed NMRbox, a shared resource for NMR software and computation. NMRbox employs virtualization to provide a comprehensive software environment preconfigured with hundreds of software packages, available as a downloadable virtual machine or as a Platform-as-a-Service supported by a dedicated compute cloud. Ongoing development includes a metadata harvester to regularize, annotate, and preserve workflows and facilitate and enhance data depositions to BioMagResBank, and tools for Bayesian inference to enhance the robustness and extensibility of computational analyses. In addn. to facilitating use and preservation of the rich and dynamic software environment for biomol. NMR, NMRbox fosters the development and deployment of a new class of metasoftware packages. NMRbox is freely available to not-for-profit users.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXlvFWgtL8%253D&md5=a990d67aed35b62cd010f0f5396d0f82
  72. 72
    Shen, Y.; Delaglio, F.; Cornilescu, G.; Bax, A. TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts. J. Biomol. NMR 2009, 44 (4), 213– 223,  DOI: 10.1007/s10858-009-9333-z
    Google Scholar
    72
    TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts
    Shen, Yang; Delaglio, Frank; Cornilescu, Gabriel; Bax, Ad
    Journal of Biomolecular NMR (2009), 44 (4), 213-223CODEN: JBNME9; ISSN:0925-2738. (Springer)
    NMR chem. shifts in proteins depend strongly on local structure. The program TALOS establishes an empirical relation between 13C, 15N and 1H chem. shifts and backbone torsion angles .vphi. and ψ. Extension of the original 20-protein database to 200 proteins increased the fraction of residues for which backbone angles could be predicted from 65 to 74%, while reducing the error rate from 3 to 2.5%. Addn. of a two-layer neural network filter to the database fragment selection process forms the basis for a new program, TALOS+, which further enhances the prediction rate to 88.5%, without increasing the error rate. Excluding the 2.5% of residues for which TALOS+ makes predictions that strongly differ from those obsd. in the cryst. state, the accuracy of predicted .vphi. and ψ angles, equals ±13°. Large discrepancies between predictions and crystal structures are primarily limited to loop regions, and for the few cases where multiple X-ray structures are available such residues are often found in different states in the different structures. The TALOS+ output includes predictions for individual residues with missing chem. shifts, and the neural network component of the program also predicts secondary structure with good accuracy.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXovFajtr8%253D&md5=ee3f8647a02e19937988056fd7af04b2
  73. 73
    Tian, Y.; Schwieters, C. D.; Opella, S. J.; Marassi, F. M. A practical implicit solvent potential for NMR structure calculation. J. Magn. Reson. 2014, 243, 54– 64,  DOI: 10.1016/j.jmr.2014.03.011
    Google Scholar
    73
    A practical implicit solvent potential for NMR structure calculation
    Tian, Ye; Schwieters, Charles D.; Opella, Stanley J.; Marassi, Francesca M.
    Journal of Magnetic Resonance (2014), 243 (), 54-64CODEN: JMARF3; ISSN:1090-7807. (Elsevier B.V.)
    The benefits of protein structure refinement in water are well documented. However, performing structure refinement with explicit at. representation of the solvent mols. is computationally expensive and impractical for NMR-restrained structure calcns. that start from completely extended polypeptide templates. Here we describe a new implicit solvation potential, EEFx (Effective Energy Function for XPLOR-NIH), for NMR-restrained structure calcns. of proteins in XPLOR-NIH. The key components of EEFx are an energy term for solvation energy that works together with other nonbonded energy functions, and a dedicated force field for conformational and nonbonded protein interaction parameters. The initial results obtained with EEFx show that significant improvements in structural quality can be obtained. EEFx is computationally efficient and can be used both to fold and refine structures. Overall, EEFx improves the quality of protein conformation and nonbonded at. interactions. Moreover, such benefits are accompanied by enhanced structural precision and enhanced structural accuracy, reflected in improved agreement with the cross-validated dipolar coupling data. Finally, implementation of EEFx calcns. is straightforward and computationally efficient. Overall, EEFx provides a useful method for the practical calcn. of exptl. protein structures in a phys. realistic environment.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXovVahu70%253D&md5=68ad01c9ceb6078a4291b0d6e96408e8
  74. 74
    Gore, S.; Sanz García, E.; Hendrickx, P. M. S.; Gutmanas, A.; Westbrook, J. D.; Yang, H.; Feng, Z.; Baskaran, K.; Berrisford, J. M.; Hudson, B. P. Validation of Structures in the Protein Data Bank. Structure 2017, 25 (12), 1916– 1927,  DOI: 10.1016/j.str.2017.10.009
    Google Scholar
    74
    Validation of Structures in the Protein Data Bank
    Gore, Swanand; Sanz Garcia, Eduardo; Hendrickx, Pieter M. S.; Gutmanas, Aleksandras; Westbrook, John D.; Yang, Huanwang; Feng, Zukang; Baskaran, Kumaran; Berrisford, John M.; Hudson, Brian P.; Ikegawa, Yasuyo; Kobayashi, Naohiro; Lawson, Catherine L.; Mading, Steve; Mak, Lora; Mukhopadhyay, Abhik; Oldfield, Thomas J.; Patwardhan, Ardan; Peisach, Ezra; Sahni, Gaurav; Sekharan, Monica R.; Sen, Sanchayita; Shao, Chenghua; Smart, Oliver S.; Ulrich, Eldon L.; Yamashita, Reiko; Quesada, Martha; Young, Jasmine Y.; Nakamura, Haruki; Markley, John L.; Berman, Helen M.; Burley, Stephen K.; Velankar, Sameer; Kleywegt, Gerard J.
    Structure (Oxford, United Kingdom) (2017), 25 (12), 1916-1927CODEN: STRUE6; ISSN:0969-2126. (Elsevier Ltd.)
    The Worldwide PDB recently launched a deposition, biocuration, and validation tool: OneDep. At various stages of OneDep data processing, validation reports for three-dimensional structures of biol. macromols. are produced. These reports are based on recommendations of expert task forces representing crystallog., NMR, and cryoelectron microscopy communities. The reports provide useful metrics with which depositors can evaluate the quality of the exptl. data, the structural model, and the fit between them. The validation module is also available as a stand-alone web server and as a programmatically accessible web service. A growing no. of journals require the official wwPDB validation reports (produced at biocuration) to accompany manuscripts describing macromol. structures. Upon public release of the structure, the validation report becomes part of the public PDB archive. Geometric quality scores for proteins in the PDB archive have improved over the past decade.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhvVOitbnE&md5=680c220ca1354e8a089638ae978501d3
  75. 75
    Schwarzinger, S.; Kroon, G. J.; Foss, T. R.; Wright, P. E.; Dyson, H. J. Random coil chemical shifts in acidic 8 M urea: implementation of random coil shift data in NMRView. J. Biomol. NMR 2000, 18 (1), 43– 48,  DOI: 10.1023/A:1008386816521
    Google Scholar
    75
    Random coil chemical shifts in acidic 8 M urea: implementation of random coil shift data in NMRView
    Schwarzinger, Stephan; Kroon, Gerard J. A.; Foss, Ted R.; Wright, Peter E.; Dyson, H. Jane
    Journal of Biomolecular NMR (2000), 18 (1), 43-48CODEN: JBNME9; ISSN:0925-2738. (Kluwer Academic Publishers)
    Studies of proteins unfolded in acid or chem. denaturant can help in unraveling events during the earliest phases of protein folding. In order for meaningful comparisons to be made of residual structure in unfolded states, it is necessary to use random coil chem. shifts that are valid for the exptl. system under study. We present a set of random coil chem. shifts obtained for model peptides under exptl. conditions used in studies of denatured proteins. This new set, together with previously published data sets, has been incorporated into a software interface for NMRView, allowing selection of the random coil data set that fits the exptl. conditions best.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXnvVGit7w%253D&md5=cf30cd19aa4420c807a0525789db9be1
  76. 76
    Berendsen, H. J. C.; van der Spoel, D.; van Drunen, R. GROMACS: A message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 1995, 91 (1), 43– 56,  DOI: 10.1016/0010-4655(95)00042-E
    Google Scholar
    76
    GROMACS: A message-passing parallel molecular dynamics implementation
    Berendsen, H. J. C.; van der Spoel, D.; van Drunen, R.
    Computer Physics Communications (1995), 91 (1-3), 43-56CODEN: CPHCBZ; ISSN:0010-4655. (Elsevier)
    A parallel message-passing implementation of a mol. dynamics (MD) program that is useful for bio(macro)mols. in aq. environment is described. The software has been developed for a custom-designed 32-processor ring GROMACS (Groningen MAchine for Chem. Simulation) with communication to and from left and right neighbors, but can run on any parallel system onto which a a ring of processors can be mapped and which supports PVM-like block send and receive calls. The GROMACS software consists of a preprocessor, a parallel MD and energy minimization program that can use an arbitrary no. of processors (including one), an optional monitor, and several anal. tools. The programs are written in ANSI C and available by ftp (information: [email protected]). The functionality is based on the GROMOS (Groningen Mol. Simulation) package (van Gunsteren and Berendsen, 1987; BIOMOS B.V., Nijenborgh 4, 9747 AG Groningen). Conversion programs between GROMOS and GROMACS formats are included.The MD program can handle rectangular periodic boundary conditions with temp. and pressure scaling. The interactions that can be handled without modification are variable non-bonded pair interactions with Coulomb and Lennard-Jones or Buckingham potentials, using a twin-range cut-off based on charge groups, and fixed bonded interactions of either harmonic or constraint type for bonds and bond angles and either periodic or cosine power series interactions for dihedral angles. Special forces can be added to groups of particles (for non-equil. dynamics or for position restraining) or between particles (for distance restraints). The parallelism is based on particle decompn. Interprocessor communication is largely limited to position and force distribution over the ring once per time step.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXps1Wrtr0%253D&md5=04d823aeab28ca374efb86839c705179
  77. 77
    Jurrus, E.; Engel, D.; Star, K.; Monson, K.; Brandi, J.; Felberg, L. E.; Brookes, D. H.; Wilson, L.; Chen, J.; Liles, K. Improvements to the APBS biomolecular solvation software suite. Protein Sci. 2018, 27 (1), 112– 128,  DOI: 10.1002/pro.3280
    Google Scholar
    77
    Improvements to the APBS biomolecular solvation software suite
    Jurrus, Elizabeth; Engel, Dave; Star, Keith; Monson, Kyle; Brandi, Juan; Felberg, Lisa E.; Brookes, David H.; Wilson, Leighton; Chen, Jiahui; Liles, Karina; Chun, Minju; Li, Peter; Gohara, David W.; Dolinsky, Todd; Konecny, Robert; Koes, David R.; Nielsen, Jens Erik; Head-Gordon, Teresa; Geng, Weihua; Krasny, Robert; Wei, Guo-Wei; Holst, Michael J.; McCammon, J. Andrew; Baker, Nathan A.
    Protein Science (2018), 27 (1), 112-128CODEN: PRCIEI; ISSN:1469-896X. (Wiley-Blackwell)
    The Adaptive Poisson-Boltzmann Solver (APBS) software was developed to solve the equations of continuum electrostatics for large biomol. assemblages that have provided impact in the study of a broad range of chem., biol., and biomedical applications. APBS addresses the three key technol. challenges for understanding solvation and electrostatics in biomedical applications: accurate and efficient models for biomol. solvation and electrostatics, robust and scalable software for applying those theories to biomol. systems, and mechanisms for sharing and analyzing biomol. electrostatics data in the scientific community. To address new research applications and advancing computational capabilities, we have continually updated APBS and its suite of accompanying software since its release in 2001. In this article, we discuss the models and capabilities that have recently been implemented within the APBS software package including a Poisson-Boltzmann anal. and a semi-anal. solver, an optimized boundary element solver, a geometry-based geometric flow solvation model, a graph theory-based algorithm for detg. pKa values, and an improved web-based visualization tool for viewing electrostatics.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhslSkt7vI&md5=99651d125e38f4a85d453fecf0f71652
  78. 78
    Martínez, L.; Andrade, R.; Birgin, E. G.; Martínez, J. M. PACKMOL: A package for building initial configurations for molecular dynamics simulations. J. Comput. Chem. 2009, 30 (13), 2157– 2164,  DOI: 10.1002/jcc.21224
    Google Scholar
    78
    PACKMOL: A package for building initial configurations for molecular dynamics simulations
    Martinez, L.; Andrade, R.; Birgin, E. G.; Martinez, J. M.
    Journal of Computational Chemistry (2009), 30 (13), 2157-2164CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
    Adequate initial configurations for mol. dynamics simulations consist of arrangements of mols. distributed in space in such a way to approx. represent the system's overall structure. In order that the simulations are not disrupted by large van der Waals repulsive interactions, atoms from different mols. must keep safe pairwise distances. Obtaining such a mol. arrangement can be considered a packing problem: Each type mol. must satisfy spatial constraints related to the geometry of the system, and the distance between atoms of different mols. must be greater than some specified tolerance. We have developed a code able to pack millions of atoms, grouped in arbitrarily complex mols., inside a variety of three-dimensional regions. The regions may be intersections of spheres, ellipses, cylinders, planes, or boxes. The user must provide only the structure of one mol. of each type and the geometrical constraints that each type of mol. must satisfy. Building complex mixts., interfaces, solvating biomols. in water, other solvents, or mixts. of solvents, is straightforward. In addn., different atoms belonging to the same mol. may also be restricted to different spatial regions, in such a way that more ordered mol. arrangements can be built, as micelles, lipid double-layers, etc. The packing time for state-of-the-art mol. dynamics systems varies from a few seconds to a few minutes in a personal computer. The input files are simple and currently compatible with PDB, Tinker, Molden, or Moldy coordinate files. The package is distributed as free software and can be downloaded from . © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXptleqsb8%253D&md5=2a76255c873b866a26540f7e84496272
  79. 79
    Rühle, V. Pressure coupling/barostats. 2008.
    Google Scholar
    There is no corresponding record for this reference.
  80. 80
    Essmann, U.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee, H.; Pedersen, L. G. A smooth particle mesh Ewald method. J. Chem. Phys. 1995, 103 (19), 8577– 8593,  DOI: 10.1063/1.470117
    Google Scholar
    80
    A smooth particle mesh Ewald method
    Essmann, Ulrich; Perera, Lalith; Berkowitz, Max L.; Darden, Tom; Lee, Hsing; Pedersen, Lee G.
    Journal of Chemical Physics (1995), 103 (19), 8577-93CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
    The previously developed particle mesh Ewald method is reformulated in terms of efficient B-spline interpolation of the structure factors. This reformulation allows a natural extension of the method to potentials of the form 1/rp with p ≥ 1. Furthermore, efficient calcn. of the virial tensor follows. Use of B-splines in the place of Lagrange interpolation leads to analytic gradients as well as a significant improvement in the accuracy. The authors demonstrate that arbitrary accuracy can be achieved, independent of system size N, at a cost that scales as N log(N). For biomol. systems with many thousands of atoms and this method permits the use of Ewald summation at a computational cost comparable to that of a simple truncation method of 10 Å or less.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXptlehtrw%253D&md5=092a679dd3bee08da28df41e302383a7
  81. 81
    Lindorff-Larsen, K.; Piana, S.; Palmo, K.; Maragakis, P.; Klepeis, J. L.; Dror, R. O.; Shaw, D. E. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 2010, 78, 1950– 1958,  DOI: 10.1002/prot.22711
    Google Scholar
    81
    Improved side-chain torsion potentials for the Amber ff99SB protein force field
    Lindorff-Larsen, Kresten; Piana, Stefano; Palmo, Kim; Maragakis, Paul; Klepeis, John L.; Dror, Ron O.; Shaw, David E.
    Proteins: Structure, Function, and Bioinformatics (2010), 78 (8), 1950-1958CODEN: PSFBAF ISSN:. (Wiley-Liss, Inc.)
    Recent advances in hardware and software have enabled increasingly long mol. dynamics (MD) simulations of biomols., exposing certain limitations in the accuracy of the force fields used for such simulations and spurring efforts to refine these force fields. Recent modifications to the Amber and CHARMM protein force fields, for example, have improved the backbone torsion potentials, remedying deficiencies in earlier versions. Here, the authors further advance simulation accuracy by improving the amino acid side-chain torsion potentials of the Amber ff99SB force field. First, the authors used simulations of model alpha-helical systems to identify the four residue types whose rotamer distribution differed the most from expectations based on Protein Data Bank statistics. Second, the authors optimized the side-chain torsion potentials of these residues to match new, high-level quantum-mech. calcns. Finally, the authors used microsecond-timescale MD simulations in explicit solvent to validate the resulting force field against a large set of exptl. NMR measurements that directly probe side-chain conformations. The new force field, which the authors have termed Amber ff99SB-ILDN, exhibits considerably better agreement with the NMR data. Proteins 2010. © 2010 Wiley-Liss, Inc.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXkvFegtLo%253D&md5=447a9004026e2b93f0f7beff165daa09
  82. 82
    Mark, P.; Nilsson, L. Structure and Dynamics of the TIP3P, SPC, and SPC/E Water Models at 298 K. J. Phys. Chem. A 2001, 105 (43), 9954– 9960,  DOI: 10.1021/jp003020w
    Google Scholar
    82
    Structure and Dynamics of the TIP3P, SPC, and SPC/E Water Models at 298 K
    Mark, Pekka; Nilsson, Lennart
    Journal of Physical Chemistry A (2001), 105 (43), 9954-9960CODEN: JPCAFH; ISSN:1089-5639. (American Chemical Society)
    Mol. dynamics simulations of five water models, the TIP3P (original and modified), SPC (original and refined), and SPC/E (original), were performed using the CHARMM mol. mechanics program. All simulations were carried out in the microcanonical NVE ensemble, using 901 water mols. in a cubic simulation cell furnished with periodic boundary conditions at 298 K. The SHAKE algorithm was used to keep water mols. rigid. Nanosecond trajectories were calcd. with all water models for high statistical accuracy. The characteristic self-diffusion coeffs. D and radial distribution functions, gOO, gOH, and gHH for all five water models were detd. and compared to exptl. data. The effects of velocity rescaling on the self-diffusion coeff. D were examd. All these empirical water models used in this study are similar by having three interaction sites, but the small differences in their pair potentials composed of Lennard-Jones (LJ) and Coulombic terms give significant differences in the calcd. self-diffusion coeffs., and in the height of the second peak of the radial distribution function gOO.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3MXntlWrurs%253D&md5=fecd3db40210b04e8b2a933ea07b131e
  83. 83
    de Vries, S. J.; van Dijk, M.; Bonvin, A. M. J. J. The HADDOCK web server for data-driven biomolecular docking. Nat. Protoc. 2010, 5 (5), 883– 897,  DOI: 10.1038/nprot.2010.32
    Google Scholar
    83
    The HADDOCK web server for data-driven biomolecular docking
    de Vries, Sjoerd J.; van Dijk, Marc; Bonvin, Alexandre M. J. J.
    Nature Protocols (2010), 5 (5), 883-897CODEN: NPARDW; ISSN:1750-2799. (Nature Publishing Group)
    Computational docking is the prediction or modeling of the three-dimensional structure of a biomol. complex, starting from the structures of the individual mols. in their free, unbound form. HADDOCK is a popular docking program that takes a data-driven approach to docking, with support for a wide range of exptl. data. Here the authors present the HADDOCK web server protocol, facilitating the modeling of biomol. complexes for a wide community. The main web interface is user-friendly, requiring only the structures of the individual components and a list of interacting residues as input. Addnl. web interfaces allow the more advanced user to exploit the full range of exptl. data supported by HADDOCK and to customize the docking process. The HADDOCK server has access to the resources of a dedicated cluster and of the e-NMR GRID infrastructure. Therefore, a typical docking run takes only a few minutes to prep. and a few hours to complete.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXltl2qsLw%253D&md5=dbf801d99065e8d2c3aedc016209838c
  84. 84
    Xue, L. C.; Rodrigues, J. P.; Kastritis, P. L.; Bonvin, A. M.; Vangone, A. PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics 2016, 32 (23), 3676– 3678,  DOI: 10.1093/bioinformatics/btw514
    Google Scholar
    There is no corresponding record for this reference.
  85. 85
    Schuck, P.; Zhao, H. Sedimentation Velocity Analytical Ultracentrifugation: Interacting Systems (1st ed.); CRC Press, 2017.
    Google Scholar
    There is no corresponding record for this reference.
  86. 86
    Kyte, J.; Doolittle, R. F. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 1982, 157 (1), 105– 132,  DOI: 10.1016/0022-2836(82)90515-0
    Google Scholar
    86
    A simple method for displaying the hydropathic character of a protein
    Kyte, Jack; Doolittle, Russell F.
    Journal of Molecular Biology (1982), 157 (1), 105-32CODEN: JMOBAK; ISSN:0022-2836.
    A computer program that progressively evaluates the hydrophilicity and hydrophobicity of a protein along its amino acid sequence was devised. A hydropathy scale takes into consideration the hydrophilic and hydrophobic properties of each of the 20 amino acid side chains. Correlation was demonstrated between the plotted values and known structures detd. by crystallog.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL38Xks1yjtro%253D&md5=ee67eb115939dfe56b2b2cae2c32dbd3

Cited By

Click to copy section linkSection link copied!

This article has not yet been cited by other publications.

Download PDF
Go to ACS Infectious Diseases
Get e-Alerts
Get e-Alerts

ACS Infectious Diseases

Cite this: ACS Infect. Dis. 2024, 10, 9, 3304–3319
Click to copy citationCitation copied!
https://doi.org/10.1021/acsinfecdis.4c00407
Published August 1, 2024

Copyright © 2024 The Authors. Published by American Chemical Society. This publication is licensed under

CC-BY 4.0 .

Article Views

791

Altmetric

-

Citations

-
Learn about these metrics

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.

  • Abstract

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 1

    Figure 1. GNNV atomic model and cryo-EM structures in three different pH environments. (A) Atomic models of GNNV S-domain (PDB 4WIZ) and P-domain (PDB 4RFU). (B) Surface views of GNNV virus-like particles at pH 8.0 (left panel), 6.5 (middle panel), and 5.0 (right panel). The views are colored with a heatmap according to the local radius. (C) Conformational changes of the protrusion in response to the change in pH: 8.0 (left panel), 6.5 (middle panel), and 5.0 (right panel). The protrusions rotate clockwise and shift ∼5 Å toward the capsid shell as the pH decreases. (D) Enlarged views of pores (at 5- and 3-fold axis) in the capsid shell surface for different pH environments. The size of a pore at each pH condition is indicated. To highlight the pore at the 3-fold axis, the densities underneath are colored in light purple.

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 2

    Figure 2. Effects of pH on GNNV-P in solution. (A) 1H–15N HSQC spectra of GNNV-P recorded at pH 7.0 (blue) and 5.0 (red). (B) Small box in panel (A) is enlarged. The six peaks, in green, at pH 7.0 are from Y279, L263, I222, I323, Q322, and R321, and the peaks, in red, at pH 5.0 are from E217 and I222. Y279, L263, I323, and Q322 disappear in the 1H–15N HSQC as the pH decreases. (C) Residue chemical shift perturbations (CSPs) by comparing 15N-labeled (pH 7.0) and 13C-, 15N-, and D-labeled (pH 5.0) HSQC spectra. The pH-sensitive Regions I (L263–Y279), II (W301–N303), and III (V312–V327) are highlighted in orange, yellow, and green, respectively. Residues with CSPs >2 standard deviations in pH-sensitive Regions I and III are highlighted in red and dark green, respectively. (D) pH-sensitive residues presented in panel (C) are mapped onto a surface representation of the GNNV-P crystal structure, (22) with the same color scheme as in panel (B).

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 3

    Figure 3. Structure of GNNV-P determined in solution at neutral pH. (A) Sedimentation velocity analytical ultracentrifugation (SV AUC) data of GNNV-P obtained at pH 7.0 (black), 6.0 (blue), and 5.0 (red) fitted to a continuous sedimentation coefficient distribution c(s) model. Sedimentation coefficients and the molecular weight determined by SEDFIT are denoted above each peak. (B) Backbone (N, Cα, and C′) superimposition of the ensemble of 20 low-energy conformations. The N-terminal is colored blue, and the C-terminal is colored red. (C) Cartoon representation of the GNNV-P solution structure with β-strands and α-helices highlighted in red and blue, respectively. (D) Superimposition of the GNNV-P structure determined by NMR at neutral pH (pink) and by the X-ray crystal (PDB 4RFU, blue). (E) Schematic representation of GNNV-P secondary structures, as determined by NMR (pH 7.0) and X-ray crystallography (pH 6.5). β-strands are displayed as black arrows and α-helices as gray barrels, with bordering residues indicated.

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 4

    Figure 4. MD-predicted structure of the GNNV-P oligomer at low pH. (A) Top and side view cartoon representations of the GNNV-P trimer formed under acidic pH conditions, as determined by molecular dynamics (MD) simulations. Chain A (blue), chain B (pink), and chain C (green). (B) Intermolecular interactions between neighboring GNNV-P units in the trimer. Residues at the trimeric interface are depicted as yellow sticks, and interactions are shown as dashed magenta lines. (C–E) Details of trimeric interface interactions between GNNV-P chains A and B, as boxed in panel (B).

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 5

    Figure 5. GNNV-P undergoes a low-pH-induced conformational change. (A) Secondary structure propensities of GNNV-P at pH 7.0 and 5.0, calculated using secondary chemical ΔCα–ΔCβ shifts and plotted against the amino acid sequence. The Q320–P324 region showing an increased β-strand propensity at pH 5.0 is highlighted in gray. The secondary structures are shown above each chart, with arrows and cylinders representing β-strands and α-helices, respectively. (B) MD simulations revealed the formation of a short β-strand (comprising residues Q322–L324) within the F′–G′ loop at low pH. Hydrogen bonds between R321–L324 and D266–S264 were identified as stabilizing this region. (C) Superimposition of GNNV-P at pH 7.0 (NMR structure, blue) and 5.0 (MD model, red), showing the open and closed conformations of the F′–G′ loop, respectively. (D) 1H–15N HSQC spectra of uniform U–D-, 13C-, and 15N-labeled GNNV-P at pH 5.0. The regions with duplicate signals for residues 216–220 in the linker region are marked with black boxes. (E–G) Details of the uniform U–D-, 13C-, and 15N-labeled GNNV-P 1H–15N HSQC spectra highlighted by black boxes in panel (D). (H) Sequence of the N-terminal linker T214-P221 and a stick representation of the linker region with proline residues colored red and residues presenting duplicate NMR signals colored blue. (I) Schematic representation of cis–trans isomerization of an Xaa–Pro peptide bond. Proline residues can switch between the trans (blue) and cis (red) conformations.

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 6

    Figure 6. Model of Neu5Ac-Lac binding to GNNV-P at neutral pH. (A) Surface representation of the GNNV-P monomer in complex with Neu5Ac-(α2,3)-Lac (green) and Neu5Ac-(α2,3)-Lac (yellow). (B) Details of Neu5Ac-(α2,3)-Lac binding to GNNV-P in the open pocket conformation. (C) Details of Neu5Ac-(α2,6)-Lac binding to GNNV-P in the open pocket conformation. In panels (B) and (C), residues interacting with Neu5Ac-Lac are shown as sticks, and selected contacts between GNNV-P and Neu5Ac-Lac are represented by pink dashed lines.

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 7

    Figure 7. Model of GNNV interactions with host cell surface receptors. During infection, GNNV attaches to cells by interacting with the sialic acid moiety on the host cell surface and with cellular receptors (HSP70, HSP90ab1, or nectin-4). At weakly alkaline or neutral pH for host cell entry, protrusions on the GNNV surface are in extended and loose configuration, with the sialic acid binding pocket open, and linker region (red) and host-determining region (blue) accessible for interactions with cellular receptors. Acidification in late endosomes induces conformational change of the GNNV P-domain to result in resting/prone and compact protrusions. In this configuration, the sialic acid binding pocket is closed so that sialic acids are withdrawn to the tip of the protrusions. In addition, the accessibility of the linker region to a cellular receptor is also reprogrammed, leading to its potential GNNV detachment for endosomal escape. Note that the potential of the protrusion tip in directly interacting with the endosomal membrane may be altered by the pH-induced modification of the protrusion surface hydrophobicity distribution (Figure S20).

    High Resolution Image
    Download MS PowerPoint Slide

  • References

    This article references 86 other publications.

    1. 1
      Baker, R. E.; Mahmud, A. S.; Miller, I. F.; Rajeev, M.; Rasambainarivo, F.; Rice, B. L.; Takahashi, S.; Tatem, A. J.; Wagner, C. E.; Wang, L.-F. Infectious disease in an era of global change. Nat. Rev. Microbiol. 2022, 20 (4), 193– 205,  DOI: 10.1038/s41579-021-00639-z
      1
      Infectious disease in an era of global change
      Baker, Rachel E.; Mahmud, Ayesha S.; Miller, Ian F.; Rajeev, Malavika; Rasambainarivo, Fidisoa; Rice, Benjamin L.; Takahashi, Saki; Tatem, Andrew J.; Wagner, Caroline E.; Wang, Lin-Fa; Wesolowski, Amy; Metcalf, C. Jessica E.
      Nature Reviews Microbiology (2022), 20 (4), 193-205CODEN: NRMACK; ISSN:1740-1526. (Nature Portfolio)
      A review on. The twenty-first century has witnessed a wave of severe infectious disease outbreaks, not least the COVID-19 pandemic, which has had a devastating impact on lives and livelihoods around the globe. The 2003 severe acute respiratory syndrome coronavirus outbreak, the 2009 swine flu pandemic, the 2012 Middle East respiratory syndrome coronavirus outbreak, the 2013-2016 Ebola virus disease epidemic in West Africa and the 2015 Zika virus disease epidemic all resulted in substantial morbidity and mortality while spreading across borders to infect people in multiple countries. At the same time, the past few decades have ushered in an unprecedented era of technol., demog. and climatic change: airline flights have doubled since 2000, since 2007 more people live in urban areas than rural areas, population nos. continue to climb and climate change presents an escalating threat to society. In this Review, we consider the extent to which these recent global changes have increased the risk of infectious disease outbreaks, even as improved sanitation and access to health care have resulted in considerable progress worldwide.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXit1Ciu77L&md5=e91900ca47ac5d7cf14a480ce6182ef9
    2. 2
      Doan, Q. K.; Vandeputte, M.; Chatain, B.; Morin, T.; Allal, F. Viral encephalopathy and retinopathy in aquaculture: a review. J. Fish Dis. 2017, 40 (5), 717– 742,  DOI: 10.1111/jfd.12541
      There is no corresponding record for this reference.
    3. 3
      Cann, A. J. Replication of Viruses. In Encyclopedia of Virology (Third ed.); Mahy, B. W. J.; Van Regenmortel, M. H. V., Eds.; Academic Press, 2008; pp 406– 412.
      There is no corresponding record for this reference.
    4. 4
      Bruinsma, R. F.; Wuite, G. J. L.; Roos, W. H. Physics of viral dynamics. Nat. Rev. Phys. 2021, 3 (2), 76– 91,  DOI: 10.1038/s42254-020-00267-1
      There is no corresponding record for this reference.
    5. 5
      Sherman, M. B.; Smith, H. Q.; Smith, T. J. The Dynamic Life of Virus Capsids. Viruses 2020, 12 (6), 618,  DOI: 10.3390/v12060618
      There is no corresponding record for this reference.
    6. 6
      Zhang, Y.; Cui, Y.; Sun, J.; Zhou, Z. H. Multiple conformations of trimeric spikes visualized on a non-enveloped virus. Nat. Commun. 2022, 13 (1), 550  DOI: 10.1038/s41467-022-28114-0
      There is no corresponding record for this reference.
    7. 7
      Lynch, D. L.; Pavlova, A.; Fan, Z.; Gumbart, J. C. Understanding Virus Structure and Dynamics through Molecular Simulations. J. Chem. Theory Comput. 2023, 19 (11), 3025– 3036,  DOI: 10.1021/acs.jctc.3c00116
      There is no corresponding record for this reference.
    8. 8
      Crane, M.; Hyatt, A. Viruses of fish: an overview of significant pathogens. Viruses 2011, 3 (11), 2025– 2046,  DOI: 10.3390/v3112025
      There is no corresponding record for this reference.
    9. 9
      Mori, K.-I.; Nakai, T.; Muroga, K.; Arimoto, M.; Mushiake, K.; Furusawa, I. Properties of a new virus belonging to nodaviridae found in larval striped jack (Pseudocaranx dentex) with nervous necrosis. Virology 1992, 187 (1), 368– 371,  DOI: 10.1016/0042-6822(92)90329-N
      9
      Properties of a new virus belonging to nodaviridae found in larval striped jack (Pseudocaranx dentex) with nervous necrosis
      Mori K; Nakai T; Muroga K; Arimoto M; Mushiake K; Furusawa I
      Virology (1992), 187 (1), 368-71 ISSN:0042-6822.
      Spherical virus particles were purified from larval striped jack (Pseudocaranx dentex) with nervous necrosis. The virus consists of nonenveloped particles, about 25 nm in diameter, and contains two single-stranded, positive-sense RNA molecules with molecular weights of 1.01 x 10(6)Da (RNA 1) and 0.49 x 10(6)Da (RNA 2), respectively. The RNAs do not have poly(A) sequences at the 3' terminus. Virus structural proteins consist of two proteins with molecular weights of 42 and 40 kDa. When translated into cell-free extracts of rabbit reticulocytes, RNA 1 directed the synthesis of the 1a protein (100 kDa), whereas RNA 2 synthesized the 2a protein (42 kDa), which is probably the coat protein of the virus, and a polypeptide of 40 kDa which appears to be the processed form of the 42-kDa protein. Under electron microscopic observation, the virus particles were found in the tissues of the central nervous system of the affected larval striped jack. From morphological and biochemical properties of the virus, we identified this virus as a new member of the family of Nodaviridae and designated it striped jack nervous necrosis virus.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADyaK387kt1Ghug%253D%253D&md5=2e07e4427b2baf88353ad92b9de7012d
    10. 10
      Bandín, I.; Souto, S. Betanodavirus and VER Disease: A 30-year Research Review. Pathogens 2020, 9 (2), 106,  DOI: 10.3390/pathogens9020106
      There is no corresponding record for this reference.
    11. 11
      Venter, P. A.; Schneemann, A. Nodaviruses. In Encyclopedia of Virology (Third ed.); Mahy, B. W. J.; Van Regenmortel, M. H. V., Eds.; Academic Press, 2008; pp 430– 438.
      There is no corresponding record for this reference.
    12. 12
      Tang, L.; Lin, C.-S.; Krishna, N. K.; Yeager, M.; Schneemann, A.; Johnson, J. E. Virus-like particles of a fish nodavirus display a capsid subunit domain organization different from that of insect nodaviruses. J. Virol. 2002, 76 (12), 6370– 6375,  DOI: 10.1128/JVI.76.12.6370-6375.2002
      There is no corresponding record for this reference.
    13. 13
      Iwamoto, T.; Mise, K.; Takeda, A.; Okinaka, Y.; Mori, K.-I.; Arimoto, M.; Okuno, T.; Nakai, T. Characterization of Striped jack nervous necrosis virus subgenomic RNA3 and biological activities of its encoded protein B2. J. Gen. Virol. 2005, 86, 2807– 2816,  DOI: 10.1099/vir.0.80902-0
      There is no corresponding record for this reference.
    14. 14
      Liu, W.; Hsu, C.-H.; Hong, Y.-R.; Wu, S.-C.; Wang, C.-H.; Wu, Y.-M.; Chao, C.-B.; Lin, C.-S. Early endocytosis pathways in SSN-1 cells infected by dragon grouper nervous necrosis virus. J. Gen. Virol. 2005, 86 (9), 2553– 2561,  DOI: 10.1099/vir.0.81021-0
      There is no corresponding record for this reference.
    15. 15
      Adachi, K.; Ichinose, T.; Takizawa, N.; Watanabe, K.; Kitazato, K.; Kobayashi, N. Inhibition of betanodavirus infection by inhibitors of endosomal acidification. Arch. Virol. 2007, 152 (12), 2217– 2224,  DOI: 10.1007/s00705-007-1061-7
      There is no corresponding record for this reference.
    16. 16
      Huang, R.; Zhu, G.; Zhang, J.; Lai, Y.; Xu, Y.; He, J.; Xie, J. Betanodavirus-like particles enter host cells via clathrin-mediated endocytosis in a cholesterol-, pH- and cytoskeleton-dependent manner. Vet. Res. 2017, 48 (1), 8  DOI: 10.1186/s13567-017-0412-y
      There is no corresponding record for this reference.
    17. 17
      Kumar, C. S.; Dey, D.; Ghosh, S.; Banerjee, M. Breach: Host Membrane Penetration and Entry by Nonenveloped Viruses. Trends Microbiol. 2018, 26 (6), 525– 537,  DOI: 10.1016/j.tim.2017.09.010
      There is no corresponding record for this reference.
    18. 18
      Mercer, J.; Schelhaas, M.; Helenius, A. Virus Entry by Endocytosis. Annu. Rev. Biochem. 2010, 79 (1), 803– 833,  DOI: 10.1146/annurev-biochem-060208-104626
      18
      Virus entry by endocytosis
      Mercer, Jason; Schelhaas, Mario; Helenius, Ari
      Annual Review of Biochemistry (2010), 79 (), 803-833CODEN: ARBOAW; ISSN:0066-4154. (Annual Reviews Inc.)
      A review. Although viruses are simple in structure and compn., their interactions with host cells are complex. Merely to gain entry, animal viruses make use of a repertoire of cellular processes that involve hundreds of cellular proteins. Although some viruses have the capacity to penetrate into the cytosol directly through the plasma membrane, most depend on endocytic uptake, vesicular transport through the cytoplasm, and delivery to endosomes and other intracellular organelles. The internalization may involve clathrin-mediated endocytosis (CME), macropinocytosis, caveolar/lipid raft-mediated endocytosis, or a variety of other still poorly characterized mechanisms. This review focuses on the cell biol. of virus entry and the different strategies and endocytic mechanisms used by animal viruses.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXpslSht7c%253D&md5=e083b9661ae42c15824b304f13c8e6fc
    19. 19
      Pletan, M. L.; Tsai, B. Non-enveloped virus membrane penetration: New advances leading to new insights. PLoS Pathog. 2022, 18 (12), e1010948  DOI: 10.1371/journal.ppat.1010948
      There is no corresponding record for this reference.
    20. 20
      Odegard, A.; Banerjee, M.; Johnson, J. E. Flock House Virus: A Model System for Understanding Non-Enveloped Virus Entry and Membrane Penetration. In Cell Entry by Non-Enveloped Viruses; Johnson, J. E., Ed.; Springer Berlin Heidelberg, 2010; pp 1– 22.
      There is no corresponding record for this reference.
    21. 21
      Odegard, A. L.; Kwan, M. H.; Walukiewicz, H. E.; Banerjee, M.; Schneemann, A.; Johnson, J. E. Low endocytic pH and capsid protein autocleavage are critical components of Flock House virus cell entry. J. Virol. 2009, 83 (17), 8628– 8637,  DOI: 10.1128/JVI.00873-09
      21
      Low endocytic pH and capsid protein autocleavage are critical components of flock house virus cell entry
      Odegard, Amy L.; Kwan, Maggie H.; Walukiewicz, Hanna E.; Banerjee, Manidipa; Schneemann, Anette; Johnson, John E.
      Journal of Virology (2009), 83 (17), 8628-8637CODEN: JOVIAM; ISSN:0022-538X. (American Society for Microbiology)
      The process by which nonenveloped viruses cross cell membranes during host cell entry remains poorly defined; however, common themes are emerging. Here, we use correlated in vivo and in vitro studies to understand the mechanism of Flock House virus (FHV) entry and membrane penetration. We demonstrate that low endocytic pH is required for FHV infection, that exposure to acidic pH promotes FHV-mediated disruption of model membranes (liposomes), and particles exposed to low pH in vitro exhibit increased hydrophobicity. In addn., FHV particles perturbed by heating displayed a marked increase in liposome disruption, indicating that membrane-active regions of the capsid are exposed or released under these conditions. We also provide evidence that autoproteolytic cleavage, to generate the lipophilic γ peptide (4.4 kDa), is required for membrane penetration. Mutant, cleavage-defective particles failed to mediate liposome lysis, regardless of pH or heat treatment, suggesting that these particles are not able to expose or release the requisite membrane-active regions of the capsid, namely, the γ peptides. Based on these results, we propose an updated model for FHV entry in which (i) the virus enters the host cell by endocytosis, (ii) low pH within the endocytic pathway triggers the irreversible exposure or release of γ peptides from the virus particle, and (iii) the exposed/released γ peptides disrupt the endosomal membrane, facilitating translocation of viral RNA into the cytoplasm.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtFahurrJ&md5=ca9a32cff587b467ac1302d995eff723
    22. 22
      Chen, N.-C.; Yoshimura, M.; Guan, H.-H.; Wang, T.-Y.; Misumi, Y.; Lin, C.-C.; Chuankhayan, P.; Nakagawa, A.; Chan, S. I.; Tsukihara, T. Crystal Structures of a Piscine Betanodavirus: Mechanisms of Capsid Assembly and Viral Infection. PLoS Pathog. 2015, 11 (10), e1005203,  DOI: 10.1371/journal.ppat.1005203
      There is no corresponding record for this reference.
    23. 23
      Gye, H. J.; Nishizawa, T. Sites responsible for infectivity and antigenicity on nervous necrosis virus (NNV) appear to be distinct. Sci. Rep. 2021, 11 (1), 3608  DOI: 10.1038/s41598-021-83078-3
      There is no corresponding record for this reference.
    24. 24
      Gye, H. J.; Park, M.-J.; Kim, W.-S.; Oh, M.-J.; Nishizawa, T. Heat-denaturation of conformational structures on nervous necrosis virus for generating neutralization antibodies. Aquaculture 2018, 484, 65– 70,  DOI: 10.1016/j.aquaculture.2017.10.034
      There is no corresponding record for this reference.
    25. 25
      Nishizawa, T.; Mori, K.-i.; Furuhashi, M.; Nakai, T.; Furusawa, I.; Muroga, K. Comparison of the coat protein genes of five fish nodaviruses, the causative agents of viral nervous necrosis in marine fish. J. Gen. Virol. 1995, 76 (7), 1563– 1569,  DOI: 10.1099/0022-1317-76-7-1563
      There is no corresponding record for this reference.
    26. 26
      Iwamoto, T.; Okinaka, Y.; Mise, K.; Mori, K.-I.; Arimoto, M.; Okuno, T.; Nakai, T. Identification of Host-Specificity Determinants in Betanodaviruses by Using Reassortants between Striped Jack Nervous Necrosis Virus and Sevenband Grouper Nervous Necrosis Virus. J. Virol. 2004, 78 (3), 1256– 1262,  DOI: 10.1128/JVI.78.3.1256-1262.2004
      There is no corresponding record for this reference.
    27. 27
      Moreno, P.; Souto, S.; Leiva-Rebollo, R.; Borrego, J. J.; Bandín, I.; Alonso, M. C. Capsid amino acids at positions 247 and 270 are involved in the virulence of betanodaviruses to European sea bass. Sci. Rep. 2019, 9 (1), 14068  DOI: 10.1038/s41598-019-50622-1
      There is no corresponding record for this reference.
    28. 28
      Nishizawa, T.; Lee, H. S.; Gye, H. J. Pocket structures of surface protrusions shared among serologically distinct nervous necrosis viruses (NNVs) were predicted in silico to bind to sialylated N-glycans, a host cellular receptor. Aquaculture 2023, 565, 739157,  DOI: 10.1016/j.aquaculture.2022.739157
      There is no corresponding record for this reference.
    29. 29
      Wang, C. H.; Chen, D. H.; Huang, S. H.; Wu, Y. M.; Chen, Y. Y.; Hwu, Y.; Bushnell, D.; Kornberg, R.; Chang, W. H. Sub-3 Å Cryo-EM Structures of Necrosis Virus Particles via the Use of Multipurpose TEM with Electron Counting Camera. Int. J. Mol. Sci. 2021, 22 (13), 6859,  DOI: 10.3390/ijms22136859
      There is no corresponding record for this reference.
    30. 30
      Chang, W. H.; Huang, S. H.; Lin, H. H.; Chung, S. C.; Tu, I. P. Cryo-EM Analyses Permit Visualization of Structural Polymorphism of Biological Macromolecules. Front. Bioinform. 2021, 1, 788308,  DOI: 10.3389/fbinf.2021.788308
      There is no corresponding record for this reference.
    31. 31
      Zhang, K.; Julius, D.; Cheng, Y. Structural snapshots of TRPV1 reveal mechanism of polymodal functionality. Cell 2021, 184 (20), 5138– 5150,  DOI: 10.1016/j.cell.2021.08.012
      There is no corresponding record for this reference.
    32. 32
      Chen, C.-Y.; Chang, Y.-C.; Lin, B.-L.; Huang, C.-H.; Tsai, M.-D. Temperature-Resolved Cryo-EM Uncovers Structural Bases of Temperature-Dependent Enzyme Functions. J. Am. Chem. Soc. 2019, 141 (51), 19983– 19987,  DOI: 10.1021/jacs.9b10687
      32
      Temperature-Resolved Cryo-EM Uncovers Structural Bases of Temperature-Dependent Enzyme Functions
      Chen, Chin-Yu; Chang, Yuan-Chih; Lin, Bo-Lin; Huang, Chun-Hsiang; Tsai, Ming-Daw
      Journal of the American Chemical Society (2019), 141 (51), 19983-19987CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
      Protein functions are temp.-dependent, but protein structures are usually solved at a single (often low) temp. because of limitations on the conditions of crystal growth or protein vitrification. Here we demonstrate the feasibility of solving cryo-EM structures of proteins vitrified at high temps., solve 12 structures of an archaeal ketol-acid reductoisomerase (KARI) vitrified at 4-70 °C, and show that structures of both the Mg2+ form (KARI:2Mg2+) and its ternary complex (KARI:2Mg2+:NADH:inhibitor) are temp.-dependent in correlation with the temp. dependence of enzyme activity. Furthermore, structural analyses led to dissection of the induced-fit mechanism into ligand-induced and temp.-induced effects and to capture of temp.-resolved intermediates of the temp.-induced conformational change. The results also suggest that it is preferable to solve cryo-EM structures of protein complexes at functional temps. These studies should greatly expand the landscapes of protein structure-function relationships and enhance the mechanistic anal. of enzymic functions.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitlygt7zO&md5=3866b861e15dbebe8e5657828643cbda
    33. 33
      Xie, J.; Li, K.; Gao, Y.; Huang, R.; Lai, Y.; Shi, Y.; Yang, S.; Zhu, G.; Zhang, Q.; He, J. Structural analysis and insertion study reveal the ideal sites for surface displaying foreign peptides on a betanodavirus-like particle. Vet. Res. 2016, 47 (1), 16,  DOI: 10.1186/s13567-015-0294-9
      There is no corresponding record for this reference.
    34. 34
      Štěrbová, P.; Wu, D.; Lou, Y.-C.; Wang, C.-H.; Chang, W.-H.; Tzou, D.-L. M. NMR assignments of protrusion domain of capsid protein from dragon grouper nervous necrosis virus. Biomol. NMR Assignments 2020, 14 (1), 63– 66,  DOI: 10.1007/s12104-019-09921-x
      There is no corresponding record for this reference.
    35. 35
      Walters, K. J.; Ferentz, A. E.; Hare, B. J.; Hidalgo, P.; Jasanoff, A.; Matsuo, H.; Wagner, G. Characterizing protein-protein complexes and oligomers by nuclear magnetic resonance spectroscopy. In Methods in Enzymology; Elsevier, 2001; Vol. 339, pp 238– 258.  DOI: 10.1016/S0076-6879(01)39316-3 .
      There is no corresponding record for this reference.
    36. 36
      Williamson, M. P. Using chemical shift perturbation to characterise ligand binding. Prog. Nucl. Magn. Reson. Spectrosc. 2013, 73, 1– 16,  DOI: 10.1016/j.pnmrs.2013.02.001
      36
      Using chemical shift perturbation to characterise ligand binding
      Williamson, Mike P.
      Progress in Nuclear Magnetic Resonance Spectroscopy (2013), 73 (), 1-16CODEN: PNMRAT; ISSN:0079-6565. (Elsevier B.V.)
      A review. Chem. shift perturbation (CSP, chem. shift mapping or complexation-induced changes in chem. shift, CIS) follows changes in the chem. shifts of a protein when a ligand is added, and uses these to det. the location of the binding site, the affinity of the ligand, and/or possibly the structure of the complex. A key factor in detg. the appearance of spectra during a titrn. is the exchange rate between free and bound, or more specifically the off-rate koff. When koff is greater than the chem. shift difference between free and bound, which typically equates to an affinity Kd weaker than about 3 μM, then exchange is fast on the chem. shift timescale. Under these circumstances, the obsd. shift is the population-weighted av. of free and bound, which allows Kd to be detd. from measurement of peak positions, provided the measurements are made appropriately. 1H shifts are influenced to a large extent by through-space interactions, whereas 13Cα and 13Cβ shifts are influenced more by through-bond effects. 15N and 13C' shifts are influenced both by through-bond and by through-space (hydrogen bonding) interactions. For detg. the location of a bound ligand on the basis of shift change, the most appropriate method is therefore usually to measure 15N HSQC spectra, calc. the geometrical distance moved by the peak, weighting 15N shifts by a factor of about 0.14 compared to 1H shifts, and select those residues for which the weighted shift change is larger than the std. deviation of the shift for all residues. Other methods are discussed, in particular the measurement of 13CH3 signals. Slow to intermediate exchange rates lead to line broadening, and make Kd values very difficult to obtain. There is no good way to distinguish changes in chem. shift due to direct binding of the ligand from changes in chem. shift due to allosteric change. Ligand binding at multiple sites can often be characterized, by simultaneous fitting of many measured shift changes, or more simply by adding substoichiometric amts. of ligand. The chem. shift changes can be used as restraints for docking ligand onto protein. By use of quant. calcns. of ligand-induced chem. shift changes, it is becoming possible to det. not just the position but also the orientation of ligands.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXht1yrsbzE&md5=74fc06bb4f4b3df369619bbf730016ce
    37. 37
      Krissinel, E.; Henrick, K. Inference of Macromolecular Assemblies from Crystalline State. J. Mol. Biol. 2007, 372 (3), 774– 797,  DOI: 10.1016/j.jmb.2007.05.022
      37
      Inference of Macromolecular Assemblies from Crystalline State
      Krissinel, Evgeny; Henrick, Kim
      Journal of Molecular Biology (2007), 372 (3), 774-797CODEN: JMOBAK; ISSN:0022-2836. (Elsevier Ltd.)
      The authors discuss basic phys.-chem. principles underlying the formation of stable macromol. complexes, which in many cases are likely to be the biol. units performing a certain physiol. function. The authors also consider available theor. approaches to the calcn. of macromol. affinity and entropy of complexation. The latter is shown to play an important role and make a major effect on complex size and symmetry. The authors develop a new method, based on chem. thermodn., for automatic detection of macromol. assemblies in the Protein Data Bank (PDB) entries that are the results of x-ray diffraction expts. As found, biol. units may be recovered at 80-90% success rate, which makes x-ray crystallog. an important source of exptl. data on macromol. complexes and protein-protein interactions. The method is implemented as a public WWW service (http://www.ebi.ac.uk/msd-srv/prot_int/pistart.html).
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXpvFGktb8%253D&md5=a5c764cfc7dc129f53ddc31ef9d475fa
    38. 38
      Kampmann, T.; Mueller, D. S.; Mark, A. E.; Young, P. R.; Kobe, B. The Role of histidine residues in low-pH-mediated viral membrane fusion. Structure 2006, 14 (10), 1481– 1487,  DOI: 10.1016/j.str.2006.07.011
      38
      The Role of Histidine Residues in Low-pH-Mediated Viral Membrane Fusion
      Kampmann, Thorsten; Mueller, Daniela S.; Mark, Alan E.; Young, Paul R.; Kobe, Bostjan
      Structure (Cambridge, MA, United States) (2006), 14 (10), 1481-1487CODEN: STRUE6; ISSN:0969-2126. (Cell Press)
      A central event in the invasion of a host cell by an enveloped virus is the fusion of viral and cell membranes. For many viruses, membrane fusion is driven by specific viral surface proteins that undergo large-scale conformational rearrangements, triggered by exposure to low pH in the endosome upon internalization. Here, we present evidence suggesting that in both class I (helical hairpin proteins) and class II (β-structure-rich proteins) pH-dependent fusion proteins the protonation of specific histidine residues triggers fusion via an analogous mol. mechanism. These histidines are located in the vicinity of pos. charged residues in the prefusion conformation, and they subsequently form salt bridges with neg. charged residues in the postfusion conformation. The mol. surfaces involved in the corresponding structural rearrangements leading to fusion are highly conserved and thus might provide a suitable common target for the design of antivirals, which could be active against a diverse range of pathogenic viruses.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XhtVCrtrnN&md5=ffb8d25828217e82747ef2838f53f028
    39. 39
      The PyMOL Molecular Graphics System, Version 1.8, Schrodinger, LLC, 2015.
      There is no corresponding record for this reference.
    40. 40
      Wedemeyer, W. J.; Welker, E.; Scheraga, H. A. Proline cis-trans isomerization and protein folding. Biochemistry 2002, 41 (50), 14637– 14644,  DOI: 10.1021/bi020574b
      40
      Proline Cis-Trans Isomerization and Protein Folding
      Wedemeyer, William J.; Welker, Ervin; Scheraga, Harold A.
      Biochemistry (2002), 41 (50), 14637-14644CODEN: BICHAW; ISSN:0006-2960. (American Chemical Society)
      A review. Proline cis-trans isomerization plays a key role in the rate-detg. steps of protein folding. The energetic origin of this isomerization process is summarized, and the folding and unfolding of disulfide-intact bovine pancreatic RNase A is used as an example to illustrate the kinetics and structural features of conformational changes from the heterogeneous unfolded state (consisting of cis and trans isomers of X-Pro peptide groups) to the native structure in which only one set of proline isomers is present.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38XotlOmsLw%253D&md5=f05d94502166b940f92d215d49352b6f
    41. 41
      Shen, Y.; Bax, A. Prediction of Xaa-Pro peptide bond conformation from sequence and chemical shifts. J. Biomol. NMR 2010, 46 (3), 199– 204,  DOI: 10.1007/s10858-009-9395-y
      41
      Prediction of Xaa-Pro peptide bond conformation from sequence and chemical shifts
      Shen, Yang; Bax, Ad
      Journal of Biomolecular NMR (2010), 46 (3), 199-204CODEN: JBNME9; ISSN:0925-2738. (Springer)
      The authors present a program, named Promega, to predict the Xaa-Pro peptide bond conformation on the basis of backbone chem. shifts and the amino acid sequence. Using a chem. shift database of proteins of known structure together with the PDB-extd. amino acid preference of cis Xaa-Pro peptide bonds, a cis/trans probability score is calcd. from the backbone and 13Cβ chem. shifts of the proline and its neighboring residues. For an arbitrary no. of input chem. shifts, which may include Pro-13Cγ, Promega calcs. the statistical probability that a Xaa-Pro peptide bond is cis. Besides its potential as a validation tool, Promega is particularly useful for studies of larger proteins where Pro-13Cγ assignments can be challenging, and for on-going efforts to det. protein structures exclusively on the basis of backbone and 13Cβ chem. shifts.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXivV2ku70%253D&md5=30d471955095ff1da608e18b1d3321d4
    42. 42
      Gye, H. J.; Nishizawa, T. Analysis of sialylated N-linked glycans on fish cell lines permissive to nervous necrosis virus for predicting cellular receptors of the virus. Aquaculture 2022, 555, 738198  DOI: 10.1016/j.aquaculture.2022.738198
      There is no corresponding record for this reference.
    43. 43
      Rodríguez, Y.; Cardoze, S. M.; Obineche, O. W.; Melo, C.; Persaud, A.; Fernández Romero, J. A. Small Molecules Targeting SARS-CoV-2 Spike Glycoprotein Receptor-Binding Domain. ACS Omega 2022, 7 (33), 28779– 28789,  DOI: 10.1021/acsomega.2c00844
      43
      Small Molecules Targeting SARS-CoV-2 Spike Glycoprotein Receptor-Binding Domain
      Rodriguez, Yoel; Cardoze, Scarlet Martinez; Obineche, Onyinyechi W.; Melo, Claudia; Persaud, Ashanna; Fernandez Romero, Jose A.
      ACS Omega (2022), 7 (33), 28779-28789CODEN: ACSODF; ISSN:2470-1343. (American Chemical Society)
      The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the coronavirus disease 2019 (COVID-19) pandemic. Several variants of SARS-CoV-2 have emerged worldwide. These variants show different transmissibility infectivity due to mutations in the viral spike (S) glycoprotein that interacts with the human angiotensin-converting enzyme 2 (hACE2) receptor and facilitates viral entry into target cells. Despite the effective SARS-CoV-2 vaccines, we still need to identify selective antivirals, and the S glycoprotein is a key target to neutralize the virus. We hypothesize that small mols. could disrupt the interaction of S glycoprotein with hACE2 and inhibit viral entry. We analyzed the S glycoprotein-hACE2 complex structure (PDB: 7DF4) and created models for different viral variants using visual mol. dynamics (VMD) and mol. operating environment (MOE) programs. Moreover, we started the hits search by performing structure-based mol. docking virtual screening of com. available small mols. against S glycoprotein models using OEDocking FRED-4.0.0.0 software. The FRED-4.0.0.0 Chemguass4 scoring function was used to rank the small mols. based on their affinities. The best candidate compds. were purchased and tested using a std. SARS-CoV-2 pseudotyped cell-based bioassay to investigate their antiviral activity. Three of these compds., alone or in combination, showed antiviral selectivity. These small mols. may lead to an effective antiviral treatment or serve as probes to better understand the biol. of SARS-CoV-2.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XitVKktrbO&md5=4c573b63f6c38170fbe0bff8533dd57b
    44. 44
      Chang, J.-S.; Chi, S.-C. GHSC70 is involved in the cellular entry of nervous necrosis virus. J. Virol. 2015, 89 (1), 61– 70,  DOI: 10.1128/JVI.02523-14
      There is no corresponding record for this reference.
    45. 45
      Zhang, W.; Jia, K.; Jia, P.; Xiang, Y.; Lu, X.; Liu, W.; Yi, M. Marine medaka heat shock protein 90ab1 is a receptor for red-spotted grouper nervous necrosis virus and promotes virus internalization through clathrin-mediated endocytosis. PLoS Pathog. 2020, 16 (7), e1008668  DOI: 10.1371/journal.ppat.1008668
      There is no corresponding record for this reference.
    46. 46
      Krishnan, R.; Qadiri, S. S. N.; Oh, M.-J. Functional characterization of seven-band grouper immunoglobulin like cell adhesion molecule, Nectin4 as a cellular receptor for nervous necrosis virus. Fish Shellfish Immunol. 2019, 93, 720– 725,  DOI: 10.1016/j.fsi.2019.08.019
      There is no corresponding record for this reference.
    47. 47
      Ito, Y.; Okinaka, Y.; Mori, K. I.; Sugaya, T.; Nishioka, T.; Oka, M.; Nakai, T. Variable region of betanodavirus RNA2 is sufficient to determine host specificity. Dis. Aquat. Org. 2008, 79 (3), 199– 205,  DOI: 10.3354/dao01906
      There is no corresponding record for this reference.
    48. 48
      Staring, J.; Raaben, M.; Brummelkamp, T. R. Viral escape from endosomes and host detection at a glance. J. Cell Sci. 2018, 131 (15), jcs216259  DOI: 10.1242/jcs.216259
      There is no corresponding record for this reference.
    49. 49
      Song, C.; Takai-Todaka, R.; Miki, M.; Haga, K.; Fujimoto, A.; Ishiyama, R.; Oikawa, K.; Yokoyama, M.; Miyazaki, N.; Iwasaki, K. Dynamic rotation of the protruding domain enhances the infectivity of norovirus. PLoS Pathog. 2020, 16 (7), e1008619  DOI: 10.1371/journal.ppat.1008619
      There is no corresponding record for this reference.
    50. 50
      Hu, L.; Salmen, W.; Chen, R.; Zhou, Y.; Neill, F.; Crowe, J. E.; Atmar, R. L.; Estes, M. K.; Prasad, B. V. V. Atomic structure of the predominant GII.4 human norovirus capsid reveals novel stability and plasticity. Nat. Commun. 2022, 13 (1), 1241  DOI: 10.1038/s41467-022-28757-z
      There is no corresponding record for this reference.
    51. 51
      Goto, Y.; Takahashi, N.; Fink, A. L. (1990). Mechanism of acid-induced folding of proteins. Biochemistry 1990, 29 (14), 3480– 3488,  DOI: 10.1021/bi00466a009
      51
      Mechanism of acid-induced folding of proteins
      Goto, Yuji; Takahashi, Nobuaki; Fink, Anthony L.
      Biochemistry (1990), 29 (14), 3480-8CODEN: BICHAW; ISSN:0006-2960.
      It was previously shown that β-lactamase, cytochrome c (I), and apomyoglobin (II) are maximally unfolded at pH 2 under conditions of low ionic strength, but that a further decrease in pH, by increasing the concn. of HCl, refolds the proteins to the A state with properties similar to those of a molten globule state. To understand the mechanism of acid-induced refolding of protein structure, the effects of various strong acids and their neutral salts on the acid-unfolded states of ferri-I and II were studied. The conformational transition of I was monitored at 20° by using changes in the far-UV CD and in the Soret absorption at 394 nm, and that of II was monitored by changes in the far-UV CD. Various strong acids (i.e., H2SO4, HClO4, HNO3, TCA, and trifluoroacetic acid) refolded acid-unfolded I and II to the A states as was the case with HCl. For both proteins, neutral salts of these acids caused similar conformational transitions, confirming that the anions are responsible for bringing about the transition. The order of effectiveness of anions was ferricyanide > ferrocyanide > SO42- > TCA- > SCN- > ClO4- > I- > NO3- > trifluoroacetate > Br- > Cl-. This series was similar to the electroselectivity series of anions toward the anion-exchange resins, showing that preferential binding of anions to the A states causes the conformational transitions.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3cXhsFeqs7w%253D&md5=48e21a0012913f7c73f4748b2c62a142
    52. 52
      Güthe, S.; Kapinos, L.; Möglich, A.; Meier, S.; Grzesiek, S.; Kiefhaber, T. Very fast folding and association of a trimerization domain from bacteriophage T4 fibritin. J. Mol. Biol. 2004, 337 (4), 905– 915,  DOI: 10.1016/j.jmb.2004.02.020
      There is no corresponding record for this reference.
    53. 53
      So, M.; Hall, D.; Goto, Y. Revisiting supersaturation as a factor determining amyloid fibrillation. Curr. Opin. Struct. Biol. 2016, 36, 32– 39,  DOI: 10.1016/j.sbi.2015.11.009
      53
      Revisiting supersaturation as a factor determining amyloid fibrillation
      So, Masatomo; Hall, Damien; Goto, Yuji
      Current Opinion in Structural Biology (2016), 36 (), 32-39CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)
      Amyloid fibrils involved in various diseases are formed by a nucleation-growth mechanism, similar to the crystn. of solutes from soln. Soly. and supersatn. are two of the most important factors detg. crystn. of solutes. Moreover, crystn. competes with glass formation in which solutes collapse into amorphous aggregates. Recent studies on the formation of amyloid fibrils and amorphous aggregates indicate that the partition between distinct types of aggregates can be rationally explained by a kinetic and thermodn. competition between them. Understanding the role of supersatn. in detg. aggregation-based phase transitions of denatured proteins provides an important complementary point of view to structural studies of protein aggregates.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXitVSkurjP&md5=01c17457e922fbefc12715f0bf000e1d
    54. 54
      Shih, T. C.; Ho, L. P.; Chou, H. Y.; Wu, J. L.; Pai, T. W. Comprehensive Linear Epitope Prediction System for Host Specificity in Nodaviridae. Viruses 2022, 14 (7), 1357,  DOI: 10.3390/v14071357
      There is no corresponding record for this reference.
    55. 55
      Zhang, Z.; Xing, J.; Tang, X.; Sheng, X.; Chi, H.; Zhan, W. Development and characterization of monoclonal antibodies against red-spotted grouper nervous necrosis virus and their neutralizing potency in vitro. Aquaculture 2022, 560, 738562  DOI: 10.1016/j.aquaculture.2022.738562
      There is no corresponding record for this reference.
    56. 56
      Huang, S.; Wu, Y.; Su, L.; Su, T.; Zhou, Q.; Zhang, J.; Zhao, Z.; Weng, S.; He, J.; Xie, J. A single-chain variable fragment antibody exerts anti-nervous necrosis virus activity by irreversible binding. Aquaculture 2022, 552, 738001  DOI: 10.1016/j.aquaculture.2022.738001
      There is no corresponding record for this reference.
    57. 57
      Graham, B. S.; Gilman, M. S. A.; McLellan, J. S. Structure-Based Vaccine Antigen Design. Annu. Rev. Med. 2019, 70, 91– 104,  DOI: 10.1146/annurev-med-121217-094234
      57
      Structure-Based Vaccine Antigen Design
      Graham, Barney S.; Gilman, Morgan S. A.; McLellan, Jason S.
      Annual Review of Medicine (2019), 70 (), 91-104CODEN: ARMCAH; ISSN:0066-4219. (Annual Reviews)
      A review. Enabled by new approaches for rapid identification and selection of human monoclonal antibodies, at.-level structural information for viral surface proteins, and capacity for precision engineering of protein immunogens and self-assembling nanoparticles, a new era of antigen design and display options has evolved. While HIV-1 vaccine development has been a driving force behind these technologies and concepts, clin. proof-of-concept for structure-based vaccine design may first be achieved for respiratory syncytial virus (RSV), where conformation-dependent access to neutralization-sensitive epitopes on the fusion glycoprotein dets. the capacity to induce potent neutralizing activity. Success with RSV has motivated structure-based stabilization of other class I viral fusion proteins for use as immunogens and demonstrated the importance of structural information for developing vaccines against other viral pathogens, particularly difficult targets that have resisted prior vaccine development efforts. Solving viral surface protein structures also supports rapid vaccine antigen design and application of platform manufg. approaches for emerging pathogens.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhvF2ltrw%253D&md5=ccbef186198ab5f51fe6aa228b440734
    58. 58
      Liang, J.; Edelsbrunner, H.; Woodward, C. Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design. Protein Sci. 1998, 7 (9), 1884– 1897,  DOI: 10.1002/pro.5560070905
      58
      Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design
      Liang, Jie; Edelsbrunner, Herbert; Woodward, Clare
      Protein Science (1998), 7 (9), 1884-1897CODEN: PRCIEI; ISSN:0961-8368. (Cambridge University Press)
      Identification and size characterization of surface pockets and occluded cavities are initial steps in protein structure-based ligand design. A new program, CAST, for automatically locating and measuring protein pockets and cavities, is based on precise computational geometry methods, including alpha shape and discrete flow theory. CAST identifies and measures pockets and pocket mouth openings, as well as cavities. The program specifies the atoms lining pockets, pocket openings, and buried cavities; the vol. and area of pockets and cavities; and the area and circumference of mouth openings. CAST anal. of over 100 proteins has been carried out; proteins examd. include a set of 51 monomeric enzyme-ligand structures, several elastase-inhibitor complexes, the FK506 binding protein, 30 HIV-1 protease-inhibitor complexes, and a no. of small and large protein inhibitors. Medium-sized globular proteins typically have 10-20 pockets/cavities. Most often, binding sites are pockets with 1-2 mouth openings; much less frequently they are cavities. Ligand binding pockets vary widely in size, most within the range 102-103 Å3. Statistical anal. reveals that the no. of pockets and cavities is correlated with protein size, but there is no correlation between the size of the protein and the size of binding sites. Most frequently, the largest pocket/cavity is the active site, but there are a no. of instructive exceptions. Ligand vol. and binding site vol. are somewhat correlated when binding site vol. is ≤700 Å3, but the ligand seldom occupies the entire site. Auxiliary pockets near the active site have been suggested as addnl. binding surface for designed ligands (Mattos C et al., 1994, Nat Struct Biol 1:55-58). Anal. of elastase-inhibitor complexes suggests that CAST can identify ancillary pockets suitable for recruitment in ligand design strategies. Anal. of the FK506 binding protein, and of compds. developed in SAR by NMR (Shuker SB et al., 1996, Science 274:1531-1534), indicates that CAST pocket computation may provide a priori identification of target proteins for linked-fragment design. CAST anal. of 30 HIV-1 protease-inhibitor complexes shows that the flexible active site pocket can vary over a range of 853-1,566 Å3, and that there are two pockets near or adjoining the active site that may be recruited for ligand design.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1cXmtFGjsLo%253D&md5=a6f62e03de03da0a6e51361de7a08765
    59. 59
      Naceri, S.; Marc, D.; Blot, R.; Flatters, D.; Camproux, A. C. Druggable Pockets at the RNA Interface Region of Influenza A Virus NS1 Protein Are Conserved across Sequence Variants from Distinct Subtypes. Biomolecules 2023, 13 (1), 64,  DOI: 10.3390/biom13010064
      There is no corresponding record for this reference.
    60. 60
      Gavenonis, J.; Sheneman, B. A.; Siegert, T. R.; Eshelman, M. R.; Kritzer, J. A. Comprehensive analysis of loops at protein-protein interfaces for macrocycle design. Nat. Chem. Biol. 2014, 10 (9), 716– 722,  DOI: 10.1038/nchembio.1580
      60
      Comprehensive analysis of loops at protein-protein interfaces for macrocycle design
      Gavenonis, Jason; Sheneman, Bradley A.; Siegert, Timothy R.; Eshelman, Matthew R.; Kritzer, Joshua A.
      Nature Chemical Biology (2014), 10 (9), 716-722CODEN: NCBABT; ISSN:1552-4450. (Nature Publishing Group)
      Inhibiting protein-protein interactions (PPIs) with synthetic mols. remains a frontier of chem. biol. Many PPIs have been successfully targeted by mimicking α-helixes at interfaces, but most PPIs are mediated by nonhelical, nonstrand peptide loops. We sought to comprehensively identify and analyze these loop-mediated PPIs by writing and implementing LoopFinder, a customizable program that can identify loop-mediated PPIs within all of the protein-protein complexes in the Protein Data Bank. Comprehensive anal. of the entire set of 25,005 interface loops revealed common structural motifs and unique features that distinguish loop-mediated PPIs from other PPIs. 'Hot loops', named in analogy to protein hot spots, were identified as loops with favorable properties for mimicry using synthetic mols. The hot loops and their binding partners represent new and promising PPIs for the development of macrocycle and constrained peptide inhibitors.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhtFygs7jE&md5=bf07bd724f173ea74467ab7cd5aedb6a
    61. 61
      Corbi-Verge, C.; Kim, P. M. Motif mediated protein-protein interactions as drug targets. Cell Commun. Signal 2016, 14, 8  DOI: 10.1186/s12964-016-0131-4
      There is no corresponding record for this reference.
    62. 62
      Marković, V.; Szczepańska, A.; Berlicki, Ł. Antiviral Protein-Protein Interaction Inhibitors. J. Med. Chem. 2024, 67 (5), 3205– 3231,  DOI: 10.1021/acs.jmedchem.3c01543
      There is no corresponding record for this reference.
    63. 63
      Zheng, S. Q.; Palovcak, E.; Armache, J. P.; Verba, K. A.; Cheng, Y.; Agard, D. A. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 2017, 14 (4), 331– 332,  DOI: 10.1038/nmeth.4193
      63
      MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy
      Zheng, Shawn Q.; Palovcak, Eugene; Armache, Jean-Paul; Verba, Kliment A.; Cheng, Yifan; Agard, David A.
      Nature Methods (2017), 14 (4), 331-332CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)
      A review on anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Here we describe MotionCor2, a software tool for anisotropic correction of beam-induced motion. Overall, MotionCor2 is extremely robust and sufficiently accurate at correcting local motions so that the very time-consuming and computationally intensive particle polishing in RELION can be skipped, importantly, it also works on a wide range of data sets, including cryo tomog. tilt series.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXjt1ags7g%253D&md5=5f4e225ef8123dacd8475d526175e1d2
    64. 64
      Rohou, A.; Grigorieff, N. CTFFIND4: Fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 2015, 192 (2), 216– 221,  DOI: 10.1016/j.jsb.2015.08.008
      64
      CTFFIND4: Fast and accurate defocus estimation from electron micrographs
      Rohou Alexis; Grigorieff Nikolaus
      Journal of structural biology (2015), 192 (2), 216-21 ISSN:.
      CTFFIND is a widely-used program for the estimation of objective lens defocus parameters from transmission electron micrographs. Defocus parameters are estimated by fitting a model of the microscope's contrast transfer function (CTF) to an image's amplitude spectrum. Here we describe modifications to the algorithm which make it significantly faster and more suitable for use with images collected using modern technologies such as dose fractionation and phase plates. We show that this new version preserves the accuracy of the original algorithm while allowing for higher throughput. We also describe a measure of the quality of the fit as a function of spatial frequency and suggest this can be used to define the highest resolution at which CTF oscillations were successfully modeled.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC287js1Whsg%253D%253D&md5=8500953ad4898ae82de6f8cdc95832cf
    65. 65
      Punjani, A.; Rubinstein, J. L.; Fleet, D. J.; Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 2017, 14 (3), 290– 296,  DOI: 10.1038/nmeth.4169
      65
      cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination
      Punjani, Ali; Rubinstein, John L.; Fleet, David J.; Brubaker, Marcus A.
      Nature Methods (2017), 14 (3), 290-296CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)
      Single-particle electron cryomicroscopy (cryo-EM) is a powerful method for detg. the structures of biol. macromols. With automated microscopes, cryo-EM data can often be obtained in a few days. However, processing cryo-EM image data to reveal heterogeneity in the protein structure and to refine 3D maps to high resoln. frequently becomes a severe bottleneck, requiring expert intervention, prior structural knowledge, and weeks of calcns. on expensive computer clusters. Here we show that stochastic gradient descent (SGD) and branch-and-bound max. likelihood optimization algorithms permit the major steps in cryo-EM structure detn. to be performed in hours or minutes on an inexpensive desktop computer. Furthermore, SGD with Bayesian marginalization allows ab initio 3D classification, enabling automated anal. and discovery of unexpected structures without bias from a ref. map. These algorithms are combined in a user-friendly computer program named cryoSPARC (http://www.cryosparc.com).
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXitlGisbs%253D&md5=95d468147707707e70ac0ad38dd6ebf6
    66. 66
      Yang, Z.; Lasker, K.; Schneidman-Duhovny, D.; Webb, B.; Huang, C. C.; Pettersen, E. F.; Goddard, T. D.; Meng, E. C.; Sali, A.; Ferrin, T. E. UCSF Chimera, MODELLER, and IMP: an integrated modeling system. J. Struct Biol. 2012, 179 (3), 269– 278,  DOI: 10.1016/j.jsb.2011.09.006
      There is no corresponding record for this reference.
    67. 67
      Dam, J.; Schuck, P. Calculating Sedimentation Coefficient Distributions by Direct Modeling of Sedimentation Velocity Concentration Profiles. In Methods in Enzymology; Academic Press, 2004; Vol. 384, pp 185– 212.
      There is no corresponding record for this reference.
    68. 68
      Ribaric, S.; Peterec, D.; Sketelj, J. Computer aided data acquisition and analysis of acetlycholinesterase velocity sedimentation profiles. Comput. Methods Program. Biomed. 1996, 49 (2), 149– 156,  DOI: 10.1016/0169-2607(96)01719-1
      There is no corresponding record for this reference.
    69. 69
      Johnson, B. A.; Blevins, R. A. NMR View: A computer program for the visualization and analysis of NMR data. J. Biomol. NMR 1994, 4 (5), 603– 614,  DOI: 10.1007/BF00404272
      69
      NMRView: a computer program for the visualization and analysis of NMR data
      Johnson, Bruce A.; Blevins, Richard A.
      Journal of Biomolecular NMR (1994), 4 (5), 603-14CODEN: JBNME9; ISSN:0925-2738.
      NMRView is a computer program designed for the visualization and anal. of NMR data. It allows the user to interact with a practically unlimited no. of 2D, 3D and 4D NMR data files. Any no. of spectral windows can be displayed on the screen in any size and location. Automatic peak picking and facilitated peak anal. features are included to aid in the assignment of complex NMR spectra. NMR View provides structure anal. features and data transfer to and from structural generation programs, allowing for a tight coupling between the spectral anal. and structural generation.,. Visual correlation between structures and spectra can be done with the Mol. Data Viewer, a mol. graphics program with bidirectional communication to NMR View. The used interface can be customized and a command language is provided to allow for the automation of various tasks.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2cXmt1Gkurw%253D&md5=6a93a0d9704fbbb654abc192203c68ae
    70. 70
      Schwieters, C. D.; Kuszewski, J. J.; Marius Clore, G. Using Xplor–NIH for NMR molecular structure determination. Prog. Nucl. Magn. Reson. Spectrosc. 2006, 48 (1), 47– 62,  DOI: 10.1016/j.pnmrs.2005.10.001
      70
      Using Xplor-NIH for NMR molecular structure determination
      Schwieters, Charles D.; Kuszewski, John J.; Clore, G. Marius
      Progress in Nuclear Magnetic Resonance Spectroscopy (2006), 48 (1), 47-62CODEN: PNMRAT; ISSN:0079-6565. (Elsevier B.V.)
      A review of the title program for computerized structure detn.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XjtVOiurY%253D&md5=56257dc51ddafdf8c467d30aef191ee2
    71. 71
      Maciejewski, M. W.; Schuyler, A. D.; Gryk, M. R.; Moraru, I. I.; Romero, P. R.; Ulrich, E. L.; Eghbalnia, H. R.; Livny, M.; Delaglio, F.; Hoch, J. C. NMRbox: A Resource for Biomolecular NMR Computation. Biophys. J. 2017, 112 (8), 1529– 1534,  DOI: 10.1016/j.bpj.2017.03.011
      71
      NMRbox: A Resource for Biomolecular NMR Computation
      Maciejewski, Mark W.; Schuyler, Adam D.; Gryk, Michael R.; Moraru, Ion I.; Romero, Pedro R.; Ulrich, Eldon L.; Eghbalnia, Hamid R.; Livny, Miron; Delaglio, Frank; Hoch, Jeffrey C.
      Biophysical Journal (2017), 112 (8), 1529-1534CODEN: BIOJAU; ISSN:0006-3495. (Cell Press)
      Advances in computation have been enabling many recent advances in biomol. applications of NMR. Due to the wide diversity of applications of NMR, the no. and variety of software packages for processing and analyzing NMR data is quite large, with labs relying on dozens, if not hundreds of software packages. Discovery, acquisition, installation, and maintenance of all these packages is a burdensome task. Because the majority of software packages originate in academic labs, persistence of the software is compromised when developers graduate, funding ceases, or investigators turn to other projects. To simplify access to and use of biomol. NMR software, foster persistence, and enhance reproducibility of computational workflows, the authors have developed NMRbox, a shared resource for NMR software and computation. NMRbox employs virtualization to provide a comprehensive software environment preconfigured with hundreds of software packages, available as a downloadable virtual machine or as a Platform-as-a-Service supported by a dedicated compute cloud. Ongoing development includes a metadata harvester to regularize, annotate, and preserve workflows and facilitate and enhance data depositions to BioMagResBank, and tools for Bayesian inference to enhance the robustness and extensibility of computational analyses. In addn. to facilitating use and preservation of the rich and dynamic software environment for biomol. NMR, NMRbox fosters the development and deployment of a new class of metasoftware packages. NMRbox is freely available to not-for-profit users.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXlvFWgtL8%253D&md5=a990d67aed35b62cd010f0f5396d0f82
    72. 72
      Shen, Y.; Delaglio, F.; Cornilescu, G.; Bax, A. TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts. J. Biomol. NMR 2009, 44 (4), 213– 223,  DOI: 10.1007/s10858-009-9333-z
      72
      TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts
      Shen, Yang; Delaglio, Frank; Cornilescu, Gabriel; Bax, Ad
      Journal of Biomolecular NMR (2009), 44 (4), 213-223CODEN: JBNME9; ISSN:0925-2738. (Springer)
      NMR chem. shifts in proteins depend strongly on local structure. The program TALOS establishes an empirical relation between 13C, 15N and 1H chem. shifts and backbone torsion angles .vphi. and ψ. Extension of the original 20-protein database to 200 proteins increased the fraction of residues for which backbone angles could be predicted from 65 to 74%, while reducing the error rate from 3 to 2.5%. Addn. of a two-layer neural network filter to the database fragment selection process forms the basis for a new program, TALOS+, which further enhances the prediction rate to 88.5%, without increasing the error rate. Excluding the 2.5% of residues for which TALOS+ makes predictions that strongly differ from those obsd. in the cryst. state, the accuracy of predicted .vphi. and ψ angles, equals ±13°. Large discrepancies between predictions and crystal structures are primarily limited to loop regions, and for the few cases where multiple X-ray structures are available such residues are often found in different states in the different structures. The TALOS+ output includes predictions for individual residues with missing chem. shifts, and the neural network component of the program also predicts secondary structure with good accuracy.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXovFajtr8%253D&md5=ee3f8647a02e19937988056fd7af04b2
    73. 73
      Tian, Y.; Schwieters, C. D.; Opella, S. J.; Marassi, F. M. A practical implicit solvent potential for NMR structure calculation. J. Magn. Reson. 2014, 243, 54– 64,  DOI: 10.1016/j.jmr.2014.03.011
      73
      A practical implicit solvent potential for NMR structure calculation
      Tian, Ye; Schwieters, Charles D.; Opella, Stanley J.; Marassi, Francesca M.
      Journal of Magnetic Resonance (2014), 243 (), 54-64CODEN: JMARF3; ISSN:1090-7807. (Elsevier B.V.)
      The benefits of protein structure refinement in water are well documented. However, performing structure refinement with explicit at. representation of the solvent mols. is computationally expensive and impractical for NMR-restrained structure calcns. that start from completely extended polypeptide templates. Here we describe a new implicit solvation potential, EEFx (Effective Energy Function for XPLOR-NIH), for NMR-restrained structure calcns. of proteins in XPLOR-NIH. The key components of EEFx are an energy term for solvation energy that works together with other nonbonded energy functions, and a dedicated force field for conformational and nonbonded protein interaction parameters. The initial results obtained with EEFx show that significant improvements in structural quality can be obtained. EEFx is computationally efficient and can be used both to fold and refine structures. Overall, EEFx improves the quality of protein conformation and nonbonded at. interactions. Moreover, such benefits are accompanied by enhanced structural precision and enhanced structural accuracy, reflected in improved agreement with the cross-validated dipolar coupling data. Finally, implementation of EEFx calcns. is straightforward and computationally efficient. Overall, EEFx provides a useful method for the practical calcn. of exptl. protein structures in a phys. realistic environment.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXovVahu70%253D&md5=68ad01c9ceb6078a4291b0d6e96408e8
    74. 74
      Gore, S.; Sanz García, E.; Hendrickx, P. M. S.; Gutmanas, A.; Westbrook, J. D.; Yang, H.; Feng, Z.; Baskaran, K.; Berrisford, J. M.; Hudson, B. P. Validation of Structures in the Protein Data Bank. Structure 2017, 25 (12), 1916– 1927,  DOI: 10.1016/j.str.2017.10.009
      74
      Validation of Structures in the Protein Data Bank
      Gore, Swanand; Sanz Garcia, Eduardo; Hendrickx, Pieter M. S.; Gutmanas, Aleksandras; Westbrook, John D.; Yang, Huanwang; Feng, Zukang; Baskaran, Kumaran; Berrisford, John M.; Hudson, Brian P.; Ikegawa, Yasuyo; Kobayashi, Naohiro; Lawson, Catherine L.; Mading, Steve; Mak, Lora; Mukhopadhyay, Abhik; Oldfield, Thomas J.; Patwardhan, Ardan; Peisach, Ezra; Sahni, Gaurav; Sekharan, Monica R.; Sen, Sanchayita; Shao, Chenghua; Smart, Oliver S.; Ulrich, Eldon L.; Yamashita, Reiko; Quesada, Martha; Young, Jasmine Y.; Nakamura, Haruki; Markley, John L.; Berman, Helen M.; Burley, Stephen K.; Velankar, Sameer; Kleywegt, Gerard J.
      Structure (Oxford, United Kingdom) (2017), 25 (12), 1916-1927CODEN: STRUE6; ISSN:0969-2126. (Elsevier Ltd.)
      The Worldwide PDB recently launched a deposition, biocuration, and validation tool: OneDep. At various stages of OneDep data processing, validation reports for three-dimensional structures of biol. macromols. are produced. These reports are based on recommendations of expert task forces representing crystallog., NMR, and cryoelectron microscopy communities. The reports provide useful metrics with which depositors can evaluate the quality of the exptl. data, the structural model, and the fit between them. The validation module is also available as a stand-alone web server and as a programmatically accessible web service. A growing no. of journals require the official wwPDB validation reports (produced at biocuration) to accompany manuscripts describing macromol. structures. Upon public release of the structure, the validation report becomes part of the public PDB archive. Geometric quality scores for proteins in the PDB archive have improved over the past decade.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhvVOitbnE&md5=680c220ca1354e8a089638ae978501d3
    75. 75
      Schwarzinger, S.; Kroon, G. J.; Foss, T. R.; Wright, P. E.; Dyson, H. J. Random coil chemical shifts in acidic 8 M urea: implementation of random coil shift data in NMRView. J. Biomol. NMR 2000, 18 (1), 43– 48,  DOI: 10.1023/A:1008386816521
      75
      Random coil chemical shifts in acidic 8 M urea: implementation of random coil shift data in NMRView
      Schwarzinger, Stephan; Kroon, Gerard J. A.; Foss, Ted R.; Wright, Peter E.; Dyson, H. Jane
      Journal of Biomolecular NMR (2000), 18 (1), 43-48CODEN: JBNME9; ISSN:0925-2738. (Kluwer Academic Publishers)
      Studies of proteins unfolded in acid or chem. denaturant can help in unraveling events during the earliest phases of protein folding. In order for meaningful comparisons to be made of residual structure in unfolded states, it is necessary to use random coil chem. shifts that are valid for the exptl. system under study. We present a set of random coil chem. shifts obtained for model peptides under exptl. conditions used in studies of denatured proteins. This new set, together with previously published data sets, has been incorporated into a software interface for NMRView, allowing selection of the random coil data set that fits the exptl. conditions best.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXnvVGit7w%253D&md5=cf30cd19aa4420c807a0525789db9be1
    76. 76
      Berendsen, H. J. C.; van der Spoel, D.; van Drunen, R. GROMACS: A message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 1995, 91 (1), 43– 56,  DOI: 10.1016/0010-4655(95)00042-E
      76
      GROMACS: A message-passing parallel molecular dynamics implementation
      Berendsen, H. J. C.; van der Spoel, D.; van Drunen, R.
      Computer Physics Communications (1995), 91 (1-3), 43-56CODEN: CPHCBZ; ISSN:0010-4655. (Elsevier)
      A parallel message-passing implementation of a mol. dynamics (MD) program that is useful for bio(macro)mols. in aq. environment is described. The software has been developed for a custom-designed 32-processor ring GROMACS (Groningen MAchine for Chem. Simulation) with communication to and from left and right neighbors, but can run on any parallel system onto which a a ring of processors can be mapped and which supports PVM-like block send and receive calls. The GROMACS software consists of a preprocessor, a parallel MD and energy minimization program that can use an arbitrary no. of processors (including one), an optional monitor, and several anal. tools. The programs are written in ANSI C and available by ftp (information: [email protected]). The functionality is based on the GROMOS (Groningen Mol. Simulation) package (van Gunsteren and Berendsen, 1987; BIOMOS B.V., Nijenborgh 4, 9747 AG Groningen). Conversion programs between GROMOS and GROMACS formats are included.The MD program can handle rectangular periodic boundary conditions with temp. and pressure scaling. The interactions that can be handled without modification are variable non-bonded pair interactions with Coulomb and Lennard-Jones or Buckingham potentials, using a twin-range cut-off based on charge groups, and fixed bonded interactions of either harmonic or constraint type for bonds and bond angles and either periodic or cosine power series interactions for dihedral angles. Special forces can be added to groups of particles (for non-equil. dynamics or for position restraining) or between particles (for distance restraints). The parallelism is based on particle decompn. Interprocessor communication is largely limited to position and force distribution over the ring once per time step.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXps1Wrtr0%253D&md5=04d823aeab28ca374efb86839c705179
    77. 77
      Jurrus, E.; Engel, D.; Star, K.; Monson, K.; Brandi, J.; Felberg, L. E.; Brookes, D. H.; Wilson, L.; Chen, J.; Liles, K. Improvements to the APBS biomolecular solvation software suite. Protein Sci. 2018, 27 (1), 112– 128,  DOI: 10.1002/pro.3280
      77
      Improvements to the APBS biomolecular solvation software suite
      Jurrus, Elizabeth; Engel, Dave; Star, Keith; Monson, Kyle; Brandi, Juan; Felberg, Lisa E.; Brookes, David H.; Wilson, Leighton; Chen, Jiahui; Liles, Karina; Chun, Minju; Li, Peter; Gohara, David W.; Dolinsky, Todd; Konecny, Robert; Koes, David R.; Nielsen, Jens Erik; Head-Gordon, Teresa; Geng, Weihua; Krasny, Robert; Wei, Guo-Wei; Holst, Michael J.; McCammon, J. Andrew; Baker, Nathan A.
      Protein Science (2018), 27 (1), 112-128CODEN: PRCIEI; ISSN:1469-896X. (Wiley-Blackwell)
      The Adaptive Poisson-Boltzmann Solver (APBS) software was developed to solve the equations of continuum electrostatics for large biomol. assemblages that have provided impact in the study of a broad range of chem., biol., and biomedical applications. APBS addresses the three key technol. challenges for understanding solvation and electrostatics in biomedical applications: accurate and efficient models for biomol. solvation and electrostatics, robust and scalable software for applying those theories to biomol. systems, and mechanisms for sharing and analyzing biomol. electrostatics data in the scientific community. To address new research applications and advancing computational capabilities, we have continually updated APBS and its suite of accompanying software since its release in 2001. In this article, we discuss the models and capabilities that have recently been implemented within the APBS software package including a Poisson-Boltzmann anal. and a semi-anal. solver, an optimized boundary element solver, a geometry-based geometric flow solvation model, a graph theory-based algorithm for detg. pKa values, and an improved web-based visualization tool for viewing electrostatics.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhslSkt7vI&md5=99651d125e38f4a85d453fecf0f71652
    78. 78
      Martínez, L.; Andrade, R.; Birgin, E. G.; Martínez, J. M. PACKMOL: A package for building initial configurations for molecular dynamics simulations. J. Comput. Chem. 2009, 30 (13), 2157– 2164,  DOI: 10.1002/jcc.21224
      78
      PACKMOL: A package for building initial configurations for molecular dynamics simulations
      Martinez, L.; Andrade, R.; Birgin, E. G.; Martinez, J. M.
      Journal of Computational Chemistry (2009), 30 (13), 2157-2164CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
      Adequate initial configurations for mol. dynamics simulations consist of arrangements of mols. distributed in space in such a way to approx. represent the system's overall structure. In order that the simulations are not disrupted by large van der Waals repulsive interactions, atoms from different mols. must keep safe pairwise distances. Obtaining such a mol. arrangement can be considered a packing problem: Each type mol. must satisfy spatial constraints related to the geometry of the system, and the distance between atoms of different mols. must be greater than some specified tolerance. We have developed a code able to pack millions of atoms, grouped in arbitrarily complex mols., inside a variety of three-dimensional regions. The regions may be intersections of spheres, ellipses, cylinders, planes, or boxes. The user must provide only the structure of one mol. of each type and the geometrical constraints that each type of mol. must satisfy. Building complex mixts., interfaces, solvating biomols. in water, other solvents, or mixts. of solvents, is straightforward. In addn., different atoms belonging to the same mol. may also be restricted to different spatial regions, in such a way that more ordered mol. arrangements can be built, as micelles, lipid double-layers, etc. The packing time for state-of-the-art mol. dynamics systems varies from a few seconds to a few minutes in a personal computer. The input files are simple and currently compatible with PDB, Tinker, Molden, or Moldy coordinate files. The package is distributed as free software and can be downloaded from . © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXptleqsb8%253D&md5=2a76255c873b866a26540f7e84496272
    79. 79
      Rühle, V. Pressure coupling/barostats. 2008.
      There is no corresponding record for this reference.
    80. 80
      Essmann, U.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee, H.; Pedersen, L. G. A smooth particle mesh Ewald method. J. Chem. Phys. 1995, 103 (19), 8577– 8593,  DOI: 10.1063/1.470117
      80
      A smooth particle mesh Ewald method
      Essmann, Ulrich; Perera, Lalith; Berkowitz, Max L.; Darden, Tom; Lee, Hsing; Pedersen, Lee G.
      Journal of Chemical Physics (1995), 103 (19), 8577-93CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
      The previously developed particle mesh Ewald method is reformulated in terms of efficient B-spline interpolation of the structure factors. This reformulation allows a natural extension of the method to potentials of the form 1/rp with p ≥ 1. Furthermore, efficient calcn. of the virial tensor follows. Use of B-splines in the place of Lagrange interpolation leads to analytic gradients as well as a significant improvement in the accuracy. The authors demonstrate that arbitrary accuracy can be achieved, independent of system size N, at a cost that scales as N log(N). For biomol. systems with many thousands of atoms and this method permits the use of Ewald summation at a computational cost comparable to that of a simple truncation method of 10 Å or less.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXptlehtrw%253D&md5=092a679dd3bee08da28df41e302383a7
    81. 81
      Lindorff-Larsen, K.; Piana, S.; Palmo, K.; Maragakis, P.; Klepeis, J. L.; Dror, R. O.; Shaw, D. E. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 2010, 78, 1950– 1958,  DOI: 10.1002/prot.22711
      81
      Improved side-chain torsion potentials for the Amber ff99SB protein force field
      Lindorff-Larsen, Kresten; Piana, Stefano; Palmo, Kim; Maragakis, Paul; Klepeis, John L.; Dror, Ron O.; Shaw, David E.
      Proteins: Structure, Function, and Bioinformatics (2010), 78 (8), 1950-1958CODEN: PSFBAF ISSN:. (Wiley-Liss, Inc.)
      Recent advances in hardware and software have enabled increasingly long mol. dynamics (MD) simulations of biomols., exposing certain limitations in the accuracy of the force fields used for such simulations and spurring efforts to refine these force fields. Recent modifications to the Amber and CHARMM protein force fields, for example, have improved the backbone torsion potentials, remedying deficiencies in earlier versions. Here, the authors further advance simulation accuracy by improving the amino acid side-chain torsion potentials of the Amber ff99SB force field. First, the authors used simulations of model alpha-helical systems to identify the four residue types whose rotamer distribution differed the most from expectations based on Protein Data Bank statistics. Second, the authors optimized the side-chain torsion potentials of these residues to match new, high-level quantum-mech. calcns. Finally, the authors used microsecond-timescale MD simulations in explicit solvent to validate the resulting force field against a large set of exptl. NMR measurements that directly probe side-chain conformations. The new force field, which the authors have termed Amber ff99SB-ILDN, exhibits considerably better agreement with the NMR data. Proteins 2010. © 2010 Wiley-Liss, Inc.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXkvFegtLo%253D&md5=447a9004026e2b93f0f7beff165daa09
    82. 82
      Mark, P.; Nilsson, L. Structure and Dynamics of the TIP3P, SPC, and SPC/E Water Models at 298 K. J. Phys. Chem. A 2001, 105 (43), 9954– 9960,  DOI: 10.1021/jp003020w
      82
      Structure and Dynamics of the TIP3P, SPC, and SPC/E Water Models at 298 K
      Mark, Pekka; Nilsson, Lennart
      Journal of Physical Chemistry A (2001), 105 (43), 9954-9960CODEN: JPCAFH; ISSN:1089-5639. (American Chemical Society)
      Mol. dynamics simulations of five water models, the TIP3P (original and modified), SPC (original and refined), and SPC/E (original), were performed using the CHARMM mol. mechanics program. All simulations were carried out in the microcanonical NVE ensemble, using 901 water mols. in a cubic simulation cell furnished with periodic boundary conditions at 298 K. The SHAKE algorithm was used to keep water mols. rigid. Nanosecond trajectories were calcd. with all water models for high statistical accuracy. The characteristic self-diffusion coeffs. D and radial distribution functions, gOO, gOH, and gHH for all five water models were detd. and compared to exptl. data. The effects of velocity rescaling on the self-diffusion coeff. D were examd. All these empirical water models used in this study are similar by having three interaction sites, but the small differences in their pair potentials composed of Lennard-Jones (LJ) and Coulombic terms give significant differences in the calcd. self-diffusion coeffs., and in the height of the second peak of the radial distribution function gOO.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3MXntlWrurs%253D&md5=fecd3db40210b04e8b2a933ea07b131e
    83. 83
      de Vries, S. J.; van Dijk, M.; Bonvin, A. M. J. J. The HADDOCK web server for data-driven biomolecular docking. Nat. Protoc. 2010, 5 (5), 883– 897,  DOI: 10.1038/nprot.2010.32
      83
      The HADDOCK web server for data-driven biomolecular docking
      de Vries, Sjoerd J.; van Dijk, Marc; Bonvin, Alexandre M. J. J.
      Nature Protocols (2010), 5 (5), 883-897CODEN: NPARDW; ISSN:1750-2799. (Nature Publishing Group)
      Computational docking is the prediction or modeling of the three-dimensional structure of a biomol. complex, starting from the structures of the individual mols. in their free, unbound form. HADDOCK is a popular docking program that takes a data-driven approach to docking, with support for a wide range of exptl. data. Here the authors present the HADDOCK web server protocol, facilitating the modeling of biomol. complexes for a wide community. The main web interface is user-friendly, requiring only the structures of the individual components and a list of interacting residues as input. Addnl. web interfaces allow the more advanced user to exploit the full range of exptl. data supported by HADDOCK and to customize the docking process. The HADDOCK server has access to the resources of a dedicated cluster and of the e-NMR GRID infrastructure. Therefore, a typical docking run takes only a few minutes to prep. and a few hours to complete.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXltl2qsLw%253D&md5=dbf801d99065e8d2c3aedc016209838c
    84. 84
      Xue, L. C.; Rodrigues, J. P.; Kastritis, P. L.; Bonvin, A. M.; Vangone, A. PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics 2016, 32 (23), 3676– 3678,  DOI: 10.1093/bioinformatics/btw514
      There is no corresponding record for this reference.
    85. 85
      Schuck, P.; Zhao, H. Sedimentation Velocity Analytical Ultracentrifugation: Interacting Systems (1st ed.); CRC Press, 2017.
      There is no corresponding record for this reference.
    86. 86
      Kyte, J.; Doolittle, R. F. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 1982, 157 (1), 105– 132,  DOI: 10.1016/0022-2836(82)90515-0
      86
      A simple method for displaying the hydropathic character of a protein
      Kyte, Jack; Doolittle, Russell F.
      Journal of Molecular Biology (1982), 157 (1), 105-32CODEN: JMOBAK; ISSN:0022-2836.
      A computer program that progressively evaluates the hydrophilicity and hydrophobicity of a protein along its amino acid sequence was devised. A hydropathy scale takes into consideration the hydrophilic and hydrophobic properties of each of the 20 amino acid side chains. Correlation was demonstrated between the plotted values and known structures detd. by crystallog.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL38Xks1yjtro%253D&md5=ee67eb115939dfe56b2b2cae2c32dbd3

  • Supporting Information

    Supporting Information

    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsinfecdis.4c00407.

    • Experimental methods; Figures S1–S21; Tables S1 and S2 (PDF)

    • Conformational change of GNNV VLP from pH 8.0 to 5.0 (Movie S1) (MP4)

    • Enlarged views of GNNV protrusion conformational change from pH 8.0 to 5.0 (Movie S2) (MP4)

    • Hypothetical atomic model of GNNV-P conformational change from pH 8.0 to 5.0 (Movie S3) (MP4)


    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.