In Silico Structural Modeling and Analysis of Elongation Factor-1 Alpha and Elongation Factor-like Protein
- Kotaro SakamotoKotaro SakamotoLeading Graduate School Doctoral Program in Human Biology, University of Tsukuba, Tsukuba, Ibaraki 305-8577, JapanMore by Kotaro Sakamoto,
- Megumi Kayanuma*Megumi Kayanuma*E-mail: [email protected] (M.K.).Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8577, JapanMore by Megumi Kayanuma,
- Yuji InagakiYuji InagakiCenter for Computational Sciences, Graduate School of Life and Environmental Sciences, , University of Tsukuba, Tsukuba, Ibaraki 305-8577, JapanMore by Yuji Inagaki,
- Tetsuo HashimotoTetsuo HashimotoGraduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8577, JapanMore by Tetsuo Hashimoto, and
- Yasuteru Shigeta*Yasuteru Shigeta*E-mail: [email protected] (Y.S.).Center for Computational Sciences, Graduate School of Pure and Applied Sciences, , University of Tsukuba, Tsukuba, Ibaraki 305-8577, JapanMore by Yasuteru Shigeta
Copyright © 2019 American Chemical Society
Abstract
Translation elongation factor-1alpha (EF-1α) or its paralog elongation factor-like proteins (EFL) interact with an aminoacyl-transfer RNA (aa-tRNA) to play its essential role in elongation of peptide-chain during protein synthesis. Species usually have either an EF-1α or EFL protein; however, some species have both EF-1α and EFL (dual-EF-containing species). In the dual-EF-containing species, EF-1α appeared to be highly divergent in the sequence. Homology modeling and surface analysis of EF-1α and EFL were performed to examine the hypothesis that the divergent EF-1α in the dual-EF-containing eukaryotes does not strongly interact with aa-tRNA compared to the canonical EF-1α and EFL. The subsequent molecular dynamics simulations were carried out to confirm the validity of modeled structures and to analyze their stability. It was found that the molecular surfaces of the divergent EF-1α proteins were negatively charged partly, and thus they might not interact with negatively charged aa-tRNA as strongly as the canonical ones. The molecular docking simulations between EF-1α/EFL and aa-tRNA also support the hypothesis.
Introduction
ARTICLE SECTIONS
Translation elongation factor-1alpha (EF-1α or EF1A), the eukaryotic (and archaebacterial) counterpart to elongation factor Tu (EF-Tu) in bacteria, interacts with aminoacyl-transfer RNAs (aa-tRNAs) in a guanosine triphosphate (GTP)-binding-dependent manner, and delivers them to the acceptor site of the 80S ribosome.(1,2) Due to high conservation at the amino acid sequence coupled with ubiquitous distribution in eukaryotes, EF-1α has been considered as one of the major phylogenetic markers,(3−5) and models for assessing functional divergence in protein evolution.(6,7) Besides the essential role in translation described above, EF-1α is known to be involved in diverse cellular processes also known as moonlighting functions, such as cytoskeletal remodeling, protein folding and degradation, proteolysis, cell signaling modulation, control of cell growth, nuclear export processes, apoptosis, and cell cycle.(8−10) For instance, two EF-1α isoforms in human, which share 98% similarity at the amino acid sequence, were found to have distinct arbitrary functions, one involved in apoptosis and the other in cancer development.(11)
Recent massive accumulation of sequencing data from phylogenetically diverse eukaryotes has raised a question on the ubiquity of EF-1α in eukaryotes. Some eukaryotes appeared to lack EF-1α but a distinct paralog, i.e., elongation factor-like protein (EFL), has been identified.(12) The surveys of EF-1α/EFL genes(13−20) classified eukaryotes into three types, namely “EF-1α-containing”, “EFL-containing”, and “dual-EF-containing”—the species using only EF-1α belong to the first type, whereas those using only EFL belong to the second type. So far, dual-EF-containing species, in which both EF-1α and EFL genes were found, are highly restricted among eukaryotes. The distribution of EF-1α/EFL can be explained by “differential loss” hypothesis, which assumes that the two elongation factors were present in the ancestral eukaryote and losses of one of the two proteins occurred on separate branches of the eukaryotic tree.(16−19) According to this hypothesis, dual-EF-containing species corresponds to the ancestral state, implying that these types of eukaryotes provide insights into the early molecular evolution of EF-1α/EFL.(15,19,21,22) Although EF-1α and EFL are functionally equivalent to one another as a GTPase in translation, the two factors most likely recycle guanosine-5′-diphosphate (GDP) to GTP in distinct approaches. In EF-1α-containing species, EF-1α (henceforth designated as “solo-EF-1α”) interacts with its guanine nucleotide exchange factor (GEF), EF-1β, to recycle GDP to GTP.(23) Nevertheless, EF-1β is unlikely to be the GEF for EFL, as EFL-containing species lack EF-1β with a few exceptions.(15,22) Consistent with the observation described above, a study incorporating a homology model of EFL proposed that EFL can recharge GTP with no GEF.(22)
In the dual-EF-containing species known to date, not both EF-1α and EFL unlikely participate in the elongation step in translation, as EF-1α appeared to be highly divergent at the amino acid sequence level and transcriptionally suppressed compared to the co-occurring EFL genes. The results from pioneering studies imply that dual-EF-containing species use EFL for translation, whereas the divergent EF-1α (henceforth designated as “div-EF-1α”) is in charge of arbitrary functions involved in nontranslational cellular processes.(19) In another word, div-EF-1α in dual-EF-containing species are expected to not necessarily bind to and deliver aa-tRNAs to the ribosomes, which are the principal functions of solo-EF-1α and EFL. In addition, no case of co-occurrence of div-EF-1α and EF-1β has been found in eukaryotes,(22) implying that div-EF-1α lacks EF-1β-binding capacity for GDP/GTP recycling. Nevertheless, there is no study that characterized the putative functions of div-EF-1α. We here report the results from comparative studies between solo-EF-1α/EFL and div-EF-1α by combining in silico structural modeling, molecular dynamics (MD) simulation, molecular surface analyses and molecular docking simulation. The structural models of EF-1α and EFL were then employed to molecular surface analyses and molecular docking simulations to evaluate whether div-EF-1α binds to aa-tRNAs or EF-1β.(19)
Results and Discussion
ARTICLE SECTIONS
Homology Modeling
Three-dimensional structures of EF-1α and EFL proteins were constructed by the SWISS-MODEL web server(24) using the three templates (PDB ID: 1G7C,(23)4C0S,(25) and 3WXM(26)). Tables S1–S3 show the sequence identities with the templates and the scores estimating the quality of the homology models. A qualitative model energy analysis (QMEAN) Z-score provides an estimate of the absolute quality of a model and indicates whether the quality of the generated model is comparable to the experimental structures, whereas QMEAN4 is a linear combination of the four statistical potential scores.(27,28) A global model quality estimation (GMQE) score reflects the expected accuracy of the alignment and is expressed as a number between 0 and 1.(24) For both the QMEAN Z-score and GMQE score, the higher numbers indicate the higher reliability of the generated models. The sequence identities between the template and the div-EF-1α considered in this study ranged from 40 to 70%, which were lower than those calculated from the solo-EF-1α (70–80%). By comparing the QMEAN4 scores, it was shown that the qualities of the generated models of the div-EF-1α were found to be lower than those of the solo-EF-1α. Models e.g., with yeast EF-1α as the template, the QMEAN4 Z-scores ranged from −3.82 to −0.32 in the former, whereas from −0.92 to −0.13 in the latter (Table S1). The sequence identities of EFL of both the dual-EF-containing and EFL-containing species was lower than 45% and the estimated model quality was also low accordingly (QMEAN4 was lower than −3.52). The sequence identities between the solo- and div-EF-1α sequences and the Aeropyrum sequence varied (Table S3), whereas those between the EFL sequences did not vary significantly.
Using Aeropyrum pernix EF-1α as the template structure would be the most plausible for the aa-tRNA binding form because the root-mean-square deviations (RMSD) of Aeropyrum pernix EF-1α (PDB ID: 3WXM) and the crystal structure (PDB ID: 1TTT)(29) of EF-Tu bound to Phe-tRNA was only 2.26 Å. There is no GTP-bound eukaryotic EF-1α structure found for the template search and no X-ray crystal structures of eukaryotic EF-1α and aa-tRNA complex has been solved to this date. Assuming that eukaryotic EF-1α proteins have structures similar to the archaeal or bacterial orthologues when they interact with aa-tRNAs, the homology models using the Aeropyrum EF-1α as the template were employed to the following molecular surface analyses and molecular docking simulations to evaluate whether EF-1α/EFL bind to aa-tRNAs.
MD Simulations
For more detailed analysis of the structures of EF-1α and EFL, we performed the MD simulations for some selected models. Because many amino acid sequences in an N-terminal domain are missing in many species, we limitedly selected two EF-1α proteins of Pythium ultimum DAOM BR144 (dual-EF-containing species) and Subulatomonas sp. strain PCMinv5 (EF-1α-containing species) and two EFL proteins of Thecamonas trahens ATCC50062 (dual-EF-containing species) and Fabomonas tropica strain NYK3C (EFL-containing species) for the MD simulations. To generate initial structures, we employed the MODELLER program(30) using the three template structures. The generated models contained all sequences including C-terminal regions, which were not in the models obtained by SWISS-MODEL.
We confirmed the reliability of the predicted homology models by comparing the MD simulations for EF-1α of the EF-1α-containing species (Subulatomonas EF-1α) and the dual-EF-containing species (Pythium div-EF-1α structure). Figure 1a,c,e, show the root-mean-square deviations (RMSD) against each of the initial structure which was obtained by homology modeling. Figure 1b,d,f show the radius of gyration (a measure of compactness of the protein), which were almost stable over the simulation time. Comparing the RMSD of the solo-EF-1α and div-EF-1α structures, the latter was slightly more drifted than the former like those of the other two EFLs. This negligible difference might have come from the possible difference in the actual structures in vivo; the solo-EF-1α would have the same structure as the given template; however, the other three would not.
Figure 1
Figure 1. Root-mean-square deviations (RMSD) and gyrations of the MD simulations of proteins (a, b) with GDP (c, d) and with GTP (e, f) for EF-1α of Subulatomonas sp. strain PCMinv5 (red line) and Pythium ultimum DAOM BR144 (purple line) and EFL of Fabomonas tropica strain NYK3C (green line) and Thecamonas trahens ATCC50062 (blue line).
Figure S6 shows the changes in the structures and molecular surface after the MD simulations. The structures and their molecular surfaces remained almost the same.
There was some slight diversity in the dynamics; however, from these short MD simulations, the predicted homology models were stable, thus reliable. The four proteins shifted in RMSD for about 0.4 nm and became stable within less than 10 ns (Figure 1). The RMSD of the solo-EF-1α structure grew differently from those of other three structures. The EFL of Fabomonas also had slightly different dynamics in gyration. The dynamical differences between predicted models and the instability in their time evolution were both negligible. The atomic distances for GTP-bound or GDP-bound models had slightly larger fluctuations; however, they were still negligible. We might need a longer simulation time to observe a dynamical change in their domain intervals (Figure 2).
Figure 2
To extract the essential dynamics and the dominant motions in the proteins, principal component analysis (PCA) was performed using GROMACS. Root-mean-square fluctuations (RMSF) by the first eigenvector are shown in Figure 3. The spectra had the peak at the residue number 231, the boundary region between domains I and II/III. The boundary region between II and III in the EFL models built on the template Aeropyrum EF-1α with GTP (Figure 3c,d: red) were more fluctuating than the standard EF-1α (Figure 3a) and other proteins which were fluctuating uniformly along the residues. RMSFs were also reflecting the dynamics that the GDP forms of the EFL and the EF-1α in the dual-EF-containing were more flexible among the domains I and II/III and the GTP forms of EFLs were more flexible between domains II and III. The domain I of EFL proteins where some sequences were added compared to EF-1α(22) had less thermodynamic fluctuations than those of EF-1α proteins. This might imply the thermodynamic stability of EFL without GTP or GDP. Figure 4 shows the free energy landscapes (FEL) constructed via PCA of the MD trajectories. The most stable structures of the proteins were extracted from the lowest wells of the FEL and used for the following analysis.
Figure 3
Figure 3. Root-mean-square fluctuations of MD simulations of Subulatomonas sp. strain PC Minv5 EF-1α (a), Pythium ultimum DAOM BR144 EF-1α (b), Thecamonas trahens ATCC50062 EFL (c), Fabomonas tropica strain NYK3C EFL (d), with APO: blue, GDP: green, and GTP: red.
Figure 4
Figure 4. PCA-based free energy landscape of (A) Subulatomonas EF-1α (B) with GDP (C) with GTP, (D) Pythium EF-1α (E) with GDP (F) with GTP, (G) Thecamonas EFL (H) with GDP (I) with GTP, (J) Fabomonas EFL (K) with GDP (L) with GTP. The free energy ΔG is given in kcal mol–1 and presented by colors from blue (0 kcal mol–1) to red (15 kcal mol–1).
Judging from the structural and dynamical properties analyzed here, the MD simulations supported the homology models so that the surface analysis was stable and reliable.
Surface Analyses
The molecular surfaces of the modeled EF-1α and EFL proteins were analyzed using the eF-surf web server(31) and compared to each other (see Figures 2 and S2–S4). The surface potentials are represented from anionic (red) to cationic (blue) and the hydrophobic area is shown in yellow. Comparing the EF-1α proteins of the single EF-1α-containing and dual-EF-containing species, the latter (Figures S2–S4K–O,P–T) had more negative charges than the former (Figures S2–S4A–J), which would be unfavorable for interactions with negatively charged aa-tRNAs during translation elongation, the canonical function of EF-1α. On the other hand, the EFL proteins of both the dual-EF-containing (Figures S2–S4U–X,Y–Z) and the single EFL-containing species (Figures S2–S4A′–E′) seemed to have smaller surface areas with negative charges than the div-EF-1α proteins (Figures S2–S4K–O,P–T). In particular, the aa-tRNA binding sites on EF-1α in the dual-EF-containing species were negatively charged, whereas the EFL in the same species were less negatively charged, and thus seemed to preserve the binding sites.
The aa-tRNA binding sites of EF-1α were previously inferred from the alignment of the yeast EF-1α and EF-Tu 10.(7) The 31 sequences of EF-1α and EFL were aligned by BLAST(32) with the yeast EF-1α sequence and then the aa-tRNA binding sites were marked as shown in Table S4. The eF-Surf calculates the electrostatic potentials on the Connolly polygon vertices, so the neighboring polygon vertices to the Cα atoms corresponding to the aligned binding site residues were searched. The electrostatic potentials of the binding sites or the sum of those at the vertices are shown in Table S5. “Neighborhood” values were considered; the electrostatic potentials at the vertices within 5 nm spheres were summed up. The calculation also revealed that some of the divergent EF-1α proteins of Pythium intermedium MAFF306022, Goniomonas sp. ATCC 50180, and Achnanthes kuwaitensis NIES1349 still preserved the positively charged binding sites. Differential loss hypothesis assumes that the div-EF-1α proteins have lost the canonical function including aa-tRNA binding. Nevertheless, the three div-EF-1α proteins might be capable of interacting with aa-tRNAs. Alternatively, they have already stopped interacting with aa-tRNAs; however, the surface electrostatic potentials have not been completely adapted to the loss of the particular function and are still on the way to lose the ability. A tiny swap in amino acid residues or the electron configurations can make a huge difference in the electrostatic potentials, consequently altering the function and the fate of the proteins.
All results of surface analysis support that in the dual-EF-containing species div-EF-1α would not be involved in the translation elongation and EFL might play the role instead as predicted from the sequence diversity and the low expression level of div-EF-1α.(19) In addition, comparing EF-1α and EFL, it seemed that the EF-1β binding site(33) was less hydrophobic in EFL (Figures S2–S4U–E′) than in EF-1α of the single EF-1α-containing species (Figures S2–S4A–J). The result suggests that EFL might not bind to EF-1β or other proteins at this region, which is consistent with the previous study indicating that EFL might be able to recharge GTP without EF-1β.(22) Finally, we would like to mention that the both solo- and div-EF-1α proteins and also EFL proteins seemed to conserve the domains II and III, which were considered to be important for its molecular switch or the moonlighting functions.
Docking Simulations
The docking simulations were performed by employing the MEGADOCK 4.0.2 to consider the interactions between EF-1α/EFL and Phe-tRNA. Figure 5a, Tables S6 and S7 reflect the predicted results of EF-1α/EFL–Phe-tRNA interaction. Table S6 shows the E values representing the PPI scores of the predicted docking of EF-1α/EFL and Phe-tRNA. EF-1α were generally high, whereas those for the EFL were low in the affinity with Phe-tRNA (p-value = 1.2 × 10–4, p-value = 1.2 × 10–5) (Figure 5a). Also, the div-EF-1α were low in the affinity (p-value = 0.02) (Figure 5a). This result was consistent with the hypothesis that the div-EF-1α might not interact with aa-tRNA. The docking simulations of EF-1α/EFL and EF-1β (PDB ID: 5O8W, chain B was used for the docking target) revealed that EFL was somewhat less interacting with EF-1β; however, there were no significant changes (p-value = 0.1) in the E values (Figure 1b). EFL could still weakly interact with EF-1β. Table S6 shows the results of the EF-1α/EFL-tRNA interaction before and after the MD simulations. For Subulatomonas EF-1α, the model built with the yeast EF-1α increased the E values after the MD simulation, whereas for the models with the rabbit EF-1α and Aeropyrum EF-1α, the GDP-bound and the GTP-bound forms reduced the E values. The other models were consistent with the fact that the GTP-bound forms would be more likely to interact with aa-tRNA and the GDP-bound and the normal forms do not. The decrease of the E value of the homology model of Subulatomonas EF-1α with the EF-1α/GTP template of Aeropyrum or the increase of the E values of the other models with the template of Aeropyrum before and after the MD simulation would not alter the results shown in Figure 5.
Figure 5
Figure 5. Interaction prediction (E-scores) by docking simulations between EF-1α/EFL (a) and Phe-tRNA/EF-1β (b). We took the average of all simulations. Error bars are the standard deviations.
Conclusions
ARTICLE SECTIONS
Homology modeling and surface analysis of EF-1α and EFL were performed to examine the hypothesis that divergent EF-1α in the dual-EF-containing eukaryotes do not strongly interact with aa-tRNA compared to the canonical EF-1α. The subsequent MD simulations were carried out to confirm adequacy of the predicted structures and investigate the difference in their dynamical behavior. The molecular surfaces of the divergent ones were negatively charged, and thus they might not interact with negatively charged aa-tRNA. The molecular docking simulations also support this hypothesis.
Computational Details
ARTICLE SECTIONS
Homology Modeling
There are no crystal structures available for any div-EF-1α of the target species or EFL, so their three-dimensional structures were generated using standard protein homology modeling methods. A total of 31 protein sequences were obtained from the literature,(19) UniProt and GenBank databases.(34) Structural homology models of EF-1α and EFL were generated using the SWISS-MODEL(24) with three different X-ray crystal structures as the structural templates. Multiple templates were used to avoid the template structure dependence, as well as to consider the conformational change of EF-1α between the active and inactive forms.(35) (i) Saccharomyces cerevisiae (baker’s yeast) eEF1A (EF-1α) in complex with the eEF1Bα subunit of eEF1B (EF-1β) and guanosine-5′-monophosphate (PDB ID: 1G7C(23)), (ii) post-translationally modified Oryctolagus cuniculus (rabbit) isoform 2 of eEF1α (eEF1α2) in complex with guanosine-5′-diphosphate and a Mg2+ ion crystallized as a dimer (PDB ID: 4C0S, chain A was used as the template),(25) and (iii) a crenarchaeon Aeropyrum pernix EF-1α in complex with archaeal Pelota, guanosine-5′-triphosphate, and a Mg2+ ion (PDB ID: 3WXM, chain A was used as the template)(26) were chosen to consider the EF-1β binding form, the GDP-bound inactive form, and the GTP-bound active form, respectively.
The average form of EF-1α contains 462 amino acid residues, composed of three domains, that is, domain I (residues from 10 to 240), domain II (residues from 241 to 336), and domain III (residues from 337 to 435).(36) Regarding the structural differences of the selected templates, the GDP-bound form, which is represented by rabbit EF-1α, is an open form where the distances between domain I and domain II/domain III are distant, whereas the GTP-bound form, which is represented by Aeropyrum EF-1α, domain I rotates by 90° relative to domains II and III and has narrow and closed domain intervals. The cleft created between domains I, II, and III in the GTP-bound form is the binding site for aa-tRNAs and EF-1β.(33) Domain I contains the GTP hydrolysis site.(37) The comparison of the templates is shown in Figure S1.
Molecular Dynamics Simulations
Molecular dynamics (MD) simulations of the selected homology models were carried out to investigate the thermodynamic stability of the modeled structures and to examine the interactions between GDP or GTP and the EF-1α or EFL proteins affecting the structures. MD simulations were performed for EF-1α proteins of Pythium ultimum DAOM BR144 (dual-EF-containing species), Subulatomonas sp. strain PCMinv5 (EF-1α-containing species) and EFL proteins of Thecamonas trahens ATCC50062 (dual-EF-containing species) and Fabomonas tropica strain NYK3C (EFL-containing species). The four representatives were chosen for their good quality of data containing the whole sequences. The initial structures were obtained using homology modeling by the MODELLER program(30) using yeast EF-1α, rabbit EF-1α, and Aeropyrum EF-1α. We aligned the target sequences with each of the three template sequences by Clustal Omega.(38) The MODELLER program was employed to complete the full sequences including the C-terminal regions which were missing in the models generated using the SWISS-MODEL.
The GROMACS 5.0.4 program(39) was adopted for the MD simulations. Long-range electrostatic interactions were treated with the fast smooth particle-mesh Ewald method,(40) and the cut-offs for van der Waals and Coulomb interactions were set to 10 Å. The time-step was 2 fs with P-LINCS algorithms(41) for constraining the bond lengths. Proteins were parameterized using the AMBER99SB-ILDN(42) force field. The atomic charges for GTP and GDP were determined with the restrained electrostatic potential method using Gaussian 09(43) at the B3LYP/6-31G* level of theory and the force field parameters were taken from the general Amber force field(44) using the Antechamber program,(45) and the generated topology files were converted using the ACPYPE program.(46) The models were placed on dodecahedron boxes of the TIP3P(47) water model with distances of at least 10 Å from the box edge and neutralized by adding Cl– or Na+ counter ions. First, energy minimization was carried out to relax the structure. Then, we performed short MD simulations for equilibration for 100 ps in the NVT ensemble, followed by another 100 ps at 1.0 bar in the NPT ensemble with applying a position restraining force on the heavy atoms of the protein and ligands, and production run for 100 ns at 300 K. An averaged structure located at the center of the largest cluster was chosen to analyze the molecular surface using the eF-surf web server.(31)
Quantitative characterization of the conformational dynamics of each system was performed using principal component analysis (PCA). We applied g_covar and g_anaeig utilities of the GROMACS package to attain the covariance matrix of the RMSD of the protein backbone (Cα atoms) and its diagonalisation, yielding a set of eigenvectors and corresponding eigenvalues. Some of the former provide principal axes of the large-amplitude concerted motions characterizing the essential subspace of each protein’s internal dynamics, whereas the latter represents the amplitude of the motion along the eigenvector. The projection of the trajectories on each principal axis shows the width of the essential space explored by the system as a function of time. A comparison of the conformational space sampled by different trajectories generated for the same system can be made to gain insight into the amount of essential space explored by the system during the MD simulation.
Surface Analyses
It is useful to investigate the molecular surface to understand the function of proteins, because specific intermolecular interactions such as electrostatic and hydrophobic interactions are important in molecular recognition.(48−50) Molecular surfaces of almost all protein structures registered in PDB are available in eF-site database,(51) which calculates the polygon-meshed molecular surfaces by the MSP program developed by Connolly,(52) then the electrostatic potentials were calculated by solving the Poisson–Boltzmann equations using the SCB program.(53) The molecular surfaces and electrostatic potentials for the template proteins were obtained from the eF-site database,(51) whereas those of the models were calculated using the eF-surf web server,(31) which calculates the molecular surface for the up-loaded structures in the same way as the eF-site database.
Docking Simulations
Molecular docking simulations were carried out to examine interactions(54) between EF-1α and phenylalanyl-tRNA (Phe-tRNA). MEGADOCK ver. 4.0.2(55) was employed to perform the docking simulations. We adopted the default parameter values for all calculations. For the docking score functions, the real Pairwise Shape Complementarity score,(55) electrostatics score based on FTDock potentials(56) and CHARMM19 atomic charge,(57,58) RDE desolvation free energy(59) were referred. On the basis of the docking scores of 2000 predicted models, the evaluation value E(60) or the protein–protein interaction (PPI) score was then calculated to judge whether each EF-1α/EFL (protein) and Phe-tRNA (ligand) can interact or not. The evaluation value E or the standardized variable was obtained by subtracting the mean from the top-scored decoy’s docking score for a protein–ligand pair and then dividing by the standard deviation.
ARTICLE SECTIONS
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsomega.8b03547.
Crystal structures of the template and the molecular surfaces (Figure S1);
homology models of EF-1α and EFL proteins with the yeast, rabbit, and Aeropyrum template, respectively (Tables S1–S3); molecular surface seen from the tRNA binding side of EF-1α and EFL models built with the yeast, rabbit, and Aeropyrum template, respectively (Figures S2–S4); molecular surface seen from the backside of EF-1α and EFL models built with the Aeropyrum template (Figure S5); values of net charges and electrostatic potentials (Table S5); structures and molecular surfaces of the averaged structure of 100 ns MD simulation (Figure S6); results of EF-1α/EFL-tRNA and EF-1α/EFL-EF-1β interaction prediction (E-score) (Table S6); results of EF-1α/EFL-tRNA interaction prediction (E-score) before and after MD (Table S7) (PDF)
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Author Information
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- Yasuteru Shigeta - Center for Computational Sciences, Graduate School of Pure and Applied Sciences, , , , and , University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan;
http://orcid.org/0000-0002-3219-6007;
- Kotaro Sakamoto - Leading Graduate School Doctoral Program in Human Biology, , , and , University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan;http://orcid.org/0000-0003-0007-3894
The authors declare no competing financial interest.
Acknowledgments
ARTICLE SECTIONS
All computations were performed at the Center for Computational Sciences (CCS), University of Tsukuba. We especially thank Takanori Hayashi for the useful discussion on MEGADOCK.
References
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- 8Ejiri, S.-I. Moonlighting Functions of Polypeptide Elongation Factor 1: From Actin Bundling to Zinc Finger Protein R1-Associated Nuclear Localization. Biosci., Biotechnol., Biochem. 2002, 66, 1– 21, DOI: 10.1271/bbb.66.1
- 9Mateyak, M. K.; Kinzy, T. G. eEF1A: Thinking Outside the Ribosome. J. Biol. Chem. 2010, 285, 21209– 21213, DOI: 10.1074/jbc.R110.113795[Crossref], [PubMed], [CAS], Google Scholar9https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXos1WrsL8%253D&md5=7e402e72cca9e670e273d65e3c5573deeEF1A: Thinking outside the ribosomeMateyak, Maria K.; Kinzy, Terri GossJournal of Biological Chemistry (2010), 285 (28), 21209-21213CODEN: JBCHA3; ISSN:0021-9258. (American Society for Biochemistry and Molecular Biology)A review. Eukaryotic translation elongation factor 1A (eEF-1A) is one of the most abundant protein synthesis factors. It is responsible for the delivery of all aminoacyl-tRNAs to the ribosome, aside from the initiator and selenocysteine tRNAs. In addn. to its roles in polypeptide chain elongation, unique cellular and viral activities have been attributed to eEF1A in eukaryotes from yeast to plants and mammals. From preliminary, speculative assocns. to well characterized biochem. and biol. interactions, it is clear that eEF1A, of all the translation factors, has been ascribed the most functions outside of protein synthesis. A mechanistic understanding of these non-canonical functions of eEF1A will shed light on many important biol. questions, including viral-host interaction, subcellular organization, and the integration of key cellular pathways.
- 10Sasikumar, A. N.; Perez, W. B.; Kinzy, T. G. The many roles of the eukaryotic elongation factor 1 complex. Wiley Interdiscip. Rev.: RNA 2012, 3, 543– 555, DOI: 10.1002/wrna.1118[Crossref], [PubMed], [CAS], Google Scholar10https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhtlGgsbrP&md5=8b91d46331db19df59755b893089cf78The many roles of the eukaryotic elongation factor 1 complexSasikumar, Arjun N.; Perez, Winder B.; Kinzy, Terri GossWiley Interdisciplinary Reviews: RNA (2012), 3 (4), 543-555CODEN: WIRRCU; ISSN:1757-7012. (Wiley-Blackwell)A review. The vast majority of proteins are believed to have one specific function. Throughout the course of evolution, however, some proteins have acquired addnl. functions to meet the demands of a complex cellular milieu. In some cases, changes in RNA or protein processing allow the cell to make the most of what is already encoded in the genome to produce slightly different forms. The eukaryotic elongation factor 1 (eEF1) complex subunits, however, have acquired such moonlighting functions without alternative forms. In this article, we discuss the canonical functions of the components of the eEF1 complex in translation elongation as well as the secondary interactions they have with other cellular factors outside of the translational app. The eEF1 complex itself changes in compn. as the complexity of eukaryotic organisms increases. Members of the complex are also subject to phosphorylation, a potential modulator of both canonical and non-canonical functions. Although alternative functions of the eEF1A subunit have been widely reported, recent studies are shedding light on addnl. functions of the eEF1B subunits. A thorough understanding of these alternate functions of eEF1 is essential for appreciating their biol. relevance.
- 11Abbas, W.; Kumar, A.; Herbein, G. The eEF1A Proteins: At the Crossroads of Oncogenesis, Apoptosis, and Viral Infections. Front. Oncol. 2015, 5, 75, DOI: 10.3389/fonc.2015.00075[Crossref], [PubMed], [CAS], Google Scholar11https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2MjntlGrug%253D%253D&md5=46d3ba2d08d5b663601d93d52050c4fcThe eEF1A Proteins: At the Crossroads of Oncogenesis, Apoptosis, and Viral InfectionsAbbas Wasim; Kumar Amit; Herbein GeorgesFrontiers in oncology (2015), 5 (), 75 ISSN:2234-943X.Eukaryotic translation elongation factors 1 alpha, eEF1A1 and eEF1A2, are not only translation factors but also pleiotropic proteins that are highly expressed in human tumors, including breast cancer, ovarian cancer, and lung cancer. eEF1A1 modulates cytoskeleton, exhibits chaperone-like activity and also controls cell proliferation and cell death. In contrast, eEF1A2 protein favors oncogenesis as shown by the fact that overexpression of eEF1A2 leads to cellular transformation and gives rise to tumors in nude mice. The eEF1A2 protein stimulates the phospholipid signaling and activates the Akt-dependent cell migration and actin remodeling that ultimately favors tumorigenesis. In contrast, inactivation of eEF1A proteins leads to immunodeficiency, neural and muscular defects, and favors apoptosis. Finally, eEF1A proteins interact with several viral proteins resulting in enhanced viral replication, decreased apoptosis, and increased cellular transformation. This review summarizes the recent findings on eEF1A proteins indicating that eEF1A proteins play a critical role in numerous human diseases through enhancement of oncogenesis, blockade of apoptosis, and increased viral pathogenesis.
- 12Keeling, P. J.; Inagaki, Y. A class of eukaryotic GTPase with a punctate distribution suggesting multiple functional replacements of translation elongation factor 1α. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 15380– 15385, DOI: 10.1073/pnas.0404505101
- 13Ruiz-Trillo, I.; Lane, C. E.; Archibald, J. M.; Roger, A. J. Insights into the Evolutionary Origin and Genome Architecture of the Unicellular Opisthokonts Capsaspora owcrazaki and Shaeroforma arctica. J. Eukaryotic Microbiol. 2006, 53, 379– 384, DOI: 10.1111/j.1550-7408.2006.00118.x[Crossref], [PubMed], [CAS], Google Scholar13https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28XhtVyqu7zO&md5=662c8743d5b124150c672dfbb6cedb2eInsights into the evolutionary origin and genome architecture of the unicellular opisthokonts Capsaspora owczarzaki and Sphaeroforma arcticaRuiz-Trillo, Inaki; Lane, Christopher E.; Archibald, John M.; Roger, Andrew J.Journal of Eukaryotic Microbiology (2006), 53 (5), 379-384CODEN: JEMIED; ISSN:1066-5234. (Blackwell Publishing, Inc.)Mol. phylogenetic analyses have recently shown that the unicellular amoeboid protist Capsaspora owczarzaki is unlikely to be a nucleariid or an ichthyosporean as previously described, but is more closely related to Metazoa, Choanoflagellata, and Ichthyosporea. However, the specific phylogenetic relationship of Capsaspora to other protist opisthokont lineages was poorly resolved. To test these earlier results we have expanded both the taxonomic sampling and the no. of genes from opisthokont unicellular lineages. We have sequenced the protein-coding genes elongation factor 1-α (EF1-α) and heat shock protein 70 (Hsp70) from C. owczarzaki and the ichthyosporean Sphaeroforma arctica. Our max. likelihood (ML) and Bayesian analyses of a concatenated alignment of EF1-α, Hsp70, and actin protein sequences with a better sampling of opisthokont-related protist lineages indicate that C. owczarzaki is not clearly allied with any of the unicellular opisthokonts, but represents an independent unicellular lineage closely related to animals and choanoflagellates. Moreover, we have found that the ichthyosporean S. arctica possesses an EF-like (EFL) gene copy instead of the canonical EF1-α, the first so far described in an ichthyosporean. A max. likelihood phylogenetic anal. shows that the EF-like gene of S. arctica strongly groups with the EF-like genes from choanoflagellates. Finally, to begin characterizing the Capsaspora genome, we have performed pulsed-field gel electrophoresis (PFGE) analyses, which indicate that its genome has at least 12 chromosomes with a total genome size in the range of 22-25 Mb.
- 14Noble, G. P.; Rogers, M. B.; Keeling, P. J. Complex distribution of EFL and EF-1α proteins in the green algal lineage. BMC Evol. Biol. 2007, 7, 82, DOI: 10.1186/1471-2148-7-82
- 15Kamikawa, R.; Inagaki, Y.; Sako, Y. Direct phylogenetic evidence for lateral transfer of elongation factor-like gene. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 6965– 6969, DOI: 10.1073/pnas.0711084105
- 16Gile, G. H.; Faktorová, D.; Castlejohn, C. A.; Burger, G.; Lang, B. F.; Farmer, M. A.; Lukeš, J.; Keeling, P. J. Distribution and Phylogeny of EFL and EF-1α in Euglenozoa Suggest Ancestral Co-Occurrence Followed by Differential Loss. PLoS One 2009, 4, e5162 DOI: 10.1371/journal.pone.0005162
- 17Kamikawa, R.; Yabuki, A.; Nakayama, T.; ichiro Ishida, K.; Hashimoto, T.; Inagaki, Y. Cercozoa comprises both EF-1α-containing and EFL-containing members. Eur. J. Protistol. 2011, 47, 24– 28, DOI: 10.1016/j.ejop.2010.08.002[Crossref], [PubMed], [CAS], Google Scholar17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3M7is1Kguw%253D%253D&md5=b381baa50d40dc73e8f5b9fe52ed41d0Cercozoa comprises both EF-1α-containing and EFL-containing membersKamikawa Ryoma; Yabuki Akinori; Nakayama Takuro; Ishida Ken-ichiro; Hashimoto Tetsuo; Inagaki YujiEuropean journal of protistology (2011), 47 (1), 24-8 ISSN:.Elongation factor 1α (EF-1α) and elongation factor-like protein (EFL) are considered to be functionally equivalent proteins involved in peptide synthesis. Eukaryotes can be fundamentally divided into 'EF-1α-containing' and 'EFL-containing' types. Recently, EF-1α and EFL genes have been surveyed across the diversity of eukaryotes to explore the origin and evolution of EFL genes. Although the phylum Cercozoa is a diverse group, gene data for either EFL or EF-1α are absent from all cercozoans except chlorarachniophytes which were previously defined as EFL-containing members. Our survey revealed that two members of the cercozoan subphylum Filosa (Thaumatomastix sp. and strain YPF610) are EFL-containing members. Importantly, we identified EF-1α genes from two members of Filosa (Paracercomonas marina and Paulinella chromatophora) and a member of the other subphylum Endomyxa (Filoreta japonica). All cercozoan EFL homologues could not be recovered as a monophyletic group in maximum-likelihood and Bayesian analyses, suggesting that lateral gene transfer was involved in the EFL evolution in this protist assemblage. In contrast, EF-1α analysis successfully recovered a monophyly of three homologues sampled from the two cercozoan subphyla. Based on the results, we postulate that cercozoan EF-1α genes have been vertically inherited, and the current EFL-containing species may have secondarily lost their EF-1α genes.
- 18Ishitani, Y.; Kamikawa, R.; Yabuki, A.; Tsuchiya, M.; Inagaki, Y.; Takishita, K. Evolution of elongation factor-like (EFL) protein in Rhizaria is revised by radiolarian EFL gene sequences. J. Eukaryotic Microbiol. 2012, 59, 367– 73, DOI: 10.1111/j.1550-7408.2012.00626.x[Crossref], [PubMed], [CAS], Google Scholar18https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38Xht1ChtbfN&md5=408e2b935beb437ba65528e33322f4c6Evolution of elongation factor-like (EFL) protein in rhizaria is revised by radiolarian EFL gene sequencesIshitani, Yoshiyuki; Kamikawa, Ryoma; Yabuki, Akinori; Tsuchiya, Masashi; Inagaki, Yuji; Takishita, KiyotakaJournal of Eukaryotic Microbiology (2012), 59 (4), 367-373CODEN: JEMIED; ISSN:1066-5234. (Wiley-Blackwell)Elongation factor 1α (EF-1α) and elongation factor-like (EFL) proteins are considered to carry out equiv. functions in translation in eukaryotic cells. Elongation factor 1α and EFL genes are patchily distributed in the global eukaryotic tree, suggesting that the evolution of these elongation factors cannot be reconciled without multiple lateral gene transfer and/or ancestral cooccurrence followed by differential loss of either of the two factors. The authors' current understanding of the EF-1α/EFL evolution in the eukaryotic group Rhizaria, composed of Foraminifera, Radiolaria, Filosa, and Endomyxa, remains insufficient, as no information on EF-1α/EFL gene is available for any members of Radiolaria. EFL genes were exptl. isolated from four polycystine radiolarians (i.e. Dictyocoryne, Eucyrtidium, Collozoum, and Sphaerozoum), as well as retrieved from publicly accessible expressed sequence tag data of two acantharean radiolarians (i.e. Astrolonche and Phyllostaurus) and the endomyxan Gromia. The EFL homologs from radiolarians, foraminiferans, and Gromia formed a robust clade in both max.-likelihood and Bayesian phylogenetic analyses, suggesting that EFL genes were vertically inherited from their common ancestor. The authors propose an updated model for EF-1α/EFL evolution in Rhizaria by incorporating new EFL data obtained.
- 19Kamikawa, R.; Brown, M. W.; Nishimura, Y.; Sako, Y.; Heiss, A. A.; Yubuki, N.; Gawryluk, R.; Simpson, A. G.; Roger, A. J.; Hashimoto, T. Parallel re-modeling of EF-1α function: divergent EF-1α genes co-occur with EFL genes in diverse distantly related eukaryotes. BMC Evol. Biol. 2013, 131, DOI: 10.1186/1471-2148-13-131
- 20Mikhailov, K. V.; Janouškovec, J.; Tikhonenkov, D. V.; Mirzaeva, G. S.; Diakin, A. Y.; Simdyanov, T. G.; Mylnikov, A. P.; Keeling, P. J.; Aleoshin, V. V. A Complex Distribution of Elongation Family GTPases EF1A and EFL in Basal Alveolate Lineages. Genome Biol. Evol. 2014, 6, 2361– 2367, DOI: 10.1093/gbe/evu186[Crossref], [PubMed], [CAS], Google Scholar20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXisV2qtLk%253D&md5=3b97abc83862f77c6c00e2d37aef1ea3A complex distribution of elongation family GTPases EF1A and EFL in basal alveolate lineagesMikhailov, Kirill V.; Janouskovec, Jan; Tikhonenkov, Denis V.; Mirzaeva, Gulnara S.; Diakin, Andrei Yu.; Simdyanov, Timur G.; Mylnikov, Alexander P.; Keeling, Patrick J.; Aleoshin, Vladimir V.Genome Biology and Evolution (2014), 6 (9), 2361-2367CODEN: GBEEA5; ISSN:1759-6653. (Oxford University Press)Translation elongation factor-1 alpha (EF1A) and the related GTPase EF-like (EFL) are two proteins with a complexmutually exclusive distribution across the tree of eukaryotes. Recent surveys revealed that the distribution of the two GTPases in even closely related taxa is frequently at odds with their phylogenetic relationships. Here, we investigate the distribution of EF1A and EFL in the alveolate supergroup. Alveolates comprise threemajor lineages: ciliates andapicomplexans encode EF1A, whereas dinoflagellates encode EFL. Wesearched transcriptome databases for seven early-diverging alveolate taxa thatdonot belong to anyof these groups: colpodellids, chromerids, and colponemids. Current data suggest all seven are expected to encode EF1A, but we find three genera encode EFL: Colpodella, Voromonas, and the photosynthetic Chromera. Comparing this distribution with the phylogeny of alveolates suggests that EF1A and EFL evolution in alveolates cannot be explained by a simple horizontal gene transfer event or lineage sorting.
- 21James, T. Y. Reconstructing the early evolution of the fungi using a six gene phylogeny Nature 2009, 4432006 7113.Google ScholarThere is no corresponding record for this reference.
- 22Atkinson, G. C.; Kuzmenko, A.; Chicherin, I.; Soosaar, A.; Tenson, T.; Carr, M.; Kamenski, P.; Hauryliuk, V. An evolutionary ratchet leading to loss of elongation factors in eukaryotes. BMC Evol. Biol. 2014, 14, 35, DOI: 10.1186/1471-2148-14-35
- 23Andersen, G. R.; Valente, L.; Pedersen, L.; Kinzy, T. G.; Nyborg, J. Crystal structures of nucleotide exchange intermediates in the eEF1A-eEF1Bα complex. Nat. Struct. Biol. 2001, 8, 531– 534, DOI: 10.1038/88598
- 24Biasini, M.; Bienert, S.; Waterhouse, A.; Arnold, K.; Studer, G.; Schmidt, T.; Kiefer, F.; Cassarino, T. G.; Bertoni, M.; Bordoli, L.; Schwede, T. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 2014, 42, W252– W258, DOI: 10.1093/nar/gku340[Crossref], [PubMed], [CAS], Google Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhtFCqs73I&md5=51509ba8353b06f286d954ebe7e6673aSWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary informationBiasini, Marco; Bienert, Stefan; Waterhouse, Andrew; Arnold, Konstantin; Studer, Gabriel; Schmidt, Tobias; Kiefer, Florian; Cassarino, Tiziano Gallo; Bertoni, Martino; Bordoli, Lorenza; Schwede, TorstenNucleic Acids Research (2014), 42 (W1), W252-W258CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)Protein structure homol. modeling has become a routine technique to generate 3D models for proteins when exptl. structures are not available. Fully automated servers such as SWISS-MODEL with user-friendly web interfaces generate reliable models without the need for complex software packages or downloading large databases. Here, we describe the latest version of the SWISS-MODEL expert system for protein structure modeling. The SWISS-MODEL template library provides annotation of quaternary structure and essential ligands and co-factors to allow for building of complete structural models, including their oligomeric structure. The improved SWISS-MODEL pipeline makes extensive use of model quality estn. for selection of the most suitable templates and provides ests. of the expected accuracy of the resulting models. The accuracy of the models generated by SWISS-MODEL is continuously evaluated by the CAMEO system. The new web site allows users to interactively search for templates, cluster them by sequence similarity, structurally compare alternative templates and select the ones to be used for model building. In cases where multiple alternative template structures are available for a protein of interest, a user-guided template selection step allows building models in different functional states. SWISS-MODEL is available at http://swissmodel.expasy.org/.
- 25Crepin, T.; Shalak, V. F.; Yaremchuk, A. D.; Vlasenko, D. O.; McCarthy, A.; Negrutskii, B. S.; Tukalo, M. A.; El’skaya, A. V. Mammalian translation elongation factor eEF1A2: X-ray structure and new features of GDP/GTP exchange mechanism in higher eukaryotes. Nucleic Acids Res. 2014, 42, 12939– 12948, DOI: 10.1093/nar/gku974[Crossref], [PubMed], [CAS], Google Scholar25https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhtVCqsLfM&md5=032ab85686a8aae4dc03c6aab2245837Mammalian translation elongation factor eEF1A2: X-ray structure and new features of GDP/GTP exchange mechanism in higher eukaryotesCrepin, Thibaut; Shalak, Vyacheslav F.; Yaremchuk, Anna D.; Vlasenko, Dmytro O.; McCarthy, Andrew; Negrutskii, Boris S.; Tukalo, Michail A.; El'skaya, Anna V.Nucleic Acids Research (2014), 42 (20), 12939-12948CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)Eukaryotic elongation factor eEF1A transits between the GTP- and GDP-bound conformations during the ribosomal polypeptide chain elongation. eEF1A*GTP establishes a complex with the aminoacyl-tRNA in the A site of the 80S ribosome. Correct codon-anticodon recognition triggers GTP hydrolysis, with subsequent dissocn. of eEF1A*GDP from the ribosome. The structures of both the 'GTP'- and 'GDP'-bound conformations of eEF1A are unknown. Thus, the eEF1A-related ribosomal mechanisms were anticipated only by analogy with the bacterial homolog EF-Tu. Here, we report the first crystal structure of the mammalian eEF1A2GDP complex which indicates major differences in the organization of the nucleotide-binding domain and intramol. movements of eEF1A compared to EF-Tu. Our results explain the nucleotide exchange mechanism in the mammalian eEF1A and suggest that the first step of eEF1AGDP dissocn. from the 80S ribosome is the rotation of the nucleotide-binding domain obsd. after GTP hydrolysis.
- 26Kobayashi, K.; Kikuno, I.; Kuroha, K.; Saito, K.; Ito, K.; Ishitani, R.; Inada, T.; Nureki, O. Structural basis for mRNA surveillance by archaeal Pelota and GTP-bound EF1α complex. Proc. Natl. Acad. Sci. U.S.A. 2010, 107, 17575– 17579, DOI: 10.1073/pnas.1009598107
- 27Benkert, P.; Biasini, M.; Schwede, T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 2011, 27, 343– 350, DOI: 10.1093/bioinformatics/btq662[Crossref], [PubMed], [CAS], Google Scholar27https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhs1Gisb8%253D&md5=eb1b789a978f8db7beff4dbcd03296eeToward the estimation of the absolute quality of individual protein structure modelsBenkert, Pascal; Biasini, Marco; Schwede, TorstenBioinformatics (2011), 27 (3), 343-350CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)Quality assessment of protein structures is an important part of exptl. structure validation and plays a crucial role in protein structure prediction, where the predicted models may contain substantial errors. Most current scoring functions are primarily designed to rank alternative models of the same sequence supporting model selection, whereas the prediction of the abs. quality of an individual protein model has received little attention in the field. However, reliable abs. quality ests. are crucial to assess the suitability of a model for specific biomedical applications. In this work, we present a new abs. measure for the quality of protein models, which provides an est. of the degree of nativeness' of the structural features obsd. in a model and describes the likelihood that a given model is of comparable quality to exptl. structures. Model quality ests. based on the QMEAN scoring function were normalized with respect to the no. of interactions. The resulting scoring function is independent of the size of the protein and may therefore be used to assess both monomers and entire oligomeric assemblies. Model quality scores for individual models are then expressed as Z-scores' in comparison to scores obtained for high-resoln. crystal structures. We demonstrate the ability of the newly introduced QMEAN Z-score to detect exptl. solved protein structures contg. significant errors, as well as to evaluate theor. protein models. In a comprehensive QMEAN Z-score anal. of all exptl. structures in the PDB, membrane proteins accumulate on one side of the score spectrum and thermostable proteins on the other. Proteins from the thermophilic organism Thermatoga maritima received significantly higher QMEAN Z-scores in a pairwise comparison with their homologous mesophilic counterparts, underlining the significance of the QMEAN Z-score as an est. of protein stability.
- 28Benkert, P.; Künzli, M.; Schwede, T. QMEAN server for protein model quality estimation. Nucleic Acids Res. 2009, 37, W510– W514, DOI: 10.1093/nar/gkp322[Crossref], [PubMed], [CAS], Google Scholar28https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXosFSktLY%253D&md5=3c3c7fde4434dbd4a1c73a6e10c044d1QMEAN server for protein model quality estimationBenkert, Pascal; Kuenzli, Michael; Schwede, TorstenNucleic Acids Research (2009), 37 (Web Server), W510-W514CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)Model quality estn. is an essential component of protein structure prediction, since ultimately the accuracy of a model dets. its usefulness for specific applications. Usually, in the course of protein structure prediction a set of alternative models is produced, from which subsequently the most accurate model has to be selected. The QMEAN server provides access to two scoring functions successfully tested at the eighth round of the community-wide blind test expt. CASP. The user can choose between the composite scoring function QMEAN, which derives a quality est. on the basis of the geometrical anal. of single models, and the clustering-based scoring function QMEANclust which calcs. a global and local quality est. based on a weighted all-against-all comparison of the models from the ensemble provided by the user. The web server performs a ranking of the input models and highlights potentially problematic regions for each model. The QMEAN server is available at http://swissmodel.expasy.org/qmean.
- 29Nissen, P.; Kjeldgaard, M.; Thirup, S.; Polekhina, G.; Reshetnikova, L.; Clark, B. F. C.; Nyborg, J. Crystal Structure of the Ternary Complex of Phe-tRNAPhe, EF-Tu, and a GTP Analog. Science 1995, 270, 1464– 1472, DOI: 10.1126/science.270.5241.1464[Crossref], [PubMed], [CAS], Google Scholar29https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXps12hsrc%253D&md5=51e24bc35387620abadb7ff982f5670eCrystal structure of the ternary complex of Phe-tRNAPhe, EF-Tu, and a GTP analogNissen, Poul; Kjeldgaard, Morten; Thirup, Soeren; Polekhina, Galina; Reshetnikova, Ludmila; Clark, Brian F. C.; Nyborg, JensScience (Washington, D. C.) (1995), 270 (5241), 1464-72CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)The structure of the ternary complex consisting of yeast phenylalanyl-tRNA (Phe-tRNAPhe), Thermus aquaticus elongation factor Tu (EF-Tu), and the guanosine triphosphate (GTP) analog GDPNP was detd. by x-ray crystallog. at 2.7 angstrom resoln. The ternary complex participates in placing the amino acids in their correct order when mRNA is translated into a protein sequence on the ribosome. The EF-Tu-GDPNP component binds to one side of the acceptor helix of Phe-tRNAPhe involving all three domains of EF-Tu. Binding sites for the phenylalanylated CCA end and the phosphorylated 5' end are located at domain interfaces, whereas the T stem interacts with the surface of the β-barrel domain 3. The binding involves many conserved residues in EF-Tu. The overall shape of the ternary complex is similar to that of the translocation factor, EF-G-GDP, and this suggests a novel mechanism involving "mol. mimicry" in the translational app.
- 30Webb, B.; Sali, A. Comparative Protein Structure Modeling Using MODELLER. Curr. Protoc. Bioinf. 54, 5.6.1 5.6.37. DOI: 10.1002/cpbi.3 .
- 31Murakami, Y.; Kinoshita, K.; Kinjo, A. R.; Nakamura, H. Exhaustive comparison and classification of ligand-binding surfaces in proteins. Protein Sci. 2013, 22, 1379– 1391, DOI: 10.1002/pro.2329[Crossref], [PubMed], [CAS], Google Scholar31https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsFals7rJ&md5=c1d2b3f7849da1f6c462560853a12919Exhaustive comparison and classification of ligand-binding surfaces in proteinsMurakami, Yoichi; Kinoshita, Kengo; Kinjo, Akira R.; Nakamura, HarukiProtein Science (2013), 22 (10), 1379-1391CODEN: PRCIEI; ISSN:1469-896X. (Wiley-Blackwell)Many proteins function by interacting with other small mols. (ligands). Identification of ligand-binding sites (LBS) in proteins can therefore help to infer their mol. functions. A comprehensive comparison among local structures of LBSs was previously performed, in order to understand their relationships and to classify their structural motifs. However, similar exhaustive comparison among local surfaces of LBSs (patches) has never been performed, due to computational complexity. To enhance our understanding of LBSs, it is worth performing such comparisons among patches and classifying them based on similarities of their surface configurations and electrostatic potentials. In this study, we first developed a rapid method to compare two patches. We then clustered patches corresponding to the same PDB chem. component identifier for a ligand, and selected a representative patch from each cluster. We subsequently exhaustively as compared the representative patches and clustered them using similarity score, PatSim. Finally, the resultant PatSim scores were compared with similarities of at. structures of the LBSs and those of the ligand-binding protein sequences and functions. Consequently, we classified the patches into ∼2000 well-characterized clusters. We found that about 63% of these clusters are used in identical protein folds, although about 25% of the clusters are conserved in distantly related proteins and even in proteins with cross-fold similarity. Furthermore, we showed that patches with higher PatSim score have potential to be involved in similar biol. processes.
- 32Altschul, S. F.; Madden, T. L.; Schäffer, A. A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D. J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25, 3389– 3402, DOI: 10.1093/nar/25.17.3389[Crossref], [PubMed], [CAS], Google Scholar32https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2sXlvFyhu7w%253D&md5=4e44123e5984e4aca46a9899d347a176Gapped BLAST and PSI-BLAST: a new generation of protein database search programsAltschul, Stephen F.; Madden, Thomas L.; Schaffer, Alejandro A.; Zhang, Jinghui; Zhang, Zheng; Miller, Webb; Lipman, David J.Nucleic Acids Research (1997), 25 (17), 3389-3402CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approx. three times the speed of the original. In addn., a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approx. the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biol. relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily. The source code for the new BLAST programs is available by anonymous ftp from the machine ncbi.nlm.nih.gov, within the directory 'blast', and the programs may be run from NCBIs web site at http://www.ncbi.nlm.nih.gov/.
- 33Andersen, G. R.; Pedersen, L.; Valente, L.; Chatterjee, I.; Kinzy, T. G.; Kjeldgaard, M.; Nyborg, J. Structural Basis for Nucleotide Exchange and Competition with tRNA in the Yeast Elongation Factor Complex eEF1A:eEF1Bα. Mol. Cell 2000, 6, 1261– 1266, DOI: 10.1016/S1097-2765(00)00122-2
- 34Benson, D. A.; Karsch-Mizrachi, I.; Lipman, D. J.; Ostell, J.; Sayers, E. W. GenBank. Nucleic Acids Res. 2010, 38, D46– D51, DOI: 10.1093/nar/gkp1024
- 35Kanibolotsky, D. S.; Novosyl’na, O. V.; Abbott, C. M.; Negrutskii, B. S.; El’skaya, A. V. Multiple molecular dynamics simulation of the isoforms of human translation elongation factor 1A reveals reversible fluctuations between “open” and “closed” conformations and suggests specific for eEF1A1 affinity for Ca2+-calmodulin. BMC Struct. Biol. 2008, 8, 4, DOI: 10.1186/1472-6807-8-4[Crossref], [PubMed], [CAS], Google Scholar35https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD1c3gslehtg%253D%253D&md5=d061fe2edb87b311da1a59b40118d329Multiple molecular dynamics simulation of the isoforms of human translation elongation factor 1A reveals reversible fluctuations between "open" and "closed" conformations and suggests specific for eEF1A1 affinity for Ca2+-calmodulinKanibolotsky Dmitry S; Novosyl'na Oleksandra V; Abbott Catherine M; Negrutskii Boris S; El'skaya Anna VBMC structural biology (2008), 8 (), 4 ISSN:.BACKGROUND: Eukaryotic translation elongation factor eEF1A directs the correct aminoacyl-tRNA to ribosomal A-site. In addition, eEF1A is involved in carcinogenesis and apoptosis and can interact with large number of non-translational ligands. There are two isoforms of eEF1A, which are 98% similar. Despite the strong similarity, the isoforms differ in some properties. Importantly, the appearance of eEF1A2 in tissues in which the variant is not normally expressed can be coupled to cancer development.We reasoned that the background for the functional difference of eEF1A1 and eEF1A2 might lie in changes of dynamics of the isoforms. RESULTS: It has been determined by multiple MD simulation that eEF1A1 shows increased reciprocal flexibility of structural domains I and II and less average distance between the domains, while increased non-correlated diffusive atom motions within protein domains characterize eEF1A2. The divergence in the dynamic properties of eEF1A1 and eEF1A2 is caused by interactions of amino acid residues that differ between the two variants with neighboring residues and water environment. The main correlated motion of both protein isoforms is the change in proximity of domains I and II which can lead to disappearance of the gap between the domains and transition of the protein into a "closed" conformation. Such a transition is reversible and the protein can adopt an "open" conformation again. This finding is in line with our earlier experimental observation that the transition between "open" and "closed" conformations of eEF1A could be essential for binding of tRNA and/or other biological ligands. The putative calmodulin-binding region Asn311-Gly327 is less flexible in eEF1A1 implying its increased affinity for calmodulin. The ability of eEF1A1 rather than eEF1A2 to interact with Ca2+/calmodulin is shown experimentally in an ELISA-based test. CONCLUSION: We have found that reversible transitions between "open" and "close" conformations of eEF1A provide a molecular background for the earlier observation that the eEF1A molecule is able to change the shape upon interaction with tRNA. The ability of eEF1A1 rather than eEF1A2 to interact with calmodulin is predicted by MD analysis and showed experimentally. The differential ability of the eEF1A isoforms to interact with signaling molecules discovered in this study could be associated with cancer-related properties of eEF1A2.
- 36Soares, D.; Barlow, P.; Newbery, H.; Porteous, D.; Abbott, C. Structural Models of Human eEF1A1 and eEF1A2 Reveal Two Distinct Surface Clusters of Sequence Variation and Potential Differences in Phosphorylation. PLoS One 2009, 4, e6315 DOI: 10.1371/journal.pone.0006315
- 37Gromadski, K. B.; Schümmer, T.; Strømgaard, A.; Knudsen, C. R.; Kinzy, T. G.; Rodnina, M. V. Kinetics of the Interactions between Yeast Elongation Factors 1A and 1Bα, Guanine Nucleotides, and Aminoacyl-tRNA. J. Biol. Chem. 2007, 282, 35629– 35637, DOI: 10.1074/jbc.M707245200
- 38Sievers, F.; Wilm, A.; Dineen, D.; Gibson, T. J.; Karplus, K.; Li, W.; Lopez, R.; McWilliam, H.; Remmert, M.; Söding, J.; Thompson, J. D.; Higgins, D. G. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 2011, 7, 539, DOI: 10.1038/msb.2011.75[Crossref], [PubMed], [CAS], Google Scholar38https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3MbgtlantA%253D%253D&md5=27dd8fac22447528e6d09e5fd1066133Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal OmegaSievers Fabian; Wilm Andreas; Dineen David; Gibson Toby J; Karplus Kevin; Li Weizhong; Lopez Rodrigo; McWilliam Hamish; Remmert Michael; Soding Johannes; Thompson Julie D; Higgins Desmond GMolecular systems biology (2011), 7 (), 539 ISSN:.Multiple sequence alignments are fundamental to many sequence analysis methods. Most alignments are computed using the progressive alignment heuristic. These methods are starting to become a bottleneck in some analysis pipelines when faced with data sets of the size of many thousands of sequences. Some methods allow computation of larger data sets while sacrificing quality, and others produce high-quality alignments, but scale badly with the number of sequences. In this paper, we describe a new program called Clustal Omega, which can align virtually any number of protein sequences quickly and that delivers accurate alignments. The accuracy of the package on smaller test cases is similar to that of the high-quality aligners. On larger data sets, Clustal Omega outperforms other packages in terms of execution time and quality. Clustal Omega also has powerful features for adding sequences to and exploiting information in existing alignments, making use of the vast amount of precomputed information in public databases like Pfam.
- 39Abraham, M. J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J. C.; Hess, B.; Lindahl, E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015, 1–2, 19– 25, DOI: 10.1016/j.softx.2015.06.001
- 40Essmann, U.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee, H.; Pedersen, L. G. A smooth particle mesh Ewald method. J. Chem. Phys. 1995, 103, 8577– 8593, DOI: 10.1063/1.470117[Crossref], [CAS], Google Scholar40https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXptlehtrw%253D&md5=092a679dd3bee08da28df41e302383a7A smooth particle mesh Ewald methodEssmann, 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.
- 41Hess, B. P-LINCS: A Parallel Linear Constraint Solver for Molecular Simulation. J. Chem. Theory Comput. 2008, 4, 116– 122, DOI: 10.1021/ct700200b[ACS Full Text
], [CAS], Google Scholar41https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXhtlKru7zL&md5=476d5ca2eb25574d44b775996fff7b75P-LINCS: A Parallel Linear Constraint Solver for Molecular SimulationHess, BerkJournal of Chemical Theory and Computation (2008), 4 (1), 116-122CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)By removing the fastest degrees of freedom, constraints allow for an increase of the time step in mol. simulations. In the last decade parallel simulations have become commonplace. However, up till now efficient parallel constraint algorithms have not been used with domain decompn. In this paper the parallel linear constraint solver (P-LINCS) is presented, which allows the constraining of all bonds in macromols. Addnl. the energy conservation properties of (P-)LINCS are assessed in view of improvements in the accuracy of uncoupled angle constraints and integration in single precision. - 42Lindorff-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: Struct., Funct., Bioinf. 2010, 78, 1950– 1958, DOI: 10.1002/prot.22711[Crossref], [PubMed], [CAS], Google Scholar42https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXkvFegtLo%253D&md5=447a9004026e2b93f0f7beff165daa09Improved side-chain torsion potentials for the Amber ff99SB protein force fieldLindorff-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.
- 43Frisch, M. J.; Gaussian 09, revision 01; Gaussian Inc.: Wallingford, CT, 2009.Google ScholarThere is no corresponding record for this reference.
- 44Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 2004, 25, 1157– 1174, DOI: 10.1002/jcc.20035[Crossref], [PubMed], [CAS], Google Scholar44https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXksFakurc%253D&md5=2992017a8cf51f89290ae2562403b115Development and testing of a general Amber force fieldWang, Junmei; Wolf, Romain M.; Caldwell, James W.; Kollman, Peter A.; Case, David A.Journal of Computational Chemistry (2004), 25 (9), 1157-1174CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)We describe here a general Amber force field (GAFF) for org. mols. GAFF is designed to be compatible with existing Amber force fields for proteins and nucleic acids, and has parameters for most org. and pharmaceutical mols. that are composed of H, C, N, O, S, P, and halogens. It uses a simple functional form and a limited no. of atom types, but incorporates both empirical and heuristic models to est. force consts. and partial at. charges. The performance of GAFF in test cases is encouraging. In test I, 74 crystallog. structures were compared to GAFF minimized structures, with a root-mean-square displacement of 0.26 Å, which is comparable to that of the Tripos 5.2 force field (0.25 Å) and better than those of MMFF 94 and CHARMm (0.47 and 0.44 Å, resp.). In test II, gas phase minimizations were performed on 22 nucleic acid base pairs, and the minimized structures and intermol. energies were compared to MP2/6-31G* results. The RMS of displacements and relative energies were 0.25 Å and 1.2 kcal/mol, resp. These data are comparable to results from Parm99/RESP (0.16 Å and 1.18 kcal/mol, resp.), which were parameterized to these base pairs. Test III looked at the relative energies of 71 conformational pairs that were used in development of the Parm99 force field. The RMS error in relative energies (compared to expt.) is about 0.5 kcal/mol. GAFF can be applied to wide range of mols. in an automatic fashion, making it suitable for rational drug design and database searching.
- 45Wang, J.; Wang, W.; Kollman, P. A.; Case, D. A. Automatic atom type and bond type perception in molecular mechanical calculations. J. Mol. Graphics Modell. 2006, 25, 247– 260, DOI: 10.1016/j.jmgm.2005.12.005[Crossref], [PubMed], [CAS], Google Scholar45https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28Xps1Gis7g%253D&md5=8031a21d2784d5dea12e70868522aa61Automatic atom type and bond type perception in molecular mechanical calculationsWang, Junmei; Wang, Wei; Kollman, Peter A.; Case, David A.Journal of Molecular Graphics & Modelling (2006), 25 (2), 247-260CODEN: JMGMFI; ISSN:1093-3263. (Elsevier Inc.)In mol. mechanics (MM) studies, atom types and/or bond types of mols. are needed to det. prior to energy calcns. The authors present here an automatic algorithm of perceiving atom types that are defined in a description table, and an automatic algorithm of assigning bond types just based on at. connectivity. The algorithms have been implemented in a new module of the AMBER packages. This auxiliary module, antechamber (roughly meaning "before AMBER"), can be applied to generate necessary inputs of leap-the AMBER program to generate topologies for minimization, mol. dynamics, etc., for most org. mols. The algorithms behind the manipulations may be useful for other mol. mech. packages as well as applications that need to designate atom types and bond types.
- 46Sousa da Silva, A. W.; Vranken, W. F. ACPYPE - AnteChamber PYthon Parser interfacE. BMC Res. Notes 2012, 5, 367, DOI: 10.1186/1756-0500-5-367[Crossref], [PubMed], [CAS], Google Scholar46https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC38fitlWjtg%253D%253D&md5=60f750ba98c975374b351a39a1fa0ec4ACPYPE - AnteChamber PYthon Parser interfacESousa da Silva Alan W; Vranken Wim FBMC research notes (2012), 5 (), 367 ISSN:.BACKGROUND: ACPYPE (or AnteChamber PYthon Parser interfacE) is a wrapper script around the ANTECHAMBER software that simplifies the generation of small molecule topologies and parameters for a variety of molecular dynamics programmes like GROMACS, CHARMM and CNS. It is written in the Python programming language and was developed as a tool for interfacing with other Python based applications such as the CCPN software suite (for NMR data analysis) and ARIA (for structure calculations from NMR data). ACPYPE is open source code, under GNU GPL v3, and is available as a stand-alone application at http://www.ccpn.ac.uk/acpype and as a web portal application at http://webapps.ccpn.ac.uk/acpype. FINDINGS: We verified the topologies generated by ACPYPE in three ways: by comparing with default AMBER topologies for standard amino acids; by generating and verifying topologies for a large set of ligands from the PDB; and by recalculating the structures for 5 protein-ligand complexes from the PDB. CONCLUSIONS: ACPYPE is a tool that simplifies the automatic generation of topology and parameters in different formats for different molecular mechanics programmes, including calculation of partial charges, while being object oriented for integration with other applications.
- 47Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983, 79, 926– 935, DOI: 10.1063/1.445869[Crossref], [CAS], Google Scholar47https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL3sXksF2htL4%253D&md5=a1161334e381746be8c9b15a5e56f704Comparison of simple potential functions for simulating liquid waterJorgensen, William L.; Chandrasekhar, Jayaraman; Madura, Jeffry D.; Impey, Roger W.; Klein, Michael L.Journal of Chemical Physics (1983), 79 (2), 926-35CODEN: JCPSA6; ISSN:0021-9606.Classical Monte Carlo simulations were carried out for liq. H2O in the NPT ensemble at 25° and 1 atm using 6 of the simpler intermol. potential functions for the dimer. Comparisons were made with exptl. thermodn. and structural data including the neutron diffraction results of Thiessen and Narten (1982). The computed densities and potential energies agree with expt. except for the original Bernal-Fowler model, which yields an 18% overest. of the d. and poor structural results. The discrepancy may be due to the correction terms needed in processing the neutron data or to an effect uniformly neglected in the computations. Comparisons were made for the self-diffusion coeffs. obtained from mol. dynamics simulations.
- 48Jones, S.; Thornton, J. M. Principles of protein-protein interactions. Proc. Natl. Acad. Sci. U.S.A. 1996, 93, 13– 20, DOI: 10.1073/pnas.93.1.13[Crossref], [PubMed], [CAS], Google Scholar48https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK28XjslOrsQ%253D%253D&md5=2b03d8f835104e5a7bf5da83afd4a8d1Principles of protein-protein interactionsJones, Susan; Thornton, Janet M.Proceedings of the National Academy of Sciences of the United States of America (1996), 93 (1), 13-20CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)A review, with 47 refs., examg. protein complexes in the Brookhaven Protein Databank to gain a better understanding of the principles governing the interactions involved in protein-protein recognition. The factors that influence the formation of protein-protein complexes are explored in four different types of protein-protein complexes: homodimeric proteins, heterodimeric proteins, enzyme-inhibitor complexes, and antibody-protein complexes. The comparison between the complexes highlights differences that reflect their biol. roles.
- 49Draper, D. E. Themes in RNA-protein recognition. J. Mol. Biol. 1999, 293, 255– 270, DOI: 10.1006/jmbi.1999.2991
- 50Hunter, C. A. Quantifying Intermolecular Interactions: Guidelines for the Molecular Recognition Toolbox. Angew. Chem., Int. Ed. 2004, 43, 5310– 5324, DOI: 10.1002/anie.200301739[Crossref], [CAS], Google Scholar50https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXptVanur4%253D&md5=564b7881d97ba783ce309ce7867ead3cQuantifying intermolecular interactions: Guidelines for the molecular recognition toolboxHunter, Christopher A.Angewandte Chemie, International Edition (2004), 43 (40), 5310-5324CODEN: ACIEF5; ISSN:1433-7851. (Wiley-VCH Verlag GmbH & Co. KGaA)A review. Mol. recognition events in soln. are affected by many different factors that have hampered the development of an understanding of intermol. interactions at a quant. level. Tendency is to partition these effects into discrete phenomenol. fields that are classified, named, and divorced: arom. interactions, cation-T interactions, CH-O hydrogen bonds, short strong hydrogen bonds, and hydrophobic interactions to name a few. To progress in the field, the authors need to develop an integrated quant. appreciation of the relative magnitudes of all of the different effects that might influence the mol. recognition behavior of a given system. In an effort to navigate undergraduates through the vast and sometimes contradictory literature on the subject, I have developed an approach that treats theor. ideas and exptl. observations about intermol. interactions in the gas phase, the solid state, and soln. from a single simplistic viewpoint. The key features are outlined here, and although many of the ideas will be familiar, the aim is to provide a semiquant. thermodn. ranking of these effects in soln. at room temp.
- 51Kinoshita, K.; Nakamura, H. eF-site and PDBjViewer: database and viewer for protein functional sites. Bioinformatics 2004, 20, 1329– 1330, DOI: 10.1093/bioinformatics/bth073[Crossref], [PubMed], [CAS], Google Scholar51https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXkt1Srtbs%253D&md5=a5134b63e935264f8c1b71c727d64fa8eF-site and PDBjViewer: database and viewer for protein functional sitesKinoshita, Kengo; Nakamura, HarukiBioinformatics (2004), 20 (8), 1329-1330CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)The electrostatic-surface of functional site (eF-site) is a database for the mol. surfaces of protein functional sites. To enable browsing of each mol. surface along with the at. model, a new three-dimensional interactive viewer called PDBjViewer was developed that can be used both as an applet and as a stand-alone program.
- 52Connolly, M. L. The molecular surface package. J. Mol. Graphics 1993, 11, 139– 141, DOI: 10.1016/0263-7855(93)87010-3[Crossref], [PubMed], [CAS], Google Scholar52https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3sXmsV2itbg%253D&md5=de7d3ee51d249804a4e714fd0ec46e07The molecular surface packageConnolly, Michael L.Journal of Molecular Graphics (1993), 11 (2), 139-41CODEN: JMGRDV; ISSN:0263-7855.The mol. surface package is a reimplementation, in C, of a set of earlier FORTRAN programs for computing anal. mol. surfaces, areas, vols., polyhedral mol. surfaces, and surface curvatures. The software does not do interactive mol. graphics, but it will produce pixel maps of smooth mol. surfaces. The polyhedral mol. surfaces are suited to display on graphics systems with real-time rendering of polyhedra.
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- 54Kitchen, D.; Decornez, H.; R Furr, J.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat. Rev. Drug Discovery 2004, 3, 935– 949, DOI: 10.1038/nrd1549[Crossref], [PubMed], [CAS], Google Scholar54https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXptFemtrg%253D&md5=875a2b37a4299181509a1922b11dbd2fDocking and scoring in virtual screening for drug discovery: methods and applicationsKitchen, Douglas B.; Decornez, Helene; Furr, John R.; Bajorath, JuergenNature Reviews Drug Discovery (2004), 3 (11), 935-949CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)A review. Computational approaches that 'dock' small mols. into the structures of macromol. targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization. Indeed, there are now a no. of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors. Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes. Here, we review key concepts and specific features of small-mol.-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches.
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- 59Ohue, M.; I, T.; A, Y.; Matsuzaki, Y. Improvement of the protein-protein docking prediction by introducing a simple hydrophobic interaction model: an application to interaction pathway analysis. Lect. Notes Bioinf. 2012, 7632, 178– 187, DOI: 10.1007/978-3-642-34123-6_16[Crossref], [CAS], Google Scholar59https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXmvV2isLo%253D&md5=a3b47665ce4829bc8dc9755c89ab4a4fImprovement of the protein-protein docking prediction by introducing a simple hydrophobic interaction model: an application to interaction pathway analysisOhue, Masahito; Matsuzaki, Yuri; Ishida, Takashi; Akiyama, YutakaLecture Notes in Bioinformatics (2012), 7632 (Pattern Recognition in Bioinformatics), 178-187CODEN: LNBEAR ISSN:. (Springer GmbH)We propose a new hydrophobic interaction model that applies at. contact energy for our protein-protein docking software, MEGADOCK. Previously, this software used only two score terms, shape complementarity and electrostatic interaction. We develop a modified score function incorporating the hydrophobic interaction effect. Using the proposed score function, MEGADOCK can calc. three physico-chem. effects with only one correlation function. We evaluate the proposed system against three other protein-protein docking score models, and we confirm that our method displays better performance than the original MEGADOCK system and is faster than both ZDOCK systems. Thus, we successfully improve accuracy without loosing speed.
- 60Ohue, M.; Matsuzaki, Y.; Akiyama, Y. Docking-calculation-based method for predicting protein-RNA interactions. Genome Inf. 2011, 25, 25– 39, DOI: 10.11234/gi.25.25[Crossref], [PubMed], [CAS], Google Scholar60https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC387jtFKktQ%253D%253D&md5=1e6337455af182e1af91dd72a6bc66cbDocking-calculation-based method for predicting protein-RNA interactionsOhue Masahito; Matsuzaki Yuri; Akiyama YutakaGenome informatics. International Conference on Genome Informatics (2011), 25 (1), 25-39 ISSN:0919-9454.Elucidating protein-RNA interactions (PRIs) is important for understanding many cellular systems. We developed a PRI prediction method by using a rigid-body protein-RNA docking calculation with tertiary structure data. We evaluated this method by using 78 protein-RNA complex structures from the Protein Data Bank. We predicted the interactions for pairs in 78×78 combinations. Of these, 78 original complexes were defined as positive pairs, and the other 6,006 complexes were defined as negative pairs; then an F-measure value of 0.465 was obtained with our prediction system.
Abstract
Figure 1
Figure 1. Root-mean-square deviations (RMSD) and gyrations of the MD simulations of proteins (a, b) with GDP (c, d) and with GTP (e, f) for EF-1α of Subulatomonas sp. strain PCMinv5 (red line) and Pythium ultimum DAOM BR144 (purple line) and EFL of Fabomonas tropica strain NYK3C (green line) and Thecamonas trahens ATCC50062 (blue line).
Figure 2
Figure 3
Figure 3. Root-mean-square fluctuations of MD simulations of Subulatomonas sp. strain PC Minv5 EF-1α (a), Pythium ultimum DAOM BR144 EF-1α (b), Thecamonas trahens ATCC50062 EFL (c), Fabomonas tropica strain NYK3C EFL (d), with APO: blue, GDP: green, and GTP: red.
Figure 4
Figure 4. PCA-based free energy landscape of (A) Subulatomonas EF-1α (B) with GDP (C) with GTP, (D) Pythium EF-1α (E) with GDP (F) with GTP, (G) Thecamonas EFL (H) with GDP (I) with GTP, (J) Fabomonas EFL (K) with GDP (L) with GTP. The free energy ΔG is given in kcal mol–1 and presented by colors from blue (0 kcal mol–1) to red (15 kcal mol–1).
Figure 5
Figure 5. Interaction prediction (E-scores) by docking simulations between EF-1α/EFL (a) and Phe-tRNA/EF-1β (b). We took the average of all simulations. Error bars are the standard deviations.
References
ARTICLE SECTIONSThis article references 60 other publications.
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- 17Kamikawa, R.; Yabuki, A.; Nakayama, T.; ichiro Ishida, K.; Hashimoto, T.; Inagaki, Y. Cercozoa comprises both EF-1α-containing and EFL-containing members. Eur. J. Protistol. 2011, 47, 24– 28, DOI: 10.1016/j.ejop.2010.08.002[Crossref], [PubMed], [CAS], Google Scholar17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3M7is1Kguw%253D%253D&md5=b381baa50d40dc73e8f5b9fe52ed41d0Cercozoa comprises both EF-1α-containing and EFL-containing membersKamikawa Ryoma; Yabuki Akinori; Nakayama Takuro; Ishida Ken-ichiro; Hashimoto Tetsuo; Inagaki YujiEuropean journal of protistology (2011), 47 (1), 24-8 ISSN:.Elongation factor 1α (EF-1α) and elongation factor-like protein (EFL) are considered to be functionally equivalent proteins involved in peptide synthesis. Eukaryotes can be fundamentally divided into 'EF-1α-containing' and 'EFL-containing' types. Recently, EF-1α and EFL genes have been surveyed across the diversity of eukaryotes to explore the origin and evolution of EFL genes. Although the phylum Cercozoa is a diverse group, gene data for either EFL or EF-1α are absent from all cercozoans except chlorarachniophytes which were previously defined as EFL-containing members. Our survey revealed that two members of the cercozoan subphylum Filosa (Thaumatomastix sp. and strain YPF610) are EFL-containing members. Importantly, we identified EF-1α genes from two members of Filosa (Paracercomonas marina and Paulinella chromatophora) and a member of the other subphylum Endomyxa (Filoreta japonica). All cercozoan EFL homologues could not be recovered as a monophyletic group in maximum-likelihood and Bayesian analyses, suggesting that lateral gene transfer was involved in the EFL evolution in this protist assemblage. In contrast, EF-1α analysis successfully recovered a monophyly of three homologues sampled from the two cercozoan subphyla. Based on the results, we postulate that cercozoan EF-1α genes have been vertically inherited, and the current EFL-containing species may have secondarily lost their EF-1α genes.
- 18Ishitani, Y.; Kamikawa, R.; Yabuki, A.; Tsuchiya, M.; Inagaki, Y.; Takishita, K. Evolution of elongation factor-like (EFL) protein in Rhizaria is revised by radiolarian EFL gene sequences. J. Eukaryotic Microbiol. 2012, 59, 367– 73, DOI: 10.1111/j.1550-7408.2012.00626.x[Crossref], [PubMed], [CAS], Google Scholar18https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38Xht1ChtbfN&md5=408e2b935beb437ba65528e33322f4c6Evolution of elongation factor-like (EFL) protein in rhizaria is revised by radiolarian EFL gene sequencesIshitani, Yoshiyuki; Kamikawa, Ryoma; Yabuki, Akinori; Tsuchiya, Masashi; Inagaki, Yuji; Takishita, KiyotakaJournal of Eukaryotic Microbiology (2012), 59 (4), 367-373CODEN: JEMIED; ISSN:1066-5234. (Wiley-Blackwell)Elongation factor 1α (EF-1α) and elongation factor-like (EFL) proteins are considered to carry out equiv. functions in translation in eukaryotic cells. Elongation factor 1α and EFL genes are patchily distributed in the global eukaryotic tree, suggesting that the evolution of these elongation factors cannot be reconciled without multiple lateral gene transfer and/or ancestral cooccurrence followed by differential loss of either of the two factors. The authors' current understanding of the EF-1α/EFL evolution in the eukaryotic group Rhizaria, composed of Foraminifera, Radiolaria, Filosa, and Endomyxa, remains insufficient, as no information on EF-1α/EFL gene is available for any members of Radiolaria. EFL genes were exptl. isolated from four polycystine radiolarians (i.e. Dictyocoryne, Eucyrtidium, Collozoum, and Sphaerozoum), as well as retrieved from publicly accessible expressed sequence tag data of two acantharean radiolarians (i.e. Astrolonche and Phyllostaurus) and the endomyxan Gromia. The EFL homologs from radiolarians, foraminiferans, and Gromia formed a robust clade in both max.-likelihood and Bayesian phylogenetic analyses, suggesting that EFL genes were vertically inherited from their common ancestor. The authors propose an updated model for EF-1α/EFL evolution in Rhizaria by incorporating new EFL data obtained.
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- 20Mikhailov, K. V.; Janouškovec, J.; Tikhonenkov, D. V.; Mirzaeva, G. S.; Diakin, A. Y.; Simdyanov, T. G.; Mylnikov, A. P.; Keeling, P. J.; Aleoshin, V. V. A Complex Distribution of Elongation Family GTPases EF1A and EFL in Basal Alveolate Lineages. Genome Biol. Evol. 2014, 6, 2361– 2367, DOI: 10.1093/gbe/evu186[Crossref], [PubMed], [CAS], Google Scholar20https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXisV2qtLk%253D&md5=3b97abc83862f77c6c00e2d37aef1ea3A complex distribution of elongation family GTPases EF1A and EFL in basal alveolate lineagesMikhailov, Kirill V.; Janouskovec, Jan; Tikhonenkov, Denis V.; Mirzaeva, Gulnara S.; Diakin, Andrei Yu.; Simdyanov, Timur G.; Mylnikov, Alexander P.; Keeling, Patrick J.; Aleoshin, Vladimir V.Genome Biology and Evolution (2014), 6 (9), 2361-2367CODEN: GBEEA5; ISSN:1759-6653. (Oxford University Press)Translation elongation factor-1 alpha (EF1A) and the related GTPase EF-like (EFL) are two proteins with a complexmutually exclusive distribution across the tree of eukaryotes. Recent surveys revealed that the distribution of the two GTPases in even closely related taxa is frequently at odds with their phylogenetic relationships. Here, we investigate the distribution of EF1A and EFL in the alveolate supergroup. Alveolates comprise threemajor lineages: ciliates andapicomplexans encode EF1A, whereas dinoflagellates encode EFL. Wesearched transcriptome databases for seven early-diverging alveolate taxa thatdonot belong to anyof these groups: colpodellids, chromerids, and colponemids. Current data suggest all seven are expected to encode EF1A, but we find three genera encode EFL: Colpodella, Voromonas, and the photosynthetic Chromera. Comparing this distribution with the phylogeny of alveolates suggests that EF1A and EFL evolution in alveolates cannot be explained by a simple horizontal gene transfer event or lineage sorting.
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- 23Andersen, G. R.; Valente, L.; Pedersen, L.; Kinzy, T. G.; Nyborg, J. Crystal structures of nucleotide exchange intermediates in the eEF1A-eEF1Bα complex. Nat. Struct. Biol. 2001, 8, 531– 534, DOI: 10.1038/88598
- 24Biasini, M.; Bienert, S.; Waterhouse, A.; Arnold, K.; Studer, G.; Schmidt, T.; Kiefer, F.; Cassarino, T. G.; Bertoni, M.; Bordoli, L.; Schwede, T. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 2014, 42, W252– W258, DOI: 10.1093/nar/gku340[Crossref], [PubMed], [CAS], Google Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhtFCqs73I&md5=51509ba8353b06f286d954ebe7e6673aSWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary informationBiasini, Marco; Bienert, Stefan; Waterhouse, Andrew; Arnold, Konstantin; Studer, Gabriel; Schmidt, Tobias; Kiefer, Florian; Cassarino, Tiziano Gallo; Bertoni, Martino; Bordoli, Lorenza; Schwede, TorstenNucleic Acids Research (2014), 42 (W1), W252-W258CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)Protein structure homol. modeling has become a routine technique to generate 3D models for proteins when exptl. structures are not available. Fully automated servers such as SWISS-MODEL with user-friendly web interfaces generate reliable models without the need for complex software packages or downloading large databases. Here, we describe the latest version of the SWISS-MODEL expert system for protein structure modeling. The SWISS-MODEL template library provides annotation of quaternary structure and essential ligands and co-factors to allow for building of complete structural models, including their oligomeric structure. The improved SWISS-MODEL pipeline makes extensive use of model quality estn. for selection of the most suitable templates and provides ests. of the expected accuracy of the resulting models. The accuracy of the models generated by SWISS-MODEL is continuously evaluated by the CAMEO system. The new web site allows users to interactively search for templates, cluster them by sequence similarity, structurally compare alternative templates and select the ones to be used for model building. In cases where multiple alternative template structures are available for a protein of interest, a user-guided template selection step allows building models in different functional states. SWISS-MODEL is available at http://swissmodel.expasy.org/.
- 25Crepin, T.; Shalak, V. F.; Yaremchuk, A. D.; Vlasenko, D. O.; McCarthy, A.; Negrutskii, B. S.; Tukalo, M. A.; El’skaya, A. V. Mammalian translation elongation factor eEF1A2: X-ray structure and new features of GDP/GTP exchange mechanism in higher eukaryotes. Nucleic Acids Res. 2014, 42, 12939– 12948, DOI: 10.1093/nar/gku974[Crossref], [PubMed], [CAS], Google Scholar25https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhtVCqsLfM&md5=032ab85686a8aae4dc03c6aab2245837Mammalian translation elongation factor eEF1A2: X-ray structure and new features of GDP/GTP exchange mechanism in higher eukaryotesCrepin, Thibaut; Shalak, Vyacheslav F.; Yaremchuk, Anna D.; Vlasenko, Dmytro O.; McCarthy, Andrew; Negrutskii, Boris S.; Tukalo, Michail A.; El'skaya, Anna V.Nucleic Acids Research (2014), 42 (20), 12939-12948CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)Eukaryotic elongation factor eEF1A transits between the GTP- and GDP-bound conformations during the ribosomal polypeptide chain elongation. eEF1A*GTP establishes a complex with the aminoacyl-tRNA in the A site of the 80S ribosome. Correct codon-anticodon recognition triggers GTP hydrolysis, with subsequent dissocn. of eEF1A*GDP from the ribosome. The structures of both the 'GTP'- and 'GDP'-bound conformations of eEF1A are unknown. Thus, the eEF1A-related ribosomal mechanisms were anticipated only by analogy with the bacterial homolog EF-Tu. Here, we report the first crystal structure of the mammalian eEF1A2GDP complex which indicates major differences in the organization of the nucleotide-binding domain and intramol. movements of eEF1A compared to EF-Tu. Our results explain the nucleotide exchange mechanism in the mammalian eEF1A and suggest that the first step of eEF1AGDP dissocn. from the 80S ribosome is the rotation of the nucleotide-binding domain obsd. after GTP hydrolysis.
- 26Kobayashi, K.; Kikuno, I.; Kuroha, K.; Saito, K.; Ito, K.; Ishitani, R.; Inada, T.; Nureki, O. Structural basis for mRNA surveillance by archaeal Pelota and GTP-bound EF1α complex. Proc. Natl. Acad. Sci. U.S.A. 2010, 107, 17575– 17579, DOI: 10.1073/pnas.1009598107
- 27Benkert, P.; Biasini, M.; Schwede, T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 2011, 27, 343– 350, DOI: 10.1093/bioinformatics/btq662[Crossref], [PubMed], [CAS], Google Scholar27https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhs1Gisb8%253D&md5=eb1b789a978f8db7beff4dbcd03296eeToward the estimation of the absolute quality of individual protein structure modelsBenkert, Pascal; Biasini, Marco; Schwede, TorstenBioinformatics (2011), 27 (3), 343-350CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)Quality assessment of protein structures is an important part of exptl. structure validation and plays a crucial role in protein structure prediction, where the predicted models may contain substantial errors. Most current scoring functions are primarily designed to rank alternative models of the same sequence supporting model selection, whereas the prediction of the abs. quality of an individual protein model has received little attention in the field. However, reliable abs. quality ests. are crucial to assess the suitability of a model for specific biomedical applications. In this work, we present a new abs. measure for the quality of protein models, which provides an est. of the degree of nativeness' of the structural features obsd. in a model and describes the likelihood that a given model is of comparable quality to exptl. structures. Model quality ests. based on the QMEAN scoring function were normalized with respect to the no. of interactions. The resulting scoring function is independent of the size of the protein and may therefore be used to assess both monomers and entire oligomeric assemblies. Model quality scores for individual models are then expressed as Z-scores' in comparison to scores obtained for high-resoln. crystal structures. We demonstrate the ability of the newly introduced QMEAN Z-score to detect exptl. solved protein structures contg. significant errors, as well as to evaluate theor. protein models. In a comprehensive QMEAN Z-score anal. of all exptl. structures in the PDB, membrane proteins accumulate on one side of the score spectrum and thermostable proteins on the other. Proteins from the thermophilic organism Thermatoga maritima received significantly higher QMEAN Z-scores in a pairwise comparison with their homologous mesophilic counterparts, underlining the significance of the QMEAN Z-score as an est. of protein stability.
- 28Benkert, P.; Künzli, M.; Schwede, T. QMEAN server for protein model quality estimation. Nucleic Acids Res. 2009, 37, W510– W514, DOI: 10.1093/nar/gkp322[Crossref], [PubMed], [CAS], Google Scholar28https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXosFSktLY%253D&md5=3c3c7fde4434dbd4a1c73a6e10c044d1QMEAN server for protein model quality estimationBenkert, Pascal; Kuenzli, Michael; Schwede, TorstenNucleic Acids Research (2009), 37 (Web Server), W510-W514CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)Model quality estn. is an essential component of protein structure prediction, since ultimately the accuracy of a model dets. its usefulness for specific applications. Usually, in the course of protein structure prediction a set of alternative models is produced, from which subsequently the most accurate model has to be selected. The QMEAN server provides access to two scoring functions successfully tested at the eighth round of the community-wide blind test expt. CASP. The user can choose between the composite scoring function QMEAN, which derives a quality est. on the basis of the geometrical anal. of single models, and the clustering-based scoring function QMEANclust which calcs. a global and local quality est. based on a weighted all-against-all comparison of the models from the ensemble provided by the user. The web server performs a ranking of the input models and highlights potentially problematic regions for each model. The QMEAN server is available at http://swissmodel.expasy.org/qmean.
- 29Nissen, P.; Kjeldgaard, M.; Thirup, S.; Polekhina, G.; Reshetnikova, L.; Clark, B. F. C.; Nyborg, J. Crystal Structure of the Ternary Complex of Phe-tRNAPhe, EF-Tu, and a GTP Analog. Science 1995, 270, 1464– 1472, DOI: 10.1126/science.270.5241.1464[Crossref], [PubMed], [CAS], Google Scholar29https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXps12hsrc%253D&md5=51e24bc35387620abadb7ff982f5670eCrystal structure of the ternary complex of Phe-tRNAPhe, EF-Tu, and a GTP analogNissen, Poul; Kjeldgaard, Morten; Thirup, Soeren; Polekhina, Galina; Reshetnikova, Ludmila; Clark, Brian F. C.; Nyborg, JensScience (Washington, D. C.) (1995), 270 (5241), 1464-72CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)The structure of the ternary complex consisting of yeast phenylalanyl-tRNA (Phe-tRNAPhe), Thermus aquaticus elongation factor Tu (EF-Tu), and the guanosine triphosphate (GTP) analog GDPNP was detd. by x-ray crystallog. at 2.7 angstrom resoln. The ternary complex participates in placing the amino acids in their correct order when mRNA is translated into a protein sequence on the ribosome. The EF-Tu-GDPNP component binds to one side of the acceptor helix of Phe-tRNAPhe involving all three domains of EF-Tu. Binding sites for the phenylalanylated CCA end and the phosphorylated 5' end are located at domain interfaces, whereas the T stem interacts with the surface of the β-barrel domain 3. The binding involves many conserved residues in EF-Tu. The overall shape of the ternary complex is similar to that of the translocation factor, EF-G-GDP, and this suggests a novel mechanism involving "mol. mimicry" in the translational app.
- 30Webb, B.; Sali, A. Comparative Protein Structure Modeling Using MODELLER. Curr. Protoc. Bioinf. 54, 5.6.1 5.6.37. DOI: 10.1002/cpbi.3 .
- 31Murakami, Y.; Kinoshita, K.; Kinjo, A. R.; Nakamura, H. Exhaustive comparison and classification of ligand-binding surfaces in proteins. Protein Sci. 2013, 22, 1379– 1391, DOI: 10.1002/pro.2329[Crossref], [PubMed], [CAS], Google Scholar31https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsFals7rJ&md5=c1d2b3f7849da1f6c462560853a12919Exhaustive comparison and classification of ligand-binding surfaces in proteinsMurakami, Yoichi; Kinoshita, Kengo; Kinjo, Akira R.; Nakamura, HarukiProtein Science (2013), 22 (10), 1379-1391CODEN: PRCIEI; ISSN:1469-896X. (Wiley-Blackwell)Many proteins function by interacting with other small mols. (ligands). Identification of ligand-binding sites (LBS) in proteins can therefore help to infer their mol. functions. A comprehensive comparison among local structures of LBSs was previously performed, in order to understand their relationships and to classify their structural motifs. However, similar exhaustive comparison among local surfaces of LBSs (patches) has never been performed, due to computational complexity. To enhance our understanding of LBSs, it is worth performing such comparisons among patches and classifying them based on similarities of their surface configurations and electrostatic potentials. In this study, we first developed a rapid method to compare two patches. We then clustered patches corresponding to the same PDB chem. component identifier for a ligand, and selected a representative patch from each cluster. We subsequently exhaustively as compared the representative patches and clustered them using similarity score, PatSim. Finally, the resultant PatSim scores were compared with similarities of at. structures of the LBSs and those of the ligand-binding protein sequences and functions. Consequently, we classified the patches into ∼2000 well-characterized clusters. We found that about 63% of these clusters are used in identical protein folds, although about 25% of the clusters are conserved in distantly related proteins and even in proteins with cross-fold similarity. Furthermore, we showed that patches with higher PatSim score have potential to be involved in similar biol. processes.
- 32Altschul, S. F.; Madden, T. L.; Schäffer, A. A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D. J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25, 3389– 3402, DOI: 10.1093/nar/25.17.3389[Crossref], [PubMed], [CAS], Google Scholar32https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2sXlvFyhu7w%253D&md5=4e44123e5984e4aca46a9899d347a176Gapped BLAST and PSI-BLAST: a new generation of protein database search programsAltschul, Stephen F.; Madden, Thomas L.; Schaffer, Alejandro A.; Zhang, Jinghui; Zhang, Zheng; Miller, Webb; Lipman, David J.Nucleic Acids Research (1997), 25 (17), 3389-3402CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approx. three times the speed of the original. In addn., a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approx. the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biol. relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily. The source code for the new BLAST programs is available by anonymous ftp from the machine ncbi.nlm.nih.gov, within the directory 'blast', and the programs may be run from NCBIs web site at http://www.ncbi.nlm.nih.gov/.
- 33Andersen, G. R.; Pedersen, L.; Valente, L.; Chatterjee, I.; Kinzy, T. G.; Kjeldgaard, M.; Nyborg, J. Structural Basis for Nucleotide Exchange and Competition with tRNA in the Yeast Elongation Factor Complex eEF1A:eEF1Bα. Mol. Cell 2000, 6, 1261– 1266, DOI: 10.1016/S1097-2765(00)00122-2
- 34Benson, D. A.; Karsch-Mizrachi, I.; Lipman, D. J.; Ostell, J.; Sayers, E. W. GenBank. Nucleic Acids Res. 2010, 38, D46– D51, DOI: 10.1093/nar/gkp1024
- 35Kanibolotsky, D. S.; Novosyl’na, O. V.; Abbott, C. M.; Negrutskii, B. S.; El’skaya, A. V. Multiple molecular dynamics simulation of the isoforms of human translation elongation factor 1A reveals reversible fluctuations between “open” and “closed” conformations and suggests specific for eEF1A1 affinity for Ca2+-calmodulin. BMC Struct. Biol. 2008, 8, 4, DOI: 10.1186/1472-6807-8-4[Crossref], [PubMed], [CAS], Google Scholar35https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD1c3gslehtg%253D%253D&md5=d061fe2edb87b311da1a59b40118d329Multiple molecular dynamics simulation of the isoforms of human translation elongation factor 1A reveals reversible fluctuations between "open" and "closed" conformations and suggests specific for eEF1A1 affinity for Ca2+-calmodulinKanibolotsky Dmitry S; Novosyl'na Oleksandra V; Abbott Catherine M; Negrutskii Boris S; El'skaya Anna VBMC structural biology (2008), 8 (), 4 ISSN:.BACKGROUND: Eukaryotic translation elongation factor eEF1A directs the correct aminoacyl-tRNA to ribosomal A-site. In addition, eEF1A is involved in carcinogenesis and apoptosis and can interact with large number of non-translational ligands. There are two isoforms of eEF1A, which are 98% similar. Despite the strong similarity, the isoforms differ in some properties. Importantly, the appearance of eEF1A2 in tissues in which the variant is not normally expressed can be coupled to cancer development.We reasoned that the background for the functional difference of eEF1A1 and eEF1A2 might lie in changes of dynamics of the isoforms. RESULTS: It has been determined by multiple MD simulation that eEF1A1 shows increased reciprocal flexibility of structural domains I and II and less average distance between the domains, while increased non-correlated diffusive atom motions within protein domains characterize eEF1A2. The divergence in the dynamic properties of eEF1A1 and eEF1A2 is caused by interactions of amino acid residues that differ between the two variants with neighboring residues and water environment. The main correlated motion of both protein isoforms is the change in proximity of domains I and II which can lead to disappearance of the gap between the domains and transition of the protein into a "closed" conformation. Such a transition is reversible and the protein can adopt an "open" conformation again. This finding is in line with our earlier experimental observation that the transition between "open" and "closed" conformations of eEF1A could be essential for binding of tRNA and/or other biological ligands. The putative calmodulin-binding region Asn311-Gly327 is less flexible in eEF1A1 implying its increased affinity for calmodulin. The ability of eEF1A1 rather than eEF1A2 to interact with Ca2+/calmodulin is shown experimentally in an ELISA-based test. CONCLUSION: We have found that reversible transitions between "open" and "close" conformations of eEF1A provide a molecular background for the earlier observation that the eEF1A molecule is able to change the shape upon interaction with tRNA. The ability of eEF1A1 rather than eEF1A2 to interact with calmodulin is predicted by MD analysis and showed experimentally. The differential ability of the eEF1A isoforms to interact with signaling molecules discovered in this study could be associated with cancer-related properties of eEF1A2.
- 36Soares, D.; Barlow, P.; Newbery, H.; Porteous, D.; Abbott, C. Structural Models of Human eEF1A1 and eEF1A2 Reveal Two Distinct Surface Clusters of Sequence Variation and Potential Differences in Phosphorylation. PLoS One 2009, 4, e6315 DOI: 10.1371/journal.pone.0006315
- 37Gromadski, K. B.; Schümmer, T.; Strømgaard, A.; Knudsen, C. R.; Kinzy, T. G.; Rodnina, M. V. Kinetics of the Interactions between Yeast Elongation Factors 1A and 1Bα, Guanine Nucleotides, and Aminoacyl-tRNA. J. Biol. Chem. 2007, 282, 35629– 35637, DOI: 10.1074/jbc.M707245200
- 38Sievers, F.; Wilm, A.; Dineen, D.; Gibson, T. J.; Karplus, K.; Li, W.; Lopez, R.; McWilliam, H.; Remmert, M.; Söding, J.; Thompson, J. D.; Higgins, D. G. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 2011, 7, 539, DOI: 10.1038/msb.2011.75[Crossref], [PubMed], [CAS], Google Scholar38https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3MbgtlantA%253D%253D&md5=27dd8fac22447528e6d09e5fd1066133Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal OmegaSievers Fabian; Wilm Andreas; Dineen David; Gibson Toby J; Karplus Kevin; Li Weizhong; Lopez Rodrigo; McWilliam Hamish; Remmert Michael; Soding Johannes; Thompson Julie D; Higgins Desmond GMolecular systems biology (2011), 7 (), 539 ISSN:.Multiple sequence alignments are fundamental to many sequence analysis methods. Most alignments are computed using the progressive alignment heuristic. These methods are starting to become a bottleneck in some analysis pipelines when faced with data sets of the size of many thousands of sequences. Some methods allow computation of larger data sets while sacrificing quality, and others produce high-quality alignments, but scale badly with the number of sequences. In this paper, we describe a new program called Clustal Omega, which can align virtually any number of protein sequences quickly and that delivers accurate alignments. The accuracy of the package on smaller test cases is similar to that of the high-quality aligners. On larger data sets, Clustal Omega outperforms other packages in terms of execution time and quality. Clustal Omega also has powerful features for adding sequences to and exploiting information in existing alignments, making use of the vast amount of precomputed information in public databases like Pfam.
- 39Abraham, M. J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J. C.; Hess, B.; Lindahl, E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015, 1–2, 19– 25, DOI: 10.1016/j.softx.2015.06.001
- 40Essmann, U.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee, H.; Pedersen, L. G. A smooth particle mesh Ewald method. J. Chem. Phys. 1995, 103, 8577– 8593, DOI: 10.1063/1.470117[Crossref], [CAS], Google Scholar40https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2MXptlehtrw%253D&md5=092a679dd3bee08da28df41e302383a7A smooth particle mesh Ewald methodEssmann, 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.
- 41Hess, B. P-LINCS: A Parallel Linear Constraint Solver for Molecular Simulation. J. Chem. Theory Comput. 2008, 4, 116– 122, DOI: 10.1021/ct700200b[ACS Full Text], [CAS], Google Scholar41https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXhtlKru7zL&md5=476d5ca2eb25574d44b775996fff7b75P-LINCS: A Parallel Linear Constraint Solver for Molecular SimulationHess, BerkJournal of Chemical Theory and Computation (2008), 4 (1), 116-122CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)By removing the fastest degrees of freedom, constraints allow for an increase of the time step in mol. simulations. In the last decade parallel simulations have become commonplace. However, up till now efficient parallel constraint algorithms have not been used with domain decompn. In this paper the parallel linear constraint solver (P-LINCS) is presented, which allows the constraining of all bonds in macromols. Addnl. the energy conservation properties of (P-)LINCS are assessed in view of improvements in the accuracy of uncoupled angle constraints and integration in single precision.
- 42Lindorff-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: Struct., Funct., Bioinf. 2010, 78, 1950– 1958, DOI: 10.1002/prot.22711[Crossref], [PubMed], [CAS], Google Scholar42https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXkvFegtLo%253D&md5=447a9004026e2b93f0f7beff165daa09Improved side-chain torsion potentials for the Amber ff99SB protein force fieldLindorff-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.
- 43Frisch, M. J.; Gaussian 09, revision 01; Gaussian Inc.: Wallingford, CT, 2009.Google ScholarThere is no corresponding record for this reference.
- 44Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 2004, 25, 1157– 1174, DOI: 10.1002/jcc.20035[Crossref], [PubMed], [CAS], Google Scholar44https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXksFakurc%253D&md5=2992017a8cf51f89290ae2562403b115Development and testing of a general Amber force fieldWang, Junmei; Wolf, Romain M.; Caldwell, James W.; Kollman, Peter A.; Case, David A.Journal of Computational Chemistry (2004), 25 (9), 1157-1174CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)We describe here a general Amber force field (GAFF) for org. mols. GAFF is designed to be compatible with existing Amber force fields for proteins and nucleic acids, and has parameters for most org. and pharmaceutical mols. that are composed of H, C, N, O, S, P, and halogens. It uses a simple functional form and a limited no. of atom types, but incorporates both empirical and heuristic models to est. force consts. and partial at. charges. The performance of GAFF in test cases is encouraging. In test I, 74 crystallog. structures were compared to GAFF minimized structures, with a root-mean-square displacement of 0.26 Å, which is comparable to that of the Tripos 5.2 force field (0.25 Å) and better than those of MMFF 94 and CHARMm (0.47 and 0.44 Å, resp.). In test II, gas phase minimizations were performed on 22 nucleic acid base pairs, and the minimized structures and intermol. energies were compared to MP2/6-31G* results. The RMS of displacements and relative energies were 0.25 Å and 1.2 kcal/mol, resp. These data are comparable to results from Parm99/RESP (0.16 Å and 1.18 kcal/mol, resp.), which were parameterized to these base pairs. Test III looked at the relative energies of 71 conformational pairs that were used in development of the Parm99 force field. The RMS error in relative energies (compared to expt.) is about 0.5 kcal/mol. GAFF can be applied to wide range of mols. in an automatic fashion, making it suitable for rational drug design and database searching.
- 45Wang, J.; Wang, W.; Kollman, P. A.; Case, D. A. Automatic atom type and bond type perception in molecular mechanical calculations. J. Mol. Graphics Modell. 2006, 25, 247– 260, DOI: 10.1016/j.jmgm.2005.12.005[Crossref], [PubMed], [CAS], Google Scholar45https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28Xps1Gis7g%253D&md5=8031a21d2784d5dea12e70868522aa61Automatic atom type and bond type perception in molecular mechanical calculationsWang, Junmei; Wang, Wei; Kollman, Peter A.; Case, David A.Journal of Molecular Graphics & Modelling (2006), 25 (2), 247-260CODEN: JMGMFI; ISSN:1093-3263. (Elsevier Inc.)In mol. mechanics (MM) studies, atom types and/or bond types of mols. are needed to det. prior to energy calcns. The authors present here an automatic algorithm of perceiving atom types that are defined in a description table, and an automatic algorithm of assigning bond types just based on at. connectivity. The algorithms have been implemented in a new module of the AMBER packages. This auxiliary module, antechamber (roughly meaning "before AMBER"), can be applied to generate necessary inputs of leap-the AMBER program to generate topologies for minimization, mol. dynamics, etc., for most org. mols. The algorithms behind the manipulations may be useful for other mol. mech. packages as well as applications that need to designate atom types and bond types.
- 46Sousa da Silva, A. W.; Vranken, W. F. ACPYPE - AnteChamber PYthon Parser interfacE. BMC Res. Notes 2012, 5, 367, DOI: 10.1186/1756-0500-5-367[Crossref], [PubMed], [CAS], Google Scholar46https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC38fitlWjtg%253D%253D&md5=60f750ba98c975374b351a39a1fa0ec4ACPYPE - AnteChamber PYthon Parser interfacESousa da Silva Alan W; Vranken Wim FBMC research notes (2012), 5 (), 367 ISSN:.BACKGROUND: ACPYPE (or AnteChamber PYthon Parser interfacE) is a wrapper script around the ANTECHAMBER software that simplifies the generation of small molecule topologies and parameters for a variety of molecular dynamics programmes like GROMACS, CHARMM and CNS. It is written in the Python programming language and was developed as a tool for interfacing with other Python based applications such as the CCPN software suite (for NMR data analysis) and ARIA (for structure calculations from NMR data). ACPYPE is open source code, under GNU GPL v3, and is available as a stand-alone application at http://www.ccpn.ac.uk/acpype and as a web portal application at http://webapps.ccpn.ac.uk/acpype. FINDINGS: We verified the topologies generated by ACPYPE in three ways: by comparing with default AMBER topologies for standard amino acids; by generating and verifying topologies for a large set of ligands from the PDB; and by recalculating the structures for 5 protein-ligand complexes from the PDB. CONCLUSIONS: ACPYPE is a tool that simplifies the automatic generation of topology and parameters in different formats for different molecular mechanics programmes, including calculation of partial charges, while being object oriented for integration with other applications.
- 47Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983, 79, 926– 935, DOI: 10.1063/1.445869[Crossref], [CAS], Google Scholar47https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL3sXksF2htL4%253D&md5=a1161334e381746be8c9b15a5e56f704Comparison of simple potential functions for simulating liquid waterJorgensen, William L.; Chandrasekhar, Jayaraman; Madura, Jeffry D.; Impey, Roger W.; Klein, Michael L.Journal of Chemical Physics (1983), 79 (2), 926-35CODEN: JCPSA6; ISSN:0021-9606.Classical Monte Carlo simulations were carried out for liq. H2O in the NPT ensemble at 25° and 1 atm using 6 of the simpler intermol. potential functions for the dimer. Comparisons were made with exptl. thermodn. and structural data including the neutron diffraction results of Thiessen and Narten (1982). The computed densities and potential energies agree with expt. except for the original Bernal-Fowler model, which yields an 18% overest. of the d. and poor structural results. The discrepancy may be due to the correction terms needed in processing the neutron data or to an effect uniformly neglected in the computations. Comparisons were made for the self-diffusion coeffs. obtained from mol. dynamics simulations.
- 48Jones, S.; Thornton, J. M. Principles of protein-protein interactions. Proc. Natl. Acad. Sci. U.S.A. 1996, 93, 13– 20, DOI: 10.1073/pnas.93.1.13[Crossref], [PubMed], [CAS], Google Scholar48https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK28XjslOrsQ%253D%253D&md5=2b03d8f835104e5a7bf5da83afd4a8d1Principles of protein-protein interactionsJones, Susan; Thornton, Janet M.Proceedings of the National Academy of Sciences of the United States of America (1996), 93 (1), 13-20CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)A review, with 47 refs., examg. protein complexes in the Brookhaven Protein Databank to gain a better understanding of the principles governing the interactions involved in protein-protein recognition. The factors that influence the formation of protein-protein complexes are explored in four different types of protein-protein complexes: homodimeric proteins, heterodimeric proteins, enzyme-inhibitor complexes, and antibody-protein complexes. The comparison between the complexes highlights differences that reflect their biol. roles.
- 49Draper, D. E. Themes in RNA-protein recognition. J. Mol. Biol. 1999, 293, 255– 270, DOI: 10.1006/jmbi.1999.2991
- 50Hunter, C. A. Quantifying Intermolecular Interactions: Guidelines for the Molecular Recognition Toolbox. Angew. Chem., Int. Ed. 2004, 43, 5310– 5324, DOI: 10.1002/anie.200301739[Crossref], [CAS], Google Scholar50https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXptVanur4%253D&md5=564b7881d97ba783ce309ce7867ead3cQuantifying intermolecular interactions: Guidelines for the molecular recognition toolboxHunter, Christopher A.Angewandte Chemie, International Edition (2004), 43 (40), 5310-5324CODEN: ACIEF5; ISSN:1433-7851. (Wiley-VCH Verlag GmbH & Co. KGaA)A review. Mol. recognition events in soln. are affected by many different factors that have hampered the development of an understanding of intermol. interactions at a quant. level. Tendency is to partition these effects into discrete phenomenol. fields that are classified, named, and divorced: arom. interactions, cation-T interactions, CH-O hydrogen bonds, short strong hydrogen bonds, and hydrophobic interactions to name a few. To progress in the field, the authors need to develop an integrated quant. appreciation of the relative magnitudes of all of the different effects that might influence the mol. recognition behavior of a given system. In an effort to navigate undergraduates through the vast and sometimes contradictory literature on the subject, I have developed an approach that treats theor. ideas and exptl. observations about intermol. interactions in the gas phase, the solid state, and soln. from a single simplistic viewpoint. The key features are outlined here, and although many of the ideas will be familiar, the aim is to provide a semiquant. thermodn. ranking of these effects in soln. at room temp.
- 51Kinoshita, K.; Nakamura, H. eF-site and PDBjViewer: database and viewer for protein functional sites. Bioinformatics 2004, 20, 1329– 1330, DOI: 10.1093/bioinformatics/bth073[Crossref], [PubMed], [CAS], Google Scholar51https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXkt1Srtbs%253D&md5=a5134b63e935264f8c1b71c727d64fa8eF-site and PDBjViewer: database and viewer for protein functional sitesKinoshita, Kengo; Nakamura, HarukiBioinformatics (2004), 20 (8), 1329-1330CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)The electrostatic-surface of functional site (eF-site) is a database for the mol. surfaces of protein functional sites. To enable browsing of each mol. surface along with the at. model, a new three-dimensional interactive viewer called PDBjViewer was developed that can be used both as an applet and as a stand-alone program.
- 52Connolly, M. L. The molecular surface package. J. Mol. Graphics 1993, 11, 139– 141, DOI: 10.1016/0263-7855(93)87010-3[Crossref], [PubMed], [CAS], Google Scholar52https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3sXmsV2itbg%253D&md5=de7d3ee51d249804a4e714fd0ec46e07The molecular surface packageConnolly, Michael L.Journal of Molecular Graphics (1993), 11 (2), 139-41CODEN: JMGRDV; ISSN:0263-7855.The mol. surface package is a reimplementation, in C, of a set of earlier FORTRAN programs for computing anal. mol. surfaces, areas, vols., polyhedral mol. surfaces, and surface curvatures. The software does not do interactive mol. graphics, but it will produce pixel maps of smooth mol. surfaces. The polyhedral mol. surfaces are suited to display on graphics systems with real-time rendering of polyhedra.
- 53Nakamura, H.; Nishida, S. Numerical Calculations of Electrostatic Potentials of Protein-Solvent Systems by the Self Consistent Boundary Method. J. Phys. Soc. Jpn. 1987, 56, 1609– 1622, DOI: 10.1143/JPSJ.56.1609[Crossref], [CAS], Google Scholar53https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL2sXktlKis70%253D&md5=fa7163dd6929d0e6ccb29b68d1440227Numerical calculations of electrostatic potentials of protein-solvent systems by the self consistent boundary methodNakamura, Haruki; Nishida, ShinichiJournal of the Physical Society of Japan (1987), 56 (4), 1609-22CODEN: JUPSAU; ISSN:0031-9015.A macroscopic dielec. model for a protein-solvent system has been solved by a numerical calcn. using a supercomputer. The whole space including a target protein mol. was first divided into lots of cubes, some of which were inside the protein and the others were embedded in the solvent. Poisson equation and Poisson-Boltzmann equation were then numerically solved inside and outside of the protein, resp. At the artificial boundary far from the protein for the finite difference procedure, neither the potential nor the field was set to be zero, but they were calcd. self-consistently from the field and the potential inside the boundary calcd. beforehand. The results by this method agreed well with those by the anal. calcns. for some simple model cases. The results of actual protein-solvent systems were quant. discussed comparing with the expts.
- 54Kitchen, D.; Decornez, H.; R Furr, J.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat. Rev. Drug Discovery 2004, 3, 935– 949, DOI: 10.1038/nrd1549[Crossref], [PubMed], [CAS], Google Scholar54https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXptFemtrg%253D&md5=875a2b37a4299181509a1922b11dbd2fDocking and scoring in virtual screening for drug discovery: methods and applicationsKitchen, Douglas B.; Decornez, Helene; Furr, John R.; Bajorath, JuergenNature Reviews Drug Discovery (2004), 3 (11), 935-949CODEN: NRDDAG; ISSN:1474-1776. (Nature Publishing Group)A review. Computational approaches that 'dock' small mols. into the structures of macromol. targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization. Indeed, there are now a no. of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors. Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes. Here, we review key concepts and specific features of small-mol.-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches.
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- 57Brooks, B. R.; Bruccoleri, R. E.; Olafson, B. D.; States, D. J.; Swaminathan, S.; Karplus, M. CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem. 1983, 4, 187– 217, DOI: 10.1002/jcc.540040211[Crossref], [CAS], Google Scholar57https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL3sXit1aiu7w%253D&md5=bd639b4299ac9934f4497c1a9fe750d2CHARMM: a program for macromolecular energy, minimization, and dynamics calculationsBrooks, Bernard R.; Bruccoleri, Robert E.; Olafson, Barry D.; States, David J.; Swaminathan, S.; Karplus, MartinJournal of Computational Chemistry (1983), 4 (2), 187-217CODEN: JCCHDD; ISSN:0192-8651.CHARMM (Chem. at HARvard Macromol. Mechanics) is a highly flexible computer program which uses empirical energy functions to model macromol. systems. The program can read or model build structures, energy minimize them by first- or second-deriv. techniques, perform a normal mode or mol. dynamics simulation, and analyze the structural, equil., and dynamic properties detd. in these calcns. The operations that CHARMM can perform are described, and some implementation details are given. A set of parameters for the empirical energy function and a sample run are included.
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- 59Ohue, M.; I, T.; A, Y.; Matsuzaki, Y. Improvement of the protein-protein docking prediction by introducing a simple hydrophobic interaction model: an application to interaction pathway analysis. Lect. Notes Bioinf. 2012, 7632, 178– 187, DOI: 10.1007/978-3-642-34123-6_16[Crossref], [CAS], Google Scholar59https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXmvV2isLo%253D&md5=a3b47665ce4829bc8dc9755c89ab4a4fImprovement of the protein-protein docking prediction by introducing a simple hydrophobic interaction model: an application to interaction pathway analysisOhue, Masahito; Matsuzaki, Yuri; Ishida, Takashi; Akiyama, YutakaLecture Notes in Bioinformatics (2012), 7632 (Pattern Recognition in Bioinformatics), 178-187CODEN: LNBEAR ISSN:. (Springer GmbH)We propose a new hydrophobic interaction model that applies at. contact energy for our protein-protein docking software, MEGADOCK. Previously, this software used only two score terms, shape complementarity and electrostatic interaction. We develop a modified score function incorporating the hydrophobic interaction effect. Using the proposed score function, MEGADOCK can calc. three physico-chem. effects with only one correlation function. We evaluate the proposed system against three other protein-protein docking score models, and we confirm that our method displays better performance than the original MEGADOCK system and is faster than both ZDOCK systems. Thus, we successfully improve accuracy without loosing speed.
- 60Ohue, M.; Matsuzaki, Y.; Akiyama, Y. Docking-calculation-based method for predicting protein-RNA interactions. Genome Inf. 2011, 25, 25– 39, DOI: 10.11234/gi.25.25[Crossref], [PubMed], [CAS], Google Scholar60https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC387jtFKktQ%253D%253D&md5=1e6337455af182e1af91dd72a6bc66cbDocking-calculation-based method for predicting protein-RNA interactionsOhue Masahito; Matsuzaki Yuri; Akiyama YutakaGenome informatics. International Conference on Genome Informatics (2011), 25 (1), 25-39 ISSN:0919-9454.Elucidating protein-RNA interactions (PRIs) is important for understanding many cellular systems. We developed a PRI prediction method by using a rigid-body protein-RNA docking calculation with tertiary structure data. We evaluated this method by using 78 protein-RNA complex structures from the Protein Data Bank. We predicted the interactions for pairs in 78×78 combinations. Of these, 78 original complexes were defined as positive pairs, and the other 6,006 complexes were defined as negative pairs; then an F-measure value of 0.465 was obtained with our prediction system.
Supporting Information
ARTICLE SECTIONSThe Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsomega.8b03547.
Crystal structures of the template and the molecular surfaces (Figure S1);
homology models of EF-1α and EFL proteins with the yeast, rabbit, and Aeropyrum template, respectively (Tables S1–S3); molecular surface seen from the tRNA binding side of EF-1α and EFL models built with the yeast, rabbit, and Aeropyrum template, respectively (Figures S2–S4); molecular surface seen from the backside of EF-1α and EFL models built with the Aeropyrum template (Figure S5); values of net charges and electrostatic potentials (Table S5); structures and molecular surfaces of the averaged structure of 100 ns MD simulation (Figure S6); results of EF-1α/EFL-tRNA and EF-1α/EFL-EF-1β interaction prediction (E-score) (Table S6); results of EF-1α/EFL-tRNA interaction prediction (E-score) before and after MD (Table S7) (PDF)
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