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Interconversion between Serum Amyloid A Native and Fibril Conformations

Cite this: ACS Omega 2022, 7, 14, 12186–12192
Publication Date (Web):March 30, 2022
https://doi.org/10.1021/acsomega.2c00566

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Abstract

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Overexpression of serum amyloid A (SAA) can lead to a form of amyloidosis where the fibrils are made of SAA fragments, most often SAA1–76. Using Replica Exchange with Tunneling, we study the conversion of a SAA1–76 chain between the folded conformation and a fibril conformation. We find that the basins in the free energy landscape corresponding to the two motifs are separated by barriers of only about 2–3 kBT. Crucial for the assembly into the fibril structure is the salt bridge 26E–34K that provides a scaffold for forming the fibril conformation.

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1. Introduction

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Various illnesses, such as inflammatory bowel disease, rheumatoid arthritis, or certain cancers, can cause overexpression of serum amyloid A (SAA) to concentrations 1000 times higher than what is seen under healthy conditions. The resulting elevated risk for misfolding and aggregation leads in some cases to AA amyloidosis where damage from SAA-fibril deposits to tissues and organs adds to the symptoms and complicates treatment of the primary disease. (1−5) For a review of recent experimental and theoretical work on SAA and other amyloids, see, for instance, ref (6). Given the damaging effects of SAA amyloids, it is therefore necessary for therapeutic intervention to understand the process of unfolding of the functional SAA structure and its reorganization into the conformation seen in the experimentally resolved fibrils.
Involved in cholesterol transport and the regulation of inflammation, the 104-residue long SAA forms hexamers. (7) We have studied in previous work (8) the conditions that lead to decay of the hexamer, cleavage of the released chains, and subsequent misfolding of the resulting fragments (most commonly SAA1–76), which afterward may associate into the SAA fibrils. We observed a competition between the fast degradation of SAA fragments and the tendency of these fragments to form amyloids. Hence, in order to intervene, it is necessary to understand the kinetics of fibril formation. For this purpose, we study in the present paper now explicitly the conversion of SAA by modeling this process in all-atom simulations relying on a physical force field. As it is difficult for molecules of this size to obtain sufficient statistics from regular molecular dynamics, we utilize an enhanced sampling technique that was designed specifically by us for the investigation of structural transitions. Our technique, Replica Exchange with Tunneling (RET), (9−12) allows us to observe the interconversion with sufficient detail to characterize important intermediates. Studying for a SAA1–76 fragment (Figure 1) the transition between a conformation as seen in the folded and functional protein (PDB-ID: 4IP8) (13) and the conformation seen in the experimentally resolved fibril structure (PDB-ID: 6MST), (14) we find a characteristic sequence of events for this conversion that we relate to our earlier work and experimental measurements. Note that while the fibril model 6MST was derived under nonphysiological conditions, our results are not limited by the choice of the fibril model. While we ignore in the simulation setup that amyloid fibrils from diseased tissue may be structurally different from such in vitro formed SAA fibrils, (15) such polymorphism will be also seen with our RET approach, given sufficient sampling.

Figure 1

Figure 1. Structures of serum amyloid A for the fragment SAA1–76 of the folded monomer (PDB-ID: 4IP8) (a) and for the chain in a fibril (segment SAA2–55, PDB-ID: 6MST) (b). The N-terminal residues are colored in blue.

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2. Materials and Methods

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2.1. Replica Exchange with Tunneling

Deriving from computer simulations the mechanism by which SAA assumes its fibril form requires an exhaustive and accurate sampling of the free energy landscape of the protein. This, however, is a difficult task as typical time scales in protein simulations are only on the order of microseconds. We have proposed in earlier work (9−12) a variant of the Hamilton Replica Exchange method (16,17) as a way to increase sampling for the special case of transitions between well-characterized states. For this purpose, we set up in our approach a ladder of replicas, each coupling a “physical” model with a structure-based model. On one side of the ladder, the structure-based model drives the physical system toward the native state of SAA, while on the other side the bias is toward the fibril. The replica-depending strength of the coupling is controlled by a parameter λ that is maximal at the two ends and zero for the central replica. Exchange moves between neighboring replicas lead to a random walk along the ladder by which the SAA conformation changes from one motif into the other. Accepting or rejecting these exchange moves with the criterion commonly used in Replica Exchange Sampling (18) guarantees that the correct and unbiased distribution of the (unbiased) physical model will be sampled at λ = 0. However, the acceptance probability becomes vanishingly small for large systems. In order to avoid this problem, we conditionally accept the exchange and rescale the velocities of atoms in the two conformations A (moving from replica 1 to replica 2) and B (moving from replica 2 to replica 1):
(1)
such that the total energy at both replicas stays the same before and after the exchange. After the exchange, the systems on the two replica evolve by microcanonical molecular dynamics, exchanging potential and kinetic energy. Once the velocity distribution for each replica approaches the one expected at the given temperature, the final configuration B̂ = (q̂B, v̂B) on replica 1 has a comparable potential energy to that of the configuration A, while the potential energy of  = (q̂A,v̂A) on replica 2 will be close to that of the configuration B. Defining ΔEphy(i) = Ephyi(q̂A) – Ephyi(qB), and accordingly ΔEGo(i) and ΔEλ(i), the exchange is accepted with probability
(2)
If rejected, the simulation continues with configurations A (on replica 1) and B (on replica 2). We have shown that this Replica Exchange with Tunneling (RET) procedure leads to the correct distribution for sufficiently large systems and sufficiently long microcanonical segments; for details, see refs (9and10).

2.2. Simulation Setup

In order to study the interconversion of the serum amyloid A (SAA) native and fibril conformations, we describe in our RET simulations the “physical” model of the system by an all-atom energy function, the CHARMM36m force field (19) in conjunction with TIP3P (20) water molecules. The SAA1–76 fragment and 18, 376 water molecules are put in a box of edge size 8.3 nm with periodic boundary conditions and the system neutralized with two sodium ions. The SAA fragment is capped with an acetyl group and a methylamine group on the N-terminus and the C-termini, respectively. Start configurations are generated from the experimentally resolved structure of the full-size monomer as deposited in the Protein Data Bank (PDB-ID: 4IP8) (13) by discarding residues 77–104 and randomizing the resulting fragment SAA1–76 in a 1 ns simulation at 1500 K, followed by another simulation of 1 ns at 310 K. After this, visual inspection for loss of the secondary structure follows a final minimization of the resulting conformation. The biasing Go-model uses as target structures the fragment SAA1–76 as derived from the PDB-model 4IP8 after discarding residues 77–104 and the fibril model as deposited in the PDB under PDB-ID: 6MST. (14) As the fibril model is only for the fragment SAA2–56, we had to add an arginine as the first residue and the C-terminal residues 57–76 by using CHIMERA. (21) With the so-generated two target structures as input for the SMOG-Server (22) at http://smog-server.org, we derive then expressions for the corresponding Go-model energies EGo. Note that masses in the Go-models are scaled by a factor 14.21 to account for the lack of hydrogen atoms in the Go-models. Finally, we couple “physical” and Go-models by an energy Eλ as defined in refs (23) and (24), which quantifies the similarity between the two models. The strength of the coupling differs between the replicas. We used the following distribution of λ-parameters for the 24 replicas: λ = 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, 0.007, 0.006, 0.005, 0, 0, 0.005, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1. Note that for the replicas 0–10 the coupling between the physical model and a Go-model is defined by the native conformation, while for replicas 13–23 the physical model is coupled with a Go-model defined by the fibril model. In order to avoid an odd number of replicas, we use two central replicas with λ = 0 (replicas 11 and 12), where the physical model is not biased by a Go-model.
The above setup is implemented in a version of GROMACS 4.5.6, (25) used by us to compare our data with earlier work. Hydrogen bonds are constrained for both the physical and Go models using the LINCS algorithm (26) leading to a time step of 2 fs. The van der Waals cutoffs are set to 1.2 nm, and the Velocity Verlet algorithm (27) is used to integrate the equations of motion. Temperature is controlled by the v-rescale thermostat. (28) Note that our implementation of the RET algorithm requires different temperatures for each replica, which therefore slightly changes between 310 and 310.23 K (in steps of 0.01 K). The length of the microcanonical segment is set to 1 ps in a RET move. Two independent trajectories of 100 and 85 ns were generated, taking measurements only from the replica where λ = 0, i.e., where the physical model is not biased by any Go-term. Allowing for convergence of each trajectory, we are left with a total of 125 ns for analyzing the free energy landscape of the system.

2.3. Analysis Tools and Protocols

We use GROMACS tools (25) and the MDTraj (29) software package in the analysis of the RET trajectories for measuring the root mean square deviation (RMSD), dihedral angles of residues, and number of contacts. Secondary structure analysis and evaluation of free energy landscapes is done by in-house scripts. The transition pathway between native and fibril SAA conformations is derived from the free energy landscape projected on the RMSD to the respective structures. Using Dijkstra’s algorithm (30) as implemented in the MEPSA software, (31) we construct for this purpose a minimum (free) energy pathway between the two corresponding minima, allowing us to identify the barriers among different minima along the pathway. While this pathway may not be the optimal (i.e., the physical) pathway, we found the method in past work (32) reliable and less costly than transition path sampling, (33−35) the string method, (36,37) the kinetic network model, (38) traveling-salesman based automated path searching (TAPS), (39) or other competing approaches. For visualization, we use the PyMOL software. (40)

3. Results and Discussion

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We argue that the computational difficulties in simulating the conversion between native and fibril SAA conformations is alleviated in our variant of Replica Exchange Sampling. In order to support this assumption, we show in Figure 2a the walk of a typical realization of our system along the ladder of replicas. At the start time (t = 0), the physical system sits on a replica, where it is biased with λ = 0.1 toward the folded structure but walks numerous times between this replica and the opposite end-point of the ladder where the physical system is with λmax = 0.08 maximally biased toward the fibril. The average exchange rate between neighboring replicas along the ladder is ∼25%. Monitoring the respective root mean square deviation (RMSD) toward both folded and fibril structure, we show in Figure 2b that this walk through λ-space induces indeed interconversion between the two motifs.

Figure 2

Figure 2. (a) A typical example of a replica walking through λ space starting from a replica where the physical model is initially biased toward the folded SAA structure. While the system walks between a replica with bias toward the folded structure (upper half) and a replica with bias toward the fibril structure (lower half), its configuration changes accordingly. This can be seen in (b), where we show the corresponding time evolution of the RMSD to the native structure (in magenta) and the fibril structure (in green).

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The higher rate of transitions allows for a more exhaustive sampling of the free energy landscape. A measure for the efficiency of our method, and a lower limit on the number of independent configurations sampled at the λ = 0 replica, is the number of walks across the whole ladder, from the replica with maximal bias toward the folded structure to the one with maximal bias toward the fibril, and back. The number of such tunneling events is inverse to the average time needed to cross the ladder (termed by us the tunneling time). The higher the number of tunneling events, the shorter the tunneling time and the more efficient our approach will be. This convergence of the simulation is checked by comparing for two distinct time intervals the free energy landscape projected on the two distances introduced above. Resemblance of the data for these two intervals suggests that the simulation has converged after 10 ns, and therefore, we use the last 90 ns of this specific trajectory for our analysis. In this time span, we find at least four tunneling events with an average tunneling time of ∼27 ns.
While small, the existence of at least a few tunneling events gives us confidence in the reliability of our data at λ = 0, i.e., at a replica where the “physical” model of our system is not biased toward either structure. For instance, we show in Figure 3 the free energy landscape of the SAA1–76 chain projected on the root mean square deviation (RMSD) to either folded or fibril conformation. This landscape is characterized by two prominent basins corresponding to either folded or fibril conformations, which are observed with frequencies of about 10% and 4%, respectively. The interconversion process corresponds to a path connecting the two basins. We have shown in earlier work (32) that the physical path does not necessarily correspond to the ones seen in tunneling events as RET relies on an unphysical dynamic. Hence, in order to find a more realistic pathway, we have used the MEPSA software (31) to determine the minimum energy pathway. This pathway, drawn as a black line in the landscape, proceeds through a series of basins and appears to be thermodynamically reasonable. Note that by construction this minimum energy pathway does not connect specific configurations but bins. Each bin contains a certain number of configurations sampled throughout the simulations. These configurations may differ in their secondary structure or the native contacts seen in either the folded or the fibril structure. The frequency with which the three quantities are observed allows one to identify five distinct regions along the pathway that correlate with the basins and barriers of the landscape. These regions are labeled as A to E in Figure 3, and characteristic conformations for these regions are shown in Figure 4, where in the case of region A (characterized by a high frequency of folded conformations) and region E (rich in fibril-like conformations) these characteristic conformations are superimposed on the respective reference structures. Region C appears to be a transition region spanning a large range of diverse conformations. Note, however, that the barrier height is only between two and three kBT.

Figure 3

Figure 3. Free energy landscape as obtained from RET simulations, with data taken at λ = 0, i.e., where the physical models are not biased by any Go-term. Energies are listed in units of kT. The prospective transition pathway is drawn in black, and the five regions crossed by this path are marked in capital letters.

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Figure 4

Figure 4. Characteristic conformations of of SAA1–76 as seen in each of the five regions (labeled A, B, C, D, and E) identified on the proposed transition pathway. The N-terminus of the chains is colored in blue. Unlike for the transition regions B, C, and D, these conformations superimposed on the respective reference structures for region A (dominated by folded-conformations) and region E (where fibril-like conformations are dominant).

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In order to get insight into the structural changes along the proposed transition pathway, we augment the visual inspection of the dominant conformations by an analysis of the number nNS of contacts found also in the folded structure and of the number nFS of contacts also found in the fibril structure. Here, we define a contact by the condition that the distance between at least one pair of heavy atoms in residues i and j is smaller than the cutoff value of 4.5 Å. If, in addition, the two residues are separated by at least three other residues in the protein sequence, we call this a long-range contact. The two quantities, normalized to one, are shown in Figure 5a. As expected, the number nNS of contacts found also in the folded structure decreases when going from region A, characterized by a low RMSD to the native structure, to region E (which has a low RMSD to the fibril structure). The opposite behavior is seen for the number nFS of fibril-like contacts. However, while there is a steep increase in nFS when going from region C to region E, the corresponding change for the number nNS of contacts seen also in the folded structure appears to be more gradual when going from region A to region C. This picture changes when one considers only long-range contacts seen in the folded structure. This number nLR is also shown in Figure 5a and has a more pronounced behavior. While the number decreases rapidly going from region A to region B and from region C to region E, it changes little between region B and region C. This indicates that the conversion process from the helix bundle of the folded conformations starts with the decay of interhelical contacts, while the transition region C is mainly characterized by the decay of intrahelical contacts, with the contacts found in strand-like conformations forming in region D. Note that we do not show separately the number of long-range fibril-like contacts as by definition contacts involving strands are long-range.

Figure 5

Figure 5. (a) Number of contacts (normalized to one) nNS that are shared with the folded structure as measured in each of the five regions A to E of the transition pathway. The subset of long-range contacts nLR, again normalized to one, is drawn separately. Shown are also the number nFS of contacts also found in the fibril reference structure. In (b), we show the relative frequency with which one of the three characteristic helices of the folded structure, or the two main β-strands of the fibril structure, is observed.

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For a more detailed analysis, we have also shown in Figure 5b the frequencies with which one of the three helices of the folded structure or the two main strands of the fibril structure are found in the five regions. Values are again normalized to one. The three regions of residues 2–27, 32–47, and 50–69 are considered helical if at least 30% of residues have dihedral angles as seen in an α-helix, that is, if the dihedral angle pair (Φ, Ψ) takes values of between [−100°, −67°] and [−40°, −7°]. Similarly, we define the “strandness” of the two segments of residues 6–9 and 18–23 by the requirement that at least 20% of residues in the segment have dihedral angles as in a strand ([−160°, 160°] and ([100°, 100°]). The plots show that the N-terminal helix-I is the first to dissolve, with half of it already gone in region B. The central helix-II starts to decay later, but its propensity is also much decreased in the transition region C and neglectable in regions D and E. The C-terminal helix-III is the last one to decay and observed with substantial frequency even in region E. Note, however, that helix-III covers a region that was not resolved in the fibril structure and well may have transient helicity.
We observe a complementary picture for the two segments that are strand-like in the fibril. Again we have initially a larger frequency for the N-terminal segment of residues 6–9, with only in the transition region C the second segment gaining higher propensity for strand formation. Both the early decay of the first helix and the higher propensity of the N-terminal segment to form strands are consistent with experimental observations and our earlier work that demonstrated the importance of the N-terminal residues 1–11 and the role of helix-I for fibril formation. (8,41−44) Interestingly, we observe in region B a mixture of the helix-broken and helix-weakened conformations, discussed in ref (8), while in region C the conformation either lost its helicity or resembled more the aggregation prone helix-weakened conformations.
The above picture is also supported by the contact maps calculated for each of the five regions that are shown in Figure 6. Note that the coloring does not indicate frequency of contacts but the average distance between residue pairs. As discussed in ref (8), destabilization of the folded structure starts quickly after cleavage of the full-size monomer into a SAA1–76 fragment with a loss of contacts between residues 60–69 on one side and residues 70–76 on the other side. Already in region A are only about 30% of these contacts still existing, and the frequency decreases further to about 17% in regions B and C, before disappearing. As a result, helix-III is more flexible than in the full-sized protein, allowing it to either break up (in helix-broken conformations) or partially dissolve (in helix-weakened conformations), thereby reducing contacts with helix-II. Overall is the loss of native contacts in regions A to C consistent with the process described in ref (8), where the data were derived with a different simulation technique. On the other hand, in region D starts the formation of fibril-stabilizing intrachain salt bridges and contacts between residues 26 and 34, 26 and 46, 29 and 33, and 35 and 43 that have been also described in ref (43). Note, however, that the contact between 29 and 34 in the helix-I to helix-II linker region is already observed with substantial probability in region A and is therefore likely crucial for fibril formation of SAA. The salt bridge between residues 26 and 34 is also formed early, seen in all five regions with about 10–20% probability. Hence, this contact is therefore a bottleneck/condensation point in fibril formation. Note that the contact of 26–46, which competes with the 26–34 contact, is seen in region D with 5% frequency but only with about 1% in the fibril region E. These results are also consistent with earlier work (45) that emphasized the importance of the helix-I to helix-II linker region for fibril formation, albeit we do not see the reported local unfolding of the two helices around this region.

Figure 6

Figure 6. Residue–residue map of the average minimal distance between heavy atoms in a pair of residues, shown for each of the five regions A, B, C, D, and E. Unlike in the transition regions B, C, and D, folded conformations dominate in region A, and fibril-like ones dominate in region E. Residue pairs whose average contact distance is more than 10 Å are excluded. Numbers on the X and Y axis mark residues, and the average distance in Å is given by the color coding shown in the middle row.

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4. Conclusions

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Using a variant of Replica Exchange with Tunneling (RET), we have probed the interconversion of SAA1–76 monomers between the folded structure and the one assumed in the experimentally resolved fibrils. In vivo, this conversion happens at high (micromolar) SAA concentrations and likely will therefore depend on the interaction with neighboring chains. However, modeling the conversion under these conditions is not feasible, and we therefore restrict ourselves to an isolated monomer. Our assumption is that both folded and fibril configurations are prominent local minima in the free energy landscape of the protein and connected by similar transition pathways as occurring under physiological conditions. Hence, the main limitation of our study is our conjecture that the energy landscape is not drastically altered by interactions with other chains changing barrier heights and local minima. The interaction with other chains becomes in this picture an external field that shifts the equilibrium toward the fibril structure. While this picture is an oversimplification, it allows us to propose a conversion mechanism for SAA fibril formation, identifying critical residues and intrachain interactions, that may guide future experiments. Specifically, we find only small free energy barriers (of the order 2–3kBT) separating the folded and fibril structures that can be crossed easily once a critical nucleus for fibril formation is formed. Consistent with our earlier work, (8) we find that the decay of the helix bundle of the folded structure progresses by a loss of interhelical contacts between helix-II and helix-III and helix-I and helix-II, leading to the helix-broken or helix-weakened conformations of ref (8). Dominant on the pathway are the more aggregation prone helix-weakened conformations where the C-terminus of helix-III interacts with the C-terminus of helix-I and increases the flexibility of helix-I. The resulting higher entropy leads to destabilization of helix-I, causing a loss of helicity. Especially important is the release of the first 11 N-terminal residues from helix-I, which then can misfold into strand-like configurations. This strand segment is indeed the first one that appears in the conversion process seen in our RET simulation. We had shown in earlier work (43) that the merging fibril structure is stabilized by intrachain contacts between residues 26 and 34, 29 and 33, and 35 and 43, connecting residues located on the helix-I to helix-II linker in the folded conformation. These contacts indeed appear once the barrier is crossed. Interestingly, the salt bridge between residues 26 and 34 is seen already transiently in early stages of the unfolding of the folded conformation and likely plays a crucial role for fibril formation. This could be tested by mutating one of these two residues to inhibit formation of this salt bridge.

Author Information

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  • Corresponding Authors
    • Fatih Yasar - Department of Chemistry & Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States;  Present Address: Department of Physics Engineering, Hacettepe University, Ankara 06800, Turkey;  Orcidhttps://orcid.org/0000-0003-2216-5263;  Email: [email protected]
    • Miranda S. Sheridan - Department of Chemistry & Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States;  Email: [email protected]
    • Ulrich H. E. Hansmann - Department of Chemistry & Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States;  Email: [email protected]
    • Notes
      The authors declare no competing financial interest.

    Acknowledgments

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    The simulations in this work were done using the SCHOONER cluster of the University of Oklahoma, XSEDE resources allocated under Grant MCB160005, and TACC resources allocated under Grant MCB20016 (both National Science Foundation). We acknowledge financial support from the National Institutes of Health under Grants GM120634 and GM120578. F.Y. also wants to thank the Scientific and Technological Research Council of Turkey (TUBITAK) under the BIDEB programs and the Department of Chemistry and Biochemistry for kind hospitality during the his sabbatical stay at the University of Oklahoma.

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      Rocken Christoph; Shakespeare Ann
      Virchows Archiv : an international journal of pathology (2002), 440 (2), 111-122 ISSN:0945-6317.
      Amyloid is defined as a proteinaceous tissue deposit that shows a typical green birefringence in polarised light after staining with Congo red, the presence of non-branching linear fibrils of indefinite length with an approximate diameter of 10-12 nm and a distinct X-ray diffraction pattern consistent with Pauling's model of a cross-beta fibril. Approximately 45% of generalised amyloidoses are secondary or reactive (AA) amyloidosis. Among the causes of AA amyloidosis are rheumatic diseases, idiopathic diseases, inherited diseases, infectious diseases and malignant tumours. Recent decades have provided significant advances in our understanding of the pathology and pathogenesis of AA amyloidosis. Its pathogenesis is multifactorial involving many variables such as primary structure of the precursor protein, acute phase response, the presence of non-fibril proteins (e.g. amyloid P component, apolipoprotein E, glycosaminoglycans, proteoglycans and basement membrane proteins), receptors, lipid metabolism and proteases. Study of the pathogenesis of AA amyloidosis has provided many insights into the nature of conformational diseases, which may help in the understanding of other members of this particularly heterogeneous group of diseases, such as Alzheimer's disease and transmissible spongiform encephalopathies.
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    5. 5
      Real de Asua, D.; Galvan, J. M.; Filigghedu, M. T.; Trujillo, D.; Costa, R.; Cadinanos, J. Systemic AA amyloidosis: epidemiology, diagnosis, and management. Clin. Epidemiol. 2014, 6, 369,  DOI: 10.2147/CLEP.S39981
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      5
      Systemic AA amyloidosis: epidemiology, diagnosis, and management
      Real de Asua Diego; Costa Ramon; Galvan Jose Maria; Filigheddu Maria Teresa; Trujillo Davinia; Cadinanos Julen
      Clinical epidemiology (2014), 6 (), 369-77 ISSN:1179-1349.
      The term "amyloidosis" encompasses the heterogeneous group of diseases caused by the extracellular deposition of autologous fibrillar proteins. The global incidence of amyloidosis is estimated at five to nine cases per million patient-years. While amyloid light-chain (AL) amyloidosis is more frequent in developed countries, amyloid A (AA) amyloidosis is more common in some European regions and in developing countries. The spectrum of AA amyloidosis has changed in recent decades owing to: an increase in the median age at diagnosis; a percent increase in the frequency of primary AL amyloidosis with respect to the AA type; and a substantial change in the epidemiology of the underlying diseases. Diagnosis of amyloidosis is based on clinical organ involvement and histological evidence of amyloid deposits. Among the many tinctorial characteristics of amyloid deposits, avidity for Congo red and metachromatic birefringence under unidirectional polarized light remain the gold standard. Once the initial diagnosis has been made, the amyloid subtype must be identified and systemic organ involvement evaluated. In this sense, the (123)I-labeled serum amyloid P component scintigraphy is a safe and noninvasive technique that has revolutionized the diagnosis and monitoring of treatment in systemic amyloidosis. It can successfully identify anatomical patterns of amyloid deposition throughout the body and enables not only an initial estimation of prognosis, but also the monitoring of the course of the disease and the response to treatment. Given the etiologic diversity of AA amyloidosis, common therapeutic strategies are scarce. All treatment options should be based upon a greater control of the underlying disease, adequate organ support, and treatment of symptoms. Nevertheless, novel therapeutic strategies targeting the formation of amyloid fibrils and amyloid deposition may generate new expectations for patients with AA amyloidosis.
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    6. 6
      Nguyen, P. H.; Ramamoorthy, A.; Sahoo, B. R.; Zheng, J.; Faller, P.; Straub, J. E.; Dominguez, L.; Shea, J.-E.; Dokholyan, N. V.; De Simone, A. Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer’s Disease, Parkinson’s Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis. Chem. Rev. 2021, 121, 2545– 2647,  DOI: 10.1021/acs.chemrev.0c01122
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      6
      Amyloid Oligomers: A joint experimental/computational perspective on Alzheimer's disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis
      Nguyen, Phuong H.; Ramamoorthy, Ayyalusamy; Sahoo, Bikash R.; Zheng, Jie; Faller, Peter; Straub, John E.; Dominguez, Laura; Shea, Joan-Emma; Dokholyan, Nikolay V.; De Simone, Alfonso; Ma, Buyong; Nussinov, Ruth; Najafi, Saeed; Ngo, Son Tung; Loquet, Antoine; Chiricotto, Mara; Ganguly, Pritam; McCarty, James; Li, Mai Suan; Hall, Carol; Wang, Yiming; Miller, Yifat; Melchionna, Simone; Habenstein, Birgit; Timr, Stepan; Chen, Jiaxing; Hnath, Brianna; Strodel, Birgit; Kayed, Rakez; Lesne, Sylvain; Wei, Guanghong; Sterpone, Fabio; Doig, Andrew J.; Derreumaux, Philippe
      Chemical Reviews (Washington, DC, United States) (2021), 121 (4), 2545-2647CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)
      A review. Protein misfolding and aggregation is obsd. in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by exptl. and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacol. expts. tell us about the accumulation and deposition of the oligomers of the (Aβ, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease (AD), Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, resp., for many years.
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    7. 7
      Cai, L.; De Beer, M. C.; De Beer, F. C.; Van Der Westhuyzen, D. R. Serum amyloid a is a ligand for scavenger receptor class B type I and inhibits high density lipoprotein binding and selective lipid uptake. J. Biol. Chem. 2005, 280, 2954– 2961,  DOI: 10.1074/jbc.M411555200
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      7
      Serum Amyloid A Is a Ligand for Scavenger Receptor Class B Type I and Inhibits High Density Lipoprotein Binding and Selective Lipid Uptake
      Cai, Lei; de Beer, Maria C.; de Beer, Frederick C.; van der Westhuyzen, Deneys R.
      Journal of Biological Chemistry (2005), 280 (4), 2954-2961CODEN: JBCHA3; ISSN:0021-9258. (American Society for Biochemistry and Molecular Biology)
      Serum amyloid A is an acute phase protein that is carried in the plasma largely as an apolipoprotein of high d. lipoprotein (HDL). In this study we investigated whether SAA is a ligand for the HDL receptor, scavenger receptor class B type I (SR-BI), and how SAA may influence SR-BI-mediated HDL binding and selective cholesteryl ester uptake. Studies using Chinese hamster ovary cells expressing SR-BI showed that 125I-labeled SAA, both in lipid-free form and in reconstituted HDL particles, functions as a high affinity ligand for SR-BI. SAA also bound with high affinity to the hepatocyte cell line, HepG2. Alexa-labeled SAA was shown by fluorescence confocal microscopy to be internalized by cells in a SR-BI-dependent manner. To assess how SAA assocn. with HDL influences HDL interaction with SR-BI, SAA-contg. HDL was isolated from mice overexpressing SAA through adenoviral gene transfer. SAA presence on HDL had little effect on HDL binding to SR-BI but decreased (30-50%) selective cholesteryl ester uptake. Lipid-free SAA, unlike lipid-free apoA-I, was an effective inhibitor of both SR-BI-dependent binding and selective cholesteryl ester uptake of HDL. We have concluded that SR-BI plays a key role in SAA metab. through its ability to interact with and internalize SAA and, further, that SAA influences HDL cholesterol metab. through its inhibitory effects on SR-BI-mediated selective lipid uptake.
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    8. 8
      Wang, W.; Khatua, P.; Hansmann, U. H. Cleavage, Downregulation, and Aggregation of Serum Amyloid A. J. Phys. Chem. B 2020, 124, 1009– 1019,  DOI: 10.1021/acs.jpcb.9b10843
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      8
      Cleavage, downregulation, and aggregation of serum amyloid A
      Wang, Wenhua; Khatua, Prabir; Hansmann, Ulrich H. E.
      Journal of Physical Chemistry B (2020), 124 (6), 1009-1019CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
      Various diseases cause overexpression of the serum amyloid A (SAA) protein, which in some cases, but not in all cases, leads to amyloidosis as a secondary disease. Response to the overexpression involves dissocn. of the SAA hexamer and subsequent cleavage of the released monomers, most commonly yielding fragments SAA1-76 of the full-sized SAA1-104. We report results from mol. dynamic simulations that probe the role of this cleavage for downregulating the activity and concn. of SAA. We propose a mechanism that relies on two elements. First, the probability to assemble into hexamers is lower for the fragments than it is for the full-sized protein. Second, unlike other fragments, SAA1-76 can switch between two distinct configurations. The first kind is easy to proteolyze (allowing a fast redn. of the SAA concn.) but prone to aggregation, whereas the situation is opposite for the second kind. If the time scale for amyloid formation is longer than the one for proteolysis, the aggregation-prone species dominates. However, if environmental conditions such as low pH increases the risk of amyloid formation, the ensemble shifts toward the more protected form. We speculate that SAA amyloidosis is a failure of this switching mechanism leading to accumulation of the aggregation-prone species and subsequent amyloid formation.
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    9. 9
      Yaşar, F.; Bernhardt, N. A.; Hansmann, U. H. Replica-exchange-with-tunneling for fast exploration of protein landscapes. J. Chem. Phys. 2015, 143, 224102,  DOI: 10.1063/1.4936968
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      9
      Replica-exchange-with-tunneling for fast exploration of protein landscapes
      Yasar, Fatih; Bernhardt, Nathan A.; Hansmann, Ulrich H. E.
      Journal of Chemical Physics (2015), 143 (22), 224102/1-224102/6CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
      While the use of replica-exchange mol. dynamics in protein simulations has become ubiquitous, its utility is limited in many practical applications. The authors propose to overcome some shortcomings that hold back its use in settings such as multi-scale or explicit solvent simulations by integrating ideas of hybrid MC/MD into the replica-exchange protocol. This Replica-Exchange-with-Tunneling method is tested by simulating the Trp-cage protein, a system often used in mol. biophysics for testing sampling techniques. (c) 2015 American Institute of Physics.
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    10. 10
      Bernhardt, N. A.; Xi, W.; Wang, W.; Hansmann, U. H. Simulating Protein Fold Switching by Replica Exchange with Tunneling. J. Chem. Theory Comput. 2016, 12, 5656– 5666,  DOI: 10.1021/acs.jctc.6b00826
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      10
      Simulating Protein Fold Switching by Replica Exchange with Tunneling
      Bernhardt, Nathan A.; Xi, Wenhui; Wang, Wei; Hansmann, Ulrich H. E.
      Journal of Chemical Theory and Computation (2016), 12 (11), 5656-5666CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
      Recent expts. suggest that an amino acid sequence encodes not only the native fold of a protein but also other forms essential for its function, or important during folding or assocn. These various forms populate a multi-funnel folding and assocn. landscape where mutations, changes in environment, or interaction with other mols. switch between the encoded folds. Here, the authors introduced replica-exchange-with-tunneling as a way to simulate efficiently switching between distinct folds of proteins and protein aggregates. The correctness and efficiency of this approach was demonstrated in a series of simulations covering a wide range of proteins, from a small 11-residue large designed peptide up to 2 56-residue large mutants of the A and B domain of streptococcal protein G.
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    11. 11
      Zhang, H.; Xi, W.; Hansmann, U. H.; Wei, Y. Fibril-Barrel Transitions in Cylindrin Amyloids. J. Chem. Theory Comput. 2017, 13, 3936– 3944,  DOI: 10.1021/acs.jctc.7b00383
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      11
      Fibril-Barrel Transitions in Cylindrin Amyloids
      Zhang, Huiling; Xi, Wenhui; Hansmann, Ulrich H. E.; Wei, Yanjie
      Journal of Chemical Theory and Computation (2017), 13 (8), 3936-3944CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
      We introduce Replica-Exchange-with-Tunneling (RET) simulations as a tool for studies of the conversion between polymorph amyloids. For the eleven-residue amyloid- forming cylindrin peptide we show that this technique allows for a more efficient sampling of the formation and interconversion between fibril-like and barrel-like assemblies. We describe a protocol for optimized anal. of RET simulations that allows us to propose a mechanism for formation and interconversion between various cylindrin assemblies. Esp., we show that an interchain salt bridge between residues K3 and D7 is crucial for formation of the barrel structure.
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    12. 12
      Bernhardt, N. A.; Hansmann, U. H. Multifunnel Landscape of the Fold-Switching Protein RfaH-CTD. J. Phys. Chem. B 2018, 122, 1600– 1607,  DOI: 10.1021/acs.jpcb.7b11352
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      12
      Multifunnel Landscape of the Fold-Switching Protein RfaH-CTD
      Bernhardt, Nathan A.; Hansmann, Ulrich H. E.
      Journal of Physical Chemistry B (2018), 122 (5), 1600-1607CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
      Proteins such as transcription factor RfaH can change biol. function by switching between distinct 3-dimensional folds. RfaH regulates transcription if the C-terminal domain folds into a double helix bundle, and promotes translation when this domain assumes a β-barrel form. This fold-switch has been also obsd. for the isolated C-terminal domain (RfaH-CTD) and was studied here with a variant of the RET (Replica-Exchange-with-Tunneling) approach recently introduced by us. We used the enhanced sampling properties of this technique to map the free energy landscape of RfaH-CTD and to propose a mechanism for the conversion process.
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    13. 13
      Lu, J.; Yu, Y.; Zhu, I.; Cheng, Y.; Sun, P. D. Structural mechanism of serum amyloid A-mediated inflammatory amyloidosis. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 5189– 5194,  DOI: 10.1073/pnas.1322357111
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      13
      Structural mechanism of serum amyloid A-mediated inflammatory amyloidosis
      Lu, Jinghua; Yu, Yadong; Zhu, Iowis; Cheng, Yifan; Sun, Peter D.
      Proceedings of the National Academy of Sciences of the United States of America (2014), 111 (14), 5189-5194CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      Serum amyloid A (SAA) represents an evolutionarily conserved family of inflammatory acute-phase proteins. It is also a major constituent of secondary amyloidosis. Here, to understand its function and structural transition to amyloid, the authors detd. a structure of human SAA isoform 1.1 (SAA1.1) in 2 crystal forms, representing a prototypic member of the family. Native SAA1.1 exists as a hexamer, with subunits displaying a unique 4-helix bundle fold stabilized by its long C-terminal tail. Structure-based mutational studies revealed 2 pos.-charge clusters, near the center and apex of the hexamer, that were involved in SAA assocn. with heparin. The binding of high-d. lipoprotein involved only the apex region of SAA and could be inhibited by heparin. Peptide amyloid formation assays identified N-terminal helixes 1 and 3 as amyloidogenic peptides of SAA1.1. Both peptides were secluded in the hexameric structure of SAA1.1, suggesting that native SAA is non-pathogenic. Furthermore, dissocn. of the SAA hexamer appeared insufficient to initiate the amyloidogenic transition, and proteolytic cleavage or removal of the C-terminal tail of SAA resulted in the formation of various-sized structural aggregates contg. ∼5-nm regular repeating protofibril-like units. The combined structural and functional studies provided mechanistic insights into the pathogenic contribution of glycosaminoglycan in SAA1.1-mediated AA amyloid formation.
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    14. 14
      Liberta, F.; Loerch, S.; Rennegarbe, M.; Schierhorn, A.; Westermark, P.; Westermark, G. T.; Hazenberg, B. P.; Grigorieff, N.; Fändrich, M.; Schmidt, M. Cryo-EM fibril structures from systemic AA amyloidosis reveal the species complementarity of pathological amyloids. Nature Commun. 2019, 10, 1104,  DOI: 10.1038/s41467-019-09033-z
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      14
      Cryo-EM fibril structures from systemic AA amyloidosis reveal the species complementarity of pathological amyloids
      Liberta Falk; Rennegarbe Matthies; Fandrich Marcus; Schmidt Matthias; Loerch Sarah; Grigorieff Nikolaus; Schierhorn Angelika; Westermark Per; Westermark Gunilla T; Hazenberg Bouke P C
      Nature communications (2019), 10 (1), 1104 ISSN:.
      Systemic AA amyloidosis is a worldwide occurring protein misfolding disease of humans and animals. It arises from the formation of amyloid fibrils from the acute phase protein serum amyloid A. Here, we report the purification and electron cryo-microscopy analysis of amyloid fibrils from a mouse and a human patient with systemic AA amyloidosis. The obtained resolutions are 3.0 ÅA and 2.7 ÅA for the murine and human fibril, respectively. The two fibrils differ in fundamental properties, such as presence of right-hand or left-hand twisted cross-β sheets and overall fold of the fibril proteins. Yet, both proteins adopt highly similar β-arch conformations within the N-terminal ~21 residues. Our data demonstrate the importance of the fibril protein N-terminus for the stability of the analyzed amyloid fibril morphologies and suggest strategies of combating this disease by interfering with specific fibril polymorphs.
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    15. 15
      Bansal, A.; Schmidt, M.; Rennegarbe, M.; Haupt, C.; Liberta, F.; Stecher, S.; Puscalau-Girtu, I.; Biedermann, A.; Fändrich, M. AA amyloid fibrils from diseased tissue are structurally different from in vitro formed SAA fibrils. Nature Commun. 2021, 12, 1013,  DOI: 10.1038/s41467-021-21129-z
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      15
      AA amyloid fibrils from diseased tissue are structurally different from in vitro formed SAA fibrils
      Bansal, Akanksha; Schmidt, Matthias; Rennegarbe, Matthies; Haupt, Christian; Liberta, Falk; Stecher, Sabrina; Puscalau-Girtu, Ioana; Biedermann, Alexander; Faendrich, Marcus
      Nature Communications (2021), 12 (1), 1013CODEN: NCAOBW; ISSN:2041-1723. (Nature Research)
      Systemic AA amyloidosis is a world-wide occurring protein misfolding disease of humans and animals. It arises from the formation of amyloid fibrils from serum amyloid A (SAA) protein. Using cryo electron microscopy we here show that amyloid fibrils which were purified from AA amyloidotic mice are structurally different from fibrils formed from recombinant SAA protein in vitro. Ex vivo amyloid fibrils consist of fibril proteins that contain more residues within their ordered parts and possess a higher β-sheet content than in vitro fibril proteins. They are also more resistant to proteolysis than their in vitro formed counterparts. These data suggest that pathogenic amyloid fibrils may originate from proteolytic selection, allowing specific fibril morphologies to proliferate and to cause damage to the surrounding tissue.
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    16. 16
      Fukunishi, H.; Watanabe, O.; Takada, S. On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: Application to protein structure prediction. J. Chem. Phys. 2002, 116, 9058– 9067,  DOI: 10.1063/1.1472510
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      16
      On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: Application to protein structure prediction
      Fukunishi, Hiroaki; Watanabe, Osamu; Takada, Shoji
      Journal of Chemical Physics (2002), 116 (20), 9058-9067CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
      Motivated by the protein structure prediction problem, we develop two variants of the Hamiltonian replica exchange methods (REMs) for efficient configuration sampling, (1) the scaled hydrophobicity REM and (2) the phantom chain REM, and compare their performance with the ordinary REM. We first point out that the ordinary REM has a shortage for the application to large systems such as biomols. and that the Hamiltonian REM, an alternative formulation of the REM, can give a remedy for it. We then propose two examples of the Hamiltonian REM that are suitable for a coarse-grained protein model. (1) The scaled hydrophobicity REM preps. replicas that are characterized by various strengths of hydrophobic interaction. The strongest interaction that mimics aq. soln. environment makes proteins folding, while weakened hydrophobicity unfolds proteins as in org. solvent. Exchange between these environments enables proteins to escape from misfolded traps and accelerate conformational search. This resembles the roles of mol. chaperone that assist proteins to fold in vivo. (2) The phantom chain REM uses replicas that allow various degrees of at. overlaps. By allowing at. overlap in some of replicas, the peptide chain can cross over itself, which can accelerate conformation sampling. Using a coarse-gained model we developed, we compute equil. probability distributions for poly-alanine 16-mer and for a small protein by these REMs and compare the accuracy of the results. We see that the scaled hydrophobicity REM is the most efficient method among the three REMs studied.
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    17. 17
      Kwak, W.; Hansmann, U. H. Efficient sampling of protein structures by model hopping. Phys. Rev. Lett. 2005, 95, 138102,  DOI: 10.1103/PhysRevLett.95.138102
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      17
      Efficient Sampling of Protein Structures by Model Hopping
      Kwak, Wooseop; Hansmann, Ulrich H. E.
      Physical Review Letters (2005), 95 (13), 138102/1-138102/4CODEN: PRLTAO; ISSN:0031-9007. (American Physical Society)
      A review. We introduce a novel simulation method, model hopping, that enhances sampling of low-energy configurations in complex systems. The approach is illustrated for a protein-folding problem. Thermodn. quantities of proteins with up to 46 residues are evaluated from all-atom simulations with this method.
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    18. 18
      Hansmann, U. H. Parallel tempering algorithm for conformational studies of biological molecules. Chem. Phys. Lett. 1997, 281, 140– 150,  DOI: 10.1016/S0009-2614(97)01198-6
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      18
      Parallel tempering algorithm for conformational studies of biological molecules
      Hansmann, Ulrich H. E.
      Chemical Physics Letters (1997), 281 (1,2,3), 140-150CODEN: CHPLBC; ISSN:0009-2614. (Elsevier Science B.V.)
      The effectiveness of a new algorithm, parallel tempering, is studied for numerical simulations of biol. mols. These mols. suffer from a rough energy landscape. The resulting slowing down in numerical simulations is overcome by the new method. This is demonstrated by performing simulations with high statistics for one of the simplest peptides, Met-enkephalin. The numerical effectiveness of the new technique was found to be much better than traditional methods and is comparable to sophisticated methods like generalized ensemble techniques.
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    19. 19
      Best, R. B.; Zhu, X.; Shim, J.; Lopes, P. E.; Mittal, J.; Feig, M.; MacKerell, A. D. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone ϕ, ψ and side-chain χ1 and χ2 Dihedral Angles. J. Chem. Theory Comput. 2012, 8, 3257– 3273,  DOI: 10.1021/ct300400x
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      19
      Optimization of the Additive CHARMM All-Atom Protein Force Field Targeting Improved Sampling of the Backbone .vphi., ψ and Side-Chain χ1 and χ2 Dihedral Angles
      Best, Robert B.; Zhu, Xiao; Shim, Jihyun; Lopes, Pedro E. M.; Mittal, Jeetain; Feig, Michael; MacKerell, Alexander D.
      Journal of Chemical Theory and Computation (2012), 8 (9), 3257-3273CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
      While the quality of the current CHARMM22/CMAP additive force field for proteins has been demonstrated in a large no. of applications, limitations in the model with respect to the equil. between the sampling of helical and extended conformations in folding simulations have been noted. To overcome this, as well as make other improvements in the model, we present a combination of refinements that should result in enhanced accuracy in simulations of proteins. The common (non-Gly, -Pro) backbone CMAP potential has been refined against exptl. soln. NMR data for weakly structured peptides, resulting in a rebalancing of the energies of the α-helix and extended regions of the Ramachandran map, correcting the α-helical bias of CHARMM22/CMAP. The Gly and Pro CMAPs have been refitted to more accurate quantum-mech. energy surfaces. Side-chain torsion parameters have been optimized by fitting to backbone-dependent quantum-mech. energy surfaces, followed by addnl. empirical optimization targeting NMR scalar couplings for unfolded proteins. A comprehensive validation of the revised force field was then performed against a collection of exptl. data: (i) comparison of simulations of eight proteins in their crystal environments with crystal structures; (ii) comparison with backbone scalar couplings for weakly structured peptides; (iii) comparison with NMR residual dipolar couplings and scalar couplings for both backbone and side-chains in folded proteins; (iv) equil. folding of mini-proteins. The results indicate that the revised CHARMM 36 parameters represent an improved model for modeling and simulation studies of proteins, including studies of protein folding, assembly, and functionally relevant conformational changes.
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    20. 20
      Jorgensen, 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
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      Comparison of simple potential functions for simulating liquid water
      Jorgensen, 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.
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      Pettersen, E. F.; Goddard, T. D.; Huang, C. C.; Couch, G. S.; Greenblatt, D. M.; Meng, E. C.; Ferrin, T. E. UCSF Chimera - A visualization system for exploratory research and analysis. J. Comput. Chem. 2004, 25, 1605– 1612,  DOI: 10.1002/jcc.20084
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      UCSF Chimera-A visualization system for exploratory research and analysis
      Pettersen, Eric F.; Goddard, Thomas D.; Huang, Conrad C.; Couch, Gregory S.; Greenblatt, Daniel M.; Meng, Elaine C.; Ferrin, Thomas E.
      Journal of Computational Chemistry (2004), 25 (13), 1605-1612CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
      The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale mol. assemblies such as viral coats, and Collab., which allows researchers to share a Chimera session interactively despite being at sep. locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and assocd. structures; ViewDock, for screening docked ligand orientations; Movie, for replaying mol. dynamics trajectories; and Vol. Viewer, for display and anal. of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/.
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      Noel, J. K.; Whitford, P. C.; Sanbonmatsu, K. Y.; Onuchic, J. N. SMOG@ctbp: Simplified deployment of structure-based models in GROMACS. Nucleic Acids Res. 2010, 38, W657,  DOI: 10.1093/nar/gkq498
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      SMOG@ctbp: simplified deployment of structure-based models in GROMACS
      Noel, Jeffrey K.; Whitford, Paul C.; Sanbonmatsu, Karissa Y.; Onuchic, Jose N.
      Nucleic Acids Research (2010), 38 (Web Server), W657-W661CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
      Mol. dynamics simulations with coarse-grained and/or simplified Hamiltonians are an effective means of capturing the functionally important long-time and large-length scale motions of proteins and RNAs. Structure-based Hamiltonians, simplified models developed from the energy landscape theory of protein folding, have become a std. tool for investigating biomol. dynamics. SMOG@ctbp is an effort to simplify the use of structure-based models. The purpose of the web server is two fold. First, the web tool simplifies the process of implementing a well-characterized structure-based model on a state-of-the-art, open source, mol. dynamics package, GROMACS. Second, the tutorial-like format helps speed the learning curve of those unfamiliar with mol. dynamics. A web tool user is able to upload any multi-chain biomol. system consisting of std. RNA, DNA and amino acids in PDB format and receive as output all files necessary to implement the model in GROMACS. Both Cα and all-atom versions of the model are available. SMOG@ctbp resides at http://smog.ucsd.edu.
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      Zhang, W.; Chen, J. Accelerate sampling in atomistic energy landscapes using topology-based coarse-grained models. J. Chem. Theory Comput. 2014, 10, 918– 923,  DOI: 10.1021/ct500031v
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      Accelerate Sampling in Atomistic Energy Landscapes Using Topology-Based Coarse-Grained Models
      Zhang, Weihong; Chen, Jianhan
      Journal of Chemical Theory and Computation (2014), 10 (3), 918-923CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
      We describe a multiscale enhanced sampling (MSES) method where efficient topol.-based coarse-grained models are coupled with all-atom ones to enhance the sampling of atomistic protein energy landscape. The bias from the coupling is removed by Hamiltonian replica exchange, thus allowing one to benefit simultaneously from faster transitions of coarse-grained modeling and accuracy of atomistic force fields. The method is demonstrated by calcg. the conformational equil. of several small but nontrivial β-hairpins with varied stabilities.
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      Moritsugu, K.; Terada, T.; Kidera, A. Scalable free energy calculation of proteins via multiscale essential sampling. J. Chem. Phys. 2010, 133, 224105,  DOI: 10.1063/1.3510519
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      Scalable free energy calculation of proteins via multiscale essential sampling
      Moritsugu, Kei; Terada, Tohru; Kidera, Akinori
      Journal of Chemical Physics (2010), 133 (22), 224105/1-224105/6CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
      A multiscale simulation method, multiscale essential sampling (MSES), is proposed for calcg. free energy surface of proteins in a sizable dimensional space with good scalability. In MSES, the configurational sampling of a full-dimensional model is enhanced by coupling with the accelerated dynamics of the essential degrees of freedom. Applying the Hamiltonian exchange method to MSES can remove the biasing potential from the coupling term, deriving the free energy surface of the essential degrees of freedom. The form of the coupling term ensures good scalability in the Hamiltonian exchange. As a test application, the free energy surface of the folding process of a mini-protein, chignolin, was calcd. in the continuum solvent model. Results agreed with the free energy surface derived from the multi-canonical simulation. Significantly improved scalability with the MSES method was clearly shown in the free energy calcn. of chignolin in explicit solvent, which was achieved without increasing the no. of replicas in the Hamiltonian exchange. (c) 2010 American Institute of Physics.
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      Hess, B.; Kutzner, C.; Van Der Spoel, D.; Lindahl, E. GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theory Comput. 2008, 4, 435– 447,  DOI: 10.1021/ct700301q
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      GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation
      Hess, Berk; Kutzner, Carsten; van der Spoel, David; Lindahl, Erik
      Journal of Chemical Theory and Computation (2008), 4 (3), 435-447CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
      Mol. simulation is an extremely useful, but computationally very expensive tool for studies of chem. and biomol. systems. Here, we present a new implementation of our mol. simulation toolkit GROMACS which now both achieves extremely high performance on single processors from algorithmic optimizations and hand-coded routines and simultaneously scales very well on parallel machines. The code encompasses a minimal-communication domain decompn. algorithm, full dynamic load balancing, a state-of-the-art parallel constraint solver, and efficient virtual site algorithms that allow removal of hydrogen atom degrees of freedom to enable integration time steps up to 5 fs for atomistic simulations also in parallel. To improve the scaling properties of the common particle mesh Ewald electrostatics algorithms, we have in addn. used a Multiple-Program, Multiple-Data approach, with sep. node domains responsible for direct and reciprocal space interactions. Not only does this combination of algorithms enable extremely long simulations of large systems but also it provides that simulation performance on quite modest nos. of std. cluster nodes.
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      Hess, B. P-LINCS: A parallel linear constraint solver for molecular simulation. J. Chem. Theory Comput. 2008, 4, 116– 122,  DOI: 10.1021/ct700200b
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      P-LINCS: A Parallel Linear Constraint Solver for Molecular Simulation
      Hess, Berk
      Journal 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.
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      Swope, W. C.; Andersen, H. C.; Berens, P. H.; Wilson, K. R. A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: Application to small water clusters. J. Chem. Phys. 1982, 76, 637– 649,  DOI: 10.1063/1.442716
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      A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: Application to small water clusters
      Swope, William C.; Andersen, Hans C.; Berens, Peter H.; Wilson, Kent R.
      Journal of Chemical Physics (1982), 76 (1), 637-49CODEN: JCPSA6; ISSN:0021-9606.
      A mol. dynamics computer simulation method is described for calcg. equil. consts. for the formation of phys. clusters of mols. The method is based on Hill's formal theory of phys. clusters. A mol. dynamics calcn. is used to calc. the av. potential energy of a cluster of mols. as a function of temp., and the equil. consts. are calcd. from the integral of the energy with respect to reciprocal temp. The method is illustrated by calcns. of the equil. consts. for the formation of clusters of 2-5 water mols. that interact with each other by an intermol. potential devised by Watts. The method is compared with other procedures for calcg. the thermodn. properties of clusters.
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      Bussi, G.; Donadio, D.; Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 2007, 126, 014101,  DOI: 10.1063/1.2408420
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      Canonical sampling through velocity rescaling
      Bussi, Giovanni; Donadio, Davide; Parrinello, Michele
      Journal of Chemical Physics (2007), 126 (1), 014101/1-014101/7CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
      The authors present a new mol. dynamics algorithm for sampling the canonical distribution. In this approach the velocities of all the particles are rescaled by a properly chosen random factor. The algorithm is formally justified and it is shown that, in spite of its stochastic nature, a quantity can still be defined that remains const. during the evolution. In numerical applications this quantity can be used to measure the accuracy of the sampling. The authors illustrate the properties of this new method on Lennard-Jones and TIP4P water models in the solid and liq. phases. Its performance is excellent and largely independent of the thermostat parameter also with regard to the dynamic properties.
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      McGibbon, R. T.; Beauchamp, K. A.; Harrigan, M. P.; Klein, C.; Swails, J. M.; Hernández, C. X.; Schwantes, C. R.; Wang, L. P.; Lane, T. J.; Pande, V. S. MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories. Biophys. J. 2015, 109, 1528– 1532,  DOI: 10.1016/j.bpj.2015.08.015
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      MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories
      McGibbon, Robert T.; Beauchamp, Kyle A.; Harrigan, Matthew P.; Klein, Christoph; Swails, Jason M.; Hernandez, Carlos X.; Schwantes, Christian R.; Wang, Lee-Ping; Lane, Thomas J.; Pande, Vijay S.
      Biophysical Journal (2015), 109 (8), 1528-1532CODEN: BIOJAU; ISSN:0006-3495. (Cell Press)
      As mol. dynamics (MD) simulations continue to evolve into powerful computational tools for studying complex biomol. systems, the necessity of flexible and easy-to-use software tools for the anal. of these simulations is growing. We have developed MDTraj, a modern, lightwt., and fast software package for analyzing MD simulations. MDTraj reads and writes trajectory data in a wide variety of commonly used formats. It provides a large no. of trajectory anal. capabilities including minimal root-mean-square-deviation calcns., secondary structure assignment, and the extn. of common order parameters. The package has a strong focus on interoperability with the wider scientific Python ecosystem, bridging the gap between MD data and the rapidly growing collection of industry-std. statistical anal. and visualization tools in Python. MDTraj is a powerful and user-friendly software package that simplifies the anal. of MD data and connects these datasets with the modern interactive data science software ecosystem in Python.
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      Marcos-Alcalde, I.; Setoain, J.; Mendieta-Moreno, J. I.; Mendieta, J.; Gómez-Puertas, P. MEPSA: minimum energy pathway analysis for energy landscapes. Bioinformatics 2015, 31, 3853– 3855,  DOI: 10.1093/bioinformatics/btv453
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      MEPSA: minimum energy pathway analysis for energy landscapes
      Marcos-Alcalde, Inigo; Setoain, Javier; Mendieta-Moreno, Jesus I.; Mendieta, Jesus; Gomez-Puertas, Paulino
      Bioinformatics (2015), 31 (23), 3853-3855CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
      Summary: From conformational studies to atomistic descriptions of enzymic reactions, potential and free energy landscapes can be used to describe biomol. systems in detail. However, extg. the relevant data of complex 3D energy surfaces can sometimes be laborious. In this article, we present MEPSA (Min. Energy Path Surface Anal.), a cross-platform user friendly tool for the anal. of energy landscapes from a transition state theory perspective. Some of its most relevant features are: identification of all the barriers and min. of the landscape at once, description of maxima edge profiles, detection of the lowest energy path connecting two min. and generation of transition state theory diagrams along these paths. In addn. to a built-in plotting system, MEPSA can save most of the generated data into easily parseable text files, allowing more versatile uses of MEPSA's output such as the generation of mol. dynamics restraints from a calcd. path.
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      Khatua, P.; Ray, A. J.; Hansmann, U. H. Bifurcated Hydrogen Bonds and the Fold Switching of Lymphotactin. J. Phys. Chem. B 2020, 124, 6555– 6564,  DOI: 10.1021/acs.jpcb.0c04565
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      Bifurcated Hydrogen Bonds and the Fold Switching of Lymphotactin
      Khatua, Prabir; Ray, Alan J.; Hansmann, Ulrich H. E.
      Journal of Physical Chemistry B (2020), 124 (30), 6555-6564CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
      Lymphotactin (Ltn) exists under physiol. conditions in an equil. between two interconverting structures with distinct biol. functions. Using replica-exchange-with-tunneling, we study the conversion between the 2-folds. Unlike previously proposed, we find that the fold switching does not require unfolding of lymphotactin but proceeds through a series of intermediates that remain partially structured. This process relies on two bifurcated hydrogen bonds that connect the β2 and β3 strands and ease the transition between the hydrogen bond pattern by which the central three-stranded β-sheet in the two forms differs.
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      Bolhuis, P. G.; Chandler, D.; Dellago, C.; Geissler, P. L. Transition Path Sampling: Throwing Ropes over Rough Mountain Passes, in the Dark. Annu. Rev. Phys. Chem. 2002, 53, 291– 318,  DOI: 10.1146/annurev.physchem.53.082301.113146
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      Transition path sampling: throwing ropes over rough mountain passes, in the dark
      Bolhuis, Peter G.; Chandler, David; Dellago, Christoph; Geissler, Phillip L.
      Annual Review of Physical Chemistry (2002), 53 (), 291-318CODEN: ARPLAP; ISSN:0066-426X. (Annual Reviews Inc.)
      A review is given of the concepts and methods of transition path sampling. These methods allow computational studies of rare events without requiring prior knowledge of mechanisms, reaction coordinates, and transition states. Based upon a statistical mechanics of trajectory space, they provide a perspective with which time dependent phenomena, even for systems driven far from equil., can be examd. with the same types of importance sampling tools that in the past have been applied so successfully to static equil. properties.
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      Dellago, C.; Bolhuis, P. G.; Csajka, F. S.; Chandler, D. Transition Path Sampling and the Calculation of Rate Constants. J. Chem. Phys. 1998, 108, 1964– 1977,  DOI: 10.1063/1.475562
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      Transition path sampling and the calculation of rate constants
      Dellago, Christoph; Bolhuis, Peter G.; Csajka, Felix S.; Chandler, David
      Journal of Chemical Physics (1998), 108 (5), 1964-1977CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
      We have developed a method to study transition pathways for rare events in complex systems. The method can be used to det. rate consts. for transitions between stable states by turning the calcn. of reactive flux correlation functions into the computation of an isomorphic reversible work. In contrast to previous dynamical approaches, the method relies neither on prior knowledge nor on explicit specification of transition states. Rather, it provides an importance sampling from which transition states can be characterized statistically. A simple model is analyzed to illustrate the methodol.
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      Dellago, C.; Bolhuis, P. G.; Chandler, D. Efficient Transition Path Sampling: Application to Lennard-Jones Cluster Rearrangements. J. Chem. Phys. 1998, 108, 9236– 9245,  DOI: 10.1063/1.476378
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      Efficient transition path sampling: Application to Lennard-Jones cluster rearrangements
      Dellago, Christoph; Bolhuis, Peter G.; Chandler, David
      Journal of Chemical Physics (1998), 108 (22), 9236-9245CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
      We develop an efficient Monte-Carlo algorithm to sample an ensemble of stochastic transition paths between stable states. In our description, paths are represented by chains of states linked by Markovian transition probabilities. Rate consts. and mechanisms characterizing the transition may be detd. from the path ensemble. We have previously devised several algorithms for sampling the path ensemble. For these algorithms, the numerical effort scales with the square of the path length. In the new simulation scheme, the required computation scales linearly with the length of the transition path. This improved efficiency allows the calcn. of rate consts. in complex mol. systems. As an example, we study rearrangement processes in a cluster consisting of seven Lennard-Jones particles in two dimensions. Using a quenching technique we are able to identify the relevant transition mechanisms and to locate the related transition states. We furthermore calc. transition rate consts. for various isomerization processes.
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      Weinan, E.; Ren, W.; Vanden-Eijnden, E. String Method for the Study of Rare Events. Phys. Rev. B 2002, 66, 052301,  DOI: 10.1103/PhysRevB.66.052301
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      Maragliano, L.; Fischer, A.; Vanden-Eijnden, E.; Ciccotti, G. String Method in Collective Variables: Minimum Free Energy Paths and Isocommittor Surfaces. J. Chem. Phys. 2006, 125, 024106,  DOI: 10.1063/1.2212942
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      String method in collective variables: Minimum free energy paths and isocommittor surfaces
      Maragliano, Luca; Fischer, Alexander; Vanden-Eijnden, Eric; Ciccotti, Giovanni
      Journal of Chemical Physics (2006), 125 (2), 024106/1-024106/15CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
      A computational technique is proposed which combines the string method with a sampling technique to det. min. free energy paths. The technique only requires to compute the mean force and another conditional expectation locally along the string, and therefore can be applied even if the no. of collective variables kept in the free energy calcn. is large. This is in contrast with other free energy sampling techniques which aim at mapping the full free energy landscape and whose cost increases exponentially with the no. of collective variables kept in the free energy. Provided that the no. of collective variables is large enough, the new technique captures the mechanism of transition in that it allows to det. the committor function for the reaction and, in particular, the transition state region. The new technique is illustrated on the example of alanine dipeptide, in which we compute the min. free energy path for the isomerization transition using either two or four dihedral angles as collective variables. It is shown that the mechanism of transition can be captured using the four dihedral angles, but it cannot be captured using only two of them.
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      Unarta, I. C.; Zhu, L.; Tse, C. K. M.; Cheung, P. P.-H.; Yu, J.; Huang, X. Molecular Mechanisms of RNA Polymerase II Transcription Elongation Elucidated by Kinetic Network Models. Curr. Opin. Struct. Biol. 2018, 49, 54– 62,  DOI: 10.1016/j.sbi.2018.01.002
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      Molecular mechanisms of RNA polymerase II transcription elongation elucidated by kinetic network models
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      In systemic amyloidosis, serum amyloid A (SAA) fibril deposits cause widespread damages to tissues and organs that eventually may lead to death. A therapeutically intervention therefore has either to dissolve these fibrils or inhibit their formation. However, only recently has the human SAA fibril structure been resolved at a resoln. that is sufficient for development of drug candidates. Here, we use mol. dynamic simulations to probe the factors that modulate the stability of this fibril model. Our simulations suggest that fibril formation starts with the stacking of two misfolded monomers into metastable dimers, with the stacking depending on the N-terminal amyloidogenic regions of different chains forming anchors. The resulting dimers pack in a second step into a 2-fold two-layer tetramer that is stable enough to nucleate fibril formation. The stability of the initial dimers is enhanced under acidic conditions by a strong salt bridge and side-chain hydrogen bond network in the C-terminal cavity (residues 23-51) but is not affected by the presence of the disordered C-terminal tail.
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      Structural Basis for Lipid Binding and Function by an Evolutionarily Conserved Protein, Serum Amyloid A
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      Journal of Molecular Biology (2020), 432 (7), 1978-1995CODEN: JMOBAK; ISSN:0022-2836. (Elsevier Ltd.)
      Serum amyloid A (SAA) is a plasma protein that transports lipids during inflammation. To explore SAA soln. conformations and lipid-binding mechanism, we used hydrogen-deuterium exchange mass spectrometry, lipoprotein reconstitution, amino acid sequence anal., and mol. dynamics simulations. Soln. conformations of lipid-bound and lipid-free mSAA1 at pH∼7.4 agreed in details with the crystal structures but also showed important differences. The results revealed that amphipathic α-helixes h1 and h3 comprise a lipid-binding site that is partially pre-formed in soln., is stabilized upon binding lipids, and shows lipid-induced folding of h3. This site sequesters apolar ligands via a concave hydrophobic surface in SAA oligomers. The largely disordered/dynamic C-terminal region is conjectured to mediate the promiscuous binding of other ligands. The h1-h2 linker region is predicted to form an unexpected β-hairpin that may represent an early amyloidogenic intermediate. The results help establish structural underpinnings for understanding SAA interactions with its key functional ligands, its evolutional conservation, and its transition to amyloid.
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    1. Sourav Nandi, Anurup Mukhopadhyay, Pratyush Kiran Nandi, Nanigopal Bera, Ritwik Hazra, Jyotirmoy Chatterjee, Nilmoni Sarkar. Amyloids Formed by Nonaromatic Amino Acid Methionine and Its Cross with Phenylalanine Significantly Affects Phospholipid Vesicle Membrane: An Insight into Hypermethioninemia Disorder. Langmuir 2022, 38 (27) , 8252-8265. https://doi.org/10.1021/acs.langmuir.2c00648
    2. Asis K. Jana, Chance W. Lander, Andrew D. Chesney, Ulrich H. E. Hansmann. Effect of an Amyloidogenic SARS-COV-2 Protein Fragment on α-Synuclein Monomers and Fibrils. The Journal of Physical Chemistry B 2022, 126 (20) , 3648-3658. https://doi.org/10.1021/acs.jpcb.2c01254
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    • Abstract

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      Figure 1

      Figure 1. Structures of serum amyloid A for the fragment SAA1–76 of the folded monomer (PDB-ID: 4IP8) (a) and for the chain in a fibril (segment SAA2–55, PDB-ID: 6MST) (b). The N-terminal residues are colored in blue.

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      Figure 2

      Figure 2. (a) A typical example of a replica walking through λ space starting from a replica where the physical model is initially biased toward the folded SAA structure. While the system walks between a replica with bias toward the folded structure (upper half) and a replica with bias toward the fibril structure (lower half), its configuration changes accordingly. This can be seen in (b), where we show the corresponding time evolution of the RMSD to the native structure (in magenta) and the fibril structure (in green).

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      Figure 3

      Figure 3. Free energy landscape as obtained from RET simulations, with data taken at λ = 0, i.e., where the physical models are not biased by any Go-term. Energies are listed in units of kT. The prospective transition pathway is drawn in black, and the five regions crossed by this path are marked in capital letters.

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      Figure 4

      Figure 4. Characteristic conformations of of SAA1–76 as seen in each of the five regions (labeled A, B, C, D, and E) identified on the proposed transition pathway. The N-terminus of the chains is colored in blue. Unlike for the transition regions B, C, and D, these conformations superimposed on the respective reference structures for region A (dominated by folded-conformations) and region E (where fibril-like conformations are dominant).

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      Figure 5

      Figure 5. (a) Number of contacts (normalized to one) nNS that are shared with the folded structure as measured in each of the five regions A to E of the transition pathway. The subset of long-range contacts nLR, again normalized to one, is drawn separately. Shown are also the number nFS of contacts also found in the fibril reference structure. In (b), we show the relative frequency with which one of the three characteristic helices of the folded structure, or the two main β-strands of the fibril structure, is observed.

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      Figure 6

      Figure 6. Residue–residue map of the average minimal distance between heavy atoms in a pair of residues, shown for each of the five regions A, B, C, D, and E. Unlike in the transition regions B, C, and D, folded conformations dominate in region A, and fibril-like ones dominate in region E. Residue pairs whose average contact distance is more than 10 Å are excluded. Numbers on the X and Y axis mark residues, and the average distance in Å is given by the color coding shown in the middle row.

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    • References

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        Serum amyloid A is an acute phase protein that is carried in the plasma largely as an apolipoprotein of high d. lipoprotein (HDL). In this study we investigated whether SAA is a ligand for the HDL receptor, scavenger receptor class B type I (SR-BI), and how SAA may influence SR-BI-mediated HDL binding and selective cholesteryl ester uptake. Studies using Chinese hamster ovary cells expressing SR-BI showed that 125I-labeled SAA, both in lipid-free form and in reconstituted HDL particles, functions as a high affinity ligand for SR-BI. SAA also bound with high affinity to the hepatocyte cell line, HepG2. Alexa-labeled SAA was shown by fluorescence confocal microscopy to be internalized by cells in a SR-BI-dependent manner. To assess how SAA assocn. with HDL influences HDL interaction with SR-BI, SAA-contg. HDL was isolated from mice overexpressing SAA through adenoviral gene transfer. SAA presence on HDL had little effect on HDL binding to SR-BI but decreased (30-50%) selective cholesteryl ester uptake. Lipid-free SAA, unlike lipid-free apoA-I, was an effective inhibitor of both SR-BI-dependent binding and selective cholesteryl ester uptake of HDL. We have concluded that SR-BI plays a key role in SAA metab. through its ability to interact with and internalize SAA and, further, that SAA influences HDL cholesterol metab. through its inhibitory effects on SR-BI-mediated selective lipid uptake.
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        Wang, Wenhua; Khatua, Prabir; Hansmann, Ulrich H. E.
        Journal of Physical Chemistry B (2020), 124 (6), 1009-1019CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
        Various diseases cause overexpression of the serum amyloid A (SAA) protein, which in some cases, but not in all cases, leads to amyloidosis as a secondary disease. Response to the overexpression involves dissocn. of the SAA hexamer and subsequent cleavage of the released monomers, most commonly yielding fragments SAA1-76 of the full-sized SAA1-104. We report results from mol. dynamic simulations that probe the role of this cleavage for downregulating the activity and concn. of SAA. We propose a mechanism that relies on two elements. First, the probability to assemble into hexamers is lower for the fragments than it is for the full-sized protein. Second, unlike other fragments, SAA1-76 can switch between two distinct configurations. The first kind is easy to proteolyze (allowing a fast redn. of the SAA concn.) but prone to aggregation, whereas the situation is opposite for the second kind. If the time scale for amyloid formation is longer than the one for proteolysis, the aggregation-prone species dominates. However, if environmental conditions such as low pH increases the risk of amyloid formation, the ensemble shifts toward the more protected form. We speculate that SAA amyloidosis is a failure of this switching mechanism leading to accumulation of the aggregation-prone species and subsequent amyloid formation.
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      9. 9
        Yaşar, F.; Bernhardt, N. A.; Hansmann, U. H. Replica-exchange-with-tunneling for fast exploration of protein landscapes. J. Chem. Phys. 2015, 143, 224102,  DOI: 10.1063/1.4936968
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        9
        Replica-exchange-with-tunneling for fast exploration of protein landscapes
        Yasar, Fatih; Bernhardt, Nathan A.; Hansmann, Ulrich H. E.
        Journal of Chemical Physics (2015), 143 (22), 224102/1-224102/6CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
        While the use of replica-exchange mol. dynamics in protein simulations has become ubiquitous, its utility is limited in many practical applications. The authors propose to overcome some shortcomings that hold back its use in settings such as multi-scale or explicit solvent simulations by integrating ideas of hybrid MC/MD into the replica-exchange protocol. This Replica-Exchange-with-Tunneling method is tested by simulating the Trp-cage protein, a system often used in mol. biophysics for testing sampling techniques. (c) 2015 American Institute of Physics.
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      10. 10
        Bernhardt, N. A.; Xi, W.; Wang, W.; Hansmann, U. H. Simulating Protein Fold Switching by Replica Exchange with Tunneling. J. Chem. Theory Comput. 2016, 12, 5656– 5666,  DOI: 10.1021/acs.jctc.6b00826
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        10
        Simulating Protein Fold Switching by Replica Exchange with Tunneling
        Bernhardt, Nathan A.; Xi, Wenhui; Wang, Wei; Hansmann, Ulrich H. E.
        Journal of Chemical Theory and Computation (2016), 12 (11), 5656-5666CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
        Recent expts. suggest that an amino acid sequence encodes not only the native fold of a protein but also other forms essential for its function, or important during folding or assocn. These various forms populate a multi-funnel folding and assocn. landscape where mutations, changes in environment, or interaction with other mols. switch between the encoded folds. Here, the authors introduced replica-exchange-with-tunneling as a way to simulate efficiently switching between distinct folds of proteins and protein aggregates. The correctness and efficiency of this approach was demonstrated in a series of simulations covering a wide range of proteins, from a small 11-residue large designed peptide up to 2 56-residue large mutants of the A and B domain of streptococcal protein G.
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      11. 11
        Zhang, H.; Xi, W.; Hansmann, U. H.; Wei, Y. Fibril-Barrel Transitions in Cylindrin Amyloids. J. Chem. Theory Comput. 2017, 13, 3936– 3944,  DOI: 10.1021/acs.jctc.7b00383
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        11
        Fibril-Barrel Transitions in Cylindrin Amyloids
        Zhang, Huiling; Xi, Wenhui; Hansmann, Ulrich H. E.; Wei, Yanjie
        Journal of Chemical Theory and Computation (2017), 13 (8), 3936-3944CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
        We introduce Replica-Exchange-with-Tunneling (RET) simulations as a tool for studies of the conversion between polymorph amyloids. For the eleven-residue amyloid- forming cylindrin peptide we show that this technique allows for a more efficient sampling of the formation and interconversion between fibril-like and barrel-like assemblies. We describe a protocol for optimized anal. of RET simulations that allows us to propose a mechanism for formation and interconversion between various cylindrin assemblies. Esp., we show that an interchain salt bridge between residues K3 and D7 is crucial for formation of the barrel structure.
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      12. 12
        Bernhardt, N. A.; Hansmann, U. H. Multifunnel Landscape of the Fold-Switching Protein RfaH-CTD. J. Phys. Chem. B 2018, 122, 1600– 1607,  DOI: 10.1021/acs.jpcb.7b11352
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        12
        Multifunnel Landscape of the Fold-Switching Protein RfaH-CTD
        Bernhardt, Nathan A.; Hansmann, Ulrich H. E.
        Journal of Physical Chemistry B (2018), 122 (5), 1600-1607CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
        Proteins such as transcription factor RfaH can change biol. function by switching between distinct 3-dimensional folds. RfaH regulates transcription if the C-terminal domain folds into a double helix bundle, and promotes translation when this domain assumes a β-barrel form. This fold-switch has been also obsd. for the isolated C-terminal domain (RfaH-CTD) and was studied here with a variant of the RET (Replica-Exchange-with-Tunneling) approach recently introduced by us. We used the enhanced sampling properties of this technique to map the free energy landscape of RfaH-CTD and to propose a mechanism for the conversion process.
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      13. 13
        Lu, J.; Yu, Y.; Zhu, I.; Cheng, Y.; Sun, P. D. Structural mechanism of serum amyloid A-mediated inflammatory amyloidosis. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 5189– 5194,  DOI: 10.1073/pnas.1322357111
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        13
        Structural mechanism of serum amyloid A-mediated inflammatory amyloidosis
        Lu, Jinghua; Yu, Yadong; Zhu, Iowis; Cheng, Yifan; Sun, Peter D.
        Proceedings of the National Academy of Sciences of the United States of America (2014), 111 (14), 5189-5194CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
        Serum amyloid A (SAA) represents an evolutionarily conserved family of inflammatory acute-phase proteins. It is also a major constituent of secondary amyloidosis. Here, to understand its function and structural transition to amyloid, the authors detd. a structure of human SAA isoform 1.1 (SAA1.1) in 2 crystal forms, representing a prototypic member of the family. Native SAA1.1 exists as a hexamer, with subunits displaying a unique 4-helix bundle fold stabilized by its long C-terminal tail. Structure-based mutational studies revealed 2 pos.-charge clusters, near the center and apex of the hexamer, that were involved in SAA assocn. with heparin. The binding of high-d. lipoprotein involved only the apex region of SAA and could be inhibited by heparin. Peptide amyloid formation assays identified N-terminal helixes 1 and 3 as amyloidogenic peptides of SAA1.1. Both peptides were secluded in the hexameric structure of SAA1.1, suggesting that native SAA is non-pathogenic. Furthermore, dissocn. of the SAA hexamer appeared insufficient to initiate the amyloidogenic transition, and proteolytic cleavage or removal of the C-terminal tail of SAA resulted in the formation of various-sized structural aggregates contg. ∼5-nm regular repeating protofibril-like units. The combined structural and functional studies provided mechanistic insights into the pathogenic contribution of glycosaminoglycan in SAA1.1-mediated AA amyloid formation.
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      14. 14
        Liberta, F.; Loerch, S.; Rennegarbe, M.; Schierhorn, A.; Westermark, P.; Westermark, G. T.; Hazenberg, B. P.; Grigorieff, N.; Fändrich, M.; Schmidt, M. Cryo-EM fibril structures from systemic AA amyloidosis reveal the species complementarity of pathological amyloids. Nature Commun. 2019, 10, 1104,  DOI: 10.1038/s41467-019-09033-z
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        14
        Cryo-EM fibril structures from systemic AA amyloidosis reveal the species complementarity of pathological amyloids
        Liberta Falk; Rennegarbe Matthies; Fandrich Marcus; Schmidt Matthias; Loerch Sarah; Grigorieff Nikolaus; Schierhorn Angelika; Westermark Per; Westermark Gunilla T; Hazenberg Bouke P C
        Nature communications (2019), 10 (1), 1104 ISSN:.
        Systemic AA amyloidosis is a worldwide occurring protein misfolding disease of humans and animals. It arises from the formation of amyloid fibrils from the acute phase protein serum amyloid A. Here, we report the purification and electron cryo-microscopy analysis of amyloid fibrils from a mouse and a human patient with systemic AA amyloidosis. The obtained resolutions are 3.0 ÅA and 2.7 ÅA for the murine and human fibril, respectively. The two fibrils differ in fundamental properties, such as presence of right-hand or left-hand twisted cross-β sheets and overall fold of the fibril proteins. Yet, both proteins adopt highly similar β-arch conformations within the N-terminal ~21 residues. Our data demonstrate the importance of the fibril protein N-terminus for the stability of the analyzed amyloid fibril morphologies and suggest strategies of combating this disease by interfering with specific fibril polymorphs.
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      15. 15
        Bansal, A.; Schmidt, M.; Rennegarbe, M.; Haupt, C.; Liberta, F.; Stecher, S.; Puscalau-Girtu, I.; Biedermann, A.; Fändrich, M. AA amyloid fibrils from diseased tissue are structurally different from in vitro formed SAA fibrils. Nature Commun. 2021, 12, 1013,  DOI: 10.1038/s41467-021-21129-z
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        15
        AA amyloid fibrils from diseased tissue are structurally different from in vitro formed SAA fibrils
        Bansal, Akanksha; Schmidt, Matthias; Rennegarbe, Matthies; Haupt, Christian; Liberta, Falk; Stecher, Sabrina; Puscalau-Girtu, Ioana; Biedermann, Alexander; Faendrich, Marcus
        Nature Communications (2021), 12 (1), 1013CODEN: NCAOBW; ISSN:2041-1723. (Nature Research)
        Systemic AA amyloidosis is a world-wide occurring protein misfolding disease of humans and animals. It arises from the formation of amyloid fibrils from serum amyloid A (SAA) protein. Using cryo electron microscopy we here show that amyloid fibrils which were purified from AA amyloidotic mice are structurally different from fibrils formed from recombinant SAA protein in vitro. Ex vivo amyloid fibrils consist of fibril proteins that contain more residues within their ordered parts and possess a higher β-sheet content than in vitro fibril proteins. They are also more resistant to proteolysis than their in vitro formed counterparts. These data suggest that pathogenic amyloid fibrils may originate from proteolytic selection, allowing specific fibril morphologies to proliferate and to cause damage to the surrounding tissue.
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      16. 16
        Fukunishi, H.; Watanabe, O.; Takada, S. On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: Application to protein structure prediction. J. Chem. Phys. 2002, 116, 9058– 9067,  DOI: 10.1063/1.1472510
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        16
        On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: Application to protein structure prediction
        Fukunishi, Hiroaki; Watanabe, Osamu; Takada, Shoji
        Journal of Chemical Physics (2002), 116 (20), 9058-9067CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
        Motivated by the protein structure prediction problem, we develop two variants of the Hamiltonian replica exchange methods (REMs) for efficient configuration sampling, (1) the scaled hydrophobicity REM and (2) the phantom chain REM, and compare their performance with the ordinary REM. We first point out that the ordinary REM has a shortage for the application to large systems such as biomols. and that the Hamiltonian REM, an alternative formulation of the REM, can give a remedy for it. We then propose two examples of the Hamiltonian REM that are suitable for a coarse-grained protein model. (1) The scaled hydrophobicity REM preps. replicas that are characterized by various strengths of hydrophobic interaction. The strongest interaction that mimics aq. soln. environment makes proteins folding, while weakened hydrophobicity unfolds proteins as in org. solvent. Exchange between these environments enables proteins to escape from misfolded traps and accelerate conformational search. This resembles the roles of mol. chaperone that assist proteins to fold in vivo. (2) The phantom chain REM uses replicas that allow various degrees of at. overlaps. By allowing at. overlap in some of replicas, the peptide chain can cross over itself, which can accelerate conformation sampling. Using a coarse-gained model we developed, we compute equil. probability distributions for poly-alanine 16-mer and for a small protein by these REMs and compare the accuracy of the results. We see that the scaled hydrophobicity REM is the most efficient method among the three REMs studied.
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      17. 17
        Kwak, W.; Hansmann, U. H. Efficient sampling of protein structures by model hopping. Phys. Rev. Lett. 2005, 95, 138102,  DOI: 10.1103/PhysRevLett.95.138102
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        17
        Efficient Sampling of Protein Structures by Model Hopping
        Kwak, Wooseop; Hansmann, Ulrich H. E.
        Physical Review Letters (2005), 95 (13), 138102/1-138102/4CODEN: PRLTAO; ISSN:0031-9007. (American Physical Society)
        A review. We introduce a novel simulation method, model hopping, that enhances sampling of low-energy configurations in complex systems. The approach is illustrated for a protein-folding problem. Thermodn. quantities of proteins with up to 46 residues are evaluated from all-atom simulations with this method.
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      18. 18
        Hansmann, U. H. Parallel tempering algorithm for conformational studies of biological molecules. Chem. Phys. Lett. 1997, 281, 140– 150,  DOI: 10.1016/S0009-2614(97)01198-6
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        18
        Parallel tempering algorithm for conformational studies of biological molecules
        Hansmann, Ulrich H. E.
        Chemical Physics Letters (1997), 281 (1,2,3), 140-150CODEN: CHPLBC; ISSN:0009-2614. (Elsevier Science B.V.)
        The effectiveness of a new algorithm, parallel tempering, is studied for numerical simulations of biol. mols. These mols. suffer from a rough energy landscape. The resulting slowing down in numerical simulations is overcome by the new method. This is demonstrated by performing simulations with high statistics for one of the simplest peptides, Met-enkephalin. The numerical effectiveness of the new technique was found to be much better than traditional methods and is comparable to sophisticated methods like generalized ensemble techniques.
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      19. 19
        Best, R. B.; Zhu, X.; Shim, J.; Lopes, P. E.; Mittal, J.; Feig, M.; MacKerell, A. D. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone ϕ, ψ and side-chain χ1 and χ2 Dihedral Angles. J. Chem. Theory Comput. 2012, 8, 3257– 3273,  DOI: 10.1021/ct300400x
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        19
        Optimization of the Additive CHARMM All-Atom Protein Force Field Targeting Improved Sampling of the Backbone .vphi., ψ and Side-Chain χ1 and χ2 Dihedral Angles
        Best, Robert B.; Zhu, Xiao; Shim, Jihyun; Lopes, Pedro E. M.; Mittal, Jeetain; Feig, Michael; MacKerell, Alexander D.
        Journal of Chemical Theory and Computation (2012), 8 (9), 3257-3273CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
        While the quality of the current CHARMM22/CMAP additive force field for proteins has been demonstrated in a large no. of applications, limitations in the model with respect to the equil. between the sampling of helical and extended conformations in folding simulations have been noted. To overcome this, as well as make other improvements in the model, we present a combination of refinements that should result in enhanced accuracy in simulations of proteins. The common (non-Gly, -Pro) backbone CMAP potential has been refined against exptl. soln. NMR data for weakly structured peptides, resulting in a rebalancing of the energies of the α-helix and extended regions of the Ramachandran map, correcting the α-helical bias of CHARMM22/CMAP. The Gly and Pro CMAPs have been refitted to more accurate quantum-mech. energy surfaces. Side-chain torsion parameters have been optimized by fitting to backbone-dependent quantum-mech. energy surfaces, followed by addnl. empirical optimization targeting NMR scalar couplings for unfolded proteins. A comprehensive validation of the revised force field was then performed against a collection of exptl. data: (i) comparison of simulations of eight proteins in their crystal environments with crystal structures; (ii) comparison with backbone scalar couplings for weakly structured peptides; (iii) comparison with NMR residual dipolar couplings and scalar couplings for both backbone and side-chains in folded proteins; (iv) equil. folding of mini-proteins. The results indicate that the revised CHARMM 36 parameters represent an improved model for modeling and simulation studies of proteins, including studies of protein folding, assembly, and functionally relevant conformational changes.
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      20. 20
        Jorgensen, 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
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        20
        Comparison of simple potential functions for simulating liquid water
        Jorgensen, 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.
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      21. 21
        Pettersen, E. F.; Goddard, T. D.; Huang, C. C.; Couch, G. S.; Greenblatt, D. M.; Meng, E. C.; Ferrin, T. E. UCSF Chimera - A visualization system for exploratory research and analysis. J. Comput. Chem. 2004, 25, 1605– 1612,  DOI: 10.1002/jcc.20084
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        21
        UCSF Chimera-A visualization system for exploratory research and analysis
        Pettersen, Eric F.; Goddard, Thomas D.; Huang, Conrad C.; Couch, Gregory S.; Greenblatt, Daniel M.; Meng, Elaine C.; Ferrin, Thomas E.
        Journal of Computational Chemistry (2004), 25 (13), 1605-1612CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
        The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale mol. assemblies such as viral coats, and Collab., which allows researchers to share a Chimera session interactively despite being at sep. locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and assocd. structures; ViewDock, for screening docked ligand orientations; Movie, for replaying mol. dynamics trajectories; and Vol. Viewer, for display and anal. of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/.
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      22. 22
        Noel, J. K.; Whitford, P. C.; Sanbonmatsu, K. Y.; Onuchic, J. N. SMOG@ctbp: Simplified deployment of structure-based models in GROMACS. Nucleic Acids Res. 2010, 38, W657,  DOI: 10.1093/nar/gkq498
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        SMOG@ctbp: simplified deployment of structure-based models in GROMACS
        Noel, Jeffrey K.; Whitford, Paul C.; Sanbonmatsu, Karissa Y.; Onuchic, Jose N.
        Nucleic Acids Research (2010), 38 (Web Server), W657-W661CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
        Mol. dynamics simulations with coarse-grained and/or simplified Hamiltonians are an effective means of capturing the functionally important long-time and large-length scale motions of proteins and RNAs. Structure-based Hamiltonians, simplified models developed from the energy landscape theory of protein folding, have become a std. tool for investigating biomol. dynamics. SMOG@ctbp is an effort to simplify the use of structure-based models. The purpose of the web server is two fold. First, the web tool simplifies the process of implementing a well-characterized structure-based model on a state-of-the-art, open source, mol. dynamics package, GROMACS. Second, the tutorial-like format helps speed the learning curve of those unfamiliar with mol. dynamics. A web tool user is able to upload any multi-chain biomol. system consisting of std. RNA, DNA and amino acids in PDB format and receive as output all files necessary to implement the model in GROMACS. Both Cα and all-atom versions of the model are available. SMOG@ctbp resides at http://smog.ucsd.edu.
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      23. 23
        Zhang, W.; Chen, J. Accelerate sampling in atomistic energy landscapes using topology-based coarse-grained models. J. Chem. Theory Comput. 2014, 10, 918– 923,  DOI: 10.1021/ct500031v
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        23
        Accelerate Sampling in Atomistic Energy Landscapes Using Topology-Based Coarse-Grained Models
        Zhang, Weihong; Chen, Jianhan
        Journal of Chemical Theory and Computation (2014), 10 (3), 918-923CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
        We describe a multiscale enhanced sampling (MSES) method where efficient topol.-based coarse-grained models are coupled with all-atom ones to enhance the sampling of atomistic protein energy landscape. The bias from the coupling is removed by Hamiltonian replica exchange, thus allowing one to benefit simultaneously from faster transitions of coarse-grained modeling and accuracy of atomistic force fields. The method is demonstrated by calcg. the conformational equil. of several small but nontrivial β-hairpins with varied stabilities.
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        Moritsugu, K.; Terada, T.; Kidera, A. Scalable free energy calculation of proteins via multiscale essential sampling. J. Chem. Phys. 2010, 133, 224105,  DOI: 10.1063/1.3510519
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        24
        Scalable free energy calculation of proteins via multiscale essential sampling
        Moritsugu, Kei; Terada, Tohru; Kidera, Akinori
        Journal of Chemical Physics (2010), 133 (22), 224105/1-224105/6CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
        A multiscale simulation method, multiscale essential sampling (MSES), is proposed for calcg. free energy surface of proteins in a sizable dimensional space with good scalability. In MSES, the configurational sampling of a full-dimensional model is enhanced by coupling with the accelerated dynamics of the essential degrees of freedom. Applying the Hamiltonian exchange method to MSES can remove the biasing potential from the coupling term, deriving the free energy surface of the essential degrees of freedom. The form of the coupling term ensures good scalability in the Hamiltonian exchange. As a test application, the free energy surface of the folding process of a mini-protein, chignolin, was calcd. in the continuum solvent model. Results agreed with the free energy surface derived from the multi-canonical simulation. Significantly improved scalability with the MSES method was clearly shown in the free energy calcn. of chignolin in explicit solvent, which was achieved without increasing the no. of replicas in the Hamiltonian exchange. (c) 2010 American Institute of Physics.
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        Hess, B.; Kutzner, C.; Van Der Spoel, D.; Lindahl, E. GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theory Comput. 2008, 4, 435– 447,  DOI: 10.1021/ct700301q
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        25
        GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation
        Hess, Berk; Kutzner, Carsten; van der Spoel, David; Lindahl, Erik
        Journal of Chemical Theory and Computation (2008), 4 (3), 435-447CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)
        Mol. simulation is an extremely useful, but computationally very expensive tool for studies of chem. and biomol. systems. Here, we present a new implementation of our mol. simulation toolkit GROMACS which now both achieves extremely high performance on single processors from algorithmic optimizations and hand-coded routines and simultaneously scales very well on parallel machines. The code encompasses a minimal-communication domain decompn. algorithm, full dynamic load balancing, a state-of-the-art parallel constraint solver, and efficient virtual site algorithms that allow removal of hydrogen atom degrees of freedom to enable integration time steps up to 5 fs for atomistic simulations also in parallel. To improve the scaling properties of the common particle mesh Ewald electrostatics algorithms, we have in addn. used a Multiple-Program, Multiple-Data approach, with sep. node domains responsible for direct and reciprocal space interactions. Not only does this combination of algorithms enable extremely long simulations of large systems but also it provides that simulation performance on quite modest nos. of std. cluster nodes.
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        Hess, B. P-LINCS: A parallel linear constraint solver for molecular simulation. J. Chem. Theory Comput. 2008, 4, 116– 122,  DOI: 10.1021/ct700200b
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        P-LINCS: A Parallel Linear Constraint Solver for Molecular Simulation
        Hess, Berk
        Journal 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.
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        Swope, W. C.; Andersen, H. C.; Berens, P. H.; Wilson, K. R. A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: Application to small water clusters. J. Chem. Phys. 1982, 76, 637– 649,  DOI: 10.1063/1.442716
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        A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: Application to small water clusters
        Swope, William C.; Andersen, Hans C.; Berens, Peter H.; Wilson, Kent R.
        Journal of Chemical Physics (1982), 76 (1), 637-49CODEN: JCPSA6; ISSN:0021-9606.
        A mol. dynamics computer simulation method is described for calcg. equil. consts. for the formation of phys. clusters of mols. The method is based on Hill's formal theory of phys. clusters. A mol. dynamics calcn. is used to calc. the av. potential energy of a cluster of mols. as a function of temp., and the equil. consts. are calcd. from the integral of the energy with respect to reciprocal temp. The method is illustrated by calcns. of the equil. consts. for the formation of clusters of 2-5 water mols. that interact with each other by an intermol. potential devised by Watts. The method is compared with other procedures for calcg. the thermodn. properties of clusters.
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        Bussi, G.; Donadio, D.; Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 2007, 126, 014101,  DOI: 10.1063/1.2408420
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        Canonical sampling through velocity rescaling
        Bussi, Giovanni; Donadio, Davide; Parrinello, Michele
        Journal of Chemical Physics (2007), 126 (1), 014101/1-014101/7CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
        The authors present a new mol. dynamics algorithm for sampling the canonical distribution. In this approach the velocities of all the particles are rescaled by a properly chosen random factor. The algorithm is formally justified and it is shown that, in spite of its stochastic nature, a quantity can still be defined that remains const. during the evolution. In numerical applications this quantity can be used to measure the accuracy of the sampling. The authors illustrate the properties of this new method on Lennard-Jones and TIP4P water models in the solid and liq. phases. Its performance is excellent and largely independent of the thermostat parameter also with regard to the dynamic properties.
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        McGibbon, R. T.; Beauchamp, K. A.; Harrigan, M. P.; Klein, C.; Swails, J. M.; Hernández, C. X.; Schwantes, C. R.; Wang, L. P.; Lane, T. J.; Pande, V. S. MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories. Biophys. J. 2015, 109, 1528– 1532,  DOI: 10.1016/j.bpj.2015.08.015
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        MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories
        McGibbon, Robert T.; Beauchamp, Kyle A.; Harrigan, Matthew P.; Klein, Christoph; Swails, Jason M.; Hernandez, Carlos X.; Schwantes, Christian R.; Wang, Lee-Ping; Lane, Thomas J.; Pande, Vijay S.
        Biophysical Journal (2015), 109 (8), 1528-1532CODEN: BIOJAU; ISSN:0006-3495. (Cell Press)
        As mol. dynamics (MD) simulations continue to evolve into powerful computational tools for studying complex biomol. systems, the necessity of flexible and easy-to-use software tools for the anal. of these simulations is growing. We have developed MDTraj, a modern, lightwt., and fast software package for analyzing MD simulations. MDTraj reads and writes trajectory data in a wide variety of commonly used formats. It provides a large no. of trajectory anal. capabilities including minimal root-mean-square-deviation calcns., secondary structure assignment, and the extn. of common order parameters. The package has a strong focus on interoperability with the wider scientific Python ecosystem, bridging the gap between MD data and the rapidly growing collection of industry-std. statistical anal. and visualization tools in Python. MDTraj is a powerful and user-friendly software package that simplifies the anal. of MD data and connects these datasets with the modern interactive data science software ecosystem in Python.
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        Marcos-Alcalde, I.; Setoain, J.; Mendieta-Moreno, J. I.; Mendieta, J.; Gómez-Puertas, P. MEPSA: minimum energy pathway analysis for energy landscapes. Bioinformatics 2015, 31, 3853– 3855,  DOI: 10.1093/bioinformatics/btv453
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        MEPSA: minimum energy pathway analysis for energy landscapes
        Marcos-Alcalde, Inigo; Setoain, Javier; Mendieta-Moreno, Jesus I.; Mendieta, Jesus; Gomez-Puertas, Paulino
        Bioinformatics (2015), 31 (23), 3853-3855CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
        Summary: From conformational studies to atomistic descriptions of enzymic reactions, potential and free energy landscapes can be used to describe biomol. systems in detail. However, extg. the relevant data of complex 3D energy surfaces can sometimes be laborious. In this article, we present MEPSA (Min. Energy Path Surface Anal.), a cross-platform user friendly tool for the anal. of energy landscapes from a transition state theory perspective. Some of its most relevant features are: identification of all the barriers and min. of the landscape at once, description of maxima edge profiles, detection of the lowest energy path connecting two min. and generation of transition state theory diagrams along these paths. In addn. to a built-in plotting system, MEPSA can save most of the generated data into easily parseable text files, allowing more versatile uses of MEPSA's output such as the generation of mol. dynamics restraints from a calcd. path.
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        Khatua, P.; Ray, A. J.; Hansmann, U. H. Bifurcated Hydrogen Bonds and the Fold Switching of Lymphotactin. J. Phys. Chem. B 2020, 124, 6555– 6564,  DOI: 10.1021/acs.jpcb.0c04565
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        Bifurcated Hydrogen Bonds and the Fold Switching of Lymphotactin
        Khatua, Prabir; Ray, Alan J.; Hansmann, Ulrich H. E.
        Journal of Physical Chemistry B (2020), 124 (30), 6555-6564CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
        Lymphotactin (Ltn) exists under physiol. conditions in an equil. between two interconverting structures with distinct biol. functions. Using replica-exchange-with-tunneling, we study the conversion between the 2-folds. Unlike previously proposed, we find that the fold switching does not require unfolding of lymphotactin but proceeds through a series of intermediates that remain partially structured. This process relies on two bifurcated hydrogen bonds that connect the β2 and β3 strands and ease the transition between the hydrogen bond pattern by which the central three-stranded β-sheet in the two forms differs.
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        Bolhuis, P. G.; Chandler, D.; Dellago, C.; Geissler, P. L. Transition Path Sampling: Throwing Ropes over Rough Mountain Passes, in the Dark. Annu. Rev. Phys. Chem. 2002, 53, 291– 318,  DOI: 10.1146/annurev.physchem.53.082301.113146
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        Transition path sampling: throwing ropes over rough mountain passes, in the dark
        Bolhuis, Peter G.; Chandler, David; Dellago, Christoph; Geissler, Phillip L.
        Annual Review of Physical Chemistry (2002), 53 (), 291-318CODEN: ARPLAP; ISSN:0066-426X. (Annual Reviews Inc.)
        A review is given of the concepts and methods of transition path sampling. These methods allow computational studies of rare events without requiring prior knowledge of mechanisms, reaction coordinates, and transition states. Based upon a statistical mechanics of trajectory space, they provide a perspective with which time dependent phenomena, even for systems driven far from equil., can be examd. with the same types of importance sampling tools that in the past have been applied so successfully to static equil. properties.
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        Dellago, C.; Bolhuis, P. G.; Csajka, F. S.; Chandler, D. Transition Path Sampling and the Calculation of Rate Constants. J. Chem. Phys. 1998, 108, 1964– 1977,  DOI: 10.1063/1.475562
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        Transition path sampling and the calculation of rate constants
        Dellago, Christoph; Bolhuis, Peter G.; Csajka, Felix S.; Chandler, David
        Journal of Chemical Physics (1998), 108 (5), 1964-1977CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
        We have developed a method to study transition pathways for rare events in complex systems. The method can be used to det. rate consts. for transitions between stable states by turning the calcn. of reactive flux correlation functions into the computation of an isomorphic reversible work. In contrast to previous dynamical approaches, the method relies neither on prior knowledge nor on explicit specification of transition states. Rather, it provides an importance sampling from which transition states can be characterized statistically. A simple model is analyzed to illustrate the methodol.
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        Dellago, C.; Bolhuis, P. G.; Chandler, D. Efficient Transition Path Sampling: Application to Lennard-Jones Cluster Rearrangements. J. Chem. Phys. 1998, 108, 9236– 9245,  DOI: 10.1063/1.476378
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        Efficient transition path sampling: Application to Lennard-Jones cluster rearrangements
        Dellago, Christoph; Bolhuis, Peter G.; Chandler, David
        Journal of Chemical Physics (1998), 108 (22), 9236-9245CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
        We develop an efficient Monte-Carlo algorithm to sample an ensemble of stochastic transition paths between stable states. In our description, paths are represented by chains of states linked by Markovian transition probabilities. Rate consts. and mechanisms characterizing the transition may be detd. from the path ensemble. We have previously devised several algorithms for sampling the path ensemble. For these algorithms, the numerical effort scales with the square of the path length. In the new simulation scheme, the required computation scales linearly with the length of the transition path. This improved efficiency allows the calcn. of rate consts. in complex mol. systems. As an example, we study rearrangement processes in a cluster consisting of seven Lennard-Jones particles in two dimensions. Using a quenching technique we are able to identify the relevant transition mechanisms and to locate the related transition states. We furthermore calc. transition rate consts. for various isomerization processes.
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        Weinan, E.; Ren, W.; Vanden-Eijnden, E. String Method for the Study of Rare Events. Phys. Rev. B 2002, 66, 052301,  DOI: 10.1103/PhysRevB.66.052301
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        Maragliano, L.; Fischer, A.; Vanden-Eijnden, E.; Ciccotti, G. String Method in Collective Variables: Minimum Free Energy Paths and Isocommittor Surfaces. J. Chem. Phys. 2006, 125, 024106,  DOI: 10.1063/1.2212942
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        String method in collective variables: Minimum free energy paths and isocommittor surfaces
        Maragliano, Luca; Fischer, Alexander; Vanden-Eijnden, Eric; Ciccotti, Giovanni
        Journal of Chemical Physics (2006), 125 (2), 024106/1-024106/15CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
        A computational technique is proposed which combines the string method with a sampling technique to det. min. free energy paths. The technique only requires to compute the mean force and another conditional expectation locally along the string, and therefore can be applied even if the no. of collective variables kept in the free energy calcn. is large. This is in contrast with other free energy sampling techniques which aim at mapping the full free energy landscape and whose cost increases exponentially with the no. of collective variables kept in the free energy. Provided that the no. of collective variables is large enough, the new technique captures the mechanism of transition in that it allows to det. the committor function for the reaction and, in particular, the transition state region. The new technique is illustrated on the example of alanine dipeptide, in which we compute the min. free energy path for the isomerization transition using either two or four dihedral angles as collective variables. It is shown that the mechanism of transition can be captured using the four dihedral angles, but it cannot be captured using only two of them.
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        Unarta, I. C.; Zhu, L.; Tse, C. K. M.; Cheung, P. P.-H.; Yu, J.; Huang, X. Molecular Mechanisms of RNA Polymerase II Transcription Elongation Elucidated by Kinetic Network Models. Curr. Opin. Struct. Biol. 2018, 49, 54– 62,  DOI: 10.1016/j.sbi.2018.01.002
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        Molecular mechanisms of RNA polymerase II transcription elongation elucidated by kinetic network models
        Unarta, Ilona Christy; Zhu, Lizhe; Tse, Carmen Ka Man; Cheung, Peter Pak-Hang; Yu, Jin; Huang, Xuhui
        Current Opinion in Structural Biology (2018), 49 (), 54-62CODEN: COSBEF; ISSN:0959-440X. (Elsevier Ltd.)
        Transcription elongation cycle (TEC) of RNA polymerase II (Pol II) is a process of adding a nucleoside triphosphate to the growing mRNA chain. Due to the long timescale events in Pol II TEC, an advanced computational technique, such as Markov State Model (MSM), is needed to provide atomistic mechanism and reaction rates. The combination of MSM and exptl. results can be used to build a kinetic network model (KNM) of the whole TEC. This review provides a brief protocol to build MSM and KNM of the whole TEC, along with the latest findings of MSM and other computational studies of Pol II TEC. Lastly, we offer a perspective on potentially using a sequence dependent KNM to predict genome-wide transcription error.
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      39. 39
        Zhu, L.; Sheong, F. K.; Cao, S.; Liu, S.; Unarta, I. C.; Huang, X. TAPS: A Traveling-Salesman Based Automated Path Searching Method for Functional Conformational Changes of Biological Macromolecules. J. Chem. Phys. 2019, 150, 124105,  DOI: 10.1063/1.5082633
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        TAPS: A traveling-salesman based automated path searching method for functional conformational changes of biological macromolecules
        Zhu, Lizhe; Sheong, Fu Kit; Cao, Siqin; Liu, Song; Unarta, Ilona C.; Huang, Xuhui
        Journal of Chemical Physics (2019), 150 (12), 124105/1-124105/10CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)
        Locating the min. free energy paths (MFEPs) between two conformational states is among the most important tasks of biomol. simulations. For example, knowledge of the MFEP is crit. for focusing the effort of unbiased simulations that were used for the construction of Markov state models to the biol. relevant regions of the system. Typically, existing path searching methods perform local sampling around the path nodes in a pre-selected collective variable (CV) space to allow a gradual downhill evolution of the path toward the MFEP. Despite the wide application of such a strategy, the gradual path evolution and the non-trivial a priori choice of CVs are also limiting its overall efficiency and automation. Non-local perpendicular sampling can be pursued to accelerate the search, provided that all nodes are reordered thereafter via a traveling-salesman scheme. Moreover, path-CVs can be computed on-the-fly and used as a coordinate system, minimizing the necessary prior knowledge about the system. The authors' traveling-salesman based automated path searching method achieves a 5-8 times speedup over the string method with swarms-of-trajectories for two peptide systems in vacuum and soln., making it a promising method for obtaining initial pathways when studying functional conformational changes between a pair of structures. (c) 2019 American Institute of Physics.
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        Schrödinger, L. The PyMOL Molecular Graphics System , Version 1.8; 2015.
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        Westermark, G. T.; Engström, U.; Westermark, P. The N-terminal segment of protein AA determines its fibrillogenic property. Biochem. Biophys. Res. Commun. 1992, 182, 27– 33,  DOI: 10.1016/S0006-291X(05)80107-X
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        The N-terminal segment of protein AA determines its fibrillogenic property
        Westermark, Gunilla T.; Engstroem, Ulla; Westermark, Per
        Biochemical and Biophysical Research Communications (1992), 182 (1), 27-33CODEN: BBRCA9; ISSN:0006-291X.
        The amyloid fibril protein AA consists of a varying long N-terminal part of the precursor protein serum AA. By using synthetic peptides corresponding to human and murine protein AA segments and cyanogen bromide fragments of human protein AA, evidence is shown that the amyloidogenic part of the mol. is the first 10-15 amino acid long segment. Amino acid substitutions in this part of the mol. may explain why only one of the two mouse SAA isoforms is amyloidogenic.
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        Yassine, H. N.; Trenchevska, O.; He, H.; Borges, C. R.; Nedelkov, D.; Mack, W.; Kono, N.; Koska, J.; Reaven, P. D.; Nelson, R. W. Serum Amyloid A Truncations in Type 2 Diabetes Mellitus. PLoS One 2015, 10, e0115320,  DOI: 10.1371/journal.pone.0115320
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        Wang, W.; Hansmann, U. H. Stability of Human Serum Amyloid A Fibrils. J. Phys. Chem. B 2020, 124, 10708– 10717,  DOI: 10.1021/acs.jpcb.0c08280
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        Stability of Human Serum Amyloid A Fibrils
        Wang, Wenhua; Hansmann, Ulrich H. E.
        Journal of Physical Chemistry B (2020), 124 (47), 10708-10717CODEN: JPCBFK; ISSN:1520-5207. (American Chemical Society)
        In systemic amyloidosis, serum amyloid A (SAA) fibril deposits cause widespread damages to tissues and organs that eventually may lead to death. A therapeutically intervention therefore has either to dissolve these fibrils or inhibit their formation. However, only recently has the human SAA fibril structure been resolved at a resoln. that is sufficient for development of drug candidates. Here, we use mol. dynamic simulations to probe the factors that modulate the stability of this fibril model. Our simulations suggest that fibril formation starts with the stacking of two misfolded monomers into metastable dimers, with the stacking depending on the N-terminal amyloidogenic regions of different chains forming anchors. The resulting dimers pack in a second step into a 2-fold two-layer tetramer that is stable enough to nucleate fibril formation. The stability of the initial dimers is enhanced under acidic conditions by a strong salt bridge and side-chain hydrogen bond network in the C-terminal cavity (residues 23-51) but is not affected by the presence of the disordered C-terminal tail.
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        Wang, W.; Xi, W.; Hansmann, U. H. Stability of the N-Terminal Helix and Its Role in Amyloid Formation of Serum Amyloid A. ACS Omega 2018, 3, 16184– 16190,  DOI: 10.1021/acsomega.8b02377
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        Stability of the N-Terminal Helix and Its Role in Amyloid Formation of Serum Amyloid A
        Wang, Wenhua; Xi, Wenhui; Hansmann, Ulrich H. E.
        ACS Omega (2018), 3 (11), 16184-16190CODEN: ACSODF; ISSN:2470-1343. (American Chemical Society)
        Colonic amyloidosis is the result of overexpression of the serum amyloid A protein (SAA) in inflammatory bowel disease or colon cancer. Crucial for amyloid formation are the 1st ten N-terminal residues, which in the crystal structure are part of a 27-residue long helix. Here we studied this 27-residue N-terminal region of SAA by a multi-exchange variant of replica exchange mol. dynamics (ME-REMD) simulations. An ensemble of configurations was obsd., dominated by 3 motifs: the single helix of the crystal structure, a helix-turn-helix configuration, and such where residues 14-27 were part of a helix but the 1st 13 residues formed an extended and disordered segment that was prone to aggregation. A single point mutation E9A shifted the equil. to the latter motif indicating the importance of interactions involving this residue for the stability of the SAA protein.
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        Frame, N. M.; Kumanan, M.; Wales, T. E.; Bandara, A.; Fändrich, M.; Straub, J. E.; Engen, J. R.; Gursky, O. Structural Basis for Lipid Binding and Function by an Evolutionarily Conserved Protein, Serum Amyloid A. J. Mol. Bio. 2020, 432, 1978– 1995,  DOI: 10.1016/j.jmb.2020.01.029
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        Structural Basis for Lipid Binding and Function by an Evolutionarily Conserved Protein, Serum Amyloid A
        Frame, Nicholas M.; Kumanan, Meera; Wales, Thomas E.; Bandara, Asanga; Fandrich, Marcus; Straub, John E.; Engen, John R.; Gursky, Olga
        Journal of Molecular Biology (2020), 432 (7), 1978-1995CODEN: JMOBAK; ISSN:0022-2836. (Elsevier Ltd.)
        Serum amyloid A (SAA) is a plasma protein that transports lipids during inflammation. To explore SAA soln. conformations and lipid-binding mechanism, we used hydrogen-deuterium exchange mass spectrometry, lipoprotein reconstitution, amino acid sequence anal., and mol. dynamics simulations. Soln. conformations of lipid-bound and lipid-free mSAA1 at pH∼7.4 agreed in details with the crystal structures but also showed important differences. The results revealed that amphipathic α-helixes h1 and h3 comprise a lipid-binding site that is partially pre-formed in soln., is stabilized upon binding lipids, and shows lipid-induced folding of h3. This site sequesters apolar ligands via a concave hydrophobic surface in SAA oligomers. The largely disordered/dynamic C-terminal region is conjectured to mediate the promiscuous binding of other ligands. The h1-h2 linker region is predicted to form an unexpected β-hairpin that may represent an early amyloidogenic intermediate. The results help establish structural underpinnings for understanding SAA interactions with its key functional ligands, its evolutional conservation, and its transition to amyloid.
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    Get article recommendations from ACS based on references in your Mendeley library.

    Pair your accounts.

    Export articles to Mendeley

    Get article recommendations from ACS based on references in your Mendeley library.

    You’ve supercharged your research process with ACS and Mendeley!

    STEP 1:
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    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

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