Molecular Dynamics Simulations of Protein Aggregation: Protocols for Simulation Setup and Analysis with Markov State Models and Transition Networks

Samantray, Suman; Schumann, Wibke; Illig, Alexander-Maurice; Carballo-Pacheco, Martín; Paul, Arghadwip; Barz, Bogdan; Strodel, Birgit

doi:10.1007/978-1-0716-1546-1_12

Cited by 20 publications

(16 citation statements)

References 37 publications

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“…We chose to simulate only eight-chain systems because achieving convergence of the ensembles was critical in the determination of the aggregate-dissociation temperatures and, therefore, in the assessment of whether a peptide aggregates at the experimental sample-incubation temperature. This setup is similar to that of the protocol recommended for all-atom simulations with GROMACS [ 50 ], in which six molecules are placed in a cubic periodic box with the 100 Å side [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

“…Molecular-simulation methods have long been used to study peptide aggregation, including both all-atom [ 16 , 17 , 18 ] and coarse-grained (CG) methodologies [ 19 ]. Of those, the methods based on CG models offer much longer time- and size-scales, including the possibility of simulating aggregation from scratch, even though this extension is achieved at the inevitable expense of modeling accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Aggregation of Biologically-Active Peptides with the UNRES Coarse-Grained Model

et al. 2022

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The UNited RESidue (UNRES) model of polypeptide chains was applied to study the association of 20 peptides with sizes ranging from 6 to 32 amino-acid residues. Twelve of those were potentially aggregating hexa- or heptapeptides excised from larger proteins, while the remaining eight contained potentially aggregating sequences, functionalized by attaching larger ends rich in charged residues. For 13 peptides, the experimental data of aggregation were used. The remaining seven were synthesized, and their properties were measured in this work. Multiplexed replica-exchange simulations of eight-chain systems were conducted at 12 temperatures from 260 to 370 K at concentrations from 0.421 to 5.78 mM, corresponding to the experimental conditions. The temperature profiles of the fractions of monomers and octamers showed a clear transition corresponding to aggregate dissociation. Low simulated transition temperatures were obtained for the peptides, which did not precipitate after incubation, as well as for the H-GNNQQNY-NH2 prion–protein fragment, which forms small fibrils. A substantial amount of inter-strand β-sheets was found in most of the systems. The results suggest that UNRES simulations can be used to assess peptide aggregation except for glutamine- and asparagine-rich peptides, for which a revision of the UNRES sidechain–sidechain interaction potentials appears necessary.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Aggregation of Biologically-Active Peptides with the UNRES Coarse-Grained Model

et al. 2022

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“…Structural Characterization. The simulations were analyzed using a combination of standard GROMACS tools, VMD, 57 and in-house Python scripts 58 invoking the MDAnalysis 59 and MDTraj 60 libraries. We determined the representative monomer structures of the peptides using the Gromacs clustering tool of Daura et al 61 with a cutoff of the root-mean-square deviation (RMSD) of 0.2 nm.…”

Section: Simulation Detailsmentioning

confidence: 99%

“…For the characterization of the intermediate oligomeric states and the transitions between them, we calculated transition networks (TNs). 34,58 As in our preceding study, 12 we chose to build the TNs in a two-dimensional space defined by β-strand content (x-axis) and oligomer size (y-axis) (Figure 5). The TNs for A99-d confirm that this FF does not support stable oligomer formation; for all three peptides the most populated state is the extended monomer structure.…”

Section: Structural Characterization Of the Aβ 16−22mentioning

confidence: 99%

Different Force Fields Give Rise to Different Amyloid Aggregation Pathways in Molecular Dynamics Simulations

Samantray

Yin

Kav

et al. 2020

J. Chem. Inf. Model.

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110

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The progress towards understanding the molecular basis of Alzheimers's disease is strongly connected to elucidating the early aggregation events of the amyloid-β (Aβ) peptide. Molecular dynamics (MD) simulations provide a viable technique to study the aggregation of Aβ into oligomers with high spatial and temporal resolution. However, the results of an MD simulation can only be as good as the underlying force field. A recent study by our group showed that none of the force fields tested can distinguish between aggregation-prone and non-aggregating peptide sequences, producing the same and in most cases too fast aggregation kinetics for all peptides. Since then, new force fields specially designed for intrinsically disordered proteins such as Aβ were developed. Here, we assess the applicability of these new force fields to studying peptide aggregation using the Aβ 16−22 peptide and mutations of it as test case. We investigate their performance in modeling the monomeric state, the aggregation into oligomers, and the stability of the aggregation end product, i.e., the fibrillar state. A main finding is that changing the force field has a stronger effect on the simulated aggregation pathway than changing the peptide sequence. Also the new force fields are not able to reproduce the experimental aggregation propensity order of the peptides. Dissecting the various energy contributions shows that AMBER99SB-disp overestimates the interactions between the peptides and water, thereby inhibiting peptide aggregation.More promising results are obtained with CHARMM36m and especially its version with increased protein-water interactions. It is thus recommended to use this force field for peptide aggregation simulations and base future reparameterizations on it. IntroductionIntrinsically disordered proteins (IDPs) are a class of proteins which are either completely unstructured or contain large disordered regions in their native state, which comes with a high tendency towards self-assembly leading to non-toxic or toxic aggregates and fibrils that may be related to diseases. 1 One of the IDPs is the amyloid-beta peptide (Aβ), forming 2

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“…All simulation were realized with GROMACS version 2018.3. 40−42 For the analysis of the simulation trajectories, we employed a combination of standard GROMACS tools, VMD, 43 and in-house Python scripts 28,44 invoking the MDAnalysis 45 and MDTraj 46 libraries.…”

Section: Model Systemsmentioning

confidence: 99%

The Effects of Different Glycosaminoglycans on the Structure and Aggregation of the Amyloid-β (16–22) Peptide

Samantray

Strodel

2021

J. Phys. Chem. B

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Aggregates of the amyloid-β (Aβ) peptide are implicated as a causative substance in Alzheimer’s disease. Molecular dynamics simulations provide valuable contributions for elucidating the conformational transitions of monomeric and aggregated forms of Aβ be it in solution or in the presence of other molecules. Here, we study the effects of four different glycosaminoglycans (GAGs), three sulfated ones and a nonsulfated one, on the aggregation of Aβ16–22. From experiments, it has been suggested that GAGs, which belong to the main components of the brain’s extracellular space, favor amyloid fibril formation. Our simulation results reveal that the binding of Aβ16–22 to the GAGs is driven by electrostatic attraction between the negative GAG charges and the positively charged K16 of Aβ16–22. While these interactions have only minor effects on the GAG and Aβ16–22 conformations at the 1 Aβ16–22/1 GAG ratio, at the 2:2 stoichiometry the aggregation of Aβ16–22 is considerably changed. In solution, the aggregation of Aβ16–22 is strongly influenced by K16–E22 attraction, leading to antiparallel β-sheets. In the presence of GAGs, on the other hand, the interaction of K16 with the GAGs increases the importance of the hydrophobic interactions during Aβ16–22 aggregation, which in turn yields parallel alignments. A templating and ordering effect of the GAGs on the Aβ16–22 aggregates is observed. In summary, this study provides new insight at the atomic level on GAG–amyloid interactions, strengthening the view that sulfation of the GAGs plays a major role in this context.

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Molecular Dynamics Simulations of Protein Aggregation: Protocols for Simulation Setup and Analysis with Markov State Models and Transition Networks

Cited by 20 publications

References 37 publications

Prediction of Aggregation of Biologically-Active Peptides with the UNRES Coarse-Grained Model

Prediction of Aggregation of Biologically-Active Peptides with the UNRES Coarse-Grained Model

Different Force Fields Give Rise to Different Amyloid Aggregation Pathways in Molecular Dynamics Simulations

The Effects of Different Glycosaminoglycans on the Structure and Aggregation of the Amyloid-β (16–22) Peptide

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