Higher Accuracy Achieved in the Simulations of Protein Structure Refinement, Protein Folding, and Intrinsically Disordered Proteins Using Polarizable Force Fields

2018 ◽  
Vol 9 (24) ◽  
pp. 7110-7116 ◽  
Author(s):  
Anhui Wang ◽  
Zhichao Zhang ◽  
Guohui Li
Keyword(s):  
Protein Folding ◽  
Protein Structure ◽  
Intrinsically Disordered Proteins ◽  
Force Fields ◽  
Structure Refinement ◽  
Disordered Proteins ◽  
Polarizable Force Fields ◽  
Intrinsically Disordered ◽  
Protein Structure Refinement

scholarly journals Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures

Biomolecules ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1788
Author(s):  
Vy T. Duong ◽  
Elizabeth M. Diessner ◽  
Gianmarc Grazioli ◽  
Rachel W. Martin ◽  
Carter T. Butts
Keyword(s):  
Protein Structure ◽  
Intrinsically Disordered Proteins ◽  
Dynamic Models ◽  
Structural Information ◽  
Coarse Graining ◽  
Molecular Structures ◽  
Disordered Proteins ◽  
Intrinsically Disordered ◽  
Scalable Network ◽  
Atomic Coordinates

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.

scholarly journals Different force fields give rise to different amyloid aggregation pathways in molecular dynamics simulations

2020 ◽  
Author(s):  
Suman Samantray ◽  
Feng Yin ◽  
Batuhan Kav ◽  
Birgit Strodel
Keyword(s):  
Molecular Dynamics ◽  
Force Field ◽  
Intrinsically Disordered Proteins ◽  
Force Fields ◽  
Md Simulations ◽  
Peptide Sequence ◽  
Disordered Proteins ◽  
Test Case ◽  
Peptide Aggregation ◽  
Intrinsically Disordered

AbstractThe 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 Aβ16−22 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.

The accuracy of force fields on the simulation of intrinsically disordered proteins: A benchmark test on the human p53 tumor suppressor

2020 ◽  
Vol 19 (04) ◽  
pp. 2050011
Author(s):  
Shangbo Ning ◽  
Jun Liu ◽  
Na Liu ◽  
Dazhong Yan
Keyword(s):  
Md Simulation ◽  
Intrinsically Disordered Proteins ◽  
Force Fields ◽  
Chemical Shifts ◽  
Disordered Proteins ◽  
Experimental Measurements ◽  
Intrinsically Disordered ◽  
Structural Ensembles ◽  
Water Models ◽  
Structural Ensemble

Intrinsically disordered proteins (IDPs) are a class of proteins without stable three-dimensional structures under physiological conditions. IDPs exhibit high dynamic nature and could be described by structural ensembles. As one of the most widely used tools, molecular dynamics (MD) simulation could provide the atomic descriptions of the structural ensemble of IDPs. However, the accuracy of the MD simulation largely depends on the accuracy of the force field. In this paper, we compared the structural ensembles of the activation domain 1 (AD1) in p53 tumor suppressor obtained from the widely used force fields, AMBER99SB-ILDN, CHARMM27, CHARMM36m with different water models. The results show that CHARMM36m generates more extended conformations than other force fields, while CHARMM27 prefers to sample the [Formula: see text]-helical structure. Moreover, the chemical shifts obtained by CHARMM36m are the closest to the experimental measurements. These results indicate that the CHARMM36m force field performs best in characterizing the structure properties of p53 AD1. Water models are also critical to describe the structural ensemble of IDPs. TIP4P water model can obtain more extended conformations and produce more local helical conformations than the TIP3P model in our simulation. In addition, we also compare the chemical shifts predicted by different chemical shift predicting programs with experimental measurements, the results show that SHIFTX2 obtains the best performance in the chemical shifts prediction.

Comparison and Evaluation of Force Fields for Intrinsically Disordered Proteins

2020 ◽  
Vol 60 (10) ◽  
pp. 4912-4923 ◽  
Author(s):  
Mueed Ur Rahman ◽  
Ashfaq Ur Rehman ◽  
Hao Liu ◽  
Hai-Feng Chen
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Force Fields ◽  
Disordered Proteins ◽  
Intrinsically Disordered
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