pyrexMD: Workflow-Orientated Python Package for Replica Exchange Molecular Dynamics

Voronin, Arthur; Schug, Alexander

doi:10.21105/joss.03325

Cited by 2 publications

References 17 publications

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Selection of representative structures from large biomolecular ensembles

Voronin

Schug

2022

The Journal of Chemical Physics

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Despite the incredible progress of experimental techniques, protein structure determination still remains a challenging task. Due to the rapid improvements of computer technology, simulations are often used to complement or interpret experimental data, in particular for sparse or low-resolution data. Many such in silico methods allow to obtain highly accurate models of a protein structure either de novo or via refinement of a physical model with experimental restraints. One crucial question is how to select a representative member or ensemble out of vast number of computationally generated structures. Here, we introduce such a method. As a representative task, we add co-evolutionary contact pairs as distance restraints to a physical force field and want to select a good characterization of the resulting native-like ensemble. To generate large ensembles, we run replica-exchange molecular dynamics (REMD) on five mid-sized test proteins and over a wide temperature range. High temperatures allow overcoming energetic barriers while low temperatures perform local searches of native-like conformations. The integrated bias is based on co-evolutionary contact pairs derived from a deep residual neural network to guide the simulation towards native-like conformations. We shortly compare and discuss the achieved model precision of contact-guided REMD for mid-sized proteins. Lastly, we discuss four robust ensemble-selection algorithms in great detail which are capable to extract the representative structure models with a high certainty. To assess the performance of the selection algorithms we exemplarily mimic a "blind scenario', i.e. where the target structure is unknown, and select a representative structural ensemble of native-like folds.

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Selection of representative structures from large biomolecular ensembles

Voronin

Schug

2022

The Journal of Chemical Physics

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Influence of Contact Map Topology on RNA Structure Prediction

Faber,

Upadhyay,

Taubert

et al. 2024

Preprint

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The available sequence data of RNA molecules have highly increased in the past years. Unfortunately, while computational power is still under exponential growth, the computer prediction quality from sequence to final structure is still inferior to the labour intensive experimental work. Although a reliable end-to-end procedure was already developed for proteins since Alphafold2, while its successor AlphaFold3 can also predict RNA, its confidence in particular for novel sequences and folds appear still limited. Another strategy entails two steps: (i) predicting potential contacts in the form of a contact maps from evolutionary data, and (ii) simulating the molecule with a physical force field while using the contact map as restraint. However, the quality of the structure prediction crucially depends on the quality of the contact map. Until now, only the proportion of true positive contacts was considered as a quality characteristic. We propose to also include the distribution of these contacts, and have done so in our recent studies. We observed that the clustering of contacts, as is common for many AI algorithms, has a negative impact on prediction quality. In contrast, a more distributed topology is beneficial. We have applied these findings from computer experiments to current algorithms and introduced a measure of distribution, the Gaussian score.

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pyrexMD: Workflow-Orientated Python Package for Replica Exchange Molecular Dynamics

Cited by 2 publications

References 17 publications

Selection of representative structures from large biomolecular ensembles

Selection of representative structures from large biomolecular ensembles

Influence of Contact Map Topology on RNA Structure Prediction

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