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Table · dataset · 2023

Dataset for "Generating protein folding trajectories using contact-map-driven directed walks"

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Recent advances in machine learning methods have had a significant impact on protein structure prediction, but accurate generation and characterization of protein-folding pathways remains intractable.

Description

Here, we demonstrate how protein folding trajectories can be generated using a directed walk strategy operating in the space defined by the residue-level contact-map. This double-ended strategy views protein folding as a series of discrete transitions between connected minima on the potential energy surface.

Subsequent reaction-path analysis for each transition enables thermodynamic and kinetic characterization of each protein-folding path. We validate the protein-folding paths generated by our discretized-walk strategy against direct molecular dynamics simulations for a series of model coarse-grained proteins constructed from hydrophobic and polar residues. This comparison demonstrates that ranking discretized paths based on the intermediate energy barriers provides a convenient route to generating physically-sensible folding ensembles.

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Importantly, by using directed walks in the protein contact-map space, we circumvent several of the traditional challenges associated with protein-folding studies, namely long time-scales required and unknown order parameters. As such, our approach offers a useful new route for studying the protein-folding problem.

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Inferred from text
Protein trafficking 76%
Provenance · 1 source records, 17 field assertions
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figshareoai:figshare.com:article/3386106412 d agoJSON v1
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