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Dataset for "Generating protein folding trajectories using contact-map-driven directed walks"
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Fakhoury, Ziad, Sosso, Gabriele C. and Habershon, Scott (2023) Dataset for "Generating protein folding trajectories using contact-map-driven directed walks". [Dataset] (In Press)
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Wrap_Data.zip - Unspecified Version Available under License Creative Commons Attribution 4.0. Download (248Kb) |
Official URL: https://doi.org/10.1021/acs.jcim.3c00023
Abstract
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.
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. 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.
Item Type: | Dataset | ||||||||
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Subjects: | Q Science > QD Chemistry | ||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Chemistry | ||||||||
Type of Data: | ASCII text files in csv format, in addition to python plotting file. | ||||||||
Publisher: | American Chemical Society | ||||||||
Official Date: | 10 April 2023 | ||||||||
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Status: | Peer Reviewed | ||||||||
Publication Status: | In Press | ||||||||
Media of Output (format): | .csv, .py | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
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