2026-10-14T12:24:11Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33861064 2026-09-16T16:14:42Z portal_1197 item_type_3 month_year_09_2026

Dataset for "Generating protein folding trajectories using contact-map-driven directed walks" Ziad Fakhoury (24687072) Gabriele C. Sosso (1304346) Scott Habershon (1263891) Migrated from ePrints 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. 2023-04-10T00:00:00Z Dataset Dataset 10.82444/warw.33861064.v1 https://figshare.com/articles/dataset/Dataset_for_Generating_protein_folding_trajectories_using_contact-map-driven_directed_walks_/33861064 CC BY 4.0