Generating protein folding trajectories using contact-map-driven directed walks Ziad Fakhoury , Gabrielel Sosso and Scott Habershon Department of Chemistry, Warwick University, UK 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.
P23
© The Author(s), 2023
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