Generating Protein Folding Trajectories Using Contact-Map-Driven Directed Walks

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 identifying 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 the choice of a specific order parameter to drive the folding process. As such, our approach offers a useful new route for studying the protein-folding problem.

Automated summary

Recent advances in machine learning methods have made progress in protein structure prediction, but accurately generating and characterizing protein-folding pathways is still challenging. In this study, a directed walk strategy using the residue-level contact-map space was employed to generate protein folding trajectories. The strategy considers protein folding as transitions between connected minima on the potential energy surface. The generated folding paths were validated against direct molecular dynamics simulations, demonstrating the potential of this approach for studying protein folding.