Systematic characterization of protein folding pathways using diffusion maps: application to Trp-cage miniprotein

• Published in: The Journal of chemical physics 2015-02, Vol.142 (8), p.085101-085101
• Main Authors: Kim, Sang Beom, Dsilva, Carmeline J, Kevrekidis, Ioannis G, Debenedetti, Pablo G
• Format: Article
• Language: English
• Subjects: Amino acids; Cages; Diffusion; Eigenvectors; Folding; Molecular dynamics; Molecular Dynamics Simulation; Order parameters; Peptides - chemistry; Protein Folding; Proteins; Simulation; Trajectories
• ISSN: 0021-9606; 1089-7690
• DOI: 10.1063/1.4913322

Understanding the mechanisms by which proteins fold from disordered amino-acid chains to spatially ordered structures remains an area of active inquiry. Molecular simulations can provide atomistic details of the folding dynamics which complement experimental findings. Conventional order parameters, such as root-mean-square deviation and radius of gyration, provide structural information but fail to capture the underlying dynamics of the protein folding process. It is therefore advantageous to adopt a method that can systematically analyze simulation data to extract relevant structural as well as dynamical information. The nonlinear dimensionality reduction technique known as diffusion maps automatically embeds the high-dimensional folding trajectories in a lower-dimensional space from which one can more easily visualize folding pathways, assuming the data lie approximately on a lower-dimensional manifold. The eigenvectors that parametrize the low-dimensional space, furthermore, are determined systematically, rather than chosen heuristically, as is done with phenomenological order parameters. We demonstrate that diffusion maps can effectively characterize the folding process of a Trp-cage miniprotein. By embedding molecular dynamics simulation trajectories of Trp-cage folding in diffusion maps space, we identify two folding pathways and intermediate structures that are consistent with the previous studies, demonstrating that this technique can be employed as an effective way of analyzing and constructing protein folding pathways from molecular simulations.