Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identification.

Abstract

In this study, we propose a Kernel-PCA model designed to capture structure-function relationships in a protein. This model also enables the ranking of reaction coordinates according to their impact on protein properties. By leveraging machine learning techniques, including Kernel and principal component analysis (PCA), our model uncovers meaningful patterns in the high-dimensional protein data obtained from molecular dynamics (MD) simulations. The effectiveness of our model in accurately identifying reaction coordinates has been demonstrated through its application to a G protein-coupled receptor. Furthermore, this model utilizes a residue-level dynamical network approach to uncover correlations in the structural dynamics of residues that are strongly associated with a specific protein property. These findings underscore the potential of our model as a powerful tool for protein structure-function analysis and visualization.