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 pro

SUBMITTER: Mollaei P 

PROVIDER: S-EPMC12288012 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature