In this study, we propose a Kernel-PCA model designed to capture structure-function relationships in a protein. This model also enables 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 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 network-based approach to uncover correlations in the dynamic behavior of residues 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.
@article{arxiv.2503.19186,
title = {Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identification},
author = {Parisa Mollaei and Amir Barati Farimani},
journal= {arXiv preprint arXiv:2503.19186},
year = {2025}
}