A Machine Learning Method to Characterize Conformational Changes of Amino Acids in Proteins
Amino acid dynamics are significant in determining the overall function, structure, stability, and activity of proteins. However, atomic-level descriptions of the structural features of proteins are limited by the current resolutions of experimental and computational techniques. In this study, we developed a machine learning (ML) framework for characterizing the individual aminoacids dynamic in a protein and compute its contribution to the overall function of proteins. This framewor identifies specific types of angular features in amino acids, such as bimodal-switch residues. It can assist in the analysis of various protein characteristics and provide valuable insights into the dynamic behavior of individual amino acids within a protein structure. We found that there is a strong correlation between a specific type of bimodal-switch residues and the global features in proteins. This knowledge can help us to identify key residues that are strongly correlated to the overall function of the protein.