title: Methods for Detecting Critical Residues in Proteins.
abstract: In proteins, certain amino acids may play a critical role in determining their structure and function. Examples include flexible regions, which allow domain motions, and highly conserved residues on functional interfaces, which play a role in binding and interaction with other proteins. Detecting these regions facilitates the analysis and simulation of protein rigidity and conformational changes, and aids in characterizing protein-protein binding. We present a protocol that combines graph-theory rigidity analysis and machine-learning-based methods for predicting critical residues in proteins. Our approach combines amino-acid specific information and data obtained by two complementary methods. One method, KINARI, performs graph-based analysis to find rigid clusters of amino acids in a protein, while the other method relies on evolutionary conservation scores to find functional interfaces in proteins. Our machine learning model combines both methods, in addition to amino acid type and solvent-accessible surface area.
authors.affiliation: Department of Computer Science, University of Massachusetts Boston, 100 Morrissey Blvd., Boston, MA, 02125, USA. nurit.haspel@umb.edu.
authors.forename: Nurit
authors.initials: N
authors.lastname: Haspel
authors.affiliation: Department of Computer Science, Western Washington University, Bellingham, WA, 98225, USA.
authors.forename: Filip
authors.initials: F
authors.lastname: Jagodzinski
mesh_terms: D000596:Amino Acids / Q000235:genetics; D001665:Binding Sites / Q000235:genetics*; D019295:Computational Biology / Q000379:methods; D008958:Models, Molecular; D011485:Protein Binding / Q000235:genetics; D060066:Protein Interaction Maps / Q000235:genetics; D011506:Proteins / Q000235:genetics*
chemical_list: D000596:Amino Acids; D011506:Proteins
keywords: Docking; Evolutionary conservation; Machine learning; Protein binding interfaces; Protein–protein interaction