Paper Info
Title
An Evolutionary Conservation & Rigidity Analysis Machine Learning Approach for Detecting Critical Protein Residues
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 machine-learning based method for the analysis and prediction of critical residues in proteins. We combine amino-acid specific information and data obtained by two complementary methods. One method, KINARI-Mutagen, performs graph-based analysis to find rigid clusters of amino acids in a protein, and the other method uses evolutionary conservation scores to find functional interfaces in proteins. We devised a machine learning model that combines both methods, in addition to amino acid type and solvent accessible surface area, to a dataset of proteins with experimentally known critical residues, and were able to achieve over 77% prediction rate, more than either of the methods separately.
Year
DOI
Venue
2013
10.1145/2506583.2506708
BCB
Keywords
Field
DocType
certain amino acid,amino acid type,critical residue,graph-based analysis,complementary method,amino acid,critical role,functional interface,protein rigidity,critical protein residues,evolutionary conservation,prediction rate,rigidity analysis machine learning,rigidity,machine learning,conservation
Rigidity (psychology),Graph,Cluster (physics),Conserved sequence,Structure and function,Amino acid,Computer science,Accessible surface area,Artificial intelligence,Bioinformatics,Machine learning
Conference
Citations 
PageRank 
References 
5
0.57
5
Authors
3
Name
Order
Citations
PageRank
Filip Jagodzinski17114.83
Bahar Akbal-Delibas2707.95
Nurit Haspel36014.11