title: Support vector machines for learning to identify the critical positions of a protein.
abstract: A method for identifying the positions in the amino acid sequence, which are critical for the catalytic activity of a protein using support vector machines (SVMs) is introduced and analysed. SVMs are supported by an efficient learning algorithm and can utilize some prior knowledge about the structure of the problem. The amino acid sequences of the variants of a protein, created by inducing mutations, along with their fitness are required as input data by the method to predict its critical positions. To investigate the performance of this algorithm, variants of the beta-lactamase enzyme were created in silico using simulations of both mutagenesis and recombination protocols. Results from literature on beta-lactamase were used to test the accuracy of this method. It was also compared with the results from a simple search algorithm. The algorithm was also shown to be able to predict critical positions that can tolerate two different amino acids and retain function.
authors.affiliation: School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0100, USA.
authors.forename: Anshul
authors.initials: A
authors.lastname: Dubey
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Realff
authors.forename: Jay H
authors.initials: JH
authors.lastname: Lee
authors.forename: Andreas S
authors.initials: AS
authors.lastname: Bommarius
mesh_terms: D000465:Algorithms; D000596:Amino Acids; D000818:Animals; D019143:Evolution, Molecular; D008957:Models, Genetic; D020539:Sequence Analysis, Protein; D001618:beta-Lactamases / Q000235:genetics
chemical_list: D000596:Amino Acids; D001618:beta-Lactamases