Structural classification of protein sequences based on signal processing and support vector machines

Abstract

The function of any protein depends directly on its secondary and tertiary structure. Proteins can fold into a three-dimensional shape, which is primarily depended on the arrangement of amino acids in the primary structure. In recent years, with the explosive sequencing of proteins, it is unfeasible to perform detailed experimental studies, as these methodologies are very expensive and time consuming. This leaves the structure of the majority of currently available protein sequences unknown. In this paper, a predictive model is therefore presented for the classification of protein sequence's secondary structures, namely alpha helix and beta sheet. The proteins used throughout this study were collected from the Structural Classification of Proteinsextended (SCOPe) database, which contains manually curated information from proteins with known structure. Two sets of proteins are used for all alpha and all beta protein sequences. The first set comprise of sequences with less than 40% identity, and the second set comprise of proteins with less than 95% identity. The analysis shows a strong connection between the amino acid indices used to convert protein sequences to numerical sequences and proteins' secondary structures. The total classification accuracy for the proposed classifier for the protein sequences with less than 40% identity for amino acid index BIOV880101 and BIOV880102 are 78.49% and 76.40%, respectively. The classification accuracy for sets of protein sequences with less than 95% identity for amino acid index BIOV880101 and BIOV880102 are 88.01% and 85.17%, respectively.