A discriminative method for remote homology detection based on n-peptide compositions with reduced amino acid alphabets.
In this study, n-peptide compositions are utilized for protein vectorization over a discriminative remote homology detection framework based on support vector machines (SVMs). The size of amino acid alphabet is gradually reduced for increasing values of n to make the method to conform with the memory resources in conventional workstations. A hash structure is implemented for accelerated search of n-peptides. The method is tested to see its ability to classify proteins into families on a subset of SCOP family database and compared against many of the existing homology detection methods including the most popular generative methods; SAM-98 and PSI-BLAST and the recent SVM methods; SVM-Fisher, SVM-BLAST and SVM-Pairwise. The results have demonstrated that the new method significantly outperforms SVM-Fisher, SVM-BLAST, SAM-98 and PSI-BLAST, while achieving a comparable accuracy with SVM-Pairwise. In terms of efficiency, it performs much better than SVM-Pairwise. It is shown that the information of n-peptide compositions with reduced amino acid alphabets provides an accurate and efficient means of protein vectorization for SVM-based sequence classification.
Oğul
Hasan
H
Department of Computer Engineering, Başkent University, 06530 Ankara, Turkey. hogul@baskent.edu.tr
Mumcuoğlu
Erkan U
EU
D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D008962:Models, Theoretical; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry*
D000596:Amino Acids; D010455:Peptides