Protein homology detection using string alignment kernels.
BACKGROUND
Remote homology detection between protein sequences is a central problem in computational biology. Discriminative methods involving support vector machines (SVMs) are currently the most effective methods for the problem of superfamily recognition in the Structural Classification Of Proteins (SCOP) database. The performance of SVMs depends critically on the kernel function used to quantify the similarity between sequences.


RESULTS
We propose new kernels for strings adapted to biological sequences, which we call local alignment kernels. These kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences. When tested in combination with SVM on their ability to recognize SCOP superfamilies on a benchmark dataset, the new kernels outperform state-of-the-art methods for remote homology detection.


BACKGROUND
Software and data available upon request.
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan.
Hiroto
H
Saigo
Jean-Philippe
JP
Vert
Nobuhisa
N
Ueda
Tatsuya
T
Akutsu
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D003205:Computing Methodologies; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
D011506:Proteins