Bio-support vector machines for computational proteomics.
BACKGROUND
One of the most important issues in computational proteomics is to produce a prediction model for the classification or annotation of biological function of novel protein sequences. In order to improve the prediction accuracy, much attention has been paid to the improvement of the performance of the algorithms used, few is for solving the fundamental issue, namely, amino acid encoding as most existing pattern recognition algorithms are unable to recognize amino acids in protein sequences. Importantly, the most commonly used amino acid encoding method has the flaw that leads to large computational cost and recognition bias.


RESULTS
By replacing kernel functions of support vector machines (SVMs) with amino acid similarity measurement matrices, we have modified SVMs, a new type of pattern recognition algorithm for analysing protein sequences, particularly for proteolytic cleavage site prediction. We refer to the modified SVMs as bio-support vector machine. When applied to the prediction of HIV protease cleavage sites, the new method has shown a remarkable advantage in reducing the model complexity and enhancing the model robustness.
Yang
Zheng Rong
ZR
Department of Computer Science, Exeter University, Exeter EX4 4PT, UK. Z.R.Yang@exeter.ac.uk
Chou
Kuo-Chen
KC
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods; D003205:Computing Methodologies; D030562:Databases, Protein; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D020543:Proteome / Q000737:chemistry / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship
D011506:Proteins; D020543:Proteome