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Prediction of Protein B-Factors Using Multi-Class Bounded SVM

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In this paper, we propose the adoption of the bounded support vector machine (BSVM) to predict the B-factors of residues based on a number of distinctive properties of residues. Due to the ability of multi-class classification of the BSVM, we can elaborately distinguish our targets and obtain relatively higher accuracy.

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Keywords: B-factors; Bounded Support Vector Machine; Evolutionary rate; Hydrophobicity profile; Sequence profile

Document Type: Research Article

Affiliations: Intelligent Computing Lab,Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei Anhui, 230031, China.

Publication date: February 1, 2007

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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