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Dual-Layer Wavelet SVM for Predicting Protein Structural Class Via the General Form of Chou's Pseudo Amino Acid Composition

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Abstract:

A prior knowledge of protein structural class can provide useful information about its overall structure. So, it is vitally important to develop a computational prediction method for fast and accurately determining the protein structural class. In this paper, a dual-layer wavelet support vector machine (WSVM) is presented via the general form of Chou's pseudo amino acid composition, which is featured by introducing wavelet as a kernel and making decisions by the fusion from three individual classifiers. As a demonstration, the rigorous jackknife cross-validation tests were performed on two benchmark datasets, including the more challenging 25PDB dataset. Our success rates were reliable, and it has not escaped from our notice that the present method has specific ability to predict the most difficult case of α+β class. The program developed can be acquired freely on request from the authors.

Keywords: Gaussian kernel; NCBI's RefSeq database; Wavelet support vector machine; algorithms; fusion; in-silico; non-redundant protein; protein structural class; pseudo amino acid composition; tertiary structure

Document Type: Research Article

DOI: http://dx.doi.org/10.2174/092986612799789332

Publication date: February 1, 2012

More about this publication?
  • 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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