Peptide Vaccine Models Using Statistical Data Mining

Author: Rajani R. Joshi,

Source: Protein and Peptide Letters, Volume 14, Number 6, June 2007 , pp. 536-542(7)

Publisher: Bentham Science Publishers

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Abstract:

Design and synthesis of peptide vaccines is of significant pharmaceutical importance. A knowledge based statistical model is fitted here for prediction of binding of an antigenic site of a protein or a B-cell epitope on a CDR (complementarity determining region) of an immunoglobulin. Linear analogues of the 3D structure of the epitopes are computed using this model. Extension for prediction of peptide epitopes from the protein sequence alone is also presented. Validation results show promising potential of this approach in computer-aided peptide vaccine production. The computed probabilities of binding also provide a pioneering approach for ab-initio prediction of `potency' of protein or peptide vaccines modeled by this method.

Keywords: B-cell epitope; paratope; logistic regression; sequence-alignment; solvent accessibility; heuristics

Document Type: Research article

Publication date: 2007-06-01

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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.