@article {Rajani:2007:0929-8665:536,
title = "Peptide Vaccine Models Using Statistical Data Mining",
journal = "Protein and Peptide Letters",
parent_itemid = "infobike://ben/ppl",
publishercode ="ben",
year = "2007",
volume = "14",
number = "6",
publication date ="2007-06-01T00:00:00",
pages = "536-542",
itemtype = "ARTICLE",
issn = "0929-8665",
url = "https://www.ingentaconnect.com/content/ben/ppl/2007/00000014/00000006/art00006",
doi = "doi:10.2174/092986607780990000",
keyword = "paratope, logistic regression, solvent accessibility, heuristics, sequence-alignment, B-cell epitope",
author = "Rajani R. Joshi",
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. ",
}