title: Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification.
abstract: Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%).
authors.affiliation: Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan.
authors.forename: Hsin-Wei
authors.initials: HW
authors.lastname: Wang
authors.forename: Ya-Chi
authors.initials: YC
authors.lastname: Lin
authors.forename: Tun-Wen
authors.initials: TW
authors.lastname: Pai
authors.forename: Hao-Teng
authors.initials: HT
authors.lastname: Chang
mesh_terms: D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D018985:Epitopes, B-Lymphocyte / Q000032:analysis* / Q000737:chemistry; D008958:Models, Molecular; D015233:Models, Statistical; D060388:Support Vector Machine; D014584:User-Computer Interface
chemical_list: D018985:Epitopes, B-Lymphocyte