title: Predicting flexible length linear B-cell epitopes.
abstract: Identifying B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting B-cell epitopes are highly desirable. We explore two machine learning approaches for predicting flexible length linear B-cell epitopes. The first approach utilizes four sequence kernels for determining a similarity score between any arbitrary pair of variable length sequences. The second approach utilizes four different methods of mapping a variable length sequence into a fixed length feature vector. Based on our empirical comparisons, we propose FBCPred, a novel method for predicting flexible length linear B-cell epitopes using the subsequence kernel. Our results demonstrate that FBCPred significantly outperforms all other classifiers evaluated in this study. An implementation of FBCPred and the datasets used in this study are publicly available through our linear B-cell epitope prediction server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/.
authors.affiliation: Artificial Intelligence Laboratory, Iowa State University, Ames, IA 50010, USA. yasser@iastate.edu
authors.forename: Yasser
authors.initials: Y
authors.lastname: El-Manzalawy
authors.forename: Drena
authors.initials: D
authors.lastname: Dobbs
authors.forename: Vasant
authors.initials: V
authors.lastname: Honavar
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D003198:Computer Simulation; D018604:Epitope Mapping / Q000379:methods*; D018985:Epitopes, B-Lymphocyte / Q000737:chemistry*; D016014:Linear Models; D008956:Models, Chemical; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D018985:Epitopes, B-Lymphocyte