title: Epitope prediction based on random peptide library screening: benchmark dataset and prediction tools evaluation.
abstract: Epitope prediction based on random peptide library screening has become a focus as a promising method in immunoinformatics research. Some novel software and web-based servers have been proposed in recent years and have succeeded in given test cases. However, since the number of available mimotopes with the relevant structure of template-target complex is limited, a systematic evaluation of these methods is still absent. In this study, a new benchmark dataset was defined. Using this benchmark dataset and a representative dataset, five examples of the most popular epitope prediction software products which are based on random peptide library screening have been evaluated. Using the benchmark dataset, in no method did performance exceed a 0.42 precision and 0.37 sensitivity, and the MCC scores suggest that the epitope prediction results of these software programs are greater than random prediction about 0.09-0.13; while using the representative dataset, most of the values of these performance measures are slightly improved, but the overall performance is still not satisfactory. Many test cases in the benchmark dataset cannot be applied to these pieces of software due to software limitations. Moreover chances are that these software products are overfitted to the small dataset and will fail in other cases. Therefore finding the correlation between mimotopes and genuine epitope residues is still far from resolved and much larger dataset for mimotope-based epitope prediction is desirable.
authors.affiliation: Faculty of Chemistry, Northeast Normal University, Changchun 130024, China.
authors.forename: Pingping
authors.initials: P
authors.lastname: Sun
authors.forename: Wenhan
authors.initials: W
authors.lastname: Chen
authors.forename: Yanxin
authors.initials: Y
authors.lastname: Huang
authors.forename: Hongyan
authors.initials: H
authors.lastname: Wang
authors.forename: Zhiqiang
authors.initials: Z
authors.lastname: Ma
authors.forename: Yinghua
authors.initials: Y
authors.lastname: Lv
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D016208:Databases, Factual; D018604:Epitope Mapping; D018985:Epitopes, B-Lymphocyte / Q000737:chemistry*; D019151:Peptide Library; D012680:Sensitivity and Specificity; D012984:Software
chemical_list: D018985:Epitopes, B-Lymphocyte; D019151:Peptide Library