Epitope Prediction Based on Random Peptide Library Screening: Benchmark Dataset and Prediction Tools Evaluation

Molecules

Pingping Sun¹  Wenhan Chen¹  Yanxin Huang¹  Hongyan Wang¹  Zhiqiang Ma¹ 

[1] ¹Faculty of Chemistry, Northeast Normal University, Changchun 130024, China ²School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China ³National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China

关键词: epitope prediction;    mimotope;    template-target complex;    benchmark dataset;    evaluation;   

DOI  :  10.3390/molecules16064971

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.