Positive-unlabeled learning for the prediction of conformational B-cell epitopes. Issue 18 (December 2015)

• Record Type: Journal Article
• Authors: Ren, Jing; Liu, Qian; Ellis, John; Li, Jinyan
• Is Part Of: BMC bioinformatics. Volume 16:Issue 18(2015)
• Page Start: 1
• Page End: 15
• Publication Date: 2015-12
• Subjects: epitope prediction -- positive-unlabeled learning -- unbound structure -- epitopes of Ebola antigen -- species-specific analysis; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/1471-2105-16-S18-S12
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background The incomplete ground truth of training data of B-cell epitopes is a demanding issue in computational epitope prediction. The challenge is that only a small fraction of the surface residues of an antigen are confirmed as antigenic residues (positive training data); the remaining residues are unlabeled. As some of these uncertain residues can possibly be grouped to form novel but currently unknown epitopes, it is misguided to unanimously classify all the unlabeled residues as negative training data following the traditional supervised learning scheme. Results We propose a positive-unlabeled learning algorithm to address this problem. The key idea is to distinguish between epitope-likely residues and reliable negative residues in unlabeled data. The method has two steps: (1) identify reliable negative residues using a weighted SVM with a high recall; and (2) construct a classification model on the positive residues and the reliable negative residues. Complex-based 10-fold cross-validation was conducted to show that this method outperforms those commonly used predictors DiscoTope 2.0, ElliPro and SEPPA 2.0 in every aspect. We conducted four case studies, in which the approach was tested on antigens of West Nile virus, dihydrofolate reductase, beta-lactamase, and two Ebola antigens whose epitopes are currently unknown. All the results were assessed on a newly-established data set of antigen structures not bound by antibodies, instead of on antibody-bound antigen structures. These bound structures may contain unfair binding information such as bound-state B-factors and protrusion index which could exaggerate the epitope prediction performance. Source codes are available on request.