Sweredoski Michael J, Baldi Pierre
Department of Computer Science, University of California, Irvine, 92697-3435, USA.
Protein Eng Des Sel. 2009 Mar;22(3):113-20. doi: 10.1093/protein/gzn075. Epub 2008 Dec 10.
Accurate prediction of B-cell epitopes has remained a challenging task in computational immunology despite several decades of research. Only 10% of the known B-cell epitopes are estimated to be continuous, yet they are often the targets of predictors because a solved tertiary structure is not required and they are integral to the development of peptide vaccines and engineering therapeutic proteins. In this article, we present COBEpro, a novel two-step system for predicting continuous B-cell epitopes. COBEpro is capable of assigning epitopic propensity scores to both standalone peptide fragments and residues within an antigen sequence. COBEpro first uses a support vector machine to make predictions on short peptide fragments within the query antigen sequence and then calculates an epitopic propensity score for each residue based on the fragment predictions. Secondary structure and solvent accessibility information (either predicted or exact) can be incorporated to improve performance. COBEpro achieved a cross-validated area under the curve (AUC) of the receiver operating characteristic up to 0.829 on the fragment epitopic propensity scoring task and an AUC up to 0.628 on the residue epitopic propensity scoring task. COBEpro is incorporated into the SCRATCH prediction suite at http://scratch.proteomics.ics.uci.edu.
尽管经过了几十年的研究，但在计算免疫学中，准确预测B细胞表位仍然是一项具有挑战性的任务。据估计，已知的B细胞表位中只有10%是连续的，但它们往往是预测工具的目标，因为预测不需要已解析的三级结构，而且它们对于肽疫苗的开发和工程治疗性蛋白质至关重要。在本文中，我们介绍了COBEpro，这是一种用于预测连续B细胞表位的新型两步系统。COBEpro能够为独立的肽片段和抗原序列中的残基分配表位倾向得分。COBEpro首先使用支持向量机对查询抗原序列中的短肽片段进行预测，然后根据片段预测为每个残基计算表位倾向得分。可以纳入二级结构和溶剂可及性信息（预测的或确切的）以提高性能。在片段表位倾向评分任务中，COBEpro在交叉验证的受试者工作特征曲线下面积（AUC）高达0.829，在残基表位倾向评分任务中AUC高达0.628。COBEpro已被纳入http://scratch.proteomics.ics.uci.edu的SCRATCH预测套件中。
