Karpenko Oleksiy, Huang Lei, Dai Yang
Department of Bioengineering (MC063), University of Illinois at Chicago, 851 South Morgan Street, Chicago, IL 60607, USA.
Immunogenetics. 2008 Jan;60(1):25-36. doi: 10.1007/s00251-007-0266-y. Epub 2007 Dec 19.
Several computational methods for the prediction of major histocompatibility complex (MHC) class II binding peptides embodying different strengths and weaknesses have been developed. To provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. The construction of a meta-predictor of this type based on a probabilistic approach is introduced in this paper. The design permits the easy incorporation of results obtained from any number of individual predictors. It is demonstrated that this integrated method outperforms six state-of-the-art individual predictors based on computational studies using MHC class II peptides from 13 HLA alleles and three mouse MHC alleles obtained from the Immune Epitope Database and Analysis Resource. It is concluded that this integrative approach provides a clearly enhanced reliability of prediction. Moreover, this computational framework can be directly extended to MHC class I binding predictions.
已经开发出几种用于预测主要组织相容性复合体（MHC）II类结合肽的计算方法，这些方法各有优缺点。为了提供可靠的预测，设计一个能够整合各种预测器结果的系统很重要。本文介绍了基于概率方法构建的此类元预测器。这种设计允许轻松纳入从任意数量的个体预测器获得的结果。基于使用从免疫表位数据库和分析资源获得的13个HLA等位基因和3个小鼠MHC等位基因的MHC II类肽进行的计算研究表明，这种综合方法优于六种最先进的个体预测器。得出的结论是，这种整合方法显著提高了预测的可靠性。此外，这种计算框架可以直接扩展到MHC I类结合预测。
