Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications
Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications
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DOI：
10.1016/j.ymeth.2004.06.006
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发表时间：
2004-12-01
期刊：
影响因子：
4.8
通讯作者：
Petrovsky, N
中科院分区：
文献类型：
--
作者：
Brusic, V;Bajic, VB;Petrovsky, N
计算模型补充了实验室实验，可有效识别 MHC 结合肽和 T 细胞表位。 MHC 结合肽的预测方法包括结合基序、定量矩阵、人工神经网络、隐马尔可夫模型和分子建模。通过这些方法衍生的模型已成功用于预测癌症、自身免疫、传染病和过敏中的 T 细胞表位。为了获得最大利益，计算机模型的使用必须被视为类似于标准实验室程序的实验，并按照严格的标准进行。这需要仔细选择用于模型构建的数据，以及充分的测试和验证。有一系列基于网络的数据库和 MHC 结合预测程序可供使用。尽管某些针对特定 MHC 等位基因的可用预测程序具有合理的准确性，但不能保证所有模型都能产生高质量的预测。在本文中，我们提出并讨论了用于 T 细胞表位预测的计算方法的建模、测试和应用的框架。 (C) 2004 Elsevier Inc. 保留所有权利。
Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.