Mallios R R
Medical Information Resources, University of California at San Francisco, Fresno 93703, USA.
J Comput Biol. 1998 Winter;5(4):703-11. doi: 10.1089/cmb.1998.5.703.
An algorithm is presented for detecting a quantitative pattern in peptide fragments that bind class II major histocompatibility complex (MHC) molecules. It is referred to as a meta-algorithm because it requires successive applications of Stepwise Discriminate Analysis (SDA). On every iteration the best subsequence candidates are selected from sequences known to bind class II MHC molecules. When SDA compares probable binding subsequences with subsequences known not to bind class II MHC molecules, a quantitative model emerges that is capable of classifying subsequences as binding or non-binding. In an iterative manner, the resultant model is utilized as a criterion for selecting probable binding subsequence candidates. The procedure is repeated until models converge. In the illustrated examples, the final models correctly classify over 95% of the peptides in a database of peptides whose binding affinity for HLA-DR1 is known. The final model can then be used to predict the binding affinity of peptides that have not yet been laboratory tested.
本文提出了一种用于检测与II类主要组织相容性复合体（MHC）分子结合的肽片段定量模式的算法。它被称为元算法，因为它需要连续应用逐步判别分析（SDA）。在每次迭代中，从已知与II类MHC分子结合的序列中选择最佳子序列候选。当SDA将可能的结合子序列与已知不与II类MHC分子结合的子序列进行比较时，就会出现一个能够将子序列分类为结合或非结合的定量模型。以迭代方式，将所得模型用作选择可能的结合子序列候选的标准。重复该过程，直到模型收敛。在所示示例中，最终模型正确分类了已知对HLA-DR1结合亲和力的肽数据库中超过95%的肽。然后，最终模型可用于预测尚未经过实验室测试的肽的结合亲和力。
