Two complementary methods for predicting peptides binding major histocompatibility complex molecules
Two complementary methods for predicting peptides binding major histocompatibility complex molecules
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DOI：
10.1006/jmbi.1997.0937
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发表时间：
1997-04-18
影响因子：
5.6
通讯作者：
DeLisi, C
中科院分区：
文献类型：
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作者：
Gulukota, K;Sidney, J;DeLisi, C
已知与主要组织相容性复合体产物（MHC）结合的肽具有一定的序列基序，这些基序虽然常见，但对于结合来说既不是必要的，也不是充分的：MHC结合的某些肽不具有特征基序，只有约30%具有所需基序的肽结合。为了开发和测试更准确的方法，我们测量了463个非特异性肽与HLA-A2.1的结合亲和力。我们描述了两种方法来预测是否一个给定的肽将与MHC结合，并将它们应用于这些肽。一种方法是基于模拟神经网络，另一种称为多项式方法，是基于假设残基侧链独立结合的统计参数估计。我们将这些方法相互比较，并与标准的基于图案的方法进行比较。这两种方法是互补的，都优于序列基序。神经网络在消除误报方面优于简单的基序搜索。它的行为可以粗略地调整到所需的结合强度，并且可以以直接的方式扩展到其他等位基因。另一方面，多项式方法具有较高的灵敏度，是消除假阴性的优越方法。我们讨论了这种预测中独立约束假设的有效性。(C) 1997学术出版社有限公司
Peptides that bind to major histocompatibility complex products (MHC) are known to exhibit certain sequence motifs which, though common, are neither necessary nor sufficient for binding: MHCs bind certain peptides that do not have the characteristic motifs and only about 30% of the peptides having the required motif, bind. In order to develop and test more accurate methods we measured the binding affinity of 463 nonamer peptides to HLA-A2.1. We describe two methods for predicting whether a given peptide will bind to an MHC and apply them to these peptides. One method is based on simulating a neural network and another, called the polynomial method, is based on statistical parameter estimation assuming independent binding of the side-chains of residues. We compare these methods with each other and with standard motif-based methods. The two methods are complementary, and both are superior to sequence motifs. The neural net is superior to simple motif searches in eliminating false positives. Its behavior can be coarsely tuned to the strength of binding desired and it is extendable in a straightforward fashion to other alleles. The polynomial method, on the other hand, has high sensitivity and is a superior method for eliminating false negatives. We discuss the validity of the independent binding assumption in such predictions. (C) 1997 Academic Press Limited.