Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers
Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers
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DOI：
10.1093/bioinformatics/btn128
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发表时间：
2008-06-01
期刊：
影响因子：
5.8
通讯作者：
Nielsen, Morten
中科院分区：
文献类型：
--
作者：
Lundegaard, Claus;Lund, Ole;Nielsen, Morten
已经开发了几个准确的预测系统来预测I类主要组织相容性复合体(MHC)：肽结合。其中大多数训练是关于主要是9聚体多肽的结合亲和力数据。在这里，我们展示了如何使用基于9mer数据训练的预测方法来准确预测长度为8、10和11的多肽的结合亲和力。该方法使预测长度不同于9的MHC等位基因的多肽成为可能，而这些多肽是尚未被测量到的。作为验证，将该方法的性能与对相关多肽长度的多肽训练的预测器进行了比较。在这种验证中，近似方法具有与训练与预测的多肽长度相同的多肽的方法相当或更好的精度。
Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (MHC):peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate binding affinity prediction of peptides of length 8, 10 and 11. The method gives the opportunity to predict peptides with a different length than nine for MHC alleles where no such peptides have been measured. As validation, the performance of this approach is compared to predictors trained on peptides of the peptide length in question. In this validation, the approximation method has an accuracy that is comparable to or better than methods trained on a peptide length identical to the predicted peptides.