Gapped sequence alignment using artificial neural networks: application to the MHC class I system
Gapped sequence alignment using artificial neural networks: application to the MHC class I system
复制标题
DOI：
10.1093/bioinformatics/btv639
复制
发表时间：
2016-02-15
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：
Andreatta, Massimo;Nielsen, Morten
动机：许多生物学过程是由受体与可变长度的线性配体相互作用指导的。一种这样的受体是MHC I类分子。长度偏好根据MHC等位基因而变化，但通常限于长度为8-11个氨基酸的肽。在这个相对简单的系统上，我们开发了一种基于人工神经网络的序列比对方法，该方法允许在align.Results中插入和缺失：我们表明，基于包括插入和缺失的比对的预测方法具有显着更高的性能比单一长度的肽训练的方法。此外，我们说明了如何删除的位置可以帮助解释的肽-MHC的结合模式，如在长肽凸出的MHC沟或突出在任一末端的情况下。最后，我们证明了该方法可以学习不同MHC分子的长度谱，并量化了使用我们的预测算法识别潜在表位所需的实验工作量的减少。
Motivation: Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8-11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment.Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods trained on peptides of single lengths. Also, we illustrate how the location of deletions can aid the interpretation of the modes of binding of the peptide-MHC, as in the case of long peptides bulging out of the MHC groove or protruding at either terminus. Finally, we demonstrate that the method can learn the length profile of different MHC molecules, and quantified the reduction of the experimental effort required to identify potential epitopes using our prediction algorithm.