NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets.
NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets.
复制标题
DOI：
10.1186/s13073-016-0288-x
复制
发表时间：
2016-03-30
期刊：
影响因子：
12.3
通讯作者：
Andreatta M
中科院分区：
文献类型：
--
作者：
Nielsen M;Andreatta M
肽与MHC I类分子（MHC-I）的结合对于抗原提呈至细胞毒性T细胞至关重要。在这里，我们展示了一个简单的比对步骤，允许在泛特异性MHC-I结合机器学习模型中插入和删除，使多个MHC分子和肽长度的信息相结合。这种泛等位基因/泛长度算法显著优于最先进的方法，并捕获不同MHC分子的结合剂的长度谱的差异，从而提高配体鉴定的准确性。使用这个模型，我们表明，百分位数的排名与亲和力为基础的阈值是最佳的配体识别由于均匀采样的MHC空间。我们开发了一种基于神经网络的机器学习算法，利用多种受体特异性和配体长度尺度的信息，并证明了这种方法如何显着提高预测肽结合和鉴定MHC配体的准确性。该方法可在www.cbs.dtu.dk/services/NetMHCpan-3.0上获得。本文的在线版本（doi：10.1186/s13073-016-0288-x）包含补充材料，可供授权用户使用。
Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells. Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining information across both multiple MHC molecules and peptide lengths. This pan-allele/pan-length algorithm significantly outperforms state-of-the-art methods, and captures differences in the length profile of binders to different MHC molecules leading to increased accuracy for ligand identification. Using this model, we demonstrate that percentile ranks in contrast to affinity-based thresholds are optimal for ligand identification due to uniform sampling of the MHC space. We have developed a neural network-based machine-learning algorithm leveraging information across multiple receptor specificities and ligand length scales, and demonstrated how this approach significantly improves the accuracy for prediction of peptide binding and identification of MHC ligands. The method is available at www.cbs.dtu.dk/services/NetMHCpan-3.0. The online version of this article (doi:10.1186/s13073-016-0288-x) contains supplementary material, which is available to authorized users.