Key Points Computational docking and conformational analyses were used predict p–MHC binding. This structure-based approach is applicable even for alleles with limited training data. Combining sequence and structure-based approaches improves p–MHC binding prediction PPV. Visual Abstract The reliable prediction of the affinity of candidate peptides for the MHC is important for predicting their potential antigenicity and thus influences medical applications, such as decisions on their inclusion in T cell–based vaccines. In this study, we present a rapid, predictive computational approach that combines a popular, sequence-based artificial neural network method, NetMHCpan 4.0, with three-dimensional structural modeling. We find that the ensembles of bound peptide conformations generated by the programs MODELLER and Rosetta FlexPepDock are less variable in geometry for strong binders than for low-affinity peptides. In tests on 1271 peptide sequences for which the experimental dissociation constants of binding to the well-characterized murine MHC allele H-2Db are known, by applying thresholds for geometric fluctuations the structure-based approach in a standalone manner drastically improves the statistical specificity, reducing the number of false positives. Furthermore, filtering candidates generated with NetMHCpan 4.0 with the structure-based predictor led to an increase in the positive predictive value (PPV) of the peptides correctly predicted to bind very strongly (i.e., Kd < 100 nM) from 40 to 52% (p = 0.027). The combined method also significantly improved the PPV when tested on five human alleles, including some with limited data for training. Overall, an average increase of 10% in the PPV was found over the standalone sequence-based method. The combined method should be useful in the rapid design of effective T cell–based vaccines.

中文翻译：

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将三维建模与人工智能相结合，提高肽-MHC 结合预测的特异性和精确度


要点 使用计算对接和构象分析来预测 p-MHC 结合。这种基于结构的方法甚至适用于训练数据有限的等位基因。结合序列和基于结构的方法改进了 p-MHC 结合预测 PPV。视觉摘要 候选肽与 MHC 的亲和力的可靠预测对于预测其潜在抗原性非常重要，从而影响医学应用，例如将其纳入基于 T 细胞的疫苗的决策。在这项研究中，我们提出了一种快速的预测计算方法，它将流行的基于序列的人工神经网络方法 NetMHCpan 4.0 与三维结构建模相结合。我们发现，与低亲和力肽相比，由程序 MODELLER 和 Rosetta FlexPepDock 生成的结合肽构象集合在几何形状上的变化较小。在对 1271 个肽序列进行的测试中，已知与已充分表征的小鼠 MHC 等位基因 H-2Db 结合的实验解离常数，通过应用几何波动阈值，基于结构的方法以独立方式极大地提高了统计特异性，减少了误报的数量。此外，使用基于结构的预测器过滤 NetMHCpan 4.0 生成的候选序列导致正确预测结合非常强（即 Kd < 100 nM）的肽的阳性预测值 (PPV) 从 40% 增加到 52%（p = 0.027）。当对五个人类等位基因进行测试时，该组合方法还显着提高了 PPV，其中包括一些训练数据有限的等位基因。总体而言，与基于序列的独立方法相比，PPV 平均增加了 10%。 该组合方法应该有助于快速设计有效的 T 细胞疫苗。