Accurate prediction of peptide binding affinity to the major histocompatibility complex (MHC) proteins has the potential to design better therapeutic vaccines. Previous work has shown that pan-specific prediction algorithms can achieve better prediction performance than other approaches. However, most of the top algorithms are neural networks based black box models. Here, we propose DeepAttentionPan, an improved pan-specific model, based on convolutional neural networks and attention mechanisms for more flexible, stable and interpretable MHC-I binding prediction. With the attention mechanism, our ensemble model consisting of 20 trained networks achieves high and more stabilized prediction performance. Extensive tests on IEDB's weekly benchmark dataset show that our method achieves state-of-the-art prediction performance on 21 test allele datasets. Analysis of the peptide positional attention weights learned by our model demonstrates its capability to capture critical binding positions of the peptides, which leads to mechanistic understanding of MHC-peptide binding with high alignment with experimentally verified results. Furthermore, we show that with transfer learning, our pan model can be fine-tuned for alleles with few samples to achieve additional performance improvement. DeepAttentionPan is freely available as an open-source software at https://github.com/jjin49/DeepAttentionPan.

中文翻译：

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具有改进的注意力机制的可解释 MHC-I 肽结合预测的深度学习泛特异性模型

准确预测肽对主要组织相容性复合体 (MHC) 蛋白的亲和力有可能设计出更好的治疗性疫苗。先前的工作表明，泛特定预测算法可以实现比其他方法更好的预测性能。然而，大多数顶级算法都是基于神经网络的黑盒模型。在这里，我们提出了 DeepAttentionPan，一种改进的 pan-specific 模型，基于卷积神经网络和注意力机制，用于更灵活、稳定和可解释的 MHC-I 绑定预测。通过注意力机制，我们由 20 个训练网络组成的集成模型实现了更高且更稳定的预测性能。对 IEDB' 的广泛测试 s 每周基准数据集表明，我们的方法在 21 个测试等位基因数据集上实现了最先进的预测性能。对我们的模型学习的肽位置注意力权重的分析证明了它能够捕获肽的关键结合位置，这导致对 MHC 肽结合的机制理解，与实验验证的结果高度对齐。此外，我们表明，通过迁移学习，我们的泛模型可以针对样本较少的等位基因进行微调，以实现额外的性能改进。DeepAttentionPan 可作为开源软件在 https://github.com/jjin49/DeepAttentionPan 上免费获得。这导致对 MHC 肽结合的机制理解与实验验证的结果高度对齐。此外，我们表明，通过迁移学习，我们的泛模型可以针对样本较少的等位基因进行微调，以实现额外的性能改进。DeepAttentionPan 可作为开源软件在 https://github.com/jjin49/DeepAttentionPan 上免费获得。这导致对 MHC 肽结合的机制理解与实验验证的结果高度对齐。此外，我们表明，通过迁移学习，我们的泛模型可以针对样本较少的等位基因进行微调，以实现额外的性能改进。DeepAttentionPan 可作为开源软件在 https://github.com/jjin49/DeepAttentionPan 上免费获得。