School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Department of Computer Science, Florida State University, Tallahassee, 32306, USA.
BMC Genomics. 2024 Jan 30;25(1):127. doi: 10.1186/s12864-023-09900-6.
The prediction of major histocompatibility complex (MHC)-peptide binding affinity is an important branch in immune bioinformatics, especially helpful in accelerating the design of disease vaccines and immunity therapy. Although deep learning-based solutions have yielded promising results on MHC-II molecules in recent years, these methods ignored structure knowledge from each peptide when employing the deep neural network models. Each peptide sequence has its specific combination order, so it is worth considering adding the structural information of the peptide sequence to the deep model training. In this work, we use positional encoding to represent the structural information of peptide sequences and validly combine the positional encoding with existing models by different strategies. Experiments on three datasets show that the introduction of position-coding information can further improve the performance built upon the existing model. The idea of introducing positional encoding to this field can provide important reference significance for the optimization of the deep network structure in the future.
主要组织相容性复合体（MHC）-肽结合亲和力的预测是免疫生物信息学的一个重要分支，特别是在加速疾病疫苗和免疫治疗的设计方面。虽然近年来基于深度学习的解决方案在 MHC-II 分子上取得了有前景的结果，但这些方法在使用深度神经网络模型时忽略了每个肽的结构知识。每个肽序列都有其特定的组合顺序，因此值得考虑在深度模型训练中添加肽序列的结构信息。在这项工作中，我们使用位置编码来表示肽序列的结构信息，并通过不同的策略有效地将位置编码与现有模型相结合。在三个数据集上的实验表明，引入位置编码信息可以进一步提高基于现有模型的性能。将位置编码引入该领域的想法可为未来深度网络结构的优化提供重要的参考意义。
