用循环神经网络预测SARS-CoV-2肽与主要组织相容性复合物的结合
Predicting the Binding of SARS-CoV-2 Peptides to the Major Histocompatibility Complex with Recurrent Neural Networks
摘要：
用机器学习预测病毒肽与主要组织相容性复合体的结合可能会扩展疫苗开发的计算免疫学工具包，并作为对抗大流行病的关键组成部分。在这项工作中，我们适应和扩展USMPep，最近提出的，概念上简单的预测算法的基础上循环神经网络。最值得注意的是，我们结合回归（结合亲和力数据）和分类器（质谱数据）从定性不同的数据源，以获得更全面的预测工具。我们在最近发布的SARS-CoV-2数据集上评估了结合稳定性测量的性能。USMPep不仅为选定的单个等位基因设定了新的基准，而且一直是性能最好的方法之一，或者对于某些指标，甚至是这项任务的整体性能最好的方法。
英文摘要：
Predicting the binding of viral peptides to the major histocompatibility complex with machine learning can potentially extend the computational immunology toolkit for vaccine development, and serve as a key component in the fight against a pandemic. In this work, we adapt and extend USMPep, a recently proposed, conceptually simple prediction algorithm based on recurrent neural networks. Most notably, we combine regressors (binding affinity data) and classifiers (mass spectrometry data) from qualitatively different data sources to obtain a more comprehensive prediction tool. We evaluate the performance on a recently released SARS-CoV-2 dataset with binding stability measurements. USMPep not only sets new benchmarks on selected single alleles, but consistently turns out to be among the best-performing methods or, for some metrics, to be even the overall best-performing method for this task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
