Abstract：
Proteins are essential to life, and understanding their intrinsic roles requires determining their structure. The field of proteomics has opened up new opportunities by applying deep learning algorithms to large databases of solved protein structures. With the availability of large data sets and advanced machine learning methods, the prediction of protein residue interactions has greatly improved. Protein contact maps provide empirical evidence of the interacting residue pairs within a protein sequence. Template-free protein structure prediction systems rely heavily on this information. This article proposes UNet-CON, an attention-integrated UNet architecture, trained to predict residue-residue contacts in protein sequences. With the predicted contacts being more accurate than state-of-the-art methods on the PDB25 test set, the model paves the way for the development of more powerful deep learning algorithms for predicting protein residue interactions.
摘要：
蛋白质对生命至关重要，理解它们的内在角色需要确定它们的结构。通过将深度学习算法应用于已解决的蛋白质结构的大型数据库，蛋白质组学领域开辟了新的机遇。随着大型数据集和先进的机器学习方法的可用性，蛋白质残基相互作用的预测有了很大的提高。蛋白质接触图提供了蛋白质序列内相互作用的残基对的经验证据。无模板的蛋白质结构预测系统严重依赖于这些信息。本文提出了UNet-CON，注意综合的UNet架构，训练预测蛋白质序列中的残基-残基接触。由于预测的接触比PDB25测试装置上的最新方法更准确，该模型为预测蛋白质残基相互作用的更强大的深度学习算法的发展铺平了道路。源代码可在GitHub链接中找到：(https://github.com/jisnava/UNetCON)。
