基于注意力机制的蛋白质残基相互作用预测方法  

PROTEIN RESIDUE INTERACTION PREDICTION METHOD BASED ON ATTENTION MECHANISM

作　　者：何如吉 权丽君[1,2] 吕强 He Ruji;Quan Lijun;Lü Qiang(School of Computer Science and Technology,Soochow University,Suzhou 215006,Jiangsu,China;Jiangsu Province Key Lab for Information Processing Technologies,Suzhou 215006,Jiangsu,China)

机构地区：[1]苏州大学计算机科学与技术学院,江苏苏州215006 [2]江苏省计算机信息处理技术重点实验室,江苏苏州215006

出　　处：《计算机应用与软件》2024年第6期55-61,共7页Computer Applications and Software

基　　金：国家自然科学基金项目(31801108);“苏州大学江苏省计算机信息处理重点实验室”开放课题(KJS1843);江苏高校优势学科建设工程资助项目。

摘　　要：蛋白质残基相互作用预测对于构建蛋白质三级结构具有重要作用,许多基于深度学习的预测方法陆续提出。残基对的相互作用不仅由残基对本身属性决定,还受到所有其他残基对的影响,但多数深度模型采用的卷积神经网络更关注对局部特征的提取,而忽略了残基对相互作用的全局因素。针对该问题,提出一种基于注意力机制的预测模型,能够兼顾残基对的局部属性以及全局属性。实验结果表明,该模型与其他方法相比具有一定优势。Protein residue interaction prediction plays an important role in constructing protein tertiary structure,and many prediction methods based on deep learning have been proposed.The interaction of the residue pairs is not only determined by the characteristics of the residue pairs themselves,but also affected by all other residue pairs.However,the convolutional neural networks used in most depth models pay more attention to the extraction of local features,while ignoring the global factors of the residue pair interaction.To solve this problem,a prediction model based on attention mechanism is proposed,which can take into account both local and global properties of residue pairs.Experimental results show that the proposed model outperforms other models.

关 键 词：蛋白质 残基相互作用 注意力机制 深度学习 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术] Q811.4[生物学—生物工程]