融合注意力机制和多尺度信息的蛋白质结合位点预测OA
Protein Binding Site Prediction by Integrating Attention Mechanism and Multi-scale Information
为了有效解决 3D U-Net在蛋白质结合位点预测中存在的噪声干扰和多尺度信息缺乏问题,提出了一种融合注意力机制和多尺度信息的蛋白质结合位点预测模型AMPocket.引入压缩注意力机制,使得模型能够聚焦于关键通道的蛋白质特征,减少无关通道特征对结合位点预测的影响,从而提高分割的精度;在编码器中引入多尺度信息,使模型能够从不同层次捕捉特征,进而获得更加全面和丰富的空间信息.实验结果表明:AMPocket在 4 个广泛使用的测试集上均取得了优异的预测结果,特别是在 SC6K数据集上的 DCC成功率和 DVO 优于所有对比方法,分别为 93.04%和 55.01%,并且 AMPocket具有较少的参数量,表明模型具有更好的预测性能.
To effectively address the issues of noise interference and insufficient multi-scale information within 3D U-Net for protein binding site prediction,a novel model named AMPocket was proposed which incorporated both at-tention mechanisms and multi-scale information to improve the accuracy of binding site prediction.AMPocket ini-tially employed squeezed attention mechanism that enabled the model to focus on the most critical channels of pro-tein features while diminishing the impact of irrelevant channels,thereby enhancing segmentation accuracy.Addi-tionally,the multi-scale information was introduced to the encoder component,allowing the model to capture fea-ture representations at various levels and thus obtained more comprehensive and robust spatial information.The ex-perimental results demonstrated that AMPocket achieved superior predictive performance across four widely used test sets,in particular,the DCC success rate and DVO metrics on the SC6K dataset outperformed all other compe-ting methods by 93.04%and 55.01%respectively,with a smaller number of parameters.It indicated that the mo-del had better predictive performance.
LU Shuai;YIN Shuailing;YUAN Mengchao;WU Di;ZHOU Qinglei
School of Computer Science and Artificial Intelligence,Zhengzhou University,Zhengzhou 450001,China||National Supercom-puting Center in Zhengzhou,Zhengzhou University,Zhengzhou 450001,ChinaSchool of Cyber Science and Engineering,Zheng-zhou University,Zhengzhou 450002,ChinaSchool of Computer Science and Artificial Intelligence,Zhengzhou University,Zhengzhou 450001,China||National Supercom-puting Center in Zhengzhou,Zhengzhou University,Zhengzhou 450001,ChinaSchool of Computer Science and Artificial Intelligence,Zhengzhou University,Zhengzhou 450001,China||National Supercom-puting Center in Zhengzhou,Zhengzhou University,Zhengzhou 450001,ChinaSchool of Computer Science and Artificial Intelligence,Zhengzhou University,Zhengzhou 450001,China||National Supercom-puting Center in Zhengzhou,Zhengzhou University,Zhengzhou 450001,China||School of Cyber Science and Engineering,Zheng-zhou University,Zhengzhou 450002,China
信息技术与安全科学
蛋白质结合位点预测3D U-Net压缩注意力机制多尺度信息噪声干扰
protein binding site prediction3D U-Netsqueezed attention mechanismmulti-scale informationnoise interference
《郑州大学学报（工学版）》 2026 (1)
66-72,7
科技部科技创新2030"新一代人工智能"重大项目(2023ZD020600)河南省重点研发专项(241111210500)河南省科技攻关项目(252102211042)
评论