基于标签依赖性的多功能活性肽预测模型  

Multi-functional active peptides prediction model based on label dependence

作　　者：任建雪 韩笑 程昊 石晋雪 王会青[1] REN Jianxue;HAN Xiao;CHENG Hao;SHI Jinxue;WANG Huiqing(College of Computer Science and Technology(College of Data Science),Taiyuan University of Technology,Jinzhong 030600,Shanxi,China)

机构地区：[1]太原理工大学计算机科学与技术学院(大数据学院),山西晋中030600

出　　处：《陕西师范大学学报(自然科学版)》2025年第1期22-32,共11页Journal of Shaanxi Normal University:Natural Science Edition

基　　金：山西省自然科学基金(202203021211121)。

摘　　要：多功能活性肽是一种源于蛋白质的化合物,能够作用于多个靶标并传递多种生理效应,对多种疾病具有显著的治疗效果。现有多功能活性肽预测模型在特征表示阶段未能充分考虑到氨基酸之间存在的关联性,使得模型的特征表示能力降低;现有方法采用将多标签分类问题转换为多个二分类预测问题的策略,导致模型在预测阶段无法考虑活性肽多个功能之间的依赖性,使得模型对多功能活性肽的预测准确度下降。针对以上问题,提出一种基于标签依赖性的多功能活性肽预测模型TCLD,通过Transformer编码器提取活性肽序列中氨基酸之间的关联性,利用ZLPR损失函数来捕获多个功能之间对应的标签依赖性,用于提高多功能活性肽预测模型的性能。实验结果表明,TCLD的预测性能优于现有的多功能活性肽预测方法,有助于研究人员快速筛选出具有潜在治疗价值的多功能活性肽候选物,从而加速新型药物的研发进程。Multi-functional active peptide is a protein-derived compound that can act on multiple targets and deliver a variety of physiological effects,and has significant therapeutic effects on a variety of diseases.The existing multi-functional active peptide prediction model fails to fully consider the correlation between amino acids in the feature representation stage,which reduces the feature representation ability of the model,and the existing method adopts the strategy of converting the multi-label classification problem into multiple binary classification prediction problems,which leads to the inability of the model to consider the dependence between multiple functions of the active peptide in the prediction stage,which reduces the prediction accuracy of the model for multi-functional active peptides.In order to solve the above problems,a multi-functional active peptide prediction model based on label dependence is proposed,TCLD,which extracts the correlation between amino acids in the active peptide sequence through the Transformer encoder,and uses the ZLPR loss function to capture the dependence between multiple functions,which is used to improve the performance of the multi-functional active peptide prediction model.The experimental results show that the prediction performance of TCLD is better than that of the existing multi-functional active peptide prediction methods,which is helpful for researchers to quickly screen out multi-functional active peptide candidates with potential therapeutic value,thereby accelerating the research and development process of new drugs.

关 键 词：多功能活性肽 TRANSFORMER 多尺度卷积网络 混合池化 标签依赖性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]