AMHF-TP:Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features  

作　　者：Shouheng Tuo YanLing Zhu Jiangkun Lin Jiewei Jiang 

机构地区：[1]School of Computer Science and Technology,Xi’an University of Posts and Telecommunications,Xi’an,China [2]Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing,Xi’an,China [3]Xi’an Key Laboratory of Big Data and Intelligent Computing,Xi’an,China [4]School of Electronic Engineering,Xi’an University of Posts and Telecommunications,Xi’an,China

出　　处：《Quantitative Biology》2025年第1期127-141,共15页定量生物学(英文版)

基　　金：National Natural Science Foundation of China,Grant/Award Number:62276210;Natural Science Basic Research Program of Shaanxi,Grant/Award Number:2022JM-380。

摘　　要：Multifunctional therapeutic peptides(MFTP)hold immense potential in diverse therapeutic contexts,yet their prediction and identification remain challenging due to the limitations of traditional methodologies,such as extensive training durations,limited sample sizes,and inadequate generalization capabilities.To address these issues,we present AMHF-TP,an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance performance.The AMHF-TP is composed of four key components:a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences;a convolutional neural network and selfattention module that refine feature extraction from amino acid sequences and their secondary structures;a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences;and a hierarchical feature extraction module that integrates multimodal peptide sequence features.Compared with leading methods,the proposed AMHF-TP demonstrates superior precision,accuracy,and coverage,underscoring its effectiveness and robustness in MFTP recognition.The comparative analysis of separate hierarchical models and the combined model,as well as with five contemporary models,reveals AMHFTP’s exceptional performance and stability in recognition tasks.

关 键 词：deep learning hypergraph multifunctional therapeutic peptides multi-granularity hierarchical features 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]