Multimodal geometric learning for antimicrobial peptide identification by leveraging alphafold2-predicted structures and surface features

作者：Zehua Sun, Jing Xu, Yumeng Zhang, Yiwen Zhang, Zhikang Wang, Xiaoyu Wang, Shanshan Li, Yuming Guo, Hsin‐Hui Shen, Jiangning Song · 发表于：Briefings in Bioinformatics · 年份：2025 · DOI：10.1093/bib/bbaf261 · 被引用次数：13 · 研究领域：Antimicrobial Peptides and Activities、Biochemical and Structural Characterization、vaccines and immunoinformatics approaches

Antimicrobial peptides (AMPs) are short peptides that play critical roles in diverse biological processes and exhibit functional activities against target organisms. While numerous methods have demonstrated the effectiveness of deep neural networks for AMP identification using sequence features; nevertheless, higher-level peptide characteristics-such as 3D structure and geometric surface features-have not been comprehensively explored. To address this gap, we introduce the SSFGM-Model (Sequence, Structure, Surface, Graph, and Geometric-based Model), a novel framework that integrates multiple feature types to enhance AMP identification. The model represents each peptide sequence as a graph, where nodes are characterized by amino acid features derived from ProteinBERT, ESM-2, and One-hot embeddings. Graph convolutional networks and an attention mechanism are employed to capture high-order structural and sequential relationships. Additionally, surface geometry and physicochemical properties are processed using a geometric neural network. Finally, a feature fusion strategy combines the outputs from these subnetworks to enable robust AMP identification. Extensive benchmarking experiments demonstrate that the SSFGM-Model outperforms current state-of-the-art methods. An ablation study further confirms the critical role of sequence, structural, and surface features in AMP identification. The key contribution of this work is the innovative integration of multiple levels of peptide cha...