{ "abstract": "Accurate prediction of effector proteins secreted by Gram-negative bacteria is important for elucidating bacterial pathogenic mechanisms and developing precise anti-infective strategies. Although existing methods have benefited from the strong sequence feature extraction capacity of pretrained protein language models, reliance on linear sequence information alone often fails to fully capture the three-dimensional conformational signals required for virulence functions. Meanwhile, conventional structure-based methods are limited by the scarcity of experimentally resolved protein structures. To address these challenges, we propose GeoEPred, a multimodal deep learning framework designed for the synergistic modeling of protein sequence and structure to identify Gram-negative bacterial effector proteins. Specifically, the model integrates sequence-contextual embeddings from a pretrained protein language model with three-dimensional structural representations predicted by ESMFold. A feature projection network refines fine-grained sequence signals associated with effector functions, while geometric vector perceptrons characterize inter-residue orientations, distances, and local spatial topology to capture potential structural conformational motifs. To further enable effective cross-modal fusion, we design a cross-modal alignment and feature-tokenized self-attention module. This module enhances consistency between the sequence-semantic and structural-geometric spaces through contrastive learning and models associations between linear functional motifs and spatial conformational patterns at a fine-grained token level. Extensive evaluations on multiple benchmark datasets show that GeoEPred achieves better predictive performance than existing leading models in T3SE, T4SE, and T6SE prediction tasks, while maintaining stable performance in remote homolog recognition scenarios. Moreover, the modular and extensible architecture of GeoEPred demonstrates strong generalization ability and substantial application potential for genome-scale effector protein discovery. Author summary: Secreted effector proteins are central virulence factors used by many Gram-negative bacterial pathogens to execute infection strategies. Their functions are governed not only by secretion signals and short linear motifs in the amino acid sequence, but also by three-dimensional folds, local domains, and surface geometric patterns. However, current predictors mainly exploit sequence-contextual features, limiting their ability to model the correspondence between linear sequence signals and spatial conformational motifs, and thereby constraining accuracy and interpretability. Here, we present GeoEPred, a multimodal deep learning framework for secreted effector protein identification. GeoEPred couples sequence-semantic embeddings from a pretrained protein language model with structural representations learned by geometric vector perceptrons. A cross-modal alignment and interaction module uses contrastive learning to improve functional consistency between sequence and structure modalities, while feature-token attention captures fine-grained links between key linear and conformational motifs. Across benchmark datasets covering multiple effector types, GeoEPred outperforms existing state-of-the-art methods and provides interpretable evidence from sequence fragments, structural regions, and cross-modal associations, supporting functional annotation, pathogenic mechanism analysis, and experimental validation.", "abstract_shown": true, "affiliations": [ "School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, Fujian, China", "School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Pulau Pinang, Malaysia", "Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China", "Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, China", "Faculty of Applied Sciences, Macao Polytechnic University, Macao, China" ], "article_type": "Research Article", "arxiv": null, "author_total": 8, "authors": [ { "affs": [ 0 ], "name": "Shouzhen Song" }, { "affs": [ 1 ], "name": "Hua Shi" }, { "affs": [ 2 ], "name": "Hongfeng Wu" }, { "affs": [ 3 ], "name": "Dachen Liu" }, { "affs": [ 4 ], "name": "Yihang Lin" }, { "name": "Nor Ashidi Mat Isa" }, { "name": "Quan Zou" }, { "name": "Leyi Wei" } ], "bibtex": "https://research.draconitic.com/paper/0x7h03xgdwa41.bib", "cited_by": 0, "doi": "10.1371/journal.pcbi.1014344", "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014344", "id": "0x7h03xgdwa41", "issns": [ "1553-7358" ], "landing_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014344", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "1312|Machine Learning in Bioinformatics" ], [ "plos", "/Biology and life sciences/Biochemistry/Proteins/Protein domains" ], [ "plos", "/Biology and life sciences/Biochemistry/Proteins/Protein structure" ], [ "plos", "/Biology and life sciences/Biochemistry/Proteins/Protein structure/Protein structure comparison" ], [ "plos", "/Biology and life sciences/Biochemistry/Proteins/Protein structure/Protein structure prediction" ], [ "plos", "/Biology and life sciences/Genetics/Genomics/Microbial genomics/Bacterial genomics" ], [ "plos", "/Biology and life sciences/Genetics/Microbial genetics/Bacterial genetics/Bacterial genomics" ], [ "plos", "/Biology and life sciences/Microbiology/Bacteriology/Bacterial genetics/Bacterial genomics" ], [ "plos", "/Biology and life sciences/Microbiology/Bacteriology/Bacterial physiology/Secretion systems" ], [ "plos", "/Biology and life sciences/Microbiology/Microbial genomics/Bacterial genomics" ], [ "plos", "/Biology and life sciences/Microbiology/Microbial physiology/Bacterial physiology/Secretion systems" ], [ "plos", "/Biology and life sciences/Molecular biology/Macromolecular structure analysis/Protein structure" ], [ "plos", "/Biology and life sciences/Molecular biology/Macromolecular structure analysis/Protein structure/Protein structure comparison" ], [ "plos", "/Biology and life sciences/Molecular biology/Macromolecular structure analysis/Protein structure/Protein structure prediction" ], [ "plos", "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Language" ], [ "plos", "/Biology and life sciences/Psychology/Cognitive psychology/Language" ], [ "plos", "/Medicine and health sciences/Pathology and laboratory medicine/Pathogens/Virulence factors/Secretion systems" ], [ "plos", "/Physical sciences/Mathematics/Statistics/Statistical methods/Forecasting" ], [ "plos", "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Forecasting" ], [ "plos", "/Social sciences/Psychology/Cognitive psychology/Language" ] ], "openalex": "W7214898468", "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014344&type=printable", "pmcid": null, "pmid": null, "published": "2026-09-30", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/0x7h03xgdwa41.ris", "sources": [ { "first_seen": "2026-10-30T12:17:37Z", "native_id": "10.1371/journal.pcbi.1014344", "source": "plos", "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014344" } ], "subjects": [ { "field": "biochemistry", "origin": "plos:molecular biology", "subfield": "molecular-biology" }, { "field": "biology", "origin": "plos:genomics", "subfield": "genetics-genomics" }, { "field": "biology", "origin": "plos:microbiology", "subfield": "microbiology" } ], "title": "GeoEPred: A multimodal structure-aware geometric deep learning framework for Gram-negative bacterial secreted effector prediction with sequence semantics", "title_text": "GeoEPred: A multimodal structure-aware geometric deep learning framework for Gram-negative bacterial secreted effector prediction with sequence semantics", "type": "peer-reviewed", "updated_at": "2026-10-30T12:17:37Z", "url": "https://research.draconitic.com/paper/0x7h03xgdwa41", "venue": "PLOS Computational Biology", "versions": [] }