文献类型：期刊文献

英文题名：Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction

作者：Peng, Yue[1];Wu, Junze[1];Sun, Yi[1];Zhang, Yuanxing[2];Wang, Qiyao[1,3,4];Shao, Shuai[1,3,4]

机构：[1]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai, Peoples R China;[2]Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519000, Peoples R China;[3]Shanghai Engn Res Ctr Maricultured Anim Vaccines, Shanghai, Peoples R China;[4]Lab Aquat Anim Dis MOA, Shanghai, Peoples R China

年份：2025

卷号：16

期号：1

外文期刊名：NATURE COMMUNICATIONS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001413840600023)】；

基金：This work was supported by grants from the National Natural Science Foundation of China (32130108 to QYW, 32373183 to SS), the National Key Research and Development Program (2023YFD2400702 to QYW, 2022YFE0101200 to QYW), and the China Agriculture Research System of MOF and MARA (CARS-47).

语种：英文

摘要：Identifying and characterizing virulence proteins secreted by Gram-negative bacteria are fundamental for deciphering microbial pathogenicity as well as aiding the development of therapeutic strategies. Effector predictors utilizing pre-trained protein language models (PLMs) have shown sound performance by leveraging extensive evolutionary and sequential protein features. However, the accuracy and sensitivity of effector prediction remain challenging. Here, we introduce a model named Contrastive-learning of Language Embedding and Biological Features (CLEF) leveraging contrastive learning to integrate PLM representations with supplementary biological features. Biologically information is captured in learned contextualized embeddings to yield meaningful representations. With cross-modality biological features, CLEF outperforms state-of-the-art (SOTA) models in predicting type III, type IV, and type VI secreted effectors (T3SEs/T4SEs/T6SEs) in enteric pathogens. All experimentally verified effectors in Enterohemorrhagic Escherichia coli and 41 of 43 experimentally verified T3SEs of Salmonella Typhimurium are recognized. Moreover, 12 predicted T3SEs and 11 predicted T6SEs are validated by extensive experiments in Edwardsiella piscicida. Furthermore, integrating omics data via CLEF framework enhances protein representations to illustrate effector-effector interactions and determine in vivo colonization-essential genes. Collectively, CLEF provides a blueprint to bridge the gap between in silico PLM's capacity and experimental biological information to fulfill complicated tasks.