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bioRxiv · 10.64898/2026.03.19.713066

A Deep Learning Framework for Predicting Gut Microbe-Host Receptor Interactions

li, H.·Zhao, R.·Zhu, C.·Jiang, R.·Chen, T.·li, X.·Yang, Y.
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Abstract

MotivationGut microbiota regulates host health through complex protein-protein interactions. However, deciphering this specific interactions between microbiota and human receptors remains a significant challenge due to the lack of specialized computational tools. ResultsLeveraging the hypothesis of cell communication and relevant data, HMI-Pred initially builds an ensemble classifier to screen for potential ligand sequences within microbial genomes. It then jointly evaluates sequence semantics and molecular docking to predict potential microbe-host receptor interactions.HMI-Pred achieved robust performance with F1-scores of 0.901 for microbial ligand identification and 0.883 for interaction prediction. Application to 332,381 microbial proteins revealed distinct interaction patterns: histone deacetylases (HDACs) served as broad-spectrum targets (mean score > 0.80), while G protein-coupled receptors (GPCRs) exhibited high specificity (scores 0.42-0.61). Furthermore, literature mining validated over 47% of the functional predictions, and specific immunomodulatory interactions were confirmed in Akkermansia muciniphila.HMI-Pred provides a valuable computational tool for decoding host-microbe signaling networks and facilitating the discovery of microbiome-based therapeutic targets. AvailabilityThe source code and documentation are available at https://github.com/YangLab-BUPT/HMI-Pred. Contactlihm@bupt.edu.cn

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li, H., Zhao, R., Zhu, C., Jiang, R., Chen, T., li, X., Yang, Y.. 2026-03-23. A Deep Learning Framework for Predicting Gut Microbe-Host Receptor Interactions. https://doi.org/10.64898/2026.03.19.713066

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