Computational Molecular Biology 2025, Vol.15, No.5, 218-226 http://bioscipublisher.com/index.php/cmb 22 5 reinforcement learning can generate samples or optimize experimental designs, while structural models such as AlphaFold2 show the potential of "general interaction prediction". Ultimately, computation and experimentation will form a closed-loop system: AI prediction, experimental verification, and model update. The combination of multi-dimensional data and intelligent algorithms will drive PPI prediction into a new stage, providing more systematic support for the analysis of infection mechanisms and antibacterial strategies. Acknowledgments The authors extend sincere thanks to two anonymous peer reviewers for their invaluable feedback on the manuscript. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Charih F., Green J.R., and Biggar K.K., 2025, Sequence-based protein-protein interaction prediction and its applications in drug discovery, Cells, 14(18): 1449. https://doi.org/10.3390/cells14181449 Chen M., Ju C., Zhou G., Chen X., Zhang T., Chang K., Zaniolo C., and Wang W., 2019, Multifaceted protein-protein interaction prediction based on Siamese residual RCNN, Bioinformatics, 35(14): i305-i314. https://doi.org/10.1093/bioinformatics/btz328 Ding Z., and Kihara D., 2018, Computational methods for predicting protein-protein interactions using various protein features, Current Protocols in Protein Science, 93(1): e62. https://doi.org/10.1002/cpps.62 Gonzalez-Lopez F., Morales-Cordovilla J.A., Villegas-Morcillo A., Gomez A.M., and Sanchez V., 2018, End-to-end prediction of protein-protein interaction based on embedding and recurrent neural networks, In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp.2344-2350. https://doi.org/10.1109/BIBM.2018.8621328 Hashemifar S., Neyshabur B., Khan A.A., and Xu J., 2018, Predicting protein-protein interactions through sequence-based deep learning, Bioinformatics, 34(17): i802-i810. https://doi.org/10.1093/bioinformatics/bty573 Humphreys I.R., Zhang J., Baek M., Wang Y., Krishnakumar A., Pei J., Anishchenko I., Tower C., Jackson B., Warrier T., Hung D., Peterson S., Mougous J., Cong Q., and Baker D., 2024, Protein interactions in human pathogens revealed through deep learning, Nature Microbiology, 9(10): 2642-2652. https://doi.org/10.1038/s41564-024-01791-x James K., and Muñoz-Muñoz J., 2022, Computational network inference for bacterial interactomics, Msystems, 7(2): e01456-21. https://doi.org/10.1128/msystems.01456-21 Jha K., Saha S., and Singh H., 2022, Prediction of protein-protein interactions using graph neural networks, Scientific Reports, 12(1): 8360. https://doi.org/10.1038/s41598-022-12201-9 Jumper J., Evans R., Pritzel A., Green T., Figurnov M., Ronneberger O., Tunyasuvunakool K., Bates R., Žídek A., Potapenko A., Bridgland A., Meyer C., Kohl S., Ballard A., Cowie A., Romera-Paredes B., Nikolov S., Jain R., Adler J., Back T., Petersen S., Reiman D., Clancy E., Zielinski M., Steinegger M., Pacholska M., Berghammer T., Bodenstein S., Silver D., Vinyals O., Senior A., Kavukcuoglu K., Kohli P., and Hassabis D., 2021, Highly accurate protein structure prediction with AlphaFold, Nature, 596(7873): 583-589. https://doi.org/10.1038/s41586-021-03819-2 Khemani B., Patil S., Kotecha K., and Tanwar S., 2024, A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions, Journal of Big Data, 11(1): 18. https://doi.org/10.1186/s40537-023-00876-4 Kotlyar M., Pastrello C., Malik Z., and Jurisica I., 2019, IID 2018 update: context-specific physical protein-protein interactions in human, model organisms and domesticated species, Nucleic Acids Research, 47(D1): D581-D589. https://doi.org/10.1093/nar/gky1037 Lee M., 2023, Recent advances in deep learning for protein-protein interaction analysis: a comprehensive review, Molecules, 28(13): 5169. https://doi.org/10.3390/molecules28135169 Li Y., and Ilie L., 2017, SPRINT: ultrafast protein-protein interaction prediction of the entire human interactome, BMC Bioinformatics, 18(1): 485. https://doi.org/10.1186/s12859-017-1871-x Lian X., Yang S., Li H., Fu C., and Zhang Z., 2019, Machine-learning-based predictor of human-bacteria protein-protein interactions by incorporating comprehensive host-network properties, Journal of Proteome Research, 18(5): 2195-2205. https://doi.org/10.1021/acs.jproteome.9b00074 Maj P., and Trylska J., 2025, Protein-protein interactions as promising molecular targets for novel antimicrobials aimed at Gram-negative bacteria, International Journal of Molecular Sciences, 26(22): 10861. https://doi.org/10.3390/ijms262210861
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