HMI-PRED: Konak-mikrop protein etkileşiminin tahmini için web sunucusu tasarımı ve geliştirilmesi

Microbes, commensals and pathogens, control numerous functions in host cells. They can alter host signaling and modulate immune surveillance by interacting with host proteins through mimicking the host protein-protein interfaces. To shed light on the contribution of microbes to health and disease, it is vital to discern how microbial proteins rewire host signaling and through which host proteins. Current host-microbe interaction data is a long way from complete, and experimental methods for large-scale identification of HMIs is challenging. Most of the currently available methods for HMI prediction are based on global sequences or structural similarity. On the other hand, there is only one available webserver for these methods, which limits the usage of these tools by the researches and scientists. To address both issues, we developed Host-Microbe Interaction PREDictor (HMI-PRED), a user-friendly webserver for template-based structural prediction of protein-protein interactions (PPIs) between host (i.e., human) and any microbial species, including bacteria, viruses, fungi, and protozoa. HMI-PRED relies on "interface mimicry" through which the microbial proteins hijack host binding surfaces. HMI-PRED server was optimized by clustering the template interface set, which is used as bases for predictions. Given the 3D structure of a microbial protein of interest, HMI-PRED will return detailed 3D structural models of potential host-microbe interaction (HMI) complexes, the list of host endogenous and exogenous PPIs that can be disrupted by the microbe protein, and the functional annotation and tissue expression of the microbe-targeted host proteins. Also, the server offers 3D visualizations of the predicted structures with highlighted contact residues, as well as visualization of the predicted structure superimposed on the original template interface. The server also allows users to upload homology models of microbial proteins. The prediction results are stored in a repository for the community access. Users can examine and search the accumulated results. HMI-PRED is available for public at https://interactome.ku.edu.tr/hmi. We also introduce a ranking method for the predicted interactions using a deep learning model based on 3D structures which is trained to identify valid protein-protein interfaces.