Ozdemir Emine Sila, Nussinov Ruth
Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States.
Cancer Innovation Laboratory, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, United States.
Front Oncol. 2023 Feb 23;13:1061595. doi: 10.3389/fonc.2023.1061595. eCollection 2023.
Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.
宿主-病原体相互作用（HPIs）在病原体和宿主中涉及并影响多种机制。病原体相互作用破坏宿主细胞的内稳态，其毒素干扰宿主机制，导致感染、疾病和功能紊乱，范围从艾滋病、新冠病毒病到癌症。对宿主-病原体复合物三维（3D）结构的研究旨在了解病原体如何与宿主相互作用。这些研究还旨在推动合理治疗方法以及预防措施的开发。然而，针对这些目标的结构研究充满挑战。本综述从结构角度描述了宿主与病原体之间蛋白质-蛋白质相互作用（PPIs）的最新进展。它讨论了预测这些PPIs的计算方面，包括机器学习（ML）和人工智能（AI）驱动的方法，并概述了可用的计算方法及其面临的挑战。文章最后列举了理论计算方法如何能产生一种有潜力用于临床的治疗药物的实例，以及未来的发展方向。
