Lim Hansaim, Cankara Fatma, Tsai Chung-Jung, Keskin Ozlem, Nussinov Ruth, Gursoy Attila
Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA.
Graduate School of Sciences and Engineering, Koç University, Istanbul, 34450, Turkey.
Curr Opin Struct Biol. 2022 Apr;73:102328. doi: 10.1016/j.sbi.2022.102328. Epub 2022 Feb 10.
Host-microbiome interactions play significant roles in human health and disease. Artificial intelligence approaches have been developed to better understand and predict the molecular interplay between the host and its microbiome. Here, we review recent advancements in computational methods to predict microbial effects on human cells with a special focus on protein-protein interactions. We categorize recent methods from traditional ones to more recent deep learning methods, followed by several challenges and potential solutions in structure-based approaches. This review serves as a brief guide to the current status and future directions in the field.
宿主-微生物组相互作用在人类健康和疾病中发挥着重要作用。人们已经开发出人工智能方法，以更好地理解和预测宿主与其微生物组之间的分子相互作用。在此，我们回顾了计算方法在预测微生物对人类细胞影响方面的最新进展，特别关注蛋白质-蛋白质相互作用。我们将近期的方法从传统方法分类到更新的深度学习方法，随后讨论基于结构的方法中的几个挑战和潜在解决方案。本综述可作为该领域当前状况和未来方向的简要指南。
