TY - JOUR
T1 - Artificial intelligence approaches to human-microbiome protein–protein interactions
AU - Lim, Hansaim
AU - Cankara, Fatma
AU - Tsai, Chung Jung
AU - Keskin, Ozlem
AU - Nussinov, Ruth
AU - Gursoy, Attila
N1 - Publisher Copyright:
© 2022 The Authors
PY - 2022/4
Y1 - 2022/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85124273887&partnerID=8YFLogxK
U2 - 10.1016/j.sbi.2022.102328
DO - 10.1016/j.sbi.2022.102328
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C2 - 35152186
AN - SCOPUS:85124273887
SN - 0959-440X
VL - 73
JO - Current Opinion in Structural Biology
JF - Current Opinion in Structural Biology
M1 - 102328
ER -