Details

Autor(en) / Beteiligte

• Wang, Guangyu
• Liu, Xiaohong
• Wang, Kai
• Gao, Yuanxu
• Li, Gen
• Baptista-Hon, Daniel T
• Yang, Xiaohong Helena
• Xue, Kanmin
• Tai, Wa Hou
• Jiang, Zeyu
• Cheng, Linling
• Fok, Manson
• Lau, Johnson Yiu-Nam
• Yang, Shengyong
• Lu, Ligong
• Zhang, Ping
• Zhang, Kang

Titel

Deep-learning-enabled protein-protein interaction analysis for prediction of SARS-CoV-2 infectivity and variant evolution

Ist Teil von

• Nature medicine, 2023-08, Vol.29 (8), p.2007-2018

Ort / Verlag

United States: Nature Publishing Group

Erscheinungsjahr

2023

Beschreibungen/Notizen

• Host-pathogen interactions and pathogen evolution are underpinned by protein-protein interactions between viral and host proteins. An understanding of how viral variants affect protein-protein binding is important for predicting viral-host interactions, such as the emergence of new pathogenic SARS-CoV-2 variants. Here we propose an artificial intelligence-based framework called UniBind, in which proteins are represented as a graph at the residue and atom levels. UniBind integrates protein three-dimensional structure and binding affinity and is capable of multi-task learning for heterogeneous biological data integration. In systematic tests on benchmark datasets and further experimental validation, UniBind effectively and scalably predicted the effects of SARS-CoV-2 spike protein variants on their binding affinities to the human ACE2 receptor, as well as to SARS-CoV-2 neutralizing monoclonal antibodies. Furthermore, in a cross-species analysis, UniBind could be applied to predict host susceptibility to SARS-CoV-2 variants and to predict future viral variant evolutionary trends. This in silico approach has the potential to serve as an early warning system for problematic emerging SARS-CoV-2 variants, as well as to facilitate research on protein-protein interactions in general.