tetano
Editor, Senior Moderator
IEEE/ACM Trans Comput Biol Bioinform
. 2021 Feb 9;PP.
doi: 10.1109/TCBB.2021.3058265. Online ahead of print.
In silico prediction of new mutations that can improve the binding abilities between 2019-nCoV coronavirus and human ACE2
Senbiao Fang, Ruoqian Zheng, Chuqi Lei, Renyi Zhou, Jianxin Wang, Min Li
Abstract
The 2019-nCoV coronavirus protein was confirmed to be highly susceptible to various mutations, which can trigger apparent changes of virus' transmission capacity and even the pathogenic mechanism. In this article, the binding interface was obtained by analyzing the interaction modes between 2019-nCoV coronavirus and the human specific target protein ACE2. Based on the "SIFT server" and the "bubble" identification mechanism, 9 amino acid sites were selected as potential mutation-sites from the 2019-nCoV-S1-ACE2 binding interface. Subsequently, one total number of 171 mutant systems for 9 mutation-sites were optimized for binding-pattern comparsion analysis, and 14 mutations that may improve the binding capacity of 2019-nCoV-S1 to ACE2 were selected. The Molecular Dynamic Simulations were conducted to calculate the binding free energies of all 14 mutant systems. Finally, we found that most of the 14 mutations on the 2019-nCoV-S1 protein could enhance the binding ability between the 2019-nCoV coronavirus and the human protein ACE2. Among which, the binding capacities for G446R, Y449R and F486Y mutations could be increased by 20%, and that for S494R mutant increased even by 38.98%. We hope this research could provide significant help for the future epidemic detection, drug development research, and vaccine development and administration.
. 2021 Feb 9;PP.
doi: 10.1109/TCBB.2021.3058265. Online ahead of print.
In silico prediction of new mutations that can improve the binding abilities between 2019-nCoV coronavirus and human ACE2
Senbiao Fang, Ruoqian Zheng, Chuqi Lei, Renyi Zhou, Jianxin Wang, Min Li
- PMID: 33560990
- DOI: 10.1109/TCBB.2021.3058265
Abstract
The 2019-nCoV coronavirus protein was confirmed to be highly susceptible to various mutations, which can trigger apparent changes of virus' transmission capacity and even the pathogenic mechanism. In this article, the binding interface was obtained by analyzing the interaction modes between 2019-nCoV coronavirus and the human specific target protein ACE2. Based on the "SIFT server" and the "bubble" identification mechanism, 9 amino acid sites were selected as potential mutation-sites from the 2019-nCoV-S1-ACE2 binding interface. Subsequently, one total number of 171 mutant systems for 9 mutation-sites were optimized for binding-pattern comparsion analysis, and 14 mutations that may improve the binding capacity of 2019-nCoV-S1 to ACE2 were selected. The Molecular Dynamic Simulations were conducted to calculate the binding free energies of all 14 mutant systems. Finally, we found that most of the 14 mutations on the 2019-nCoV-S1 protein could enhance the binding ability between the 2019-nCoV coronavirus and the human protein ACE2. Among which, the binding capacities for G446R, Y449R and F486Y mutations could be increased by 20%, and that for S494R mutant increased even by 38.98%. We hope this research could provide significant help for the future epidemic detection, drug development research, and vaccine development and administration.