Evidence map›Paper›PMID 37093365›Full record

ArticleJournal of molecular modeling2023

Binding affinity improvement analysis of multiple-mutant Omicron on 2019-nCov to human ACE2 by in silico predictions.

Bo Li, Jindan Guo, Wenxiang Hu, Yubao Chen

Abstract read
In one paragraph

Article in Journal of molecular modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Bo LiSchool of Chemistry and Environmental Engineering, Wuhan Institute of Technology, Wuhan, 430205, China.
Jindan GuoNHC Key Laboratory of Human Disease Comparative Medicine, Beijing Engineering Research Center for Experimental Animal Models of Human Critical Diseases, Institute of Laboratory Animal Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100021, China.ORCID http://orcid.org/0000-0002-8673-6267
Wenxiang HuSchool of Chemistry and Environmental Engineering, Wuhan Institute of Technology, Wuhan, 430205, China. huwx66@163.com.
Yubao ChenNHC Key Laboratory of Human Disease Comparative Medicine, Beijing Engineering Research Center for Experimental Animal Models of Human Critical Diseases, Institute of Laboratory Animal Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100021, China. chenyubao@cnilas.org.

Funding

Beijing-Tianjin-Hebei Special Project of Beijing Natural Science Foundation 19JCZDJC64700(Z)CAMS Innovation Fund for Medical Sciences (CIFMS) grant 2021-1-I2M-034National Natural Science Foundation of China Grant 82061138007R&D Program of Beijing Municipal Education Commission Z211100002521016
6 · The paper itself

Abstract

contextSince the outbreak of COVID-19 in 2019, the 2019-nCov coronavirus has appeared diverse mutational characteristics due to its own flexible conformation. One multiple-mutant strain (Omicron) with surprisingly infective activity outburst, and affected the biological activities of current drugs and vaccines, making the epidemic significantly difficult to prevent and control, and seriously threaten health around the world. Importunately exploration of mutant characteristics for novel coronavirus Omicron can supply strong theoretical guidance for learning binding mechanism of mutant viruses. What's more, full acknowledgement of key mutated-residues on Omicron strain can provide new methodology of the novel pathogenic mechanism to human ACE2 receptor, as well as the subsequent vaccine development.

methodsIn this research, 3D structures of 32 single-point mutations of 2019-nCov were firstly constructed, and 32-sites multiple-mutant Omicron were finally obtained based one the wild-type virus by homology modeling method. One total number of 33 2019-nCov/ACE2 complex systems were acquired by protein-protein docking, and optimized by using preliminary molecular dynamic simulations. Binding free energies between each 2019-nCov mutation system and human ACE2 receptor were calculated, and corresponding binding patterns especially the regions adjacent to mutation site were analyzed. The results indicated that one total number of 6 mutated sites on the Omicron strain played crucial role in improving binding capacities from 2019-nCov to ACE2 protein. Subsequently, we performed long-term molecular dynamic simulations and protein-protein binding energy analysis for the selected 6 mutations. 3 infected individuals, the mutants T478K, Q493R and G496S with lower binding energies -66.36, -67.98 and -67.09 kcal/mol also presents the high infectivity. These findings indicated that the 3 mutations T478K, Q493R and G496S play the crucial roles in enhancing binding affinity of Omicron to human ACE2 protein. All these results illuminate important theoretical guidance for future virus detection of the Omicron epidemic, drug research and vaccine development.

Indexed as

COVID-19SARS-CoV-2Angiotensin-Converting Enzyme 2HumansMutationPoint MutationProtein BindingAngiotensin-Converting Enzyme 2ACE2 receptorBinding affinity

Identifiers

PMID37093365
PMCPMC10123576

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