Rapid Assessment of Binding Affinity of SARS-COV-2 Spike Protein to the Human Angiotensin-Converting Enzyme 2 Receptor and to Neutralizing Biomolecules Based on Computer Simulations.
Rapid Assessment of Binding Affinity of SARS-COV-2 Spike Protein to the Human Angiotensin-Converting Enzyme 2 Receptor and to Neutralizing Biomolecules Based on Computer Simulations.
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基于计算机模拟快速评估 SARS-COV-2 刺突蛋白与人血管紧张素转换酶 2 受体和中和生物分子的结合亲和力
DOI:
10.3389/fimmu.2021.730099
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发表时间:
2021
影响因子:
7.3
通讯作者:
Zonta F
中科院分区:
文献类型:
--
作者:
Buratto D;Saxena A;Ji Q;Yang G;Pantano S;Zonta F
SARS-CoV-2 infects humans and causes Coronavirus disease 2019 (COVID-19). The S1 domain of the spike glycoprotein of SARS-CoV-2 binds to human angiotensin-converting enzyme 2 (hACE2) via its receptor-binding domain, while the S2 domain facilitates fusion between the virus and the host cell membrane for entry. The spike glycoprotein of circulating SARS-CoV-2 genomes is a mutation hotspot. Some mutations may affect the binding affinity for hACE2, while others may modulate S-glycoprotein expression, or they could result in a virus that can escape from antibodies generated by infection with the original variant or by vaccination. Since a large number of variants are emerging, it is of vital importance to be able to rapidly assess their characteristics: while changes of binding affinity alone do not always cause direct advantages for the virus, they still can provide important insights on where the evolutionary pressure is directed. Here, we propose a simple and cost-effective computational protocol based on Molecular Dynamics simulations to rapidly screen the ability of mutated spike protein to bind to the hACE2 receptor and selected neutralizing biomolecules. Our results show that it is possible to achieve rapid and reliable predictions of binding affinities. A similar approach can be used to perform preliminary screenings of the potential effects of S-RBD mutations, helping to prioritize the more time-consuming and expensive experimental work.
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影响因子:
5.9
作者:
Bolles, Meagan;Donaldson, Eric;Baric, Ralph
通讯作者:
Baric, Ralph
影响因子:
3
作者:
Pettersen, EF;Goddard, TD;Ferrin, TE
通讯作者:
Ferrin, TE
影响因子:
5.6
作者:
Cournia, Zoe;Allen, Bryce;Sherman, Woody
通讯作者:
Sherman, Woody
影响因子:
56.9
作者:
Chandrashekar, Abishek;Liu, Jinyan;Barouch, Dan H.
通讯作者:
Barouch, Dan H.
DOI:
10.1126/science.abd9909
发表时间:
2020-10-23
期刊:
Science (New York, N.Y.)
影响因子:
--
作者:
Cao L;Goreshnik I;Coventry B;Case JB;Miller L;Kozodoy L;Chen RE;Carter L;Walls AC;Park YJ;Strauch EM;Stewart L;Diamond MS;Veesler D;Baker D
通讯作者:
Baker D