Allosteric Hotspots in the Main Protease of SARS-CoV-2

被引:9
|
作者
Stromich, Leonie [1 ]
Wu, Nan [1 ]
Barahona, Mauricio [2 ]
Yaliraki, Sophia N. [1 ]
机构
[1] Imperial Coll London, Dept Chem, London, England
[2] Imperial Coll London, Dept Math, London, England
基金
英国工程与自然科学研究理事会; 英国惠康基金;
关键词
graph theory; allosteric site prediction; atomistic graph representation; SARS-CoV-2; 3C-LIKE PROTEASE; SARS; DESIGN; SITES; PREDICTION; REVEALS; COMPLEX; IDENTIFICATION; PROTEINS; TARGET;
D O I
10.1016/j.jmb.2022.167748
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Inhibiting the main protease of SARS-CoV-2 is of great interest in tackling the COVID-19 pandemic caused by the virus. Most efforts have been centred on inhibiting the binding site of the enzyme. However, considering allosteric sites, distant from the active or orthosteric site, broadens the search space for drug candidates and confers the advantages of allosteric drug targeting. Here, we report the allosteric commu-nication pathways in the main protease dimer by using two novel fully atomistic graph-theoretical meth-ods: Bond-to-bond propensity, which has been previously successful in identifying allosteric sites in extensive benchmark data sets without a priori knowledge, and Markov transient analysis, which has pre-viously aided in finding novel drug targets in catalytic protein families. Using statistical bootstrapping, we score the highest ranking sites against random sites at similar distances, and we identify four statistically significant putative allosteric sites as good candidates for alternative drug targeting.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecom-mons.org/licenses/by/4.0/).
引用
收藏
页数:15
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