Open AccessElucidation of Cryptic and Allosteric Pockets within the SARS-CoV-2 Main Protease
Author(s) -
Terra Sztain,
Rommie E. Amaro,
J. Andrew McCammon
Publication year - 2021
Publication title -
journal of chemical information and modeling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 160
eISSN - 1549-960X
pISSN - 1549-9596
DOI - 10.1021/acs.jcim.1c00140
Subject(s) - druggability , allosteric regulation , molecular dynamics , in silico , computational biology , covid-19 , docking (animal) , gaussian network model , protease , biology , protein dynamics , biophysics , physics , gaussian , chemistry , computational chemistry , genetics , medicine , biochemistry , enzyme , nursing , disease , pathology , gene , infectious disease (medical specialty)
The SARS-CoV-2 pandemic has rapidly spread across the globe, posing an urgent health concern. Many quests to computationally identify treatments against the virus rely on in silico small molecule docking to experimentally determined structures of viral proteins. One limit to these approaches is that protein dynamics are often unaccounted for, leading to overlooking transient, druggable conformational states. Using Gaussian accelerated molecular dynamics to enhance sampling of conformational space, we identified cryptic pockets within the SARS-CoV-2 main protease, including some within regions far from the active site. These simulations sampled comparable dynamics and pocket volumes to conventional brute force simulations carried out on two orders of magnitude greater timescales.