Profiling Molecular Simulations of SARS-CoV-2 Main Protease (Mpro) Binding to Repurposed Drugs Using Neural Network Force Fields

Aayush Gupta

10.26434/chemrxiv.12376094.v1

Research curated

Abstract

With the current pandemic situation caused by a novel coronavirus disease (COVID-19), there is an urgent call to develop a working therapeutic against it. Efficient computations can minimize the efforts by identifying a subset of drugs that can potentially bind to the COVID-19 main protease or target protein (MPRO). The results of computations are accompanied by an evaluation of their accuracy, which depends on the details described by the model used. Neural network models trained on millions of points and with unmatched accuracies are the best approach to employ in this process. In this work, I first identified and described the interaction sites of the MPRO protein using a geometric deep learning model. Second, I conducted virtual screening (at one of the sites identified) on FDA-approved drugs and selected 91 drugs with the highest binding affinities (below -8.0 kcal/mol). Then, I conducted 10 ns of molecular dynamics (MD) simulations using classical force fields and classified 37 drugs to be binding (including Lopinavir, Saquinavir, and Indinavir) based on the RMSD between MD-binding trajectories. To drastically improve the dynamics profile of the 37 selected drugs, I used the highly accurate neural network force field (ANI) method trained on coupled-cluster method (CCSD(T)/CBS) data points and performed 1 ns of binding dynamics for each drug with the protein. Using this approach, 19 drugs were qualified based on their RMSD cutoffs, and based on free energy (ANI/MM/PBSA) computations, 7 of the drugs were rejected. The final selection of 12 drugs was validated based on an MD trajectory clustering approach where 11 of the 12 drugs (Targretin, Eltrombopag, Rifaximin, Deflazacort, Ergotamine, Doxazosin, Lastacaft, Rifampicin, Victrelis, Trajenta, Toposar, and Indinavir) were confirmed to exhibit binding. Further investigations were performed to study their interactions with the protein and an accurate 2D-interaction map was generated. These findings and mappings of drug-protein interactions are highly accurate and may be potentially used to guide rational drug discovery against COVID-19.

Source: ChemRxiv

Related molecules

Name	Synonyms	Genes
Deflazacort
Doxazosin	(RS)-1-(4-Amino-6,7-dimethoxychinazolin-2-yl)-4-(2,3-dihydro-1,4-benzodioxin-2-ylcarbonyl)piperazin, DL-1-(4-Amino-6,7-dimethoxy-2-chinazolinyl)-4-[(2,3-dihydro-1,4-benzodioxin-2-yl)carbonyl]-piperazin
Alcaftadine	Lastacaft
Linagliptin	Trajenta
Rifampicin	Rifampin
Rifaximin
Ergotamine
Boceprevir	Victrelis
Etoposide	Toposar
3C-like protease	3CLpro, Mpro, SARS coronavirus main peptidase, 3CLpro-SARS-CoV-2	rep, ORF1a-1b
Bexarotene	Targretin
Eltrombopag
Indinavir

Related interactions

Target	Drug	Type	Result
3C-like protease	Bexarotene	Computational	positive
3C-like protease	Rifaximin	Computational	positive
3C-like protease	Deflazacort	Computational	positive
3C-like protease	Doxazosin	Computational	positive
3C-like protease	Alcaftadine	Computational	positive
3C-like protease	Rifampicin	Computational	positive
3C-like protease	Linagliptin	Computational	positive
3C-like protease	Ergotamine	Computational	positive
3C-like protease	Boceprevir	Computational	positive
3C-like protease	Etoposide	Computational	positive
3C-like protease	Indinavir	Computational	positive
3C-like protease	Eltrombopag	Computational	positive