Prediction of the SARS-CoV-2 (2019-nCoV) 3C-like protease (3CL<sup>pro</sup>) structure: virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates

Yu Wai Chen; Chin-Pang Bennu Yiu; Kwok-Yin Wong

doi:10.12688/f1000research.22457.1

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Prediction of the SARS-CoV-2 (2019-nCoV) 3C-like protease (3CL^pro) structure: virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates

[version 1; peer review: 3 approved]

Yu Wai Chen ^1,2, Chin-Pang Bennu Yiu³, Kwok-Yin Wong^1,2

PUBLISHED 21 Feb 2020

Author details Author details

¹ State Key Laboratory of Chemical Biology and Drug Discovery, Hunghom, Hong Kong
² Department of Applied Biology & Chemical Technology, Hong Kong Polytechnic University, Hunghom, Hong Kong
³ Independent Researcher, La Costa, Ma On Shan, Hong Kong

Yu Wai Chen
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Chin-Pang Bennu Yiu
Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Kwok-Yin Wong
Roles: Funding Acquisition, Project Administration, Resources, Supervision, Visualization, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Emerging Diseases and Outbreaks gateway.

This article is included in the Coronavirus collection.

Abstract

We prepared the three-dimensional model of the SARS-CoV-2 (aka 2019-nCoV) 3C-like protease (3CL^pro) using the crystal structure of the highly similar (96% identity) ortholog from the SARS-CoV. All residues involved in the catalysis, substrate binding and dimerisation are 100% conserved. Comparison of the polyprotein PP1AB sequences showed 86% identity. The 3C-like cleavage sites on the coronaviral polyproteins are highly conserved. Based on the near-identical substrate specificities and high sequence identities, we are of the opinion that some of the previous progress of specific inhibitors development for the SARS-CoV enzyme can be conferred on its SARS-CoV-2 counterpart. With the 3CL^pro molecular model, we performed virtual screening for purchasable drugs and proposed 16 candidates for consideration. Among these, the antivirals ledipasvir or velpatasvir are particularly attractive as therapeutics to combat the new coronavirus with minimal side effects, commonly fatigue and headache. The drugs Epclusa (velpatasvir/sofosbuvir) and Harvoni (ledipasvir/sofosbuvir) could be very effective owing to their dual inhibitory actions on two viral enzymes.

Keywords

COVID-19, SARS, 2019-nCoV, 3C-like protease, drug repurpose, antiviral, coronavirus, virtual screening, molecular modelling, ledipasvir, velpatasvir, Hepatitis C virus, HCV

Corresponding authors: Yu Wai Chen, Kwok-Yin Wong

Competing interests: No competing interests were disclosed.

Grant information: We acknowledge support from the Innovation and Technology Commission of Hong Kong, the Hong Kong Polytechnic University and the Life Science Area of Strategic Fund 1-ZVH9.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2020 Chen YW et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Chen YW, Yiu CPB and Wong KY. Prediction of the SARS-CoV-2 (2019-nCoV) 3C-like protease (3CL^pro) structure: virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates [version 1; peer review: 3 approved]. F1000Research 2020, 9:129 (https://doi.org/10.12688/f1000research.22457.1) First published: 21 Feb 2020, 9:129 (https://doi.org/10.12688/f1000research.22457.1) Latest published: 09 Apr 2020, 9:129 (https://doi.org/10.12688/f1000research.22457.2)

Introduction

On 7 January 2020, a new coronavirus, 2019-nCoV (now officially named SARS-CoV-2) was implicated in an alarming outbreak of a pneumonia-like illness COVID-19, originating from Wuhan City, Hubei, China. Human-to-human transmission was first confirmed in Guangdong, China¹. The World Health Organisation has declared this a global public health emergency — on 15 February 2020, there are more than 65,000 confirmed cases reported, and the death toll is over 1500. In the height of the crisis, this virus is spreading at a rate and scale far worse than previous coronaviral epidemics.

It was immediately evident from its genome that the coronavirus is evolutionarily related (80% identity) to the beta-coronavirus implicated in the severe acute respiratory syndrome (SARS), which originated in bats and was causative of a global outbreak in 2003. The momentum of research on developing antiviral agents against the SARS-CoV carried on after the epidemic subsided. Despite this, no SARS treatment has yet come to fruition; however, knowledge acquired from the extensive research and development efforts may be of use to inform the current therapeutic options.

The viral genome encodes more than 20 proteins, among which are two proteases (PL^pro and 3CL^pro) that are vital to virus replication; they cleave the two translated polyproteins (PP1A and PP1AB) into individual functional components. The 3-chymotrypsin-like protease (3CL^pro, aka main protease, M^pro) is considered to be a promising drug target. Tremendous effort has been spent on studying this protein in order to identify therapeutics against the SARS-CoV in particular and other pathogenic coronaviruses (e.g. MERS-CoV, the Middle East respiratory syndrome coronavirus) in general because they share similar active sites and enzymatic mechanisms. The purpose of this study is to build a molecular model of the 3CL^pro of the SARS-CoV-2 and to carry out virtual screening to identify readily usable therapeutics. It was not our intention, however, to comment on other structure-based drug design research as these will not be timely for the current epidemic.

Methods

Analysis of protein sequences

The translated polyprotein (PP1AB) sequence was obtained from the annotation of the GenBank entry of the SARS-CoV-2 genome (accession number MN908947). By comparing this sequence with the SARS-CoV PP1AB sequence (accession number ABI96956), the protease cleavage sites and all mature protein sequences were obtained. Sequence comparison and alignment were performed with BLASTp.

Preparation of structural model

The high-resolution apo-enzyme structure of SARS-CoV 3CL^pro (PDBID: 2DUC)² was employed as the template. The variant residues were “mutated” in silico by SCWRL4³, followed by manual adjustment to ensure that the best side-chain rotamer was employed (Table 2). The rebuilt model was subjected to steepest descent energy minimisation by Gromacs 2018.4 using the Gromos 54A7 forcefield, with a restraint force constant of 1000 kJ mol^-1 nm^-2 applied on all backbone atoms and all atoms of the vital residues (Table 1). Accessible surface area of residues were calculated with areaimol of the CCP4 suite v7.0.

Table 1. SARS-CoV 3CL^pro important residues and SARS-CoV-2 variant residues.

Function	Residue Number	Reference
Catalytic	41, 145	⁸
Substrate binding	41, 49, 143–144, 163–167, 187–192	2,9
Dimerisation	10, 11, 14, 28, 139, 140, 147, 298	10–13
SARS-CoV-2 variants	35, 46, 65, 86, 88, 94, 134, 180, 202, 267, 285, 286	This work

Table 2. In silico mutagenesis of the SARS-CoV-2 3CL^pro.

The 12 variant residues with reference to the SARS-CoV enzyme are shown with the respective treatment of rotamer. “A” and “B” refers to the individual chains of the dimeric model. Both chains are in the crystal asymmetric unit and are not identical. The rotamer symbol (bracketed) is defined according to the conventions of Richardson¹⁵, followed by its respective rank of popularity. ASA: accessible surface area (average of A and B chains) of the residue in the SARS-CoV 3CL^pro structure, in Å² and in % relative to the ASA of a residue X in the Gly-X-Gly conformation.

Residue	Rotamer	ASA, Å² (%)	Remarks on replacement
T35V	AB: (t-), top	19 (14%)	conservative
A46S	A: (t-), 3rd; B: (p-), top	73 (63%)	A chain disordered, rotamer chosen to minimise steric clash
S65N	AB: (m-20), top	38 (28%)
L86V	A: (m), 2nd; B: (t), top	0 (0%)	A chain rotamer to avoid clash
R88K	A: (mtpt), 9th; B: (mtpp), 19th	81 (33%)	AB: real-space refined with good fit to arginine densities
S94A	not applicable	64 (51%)
H134F	AB: (m-85), top	57 (29%)	occupy similar but larger space
K180N	AB: (m-20), top	102 (50%)
L202V	AB: (p), 3rd	22 (12%)	avoid steric clash
A267S	AB: (m), 2nd	0 (0%)	avoid steric clash
T285A	not applicable	68 (44%)	at dimeric interface
I286L	(mt), top	75 (46%)	at dimeric interface

Virtual screening

MTiOpenScreen web service⁴ was used for screening against its library of 7173 purchasable drugs (Drugs-lib), with the binding site grid specified by the active-site residues. The active sites on chain A and chain B were screened independently with AutoDock Vina⁵. When the crystal structure was released, it was stripped of its inhibitor and subjected to a screening. A list of 4,500 target:ligand docking combinations ranked by binding energies was produced for each screen. The top 10 or 11 (ranked using a binding energy cut-off) hits for chains A and B were examined visually in PyMOL (version 1.7.X)⁶.

An earlier version of this article can be found on ChemRxiv (DOI: 10.26434/chemrxiv.11831103.v2).

Results

High sequence homology with SARS-CoV

The first available genome was GenBank MN908947, now NCBI Reference Sequence NC_045512. From it, the PP1AB sequence of SARS-CoV-2 was extracted and aligned with that of SARS-CoV. The overall amino-acid sequence identity is very high (86%). The conservation is noticeable at the polyprotein cleavage sites. All 11 3CL^pro sites² are highly conserved or identical (Extended data⁷, Table S1), inferring that their respective proteases have very similar specificities. The 3CL^pro sequence of SARS-CoV-2 has only 12 out of 306 residues different from that of SARS-CoV (identity = 96%).

Conserved sequence identity among SARS-CoV-2

We compared the polyprotein PP1AB and the 3CL^pro sequences among all 11 SARS-CoV-2 genomes (GenBank MN908947, MN938384, MN975262, MN985325, MN988668, MN988669, MN988713, MN994467, MN994468, MN996527 and MN996528) that were available on 1 February 2020. With reference to MN908947 (NC_045512), among the 7096 residues, there is only one variable residue in each of MN975262 (in NSP-4), MN994467 (in NSP-2), MN994468 (in NSP-13), MN996527 (in NSP-16); and two in MN988713 (in NSP-1 and NSP-3). The remaining five have no difference. To summarise, all SARS-CoV-2 3CL^pro sequences and all their cleavage junctions on their polyproteins are 100% conserved.

3D model of the SARS-CoV-2 3CL^pro

The amino acids that are known to be important for the enzyme’s functions are listed in Table 1. Not unexpectedly, none of the 12 variant positions are involved in major roles. Therefore, we are confident to prepare a structural model of the SARS-CoV-2 3CL^pro by molecular modelling (Extended data⁷, Figure S1), which will be immediately useful for in silico development of targeted treatment. After we submitted the first draft of this study, the crystal structure of SARS-CoV-2 3CL^pro was solved and released (PDB ID 6LU7), which confirms that the predicted model is good within experimental errors (Extended data⁷, Figure S2).

Virtual screening for readily available drugs

When examined in molecular graphics⁶, all solutions were found to fit into their respective active sites convincingly. The binding energies of chain A complexes were generally higher than those of chain B by approximately 1.4 kcal mol^-1 (Table 3). This presumably demonstrates the intrinsic conformational variability between the A- and B-chain active sites in the crystal structure (the average root-mean-square deviation (rmsd) in Cα atomic positions of active-site residues is 0.83 Å). In each screen, the differences in binding energies are small, suggesting that the ranking is not discriminatory, and all top scorers should be examined. We combined the two screens and found 16 candidates which give promising binding models (etoposide and its phosphate counted as one) (Table 3).

Table 3. The results of virtual screening of drugs on the active sites of SARS-CoV-2 3CL^pro model.

The left and right columns are the results of A and B chains, respectively. The top scorers are listed first, then the equivalent top scorers of the other chain listed at the lower half. B.E.: AutoDock Vina binding energy in kcal mol^-1. The number of hits of a drug is the times it appears among all results within a screen regardless of rank, only the binding energy of the top-ranking hit was shown. Etoposide and its phosphate are listed separately in the screens. Approved and pre-approved drugs are shown in green and orange, respectively. Except dihydroergocristine and ditercalinium, all approved drugs have undergone post-market surveillance, i.e. Phase 4.

A Chain			B Chain
A Top scorers	B.E.	Hits	B Top scorers	B.E.	Hits
diosmin	-10.1	1	etoposide	-8.7	32
hesperidin	-10.1	38	R428	-8.6	2
MK-3207	-10.1	4	MK-3207	-8.6	4
venetoclax	-10.0	1	teniposide	-8.5	34
dihydroergocristine	-9.8	6	UK-432097	-8.5	2
bolazine	-9.8	1	eluxadoline	-8.4	1
R428	-9.8	2	venetoclax	-8.4	1
ditercalinium	-9.8	1	ledipasvir	-8.4	3
etoposide-phosphate	-9.8	21	irinotecan	-8.4	1
			lumacaftor	-8.4	1
			velpatasvir	-8.4	5
(B Top scorers)			(A Top scorers)
teniposide	-9.7	34	hesperidin	-8.3	38
etoposide	-9.7	32	etoposide-phosphate	-8.3	21
UK-432097	-9.6	2	bolazine	-8.3	1
irinotecan	-9.5	1	dihydroergocristine	-8.1	6
lumacaftor	-8.9	1	diosmin	-7.9	1
velpatasvir	-8.5	2	ditercalinium	-7.7	1
eluxadoline	-8.0	1
ledipasvir		0

Assessment of the candidate drugs

We checked the actions, targets and side effects of the 16 candidates. Among these, we first noticed velpatasvir (Figure 1A, D) and ledipasvir, which are inhibitors of the NS5A protein of the hepatitis C virus (HCV). Both are marketed as approved drugs in combination with sofosbuvir, which is a prodrug nucleotide analogue inhibitor of RNA-dependent RNA polymerase (RdRp, or NS5B). Interestingly, sofosbuvir has recently been proposed as an antiviral for the SARS-CoV-2 based on the similarity between the replication mechanisms of the HCV and the coronaviruses¹⁴. Our results further strengthen that these dual-component HCV drugs, Epclusa (velpatasvir/sofosbuvir) and Harvoni (ledipasvir/sofosbuvir), may be attractive candidates to repurpose because they may inhibit two coronaviral enzymes. A drug that can target two viral proteins substantially reduces the ability of the virus to develop resistance. These direct-acting antiviral drugs are also associated with very minimal side effects and are conveniently orally administered (Table 4).

Figure 1. Virtual screening results for the SARS-CoV-2 3CL^pro protease.

Docking of representative drugs into the active sites of A chain (A, B, C) and that of B chain (D, E, F). The catalytic residue surfaces are coloured in yellow. Atom colours of drug: C: cyan; O: red; N: blue; H: white; S: yellow; only polar hydrogens are shown. Prepared with PyMOL.

Table 4. Possible side effects and routes of administration of the drugs identified from virtual screening for SARS-CoV-2 3CL^pro.

Drug	Possible side effects (adverse reactions)	Admin.
Diosmin^a,b	Mild gastrointestinal disorders; skin irritations; nausea; heart arrhythmias	Topical; oral
Hesperidin^a,d	Stomach pain and upset; diarrhea; headache	Oral
MK-3207^c	No information	Oral
Venetoclax^a,b	Neutropenia; nausea; anaemia, diarrhea; upper respiratory tract infection	Oral
Dihydroergocristine^a	No information	Oral
Bolazine^b	No information	Intramuscular
R428^b	No information	Oral
Ditercalinium	No information	No info
Etoposide^a,b	Alopecia; constipation; diarrhea; nausea; vomiting; secondary malignancies	Intravenous
Teniposide^a,b	Gastrointestinal toxicity; hypersensitivity reactions; reversible alopecia	Intravenous
UK-432097^c	No information	Inhaled
Irinotecan^a,b	Gastrointestinal complication	Intravenous
Lumacaftor^a	Dyspnea; nasopharyngitis; nausea; diarrhea; upper respiratory tract infection	Oral
Velpatasvir^a,b	Headache; fatigue; nausea	Oral
Eluxadoline^a,b	Constipation; nausea; fatigue, bronchitis, viral gastroenteritis; pancreatitis	Oral
Ledipasvir^a	Fatigue; headache	Oral

Sources of information: ^a DrugBank.ca (main), ^b Wikipedia.org, ^c ClinicalTrials.gov and ^dWebMD.com.

The flavonoid glycosides diosmin (Figure 1B) and hesperidin (Figure 1E), obtained from citrus fruits, fit very well into and block the substrate binding site. Yet, these compounds cause mild adverse reactions (Table 4). Hesperidin hits showed up multiple times, suggesting it has many modes of binding (Figure 1A). Teniposide and etoposide (and its phosphate) are chemically related and turned up in multiple hits with good binding models (Figure 1F). However, these chemotherapy drugs have a lot of strong side effects and need intravenous administration (Table 4). The approved drug venetoclax (Figure 1C) and investigational drugs MK-3207 and R428 scored well in both screens. Venetoclax is another chemotherapy drug that is burdened by side effects including upper respiratory tract infection (Table 4). Not much has been disclosed about MK-3207 and R428.

We subjected the crystal structure to the same virtual screening procedures. A very similar list of candidates showed up consistently (Extended data⁷, Table S2) with high scores although ledipasvir was not found.

We noticed that most of the compounds on the list have molecular weights (MW) over 500, except lumacaftor (MW=452). The largest one is ledipasvir (MW=889). This is because the size of the peptide substrate and the deeply buried protease active site demand a large molecule that has many rotatable dynamics to fit into it.

Discussion

We identified five trials on ClinicalTrials.gov involving antiviral and immunomodulatory drug treatments for SARS (Table 5), all without reported results; i.e., at present, there are no safe and effective drug candidates against SARS-CoV. This is because once the epidemic is over, there are no patients to recruit for clinical trials. Only the study with streptokinase succeeded in completion of phase 3. It is disappointing that little progress in SARS drug development has been made in the past 17 years. After the 2003 outbreak, numerous inhibitors for the 3CL^pro enzyme have been proposed^16,17, yet no new drug candidates have succeeded to enter the clinical phase 1.

Table 5. Drugs targeting SARS that are registered for the U.S. Food and Drug Administration (USFDA) clinical trials.

Drug	Condition	Phase	Status	From	To	Location
Lopinavir / Ritonavir + Ribavirin	SARS	Unknown	Unknown			Hong Kong
Alferon LDO	SARS	Phase 2	Completed	Nov 04	Apr 06	Hong Kong
Poly-ICLC	Respiratory viruses^a	Phase 1	Completed	Mar 08	Dec 09	USA
Streptokinase	SARS, ARDS	Phase 3	Completed	Feb 16	Jan 18
Glucocorticoid (methylprednisolone) therapy	Coronavirus infections^b	Phase 2, Phase 3	Unknown	Jan 20	Dec 20 (Est.)	China

^a This covers unknown respiratory viruses. ^b This includes the COVID-19. Est. = estimated.

One record which receives a lot of attention amid the current outbreak is the lopinavir/ritonavir combination¹⁸. They are protease inhibitors originally developed against HIV. During the 2003 SARS outbreak, despite lacking a clinical trial, they were tried as an emergency measure and found to offer improved clinical outcome¹⁸. However, some scientists did express scepticism¹⁹. By analogy, these compounds were speculated to act on SARS-CoV 3CL^pro specifically, but there is as yet no crystal structure to support that, although docking studies were carried out to propose various binding modes^20–23. The IC₅₀ value of lopinavir is 50 μM (K_i = 14 μM) and that for ritonavir cannot be established²⁴. Although this is far from a cure, based on our results that the two CoV 3CL^pro enzymes are identical as far as protein sequences and substrate specificities are concerned, we are of the opinion that this is still one of the recommended routes for immediate treatment at the time of writing (early February 2020).

If we look beyond the 3CL^pro, an earlier screen produced 27 candidates that could be repurposed against both SARS-CoV and MERS-CoV²⁵. In addition, the other coronaviral proteins could be targeted for screening. Treatment of the COVID-19 with remdesivir (a repurposed drug in development targeting the RdRp) showing improved clinical outcome has just been reported and clinical trial is now underway²⁶.

We consider this work part of the global efforts responding in a timely fashion to fight this deadly communicable disease. We are aware that there are similar modelling, screening and repurposing exercises targeting 3CL^pro reported or announced^20,27–33. Our methods did not overlap, and we share no common results with these studies.

Data availability

Source data

The 11 SARS-CoV-2 polyprotein PP1AB and 3CL^pro sequences used in this study were obtained from NCBI GenBank, accession numbers MN908947 , MN938384, MN975262, MN985325, MN988668, MN988669, MN988713, MN994467, MN994468, MN996527 and MN996528, available on 1 February 2020.

The SARS-CoV PP1AB sequence wsa obtained from NCBI Protein, accession number ABI96956.

The two coronavirus protease structures used were obtained from Protein Data Bank, ID 2DUC and 6LU7.

Extended data

Open Science Framework: SARS-CoV-2 (2019-nCoV) 3CLpro Model and Screening. https://doi.org/10.17605/OSF.IO/HCU8X⁷.

The “Virtual Screening” folder contains the following extended data:

2019-nCoV-3CLpro.pdb. (3D model of the 3CL^pro: A and B chains.)
A-screen4500.pdbqt, B-screen4500.pdbqt, X-screen4500.pdbqt. (Virtual screening 3D results of Model A chain, Model B chain and the crystal-structure (A chain) in PDBQT format (can be viewed by any text editor). Use the software PyMOL to open these files. Each result file contains 4500 drug-to-protein docking hits ranked by AutoDock Vina binding energies in kcal mol^-1.)
A-screen1500.table.csv, B-screen1500.table.csv, X-screen1500.table.csv. (Virtual screening results (names only) of Model A chain, Model B chain and the crystal-structure (A chain) in CSV format (can be opened by Excel or any text editor). This is a summary of the top 1500 drug-to-protein docking hits ranked by AutoDock Vina binding energies in kcal mol^-1.)

The “Extended Results” folder contains the following extended data:

Tab S1.docx (Sequence homology of the 3CL^pro cleavage junctions of PP1AB between SARS-CoV-2 and SARS-CoV).
Tab S2.docx (The results of virtual screening of drugs on the active site of SARS-CoV-2 3CL^pro crystal structure).
Fig S1.pptx (The structural model of the SARS-CoV-2 3CL^pro protease).
Compare Crystal.docx (A comparison, with Figure S2, of the active sites of model chains A, B and the crystal structure).

Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

Faculty Opinions recommended

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32. Gao K, Nguyen DD, Wang R, et al.: Machine intelligence design of 2019-nCoV drugs. bioRxiv. 2020. Publisher Full Text
33. Li Y, Zhang J, Wang N, et al.: Therapeutic drugs targeting 2019-nCoV main protease by high-throughput screening. bioRxiv. 2020. Publisher Full Text

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Reader Comment 01 Apr 2020

Rick Sheridan, Sunny Irrigation, Nairobi, Kenya

01 Apr 2020

Reader Comment

This is beautiful research - having read many of the papers on drug simulation for SARS-CoV-2, this the first I've seen that shows results on any plant-derived compound that has ... Continue reading This is beautiful research - having read many of the papers on drug simulation for SARS-CoV-2, this the first I've seen that shows results on any plant-derived compound that has actually already shown in vitro success on SARS-CoV.
Lin et al. “Anti-SARS coronavirus 3C-like protease effects of Isatis indigotica root and plant-derived phenolic compounds”, 2005

Also, glad to see well-known coronavirus researcher Eric de Clercq having himself noted, way back in 2006 already, the efficacy of hesperidin in the same 2005 China Medical University (Taichung) in vitro study.
E. de Clerq, "Potential antivirals and antiviral strategies against SARS coronavirus infections", 2006
This is beautiful research - having read many of the papers on drug simulation for SARS-CoV-2, this the first I've seen that shows results on any plant-derived compound that has actually already shown in vitro success on SARS-CoV.
Lin et al. “Anti-SARS coronavirus 3C-like protease effects of Isatis indigotica root and plant-derived phenolic compounds”, 2005

Also, glad to see well-known coronavirus researcher Eric de Clercq having himself noted, way back in 2006 already, the efficacy of hesperidin in the same 2005 China Medical University (Taichung) in vitro study.
E. de Clerq, "Potential antivirals and antiviral strategies against SARS coronavirus infections", 2006
Competing Interests: I run a small irrigation venture in Kenya / Singapore, so I have a vested interest in horticulture value chains. Close
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Author details Author details

Kwok-Yin Wong
Roles: Funding Acquisition, Project Administration, Resources, Supervision, Visualization, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

We acknowledge support from the Innovation and Technology Commission of Hong Kong, the Hong Kong Polytechnic University and the Life Science Area of Strategic Fund 1-ZVH9.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (2)

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Chen YW, Yiu CPB and Wong KY. Prediction of the SARS-CoV-2 (2019-nCoV) 3C-like protease (3CL^pro) structure: virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates [version 1; peer review: 3 approved] F1000Research 2020, 9:129 (https://doi.org/10.12688/f1000research.22457.1)

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Reviewer Report 17 Mar 2020

Vip Viprakasit, Division of Pediatric Haematology and Oncology, Thalassemia Center, Department of Pediatrics, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand

Warut Tulalamba, Research Division and Thalassemia Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand

Approved

https://doi.org/10.5256/f1000research.24784.r60415

In this article, Chen YW and his colleagues carried out virtual screening using the computational molecular modeling of the viral protein from SARS-CoV-2 or COVID-19 to identify therapeutics targets. The authors presented that 3CLpro protease enzyme of the SARS-CoV-2 is considered to be a promising drug target and repurposing accessible drugs to challenge the globally outbreaking of the SARS-CoV-2. The authors initially used the translated polyprotein (PP1AB) sequence of SARS-CoV-2 and SARS-CoV to prepare for protein structural model. Subsequently, they performed computational virtual screening against this against its using a library of purchasable drugs with the binding site grid. Among > 7,000 repurposing drugs in the screening with their known side effects, the antivirals ledipasir or velpatasvir are potentially used against SARS-CoV-2 infection with minimal side effects. The manuscript is straightforward in both terminology and structure. The manuscript can be considered to be accepted with a minor revision and could be further improved with following points:

Table 2 is mentioned prior Table 1 in the manuscript.
More details of the setting and cut-off used in the virtual screening and analysis should be provided in the Method section.
Table 1 is quite confusing. The importance residues of the SARS-CoV 3CLpro previously reported and the variant residues found in SARS-CoV-2 (this work) should be separated. The amino acid variants of each position should be included. Using the image with the annotation could be an alternative and more informative presentation.
In Table 3, some information should be included in the table, such as molecular weight. In addition, the authors should discuss more about the results shown in the Table 3 to compare the binding energy different between A and B chains.
In the conclusion, the authors proposed velpatasvir and ledipasvir as an attractive candidate. However, based on the virtual screening on the active sites of SARS-CoV-2 3CLpro model, both of them are not ranked as the top list in Chain A screening. Could you please explain this scenario? The results from other virtual screening package (such as Glide or FlexX) should be compared?
To extend the interest of the topic as well as to compare the potential for using repurposing drug in COVID-19 treatment, the drug virtual screening with other viral enzymes might be performed and compared. In this case, since there are several clinical researches for using this drug family (e.g. Lopinavir/ritonavir) in COVID-19 treatment, therefore, the authors can compare the virtual screening model with the clinical outcomes.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Molecular genetics, Clinical haematology, Investigational New Drug, Clinical Trial.

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Author Response 09 Apr 2020

Yu Wai Chen, State Key Laboratory of Chemical Biology and Drug Discovery, Hunghom, Hong Kong

09 Apr 2020

Author Response

1. This is now corrected.

2. In reporting our virtual screening results, we listed the top 10 scorers of each model. We employed a ‘soft’ cutoff at which all results having ... Continue reading 1. This is now corrected.

2. In reporting our virtual screening results, we listed the top 10 scorers of each model. We employed a ‘soft’ cutoff at which all results having the same binding energy were included. This led to 10 (one non-human vet drug later excluded) and 11 results in chains A and B, respectively. This is now added to Methods:VS. We revised the Methods:VS section to make the procedures in MTiOpenScreen clearer.

3. Table 1 is revised so that important residues in SARS-CoV and variant residues in SARS-CoV-2 are now separated. The amino acids are identified with their single-letter codes.

We keep the two lists together because the reader can immediately compare them and see that the two sets do not overlap. The same information is conveyed in 3D on Fig. S1 in Extended data.

4. Molecular weights were added accordingly. The ‘hits’ column was revised to represent the number of stereoisomers found, out of the total possible numbers. Table 3 caption was also revised to explain in detail.

The two chains are structurally non-identical (rmsd in Cα=0.83Å) in the dimer and in the crystal structure. The differences lie primarily in side-chain conformations, especially for residues with long side chains. Thus, the two chains are two slightly-different conformational states of the protein. It is advantageous to have two states for VS because it allows some degrees of conformational variability of the active sites to be taken into account. AutoDock Vina implemented in MTiOpenScreen does not allow for the flexibility of the active site. Therefore, one would not expect the active sites on the two chains to yield the same results in terms of binding energies and ligand ranking. Further, the AutoDock Vina 'binding energy' is not a true binding free energy per se but an analogous empirical scoring function for the sake of assessment. We interpreted these results semi-quantitatively to extract trends, in VS results section, instead of exercising detailed energetic comparisons.

5. In VS results, we identified 16 candidates. Our first intention was to present all these to the readers as top scorers of docking ranked by AutoDock Vina binding energies. Next, in the Assessment section, we discussed the known properties of some of these which stood out, also considering their side effects. We highlighted velpatasvir and ledipasvir mainly because of their minimal side effects, which is a crucial factor in repurposing. In addition, we noticed the two drugs which correspond to these two compounds also contain sofosbuvir which was identified as an anti-SARS-CoV-2 candidate in a separate study. It was because of all these factors, velpatasvir and ledipasvir stood out among the 16 candidates. Therefore we highlighted them in the conclusion. We also mentioned other top-ranking candidates but they may have strong side effects. We briefly assessed these candidates to inform the clinicians who may be interested in these results.

We did not intend for a comprehensive study comparing with other VS packages. Here we employed AutoDock Vina, one of the most popular VS utilities, and produced results for follow-up development. We are moving quickly to the next stage of experimental verification in order to respond to the rapidly worsening global crisis.

6. This work was intended to be a fast response to the current pandemic — thus, we focused on one of the several potential targets for antiviral development. To this day, there are several parallel efforts on this and other targets at different scales. At the time we revised it (end of March, 2020), this article is outdated in some aspects. Therefore, we shall not extend the work further on the computational methods, as this is not the most pressing.

As the reviewer mentioned, lopinavir and ritonavir have been clinically tried. These two compounds are also expected to be specific inhibitors of 3CL^pro. Therefore, we checked to see if VS picked up these two drugs: indeed it did, with a medium score (now included in Table 3) quite a bit lower than the top candidates. Docking of lopinavir/ritonavir into the SARS-CoV 3CL^pro has been performed previously (referenced in the main text) and our models have basically the same active sites. Disappointedly, the latest trial of lopinavir/ritonavir on COVID-19 showed no clinical benefit. We added a sentence in Discussion to update the readers about this clinical trial.
1. This is now corrected.

2. In reporting our virtual screening results, we listed the top 10 scorers of each model. We employed a ‘soft’ cutoff at which all results having the same binding energy were included. This led to 10 (one non-human vet drug later excluded) and 11 results in chains A and B, respectively. This is now added to Methods:VS. We revised the Methods:VS section to make the procedures in MTiOpenScreen clearer.

3. Table 1 is revised so that important residues in SARS-CoV and variant residues in SARS-CoV-2 are now separated. The amino acids are identified with their single-letter codes.

We keep the two lists together because the reader can immediately compare them and see that the two sets do not overlap. The same information is conveyed in 3D on Fig. S1 in Extended data.

4. Molecular weights were added accordingly. The ‘hits’ column was revised to represent the number of stereoisomers found, out of the total possible numbers. Table 3 caption was also revised to explain in detail.

The two chains are structurally non-identical (rmsd in Cα=0.83Å) in the dimer and in the crystal structure. The differences lie primarily in side-chain conformations, especially for residues with long side chains. Thus, the two chains are two slightly-different conformational states of the protein. It is advantageous to have two states for VS because it allows some degrees of conformational variability of the active sites to be taken into account. AutoDock Vina implemented in MTiOpenScreen does not allow for the flexibility of the active site. Therefore, one would not expect the active sites on the two chains to yield the same results in terms of binding energies and ligand ranking. Further, the AutoDock Vina 'binding energy' is not a true binding free energy per se but an analogous empirical scoring function for the sake of assessment. We interpreted these results semi-quantitatively to extract trends, in VS results section, instead of exercising detailed energetic comparisons.

5. In VS results, we identified 16 candidates. Our first intention was to present all these to the readers as top scorers of docking ranked by AutoDock Vina binding energies. Next, in the Assessment section, we discussed the known properties of some of these which stood out, also considering their side effects. We highlighted velpatasvir and ledipasvir mainly because of their minimal side effects, which is a crucial factor in repurposing. In addition, we noticed the two drugs which correspond to these two compounds also contain sofosbuvir which was identified as an anti-SARS-CoV-2 candidate in a separate study. It was because of all these factors, velpatasvir and ledipasvir stood out among the 16 candidates. Therefore we highlighted them in the conclusion. We also mentioned other top-ranking candidates but they may have strong side effects. We briefly assessed these candidates to inform the clinicians who may be interested in these results.

We did not intend for a comprehensive study comparing with other VS packages. Here we employed AutoDock Vina, one of the most popular VS utilities, and produced results for follow-up development. We are moving quickly to the next stage of experimental verification in order to respond to the rapidly worsening global crisis.

6. This work was intended to be a fast response to the current pandemic — thus, we focused on one of the several potential targets for antiviral development. To this day, there are several parallel efforts on this and other targets at different scales. At the time we revised it (end of March, 2020), this article is outdated in some aspects. Therefore, we shall not extend the work further on the computational methods, as this is not the most pressing.

As the reviewer mentioned, lopinavir and ritonavir have been clinically tried. These two compounds are also expected to be specific inhibitors of 3CL^pro. Therefore, we checked to see if VS picked up these two drugs: indeed it did, with a medium score (now included in Table 3) quite a bit lower than the top candidates. Docking of lopinavir/ritonavir into the SARS-CoV 3CL^pro has been performed previously (referenced in the main text) and our models have basically the same active sites. Disappointedly, the latest trial of lopinavir/ritonavir on COVID-19 showed no clinical benefit. We added a sentence in Discussion to update the readers about this clinical trial.
Competing Interests: No competing interests were disclosed. Close
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COMMENTS ON THIS REPORT

Author Response 09 Apr 2020

Yu Wai Chen, State Key Laboratory of Chemical Biology and Drug Discovery, Hunghom, Hong Kong

09 Apr 2020

Author Response

1. This is now corrected.

2. In reporting our virtual screening results, we listed the top 10 scorers of each model. We employed a ‘soft’ cutoff at which all results having ... Continue reading 1. This is now corrected.

2. In reporting our virtual screening results, we listed the top 10 scorers of each model. We employed a ‘soft’ cutoff at which all results having the same binding energy were included. This led to 10 (one non-human vet drug later excluded) and 11 results in chains A and B, respectively. This is now added to Methods:VS. We revised the Methods:VS section to make the procedures in MTiOpenScreen clearer.

3. Table 1 is revised so that important residues in SARS-CoV and variant residues in SARS-CoV-2 are now separated. The amino acids are identified with their single-letter codes.

We keep the two lists together because the reader can immediately compare them and see that the two sets do not overlap. The same information is conveyed in 3D on Fig. S1 in Extended data.

4. Molecular weights were added accordingly. The ‘hits’ column was revised to represent the number of stereoisomers found, out of the total possible numbers. Table 3 caption was also revised to explain in detail.

The two chains are structurally non-identical (rmsd in Cα=0.83Å) in the dimer and in the crystal structure. The differences lie primarily in side-chain conformations, especially for residues with long side chains. Thus, the two chains are two slightly-different conformational states of the protein. It is advantageous to have two states for VS because it allows some degrees of conformational variability of the active sites to be taken into account. AutoDock Vina implemented in MTiOpenScreen does not allow for the flexibility of the active site. Therefore, one would not expect the active sites on the two chains to yield the same results in terms of binding energies and ligand ranking. Further, the AutoDock Vina 'binding energy' is not a true binding free energy per se but an analogous empirical scoring function for the sake of assessment. We interpreted these results semi-quantitatively to extract trends, in VS results section, instead of exercising detailed energetic comparisons.

5. In VS results, we identified 16 candidates. Our first intention was to present all these to the readers as top scorers of docking ranked by AutoDock Vina binding energies. Next, in the Assessment section, we discussed the known properties of some of these which stood out, also considering their side effects. We highlighted velpatasvir and ledipasvir mainly because of their minimal side effects, which is a crucial factor in repurposing. In addition, we noticed the two drugs which correspond to these two compounds also contain sofosbuvir which was identified as an anti-SARS-CoV-2 candidate in a separate study. It was because of all these factors, velpatasvir and ledipasvir stood out among the 16 candidates. Therefore we highlighted them in the conclusion. We also mentioned other top-ranking candidates but they may have strong side effects. We briefly assessed these candidates to inform the clinicians who may be interested in these results.

We did not intend for a comprehensive study comparing with other VS packages. Here we employed AutoDock Vina, one of the most popular VS utilities, and produced results for follow-up development. We are moving quickly to the next stage of experimental verification in order to respond to the rapidly worsening global crisis.

6. This work was intended to be a fast response to the current pandemic — thus, we focused on one of the several potential targets for antiviral development. To this day, there are several parallel efforts on this and other targets at different scales. At the time we revised it (end of March, 2020), this article is outdated in some aspects. Therefore, we shall not extend the work further on the computational methods, as this is not the most pressing.

As the reviewer mentioned, lopinavir and ritonavir have been clinically tried. These two compounds are also expected to be specific inhibitors of 3CL^pro. Therefore, we checked to see if VS picked up these two drugs: indeed it did, with a medium score (now included in Table 3) quite a bit lower than the top candidates. Docking of lopinavir/ritonavir into the SARS-CoV 3CL^pro has been performed previously (referenced in the main text) and our models have basically the same active sites. Disappointedly, the latest trial of lopinavir/ritonavir on COVID-19 showed no clinical benefit. We added a sentence in Discussion to update the readers about this clinical trial.
1. This is now corrected.

2. In reporting our virtual screening results, we listed the top 10 scorers of each model. We employed a ‘soft’ cutoff at which all results having the same binding energy were included. This led to 10 (one non-human vet drug later excluded) and 11 results in chains A and B, respectively. This is now added to Methods:VS. We revised the Methods:VS section to make the procedures in MTiOpenScreen clearer.

3. Table 1 is revised so that important residues in SARS-CoV and variant residues in SARS-CoV-2 are now separated. The amino acids are identified with their single-letter codes.

We keep the two lists together because the reader can immediately compare them and see that the two sets do not overlap. The same information is conveyed in 3D on Fig. S1 in Extended data.

4. Molecular weights were added accordingly. The ‘hits’ column was revised to represent the number of stereoisomers found, out of the total possible numbers. Table 3 caption was also revised to explain in detail.

The two chains are structurally non-identical (rmsd in Cα=0.83Å) in the dimer and in the crystal structure. The differences lie primarily in side-chain conformations, especially for residues with long side chains. Thus, the two chains are two slightly-different conformational states of the protein. It is advantageous to have two states for VS because it allows some degrees of conformational variability of the active sites to be taken into account. AutoDock Vina implemented in MTiOpenScreen does not allow for the flexibility of the active site. Therefore, one would not expect the active sites on the two chains to yield the same results in terms of binding energies and ligand ranking. Further, the AutoDock Vina 'binding energy' is not a true binding free energy per se but an analogous empirical scoring function for the sake of assessment. We interpreted these results semi-quantitatively to extract trends, in VS results section, instead of exercising detailed energetic comparisons.

5. In VS results, we identified 16 candidates. Our first intention was to present all these to the readers as top scorers of docking ranked by AutoDock Vina binding energies. Next, in the Assessment section, we discussed the known properties of some of these which stood out, also considering their side effects. We highlighted velpatasvir and ledipasvir mainly because of their minimal side effects, which is a crucial factor in repurposing. In addition, we noticed the two drugs which correspond to these two compounds also contain sofosbuvir which was identified as an anti-SARS-CoV-2 candidate in a separate study. It was because of all these factors, velpatasvir and ledipasvir stood out among the 16 candidates. Therefore we highlighted them in the conclusion. We also mentioned other top-ranking candidates but they may have strong side effects. We briefly assessed these candidates to inform the clinicians who may be interested in these results.

We did not intend for a comprehensive study comparing with other VS packages. Here we employed AutoDock Vina, one of the most popular VS utilities, and produced results for follow-up development. We are moving quickly to the next stage of experimental verification in order to respond to the rapidly worsening global crisis.

6. This work was intended to be a fast response to the current pandemic — thus, we focused on one of the several potential targets for antiviral development. To this day, there are several parallel efforts on this and other targets at different scales. At the time we revised it (end of March, 2020), this article is outdated in some aspects. Therefore, we shall not extend the work further on the computational methods, as this is not the most pressing.

As the reviewer mentioned, lopinavir and ritonavir have been clinically tried. These two compounds are also expected to be specific inhibitors of 3CL^pro. Therefore, we checked to see if VS picked up these two drugs: indeed it did, with a medium score (now included in Table 3) quite a bit lower than the top candidates. Docking of lopinavir/ritonavir into the SARS-CoV 3CL^pro has been performed previously (referenced in the main text) and our models have basically the same active sites. Disappointedly, the latest trial of lopinavir/ritonavir on COVID-19 showed no clinical benefit. We added a sentence in Discussion to update the readers about this clinical trial.
Competing Interests: No competing interests were disclosed. Close
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Reviewer Report 06 Mar 2020

Wai-Lung Ng, School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong (CUHK), Hong Kong, Hong Kong

Approved

https://doi.org/10.5256/f1000research.24784.r60414

Since December 2019, a plethora of cases resembling viral pneumonia has emerged exponentially in Wuhan, China, which is now coined Coronavirus Disease 2019 (COVID-19; formerly known as 2019-nCoV).

Chen et. al. reported the computational modelling and virtual screening results of the 3C-like protease (3CLpro) of SARS-CoV-2. This study is timely in view of the recent outbreak of COVID-19. The rationale of repurposing existing drugs to tackle the global viral outbreak is sound. The manuscript is also well-written and structured. It should be noted that:

The authors compared their model with the recently published crystal structure of 3CLpro and found a high similarity between the two structures. They also obtained a similar list of top-ranked drug candidates when the crystal structure was subjected to the same screening protocol.
Several studies using similar modeling and virtual screening approaches have also been published recently.

Some suggestions for improving the manuscript:

The authors proposed that the HCV drugs velpatasvir and ledipasvir, and thus Epclusa and Harvoni, could be attractive drug candidates for treating SARS-CoV-2 infection. However, there is no direct evidence to support this claim. To support this claim, the authors should connect the computational results with experimental data. To test their hypothesis, the authors should at least prove (or disprove) that the two HCV drugs could inhibit the biochemical activity of 3CLpro of SARS-CoV-2.
To further test the hypothesis, the two NS5A inhibitors should be tested using in vitro assays such as viral RNA PCR assay.
If there are no such experimental data to support the claim, the authors may consider revising their conclusion to "the computational results provide a rationale for further experimental validation of treating SARS-CoV-2 with velpatasvir and ledipasvir".

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Medicinal Chemistry, Drug Discovery, Chemical Biology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

CITE

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Author Response 09 Apr 2020

Yu Wai Chen, State Key Laboratory of Chemical Biology and Drug Discovery, Hunghom, Hong Kong

09 Apr 2020

Author Response

1. Experimental work to test these results is underway. This future plan is now included in the last sentence of the section Results:Assessment of the candidate drugs.

2. We ... Continue reading 1. Experimental work to test these results is underway. This future plan is now included in the last sentence of the section Results:Assessment of the candidate drugs.

2. We agree that it is important to also study the effects of the two NS5A inhibitors. We plan to set up collaborations for that purpose.

3. This is related to point number 1. A sentence was added at the end of the section Results:Assessment of the candidate drugs to this effect.
1. Experimental work to test these results is underway. This future plan is now included in the last sentence of the section Results:Assessment of the candidate drugs.

2. We agree that it is important to also study the effects of the two NS5A inhibitors. We plan to set up collaborations for that purpose.

3. This is related to point number 1. A sentence was added at the end of the section Results:Assessment of the candidate drugs to this effect.
Competing Interests: No competing interests were disclosed. Close
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COMMENTS ON THIS REPORT

Author Response 09 Apr 2020

Yu Wai Chen, State Key Laboratory of Chemical Biology and Drug Discovery, Hunghom, Hong Kong

09 Apr 2020

Author Response

1. Experimental work to test these results is underway. This future plan is now included in the last sentence of the section Results:Assessment of the candidate drugs.

2. We ... Continue reading 1. Experimental work to test these results is underway. This future plan is now included in the last sentence of the section Results:Assessment of the candidate drugs.

2. We agree that it is important to also study the effects of the two NS5A inhibitors. We plan to set up collaborations for that purpose.

3. This is related to point number 1. A sentence was added at the end of the section Results:Assessment of the candidate drugs to this effect.
1. Experimental work to test these results is underway. This future plan is now included in the last sentence of the section Results:Assessment of the candidate drugs.

2. We agree that it is important to also study the effects of the two NS5A inhibitors. We plan to set up collaborations for that purpose.

3. This is related to point number 1. A sentence was added at the end of the section Results:Assessment of the candidate drugs to this effect.
Competing Interests: No competing interests were disclosed. Close
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Reviewer Report 06 Mar 2020

Qiaozhu Tan, School of Life Sciences, Westlake University, Hangzhou, China

Jing Huang, School of Life Sciences, Westlake University, Hangzhou, China

Approved

https://doi.org/10.5256/f1000research.24784.r60688

Yu Wai Chen and co-workers presented a molecular modeling and docking study of the 3CL protease in the SARS-CoV-2 virus. The manuscript started with comparing polyprotein PP1AB sequences of SARS-CoV-2 and SARS-CoV, based on which the 3D structure of SARS-CoV-2 3CLPro protein was constructed. The authors then performed virtual screening against SARS-CoV-2 3CLPro using a library of 7173 purchasable drugs. Considering both binding affinities and known side effects, the authors recommend velpatasvir and ledipasvir, and further suggest combining them with another HCV RdRp inhibitor sofosbuvir, aka repurposing the Epclusa and Harvoni for treating the coronavirus. This is a concise and timely report, and has proposed new therapeutic possibilities for the SARS-CoV-2 virus. The manuscript could be further improved by addressing the following comments.

More details of the docking should be provided. What's the binding energy cutoff used? How is the hits (reported in Table 3) used? 3CLpro is catalytically active as a dimer. How is this considered in the virtual screening? What does the "(B Top scorers)" mean?
In the extended data of virtual screening, one compound could have multiple entries with different ZINC numbers. For example hesperidin corresponds to at least 20 different compounds. What are the difference? And how are different results assembled?
Table 1 is not clear. Please do a column-by-column comparison between different sites of SARS-CoV and SARS-CoV-2. Also please add one-letter amino acid codes for the residues.
The constructed protein structure is very similar to the recently solved crystal structure (6LU7), as "... confirms that the predicted model is good within experimental errors", but the docking results seem to differ significantly. Could the authors explain?

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Molecular Dynamics Simulation; Computer-aided Drug Design

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

CITE

Report a concern

Author Response 09 Apr 2020

Yu Wai Chen, State Key Laboratory of Chemical Biology and Drug Discovery, Hunghom, Hong Kong

09 Apr 2020

Author Response

1. In reporting our virtual screening results, we listed the top 10 scorers of each model. This is now added to Methods:VS.
The ‘hits’ is the number of times a ... Continue reading 1. In reporting our virtual screening results, we listed the top 10 scorers of each model. This is now added to Methods:VS.
The ‘hits’ is the number of times a compound appears (representing different stereoisomers) out of the total number of possible stereoisomers of that compound in the library. For example, hesperidin has a large number of stereoisomers (38) in the library and 8 of these are among the top scorers, it can give some confidence of reproducibility of the screening method. Stereoisomers are expected to have different binding energies. This is clarified now in the Table 3 caption.
The functional SARS-CoV 3CL^pro is a dimer. Therefore the SARS-CoV-2 enzyme was also constructed as a dimeric model, preserving all intermolecular interactions. The A- and B-chain active sites were screened independently but each was derived from the catalytically-active dimeric model. This information was now included in the Methods: Preparation of structural model and the VS sections.
The “(B Top scorers)” in the A column of Table 3 refers to those compounds which are top scorers of the B site but did not turn up among the A site top 10; they were listed to show that they still scored high (compared to the mean) although not at the top. This was explained in the Table caption.

2. The drugs-lib has 7173 purchasable drugs, with 4,574 unique compounds and their stereoisomers. Each entry is identified with the name of the compound as well as a ZINC15 ID. Stereoisomers of a compound (with same drug name but unique ZINC15 IDs) are collected together and presented as hits out of total possible stereoisomers. These are now described in Methods: VS and the Table 3 caption.

3. The purpose of Table 1 is to show that the variant positions do not overlap with any of the functional residues. It is not meant to be a detailed comparison. We amended the table to include one-letter residue identities. The list of dimerisation residues was revised to include R4, M6, E166 and E290. One reference was added.

4. The crystal structure virtual screening results (Table S2) is consistent with those of the models. Of the 9 top scorers, 6 were in common with the model top scorers (top half of table: Top scorers). For the remaining 10 of the model candidates, all of them were found in the full-list results of the crystal structure screen (bottom half of table: A, B model scorers), all with higher or similar scores to the two reference compounds, lopinavir and ritonavir. Given the semi-quantitative nature of VS and binding energy scoring using a static active site, these results from slightly different active-site conformations are considered to be agreeable.

1. In reporting our virtual screening results, we listed the top 10 scorers of each model. This is now added to Methods:VS.
The ‘hits’ is the number of times a compound appears (representing different stereoisomers) out of the total number of possible stereoisomers of that compound in the library. For example, hesperidin has a large number of stereoisomers (38) in the library and 8 of these are among the top scorers, it can give some confidence of reproducibility of the screening method. Stereoisomers are expected to have different binding energies. This is clarified now in the Table 3 caption.
The functional SARS-CoV 3CL^pro is a dimer. Therefore the SARS-CoV-2 enzyme was also constructed as a dimeric model, preserving all intermolecular interactions. The A- and B-chain active sites were screened independently but each was derived from the catalytically-active dimeric model. This information was now included in the Methods: Preparation of structural model and the VS sections.
The “(B Top scorers)” in the A column of Table 3 refers to those compounds which are top scorers of the B site but did not turn up among the A site top 10; they were listed to show that they still scored high (compared to the mean) although not at the top. This was explained in the Table caption.

2. The drugs-lib has 7173 purchasable drugs, with 4,574 unique compounds and their stereoisomers. Each entry is identified with the name of the compound as well as a ZINC15 ID. Stereoisomers of a compound (with same drug name but unique ZINC15 IDs) are collected together and presented as hits out of total possible stereoisomers. These are now described in Methods: VS and the Table 3 caption.

3. The purpose of Table 1 is to show that the variant positions do not overlap with any of the functional residues. It is not meant to be a detailed comparison. We amended the table to include one-letter residue identities. The list of dimerisation residues was revised to include R4, M6, E166 and E290. One reference was added.

4. The crystal structure virtual screening results (Table S2) is consistent with those of the models. Of the 9 top scorers, 6 were in common with the model top scorers (top half of table: Top scorers). For the remaining 10 of the model candidates, all of them were found in the full-list results of the crystal structure screen (bottom half of table: A, B model scorers), all with higher or similar scores to the two reference compounds, lopinavir and ritonavir. Given the semi-quantitative nature of VS and binding energy scoring using a static active site, these results from slightly different active-site conformations are considered to be agreeable.

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Author Response 09 Apr 2020

Yu Wai Chen, State Key Laboratory of Chemical Biology and Drug Discovery, Hunghom, Hong Kong

09 Apr 2020

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1. In reporting our virtual screening results, we listed the top 10 scorers of each model. This is now added to Methods:VS.
The ‘hits’ is the number of times a ... Continue reading 1. In reporting our virtual screening results, we listed the top 10 scorers of each model. This is now added to Methods:VS.
The ‘hits’ is the number of times a compound appears (representing different stereoisomers) out of the total number of possible stereoisomers of that compound in the library. For example, hesperidin has a large number of stereoisomers (38) in the library and 8 of these are among the top scorers, it can give some confidence of reproducibility of the screening method. Stereoisomers are expected to have different binding energies. This is clarified now in the Table 3 caption.
The functional SARS-CoV 3CL^pro is a dimer. Therefore the SARS-CoV-2 enzyme was also constructed as a dimeric model, preserving all intermolecular interactions. The A- and B-chain active sites were screened independently but each was derived from the catalytically-active dimeric model. This information was now included in the Methods: Preparation of structural model and the VS sections.
The “(B Top scorers)” in the A column of Table 3 refers to those compounds which are top scorers of the B site but did not turn up among the A site top 10; they were listed to show that they still scored high (compared to the mean) although not at the top. This was explained in the Table caption.

2. The drugs-lib has 7173 purchasable drugs, with 4,574 unique compounds and their stereoisomers. Each entry is identified with the name of the compound as well as a ZINC15 ID. Stereoisomers of a compound (with same drug name but unique ZINC15 IDs) are collected together and presented as hits out of total possible stereoisomers. These are now described in Methods: VS and the Table 3 caption.

3. The purpose of Table 1 is to show that the variant positions do not overlap with any of the functional residues. It is not meant to be a detailed comparison. We amended the table to include one-letter residue identities. The list of dimerisation residues was revised to include R4, M6, E166 and E290. One reference was added.

4. The crystal structure virtual screening results (Table S2) is consistent with those of the models. Of the 9 top scorers, 6 were in common with the model top scorers (top half of table: Top scorers). For the remaining 10 of the model candidates, all of them were found in the full-list results of the crystal structure screen (bottom half of table: A, B model scorers), all with higher or similar scores to the two reference compounds, lopinavir and ritonavir. Given the semi-quantitative nature of VS and binding energy scoring using a static active site, these results from slightly different active-site conformations are considered to be agreeable.

1. In reporting our virtual screening results, we listed the top 10 scorers of each model. This is now added to Methods:VS.
The ‘hits’ is the number of times a compound appears (representing different stereoisomers) out of the total number of possible stereoisomers of that compound in the library. For example, hesperidin has a large number of stereoisomers (38) in the library and 8 of these are among the top scorers, it can give some confidence of reproducibility of the screening method. Stereoisomers are expected to have different binding energies. This is clarified now in the Table 3 caption.
The functional SARS-CoV 3CL^pro is a dimer. Therefore the SARS-CoV-2 enzyme was also constructed as a dimeric model, preserving all intermolecular interactions. The A- and B-chain active sites were screened independently but each was derived from the catalytically-active dimeric model. This information was now included in the Methods: Preparation of structural model and the VS sections.
The “(B Top scorers)” in the A column of Table 3 refers to those compounds which are top scorers of the B site but did not turn up among the A site top 10; they were listed to show that they still scored high (compared to the mean) although not at the top. This was explained in the Table caption.

2. The drugs-lib has 7173 purchasable drugs, with 4,574 unique compounds and their stereoisomers. Each entry is identified with the name of the compound as well as a ZINC15 ID. Stereoisomers of a compound (with same drug name but unique ZINC15 IDs) are collected together and presented as hits out of total possible stereoisomers. These are now described in Methods: VS and the Table 3 caption.

3. The purpose of Table 1 is to show that the variant positions do not overlap with any of the functional residues. It is not meant to be a detailed comparison. We amended the table to include one-letter residue identities. The list of dimerisation residues was revised to include R4, M6, E166 and E290. One reference was added.

4. The crystal structure virtual screening results (Table S2) is consistent with those of the models. Of the 9 top scorers, 6 were in common with the model top scorers (top half of table: Top scorers). For the remaining 10 of the model candidates, all of them were found in the full-list results of the crystal structure screen (bottom half of table: A, B model scorers), all with higher or similar scores to the two reference compounds, lopinavir and ritonavir. Given the semi-quantitative nature of VS and binding energy scoring using a static active site, these results from slightly different active-site conformations are considered to be agreeable.

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Reader Comment 01 Apr 2020

Rick Sheridan, Sunny Irrigation, Nairobi, Kenya

01 Apr 2020

Reader Comment

This is beautiful research - having read many of the papers on drug simulation for SARS-CoV-2, this the first I've seen that shows results on any plant-derived compound that has ... Continue reading This is beautiful research - having read many of the papers on drug simulation for SARS-CoV-2, this the first I've seen that shows results on any plant-derived compound that has actually already shown in vitro success on SARS-CoV.
Lin et al. “Anti-SARS coronavirus 3C-like protease effects of Isatis indigotica root and plant-derived phenolic compounds”, 2005

Also, glad to see well-known coronavirus researcher Eric de Clercq having himself noted, way back in 2006 already, the efficacy of hesperidin in the same 2005 China Medical University (Taichung) in vitro study.
E. de Clerq, "Potential antivirals and antiviral strategies against SARS coronavirus infections", 2006
This is beautiful research - having read many of the papers on drug simulation for SARS-CoV-2, this the first I've seen that shows results on any plant-derived compound that has actually already shown in vitro success on SARS-CoV.
Lin et al. “Anti-SARS coronavirus 3C-like protease effects of Isatis indigotica root and plant-derived phenolic compounds”, 2005

Also, glad to see well-known coronavirus researcher Eric de Clercq having himself noted, way back in 2006 already, the efficacy of hesperidin in the same 2005 China Medical University (Taichung) in vitro study.
E. de Clerq, "Potential antivirals and antiviral strategies against SARS coronavirus infections", 2006
Competing Interests: I run a small irrigation venture in Kenya / Singapore, so I have a vested interest in horticulture value chains. Close
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Qiaozhu Tan, Westlake University, Hangzhou, China

Jing Huang, Westlake University, Hangzhou, China
Wai-Lung Ng, The Chinese University of Hong Kong (CUHK), Hong Kong, Hong Kong
Vip Viprakasit, Mahidol University, Bangkok, Thailand

Warut Tulalamba, Mahidol University, Bangkok, Thailand

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17 Mar 2020 | for Version 1

Vip Viprakasit, Division of Pediatric Haematology and Oncology, Thalassemia Center, Department of Pediatrics, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand

Warut Tulalamba, Research Division and Thalassemia Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand

39 Views Cite this report Responses(1)

Approved

Table 2 is mentioned prior Table 1 in the manuscript.
More details of the setting and cut-off used in the virtual screening and analysis should be provided in the Method section.
Table 1 is quite confusing. The importance residues of the SARS-CoV 3CLpro previously reported and the variant residues found in SARS-CoV-2 (this work) should be separated. The amino acid variants of each position should be included. Using the image with the annotation could be an alternative and more informative presentation.
In Table 3, some information should be included in the table, such as molecular weight. In addition, the authors should discuss more about the results shown in the Table 3 to compare the binding energy different between A and B chains.
In the conclusion, the authors proposed velpatasvir and ledipasvir as an attractive candidate. However, based on the virtual screening on the active sites of SARS-CoV-2 3CLpro model, both of them are not ranked as the top list in Chain A screening. Could you please explain this scenario? The results from other virtual screening package (such as Glide or FlexX) should be compared?
To extend the interest of the topic as well as to compare the potential for using repurposing drug in COVID-19 treatment, the drug virtual screening with other viral enzymes might be performed and compared. In this case, since there are several clinical researches for using this drug family (e.g. Lopinavir/ritonavir) in COVID-19 treatment, therefore, the authors can compare the virtual screening model with the clinical outcomes.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Molecular genetics, Clinical haematology, Investigational New Drug, Clinical Trial.

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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09 Apr 2020

Yu Wai Chen, State Key Laboratory of Chemical Biology and Drug Discovery, Hunghom, Hong Kong

1. This is now corrected.

2. In reporting our virtual screening results, we listed the top 10 scorers of each model. We employed a ‘soft’ cutoff at which all results having the same binding energy were included. This led to 10 (one non-human vet drug later excluded) and 11 results in chains A and B, respectively. This is now added to Methods:VS. We revised the Methods:VS section to make the procedures in MTiOpenScreen clearer.

3. Table 1 is revised so that important residues in SARS-CoV and variant residues in SARS-CoV-2 are now separated. The amino acids are identified with their single-letter codes.

We keep the two lists together because the reader can immediately compare them and see that the two sets do not overlap. The same information is conveyed in 3D on Fig. S1 in Extended data.

4. Molecular weights were added accordingly. The ‘hits’ column was revised to represent the number of stereoisomers found, out of the total possible numbers. Table 3 caption was also revised to explain in detail.

The two chains are structurally non-identical (rmsd in Cα=0.83Å) in the dimer and in the crystal structure. The differences lie primarily in side-chain conformations, especially for residues with long side chains. Thus, the two chains are two slightly-different conformational states of the protein. It is advantageous to have two states for VS because it allows some degrees of conformational variability of the active sites to be taken into account. AutoDock Vina implemented in MTiOpenScreen does not allow for the flexibility of the active site. Therefore, one would not expect the active sites on the two chains to yield the same results in terms of binding energies and ligand ranking. Further, the AutoDock Vina 'binding energy' is not a true binding free energy per se but an analogous empirical scoring function for the sake of assessment. We interpreted these results semi-quantitatively to extract trends, in VS results section, instead of exercising detailed energetic comparisons.

5. In VS results, we identified 16 candidates. Our first intention was to present all these to the readers as top scorers of docking ranked by AutoDock Vina binding energies. Next, in the Assessment section, we discussed the known properties of some of these which stood out, also considering their side effects. We highlighted velpatasvir and ledipasvir mainly because of their minimal side effects, which is a crucial factor in repurposing. In addition, we noticed the two drugs which correspond to these two compounds also contain sofosbuvir which was identified as an anti-SARS-CoV-2 candidate in a separate study. It was because of all these factors, velpatasvir and ledipasvir stood out among the 16 candidates. Therefore we highlighted them in the conclusion. We also mentioned other top-ranking candidates but they may have strong side effects. We briefly assessed these candidates to inform the clinicians who may be interested in these results.

We did not intend for a comprehensive study comparing with other VS packages. Here we employed AutoDock Vina, one of the most popular VS utilities, and produced results for follow-up development. We are moving quickly to the next stage of experimental verification in order to respond to the rapidly worsening global crisis.

6. This work was intended to be a fast response to the current pandemic — thus, we focused on one of the several potential targets for antiviral development. To this day, there are several parallel efforts on this and other targets at different scales. At the time we revised it (end of March, 2020), this article is outdated in some aspects. Therefore, we shall not extend the work further on the computational methods, as this is not the most pressing.

As the reviewer mentioned, lopinavir and ritonavir have been clinically tried. These two compounds are also expected to be specific inhibitors of 3CL^pro. Therefore, we checked to see if VS picked up these two drugs: indeed it did, with a medium score (now included in Table 3) quite a bit lower than the top candidates. Docking of lopinavir/ritonavir into the SARS-CoV 3CL^pro has been performed previously (referenced in the main text) and our models have basically the same active sites. Disappointedly, the latest trial of lopinavir/ritonavir on COVID-19 showed no clinical benefit. We added a sentence in Discussion to update the readers about this clinical trial.

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55 Views

06 Mar 2020 | for Version 1

Wai-Lung Ng, School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong (CUHK), Hong Kong, Hong Kong

55 Views Cite this report Responses(1)

Approved

The authors compared their model with the recently published crystal structure of 3CLpro and found a high similarity between the two structures. They also obtained a similar list of top-ranked drug candidates when the crystal structure was subjected to the same screening protocol.
Several studies using similar modeling and virtual screening approaches have also been published recently.

Some suggestions for improving the manuscript:

The authors proposed that the HCV drugs velpatasvir and ledipasvir, and thus Epclusa and Harvoni, could be attractive drug candidates for treating SARS-CoV-2 infection. However, there is no direct evidence to support this claim. To support this claim, the authors should connect the computational results with experimental data. To test their hypothesis, the authors should at least prove (or disprove) that the two HCV drugs could inhibit the biochemical activity of 3CLpro of SARS-CoV-2.
To further test the hypothesis, the two NS5A inhibitors should be tested using in vitro assays such as viral RNA PCR assay.
If there are no such experimental data to support the claim, the authors may consider revising their conclusion to "the computational results provide a rationale for further experimental validation of treating SARS-CoV-2 with velpatasvir and ledipasvir".

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Medicinal Chemistry, Drug Discovery, Chemical Biology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Author Response

09 Apr 2020

Yu Wai Chen, State Key Laboratory of Chemical Biology and Drug Discovery, Hunghom, Hong Kong

1. Experimental work to test these results is underway. This future plan is now included in the last sentence of the section Results:Assessment of the candidate drugs.

2. We agree that it is important to also study the effects of the two NS5A inhibitors. We plan to set up collaborations for that purpose.

3. This is related to point number 1. A sentence was added at the end of the section Results:Assessment of the candidate drugs to this effect.

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82 Views

06 Mar 2020 | for Version 1

Qiaozhu Tan, School of Life Sciences, Westlake University, Hangzhou, China

Jing Huang, School of Life Sciences, Westlake University, Hangzhou, China

82 Views Cite this report Responses(1)

Approved

More details of the docking should be provided. What's the binding energy cutoff used? How is the hits (reported in Table 3) used? 3CLpro is catalytically active as a dimer. How is this considered in the virtual screening? What does the "(B Top scorers)" mean?
In the extended data of virtual screening, one compound could have multiple entries with different ZINC numbers. For example hesperidin corresponds to at least 20 different compounds. What are the difference? And how are different results assembled?
Table 1 is not clear. Please do a column-by-column comparison between different sites of SARS-CoV and SARS-CoV-2. Also please add one-letter amino acid codes for the residues.
The constructed protein structure is very similar to the recently solved crystal structure (6LU7), as "... confirms that the predicted model is good within experimental errors", but the docking results seem to differ significantly. Could the authors explain?

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Molecular Dynamics Simulation; Computer-aided Drug Design

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Respond to this report

Responses (1)

Author Response

09 Apr 2020

Yu Wai Chen, State Key Laboratory of Chemical Biology and Drug Discovery, Hunghom, Hong Kong

1. In reporting our virtual screening results, we listed the top 10 scorers of each model. This is now added to Methods:VS.
The ‘hits’ is the number of times a compound appears (representing different stereoisomers) out of the total number of possible stereoisomers of that compound in the library. For example, hesperidin has a large number of stereoisomers (38) in the library and 8 of these are among the top scorers, it can give some confidence of reproducibility of the screening method. Stereoisomers are expected to have different binding energies. This is clarified now in the Table 3 caption.
The functional SARS-CoV 3CL^pro is a dimer. Therefore the SARS-CoV-2 enzyme was also constructed as a dimeric model, preserving all intermolecular interactions. The A- and B-chain active sites were screened independently but each was derived from the catalytically-active dimeric model. This information was now included in the Methods: Preparation of structural model and the VS sections.
The “(B Top scorers)” in the A column of Table 3 refers to those compounds which are top scorers of the B site but did not turn up among the A site top 10; they were listed to show that they still scored high (compared to the mean) although not at the top. This was explained in the Table caption.

2. The drugs-lib has 7173 purchasable drugs, with 4,574 unique compounds and their stereoisomers. Each entry is identified with the name of the compound as well as a ZINC15 ID. Stereoisomers of a compound (with same drug name but unique ZINC15 IDs) are collected together and presented as hits out of total possible stereoisomers. These are now described in Methods: VS and the Table 3 caption.

3. The purpose of Table 1 is to show that the variant positions do not overlap with any of the functional residues. It is not meant to be a detailed comparison. We amended the table to include one-letter residue identities. The list of dimerisation residues was revised to include R4, M6, E166 and E290. One reference was added.

4. The crystal structure virtual screening results (Table S2) is consistent with those of the models. Of the 9 top scorers, 6 were in common with the model top scorers (top half of table: Top scorers). For the remaining 10 of the model candidates, all of them were found in the full-list results of the crystal structure screen (bottom half of table: A, B model scorers), all with higher or similar scores to the two reference compounds, lopinavir and ritonavir. Given the semi-quantitative nature of VS and binding energy scoring using a static active site, these results from slightly different active-site conformations are considered to be agreeable.

View more View less

Competing Interests

No competing interests were disclosed.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Prediction of the SARS-CoV-2 (2019-nCoV) 3C-like protease (3CLpro) structure: virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates

Abstract

Keywords

Introduction

Methods

Analysis of protein sequences

Preparation of structural model

Table 1. SARS-CoV 3CLpro important residues and SARS-CoV-2 variant residues.

Table 2. In silico mutagenesis of the SARS-CoV-2 3CLpro.

Virtual screening

Results

High sequence homology with SARS-CoV

Conserved sequence identity among SARS-CoV-2

3D model of the SARS-CoV-2 3CLpro

Virtual screening for readily available drugs

Table 3. The results of virtual screening of drugs on the active sites of SARS-CoV-2 3CLpro model.

Assessment of the candidate drugs

Figure 1. Virtual screening results for the SARS-CoV-2 3CLpro protease.

Table 4. Possible side effects and routes of administration of the drugs identified from virtual screening for SARS-CoV-2 3CLpro.

Discussion

Table 5. Drugs targeting SARS that are registered for the U.S. Food and Drug Administration (USFDA) clinical trials.

Data availability

Source data

Extended data

References

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Prediction of the SARS-CoV-2 (2019-nCoV) 3C-like protease (3CL^pro) structure: virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates

Table 1. SARS-CoV 3CL^pro important residues and SARS-CoV-2 variant residues.

Table 2. In silico mutagenesis of the SARS-CoV-2 3CL^pro.

3D model of the SARS-CoV-2 3CL^pro

Table 3. The results of virtual screening of drugs on the active sites of SARS-CoV-2 3CL^pro model.

Figure 1. Virtual screening results for the SARS-CoV-2 3CL^pro protease.

Table 4. Possible side effects and routes of administration of the drugs identified from virtual screening for SARS-CoV-2 3CL^pro.