This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics
These authors have contributed equally to this work
These authors share senior authorship
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Drug repositioning continues to be the most effective, practicable possibility to treat COVID-19 patients. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters target cells by binding to the ACE2 receptor
COVID-19 consists of a spectrum of syndromes from a mild, flu-like illness to severe pneumonia caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus (
SARS-CoV-2 is an enveloped virus with a positive-sense single-stranded RNA that belongs to the beta-coronavirus genera of coronaviruses and exploits the human ACE2 receptor to enter the host cells (
The ACE2 receptor plays an essential role in transmitting the virus to the target host cells. Hence, here we aimed to identify a potential antagonist against the ACE2 receptor, which can inhibit the entry of the virus into human cells. We screened 450 FDA-approved compounds with antiviral properties toward the active pocket of ACE2 receptor using molecular docking-based virtual screening, followed by MD simulation, and subsequently,
Drug candidates were selected among antiviral datasets from published literature to identify the novel drugs which potentially interfere with the SARS-CoV-2 replication by inhibiting spike-ACE2 interactions (
The MD simulations were carried out on High Performance Computing (HPC) cluster of International Business Machines (IBM) Power9 CPU nodes (total 160 CPUs) with NVIDIA TESLA v100 32GB GPUs and Red Hat Enterprise Linux operating system.
To predict the preferred binding pocket on the ACE2 surface, molecular docking-based virtual screening was performed using Flare 5.0 and binding affinities calculated. Flare incorporates BioMolTech’s Lead Finder docking algorithm and combines its docking engine with genetic algorithm search containing local optimization procedures, enabling efficient sampling of ligand poses for refinement. The volume of the grid box was 287,154 A3, and the axis was set to be X: 116.403, Y: 97.474, and Z: 183.867 to cover all the amino acids in the box. It includes three different scoring functions (viz., LF dG, LF VSscore, and LF RankScore) for accurately predicting 3D docked ligand poses. The LF RankScore was selected for protein-ligand binding energy and rank ordering of active and inactive compounds in virtual screening experiments. The 2D plot was generated to study residue-ligand interactions, using the Schrödinger Maestro version 12.8.117, release 2021-2 suite (Schrödinger LLC, Cambridge, MA).
We used the general methodology to perform MD simulations of native ACE2 and the best-docked complexes using GROMACS (V5.18.3) (
HEK293T cells (ATCC CRL-3216) were cultured in Dulbecco’s modified Eagle medium (DMEM) with 1 g/L glucose (Life Technologies) supplemented with 10% fetal bovine serum (FBS) (Life Technologies) plus a final concentration of 100 IU/ml penicillin and 100 (μg/ml) streptomycin or without antibiotics were required for transfections.
Vero (WHO) Clone 118 cells (ECACC 88020401) were cultured in Dulbecco’s modified Eagle medium (DMEM, Life Technologies) with 1 g/L glucose (Life Technologies) supplemented with 10% heat-inactivated fetal bovine serum (FBS, Life Technologies) plus a final concentration of 100 IU/ml penicillin and 100 (μg/ml) streptomycin or without antibiotics where required for transfection. Cells were incubated at 37°C, 5% CO2.
Human ACE2 (Addgene #1786), pLVTHM/GFP (Addgene #12247), psPAX2 (Addgene 12,260), pMD2.G (Addgene #12259) were obtained from Addgene. pAAV-CMV-GFP was obtained from L. Zentilin (Molecular Medicine Lab, ICGEB). pAAV-spike-V5 and pAA-spike-d19-V5 SARS-CoV-2 spike expression vectors were used previously (
Antibodies against the following proteins were used: ACE2 (Abcam, ab15348), SARS-CoV-2 spike (GeneTex GTX632604), V5-488 (Thermo Fisher Scientific, 377500A488), α-beta-actin-HRP (Sigma-Aldrich), mouse-HRP (Abcam, ab6789), and rabbit-HRP (Abcam, ab205718).
Plasmid expressing human ACE2 reverse transfection was performed in a 96-well plate; 100 ng of plasmids were diluted in 25µl of Opti-MEM (Life Technologies) and mixed with the transfection reagent (FuGENE HD, Promega) using a ratio of 1 µg pDNA:3 µL FugeneHD. The transfection mixes were incubated for 25 min at RT and added to the 96 well plates (Cell Carrier Ultra 96, Perkin Elmer).
Vero cells (6.5 × 103) or HEK293-ACE2 (8 × 103) cells were seeded in each well. After 24 h of transfections, 100 ng of the pEC117-spike-V5 expression plasmid was transfected using a standard forward transfection protocol. After 24 h, cells were fixed in 4% PFA and processed for immunofluorescence.
After fixation in 4% PFA for 10 min at RT, cells were washed two times with 1xPBS and then permeabilized in same volumes of 0.1% Triton X100 (Sigma-Aldrich 1086431000) for 10 min at RT. Cells were then washed two times 1xPBS and blocked with 2% BSA for 1 h at RT. After blocking, the cells were stained according to the type of staining.
After blocking, a diluted primary antibody (1:500 in 1% BSA SARS-CoV-2 spike antibody or V5-488) was added to each well and incubated overnight at 4°. Cells were then washed two times with 1xPBS, and then a secondary antibody was added (45 µL/well, diluted 1:500 in 1% BSA) to each well and incubated for 2 h at RT. Cells were then washed two to three times in 1xPBS. Nuclear staining was performed using Hoechst 33,342 (1:5,000).
Image acquisition was performed using the Operetta CLS high content screening microscope (Perkin Elmer) with a Zeiss 20× (NA = 0.80) objective, a total of 25 fields were acquired per wavelength, well and replicate (∼10,000–15,000 cells per well and replicate).
Images were subsequently analyzed using the Harmony software (PerkinElmer). Images were first flat field corrected and nuclei were segmented using the “Find Nuclei” analysis module (Harmony). The thresholds for image segmentation were adjusted according to the signal-to-background ratio. The splitting coefficient was set to avoid splitting of overlapping nuclei (fused cells). The intensity of the green fluorescence (spike/GFP) was calculated using the “Calculate Intensity Properties” module (Harmony). All the cells that scored a nuclear area greater than four times (for manual quantification of syncytia, if fused nuclei >3, it counts as a syncytia) the average area of a single nucleus and were simultaneously positive for green (spike) signal in the cytoplasm area were considered as fused or syncytia. Data were expressed as a percentage of fused cells by calculating the average number of fused cells normalized to the total number of cells per well.
For pseudotyped particle entry assays: mean intensities of the segmented nucleus in the 488 (green) channel and the Hoechst channel for each nucleus across all fields were extracted. Each assay plate included a negative control, DMSO. Briefly, nuclei were segmented based on Hoechst staining, and cells were then classified as positive or negative depending on the GFP signal. Data were expressed as a percentage of GFP + cells by calculating the average number of GFP + cells normalized on the total number of cells.
A HIV-1 based lentiviral system was used to produce SARS-CoV-2 spike pseudotyped particles in HEK293T cells by the co-transfection of pMD2.G or pAAV-spike (d19), psPAX2 (packing vector), and PLVTHM (GFP) as described previously (
After 20–24 h of drug treatment, Vero cells were processed for western blot analysis. Equal amounts of total cellular proteins (15 μg), as measured with the BCA (Thermofisher, 23,227), were resolved by electrophoresis in 4–20% gradient polyacrylamide gels (Mini-PROTEAN, Biorad) and transferred to nitrocellulose/PVDF membranes (GE Healthcare). Membranes were blocked at RT for 60 min with PBST (PBS + 0.1% Tween-20) and 5% skim milk powder (Cell signalling, 9.999). Blots were then incubated (4°C, overnight) with primary antibodies against ACE2 (diluted 1:1,000), and
Functional ACE2-S interaction is essential for SARS-CoV-2 entry into host cells, as shown in (
Docked all 450 compounds with ACE2 receptor
Physicochemical properties of anidulafungin, lopinavir, indinavir, and MLN-4760.
Compounds | Anidulafungin | Lopinavir | Indinavir | MLN-4670 |
---|---|---|---|---|
MW | 1140.3 | 628.8 | 613.8 | 428.3 |
Atoms | 82 | 46 | 45 | 28 |
SlogP | 2.1 | 4.7 | 3.6 | 3.4 |
TPSA | 377.4 | 120 | 118 | 104.4 |
RB | 38 | 16 | 14 | 12 |
dG | −12.96 | −8.77 | −8.93 | −11.11 |
LF VSscore | −14.24 | −11.05 | −10.56 | −11.46 |
LF RankScore | −14.42 | −12.97 | −12.94 | −9.78 |
H-bonds | Arg518 and Thr371 | Glu398 | Gly395 and Glu402 | Glu402 |
Residues forming hydrophobic interactions | Asp206, Arg273, Phe274, Thr276, Asp367, Leu370, Thr371, His345, Pro346, His374, Glu375, Asn394, Gly395, Ala396, Asn397, Glu398, Gly399, His401, Glu402, Gly405, Glu406, Ile407, Ser409, Leu410, Lys441, Gln442, Thr445, Ile446, Gln522, Arg514, Tyr515, Lys562 and Zn | Phe40, Asp206, Tyr207, Arg273, His345, Pro346, Thr347, Ala348, Trp349, Asp350, Leu351, His374, Glu375, His378, Ile379, Tyr381, Asp382, Tyr385, Arg393, Asn394, Gly395, Ala396, Asn397, Glu398, Gly399, Phe400, His401, Glu402, Ala403, Ile513, Arg514, Tyr515, Tyr516, Thr517, Arg518, Thr519, Tyr521, Lys562 and Zn | Phe40, Asp206, His345, Pro346, Thr347, Ala348, Trp349, Asp350, Leu351, Gly352, Phe356, His374, Glu375, His378, Tyr381, Asp382, Tyr385, Phe390, Arg393, Asn394, Ala396, Asn397, Glu398, Gly399, Phe400, His401, Ala403, Arg514, Tyr515, Thr517, Arg518, Thr519 and Zn | Arg273, His345, Pro346, Thr347, Ala348, Met360, Asp367, Asp368, Thr371, His374, Glu375, His378, Asn397, Glu398, Gly399, Phe400, His401, Ala403, Gly405, Glu406, His505, Arg514, Tyr515, Tyr516, Arg518 and Zn |
MMGBSA (ΔG) | −162.28 | −67.19 | −92.1 | −73.53 |
Out of all the tested compounds, three drugs show high binding affinities toward ACE2 compared to MLN-4760, a known enzymatic inhibitor of ACE2. So, potentially, these drugs might inhibit SARS-CoV-2 replication by interfering with the formation of functional ACE2-S interactions.
To investigate molecular interactions of the docked complexes further, we performed MD simulations of ACE2-native, four selected docked complexes (ACE2-anidulafungin, ACE2-lopinavir, ACE2-indinavir) and ACE2-MLN-4670 for 100 ns. The stability, interaction profile, and structural parameters including RMSD, RMSF, Rg, SASA, and free energy calculations were also evaluated throughout the simulation run time to select the most stable receptor-drug complex.
In RMSD analysis, native ACE2 showed steady RMSD and revealed a threshold of ∼0.44 nm toward the binding with ACE2 under given simulation conditions (
The elucidation of MD simulation of native ACE2 and ACE2-docked complexes.
The RMS deviation of Cα-atoms remained stable throughout the simulation with a slight difference in the values but proposed one complex with anidulafungin, indicating strong binding due to polar interaction with Arg518 and Thr371 residues as well as various non-polar interactions. Anidulafungin displayed the least RMSD fluctuations at the ACE2 binding pocket compared to the other drug compounds. The overall results suggested that the anidulafungin was reliably stable among all the complexes.
Secondly, RMS-fluctuations play a crucial role in identifying the flexible and rigid regions of drug-receptor complexes. Hence, RMSF calculations were performed to measure the average atomic flexibility of the ACE2 receptor Cα-atoms alone and in complex with the tested compounds. The average RMSF values were recorded as —Native-ACE2 (0.18 nm), anidulafungin (0.14 nm), lopinavir (0.18 nm), indinavir (0.15 nm) and MLN-4760 (0.18 nm) (
Next, we analyzed the compactness of the native ACE2 and docked complexes by using the radius of gyration (Rg) calculations. The results showed that the Rg values of the native-ACE2 receptor and the complexes of anidulafungin, lopinavir, indinavir, and MLN-4760 compounds remained highly stable with ranges of 2.78, 2.86, 2.82, 2.76, and 2.77 nm, respectively, throughout the MD simulation period (
The average values of RMSD, Rg, and SASA of the native ACE2 and complex containing compounds anidulafungin, lopinavir, indinavir, and MLN-4760.
Complexes | Average RMSD (nm) | Average RMSF (nm) | Average SASA (nm2) | Average Rg (nm) |
---|---|---|---|---|
Native-ACE2 | 0.44 | 0.18 | 389.88 | 2.78 |
ACE2-anidulafungin | 0.39 | 0.14 | 392.60 | 2.86 |
ACE2-lopinavir | 0.41 | 0.18 | 391.05 | 2.82 |
ACE2-indinavir | 0.45 | 0.15 | 389.90 | 2.76 |
ACE2-MLN-4760 | 0.41 | 0.18 | 392.10 | 2.77 |
Finally, the docked complexes were also subjected to the overall motion of all protein and drug atoms by using Free Energy Landscapes (FEL) analysis. The conformational stabilities of the native ACE2 and the docked complexes were examined by FEL analysis using PC1 (Principal Components) and PC2 values. The values of FEL ranged from 0 to 14, 12.9, 13, 12.9, and 14.4 kJ/mol for the native ACE2, anidulafungin, lopinavir, indinavir, and MLN-4760 docked complexes, respectively
Free energy landscape analysis of
The SARS-CoV-2 spike protein in the viral envelope is essential for virus entry into the target cells. The SARS-CoV-2 S protein induces cell–cell fusion and the formation of syncytia when it is ectopically expressed on the membrane of host cells and binds ACE2 receptors of adjacent cells (
Anidulafungin and lopinavir impaired the spike-mediated syncytia formation.
Entry of SARS-CoV-2 S-viral particles mimics the entry pathway of SARS-CoV-2 virions (
Anidulafungin impaired the spike-pseudotyped particle internalization.
Collectively, these results indicate that anidulafungin impedes both S-mediated syncytia formation and S-viral particle entry into the target cells.
In this work, we screened the FDA-approved antiviral dataset using the molecular docking approach and selected the best three compounds, namely, anidulafungin, lopinavir, and indinavir, which show strong binding affinity toward the ACE2 receptor.
Both lopinavir and indinavir are antiretroviral drugs that inhibit HIV-1 replication by targeting viral protease (
Anidulafungin is an anti-fungal lipo-peptide drug approved to treat invasive candidiasis, candidemia, and esophageal candidiasis. It targets the critical enzyme 1,3-β-D-glucan synthase, essential for fungal cell wall synthesis (
The SARS-CoV-2 S protein plays a significant role in host cell viral attachment to receptor ACE2, and it also induces cell–cell fusion once expressed on the plasma membrane of ACE2-expressing cells. In our experiments, anidulafungin and lopinavir effectively blocked S-induced syncytia formation and S-pseudotyped particle entry into ACE2-expressing, target cells. Of interest, MLN-4760, an enzymatic inhibitor of ACE2 (
Our work discloses two drugs that appear to deserve further consideration as antiviral drugs for COVID-19 patients. However, further studies are required to fully understand their mechanism of action and potency against infectious SARS-CoV-2.
Mann-Whitney U significance test was used for the data analysis.
The original contributions presented in the study are included in the article/
SA contributed to literature mining, virtual screening, MD simulations, and manuscript writing. HA contributed to performing the biological assay, designing the hypothesis, major inputs, write-up, and corrections in the manuscript. IS contributed to experiments. MG contributed to the major inputs and corrections in the manuscript. DG contributed to the design of the hypothesis, major inputs, correspondence, and corrections in the manuscript.
MG acknowledges the British Heart Foundation (BHF) Programme Grant RG/19/11/34633. DG and SA acknowledge the bioinformatics infrastructure grant to ICGEB by the Department of Biotechnology, Government of India (no. BT/PR40151/BTIS/137/5/2021). SA is a recipient of a Research Associate fellowship from the Indian Council of Medical Research (ICMR), India (2019–6039 File No. ISRM/11(83)/2019).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
The Supplementary Material for this article can be found online at: