key: cord-0790256-kxvmoz5k authors: Peele, K. Abraham; Kumar, Vikas; Parate, Shraddha; Srirama, Krupanidhi; Lee, Keun Woo; Venkateswarulu, T.C. title: Insilico drug repurposing using FDA approved drugs against Membrane protein of SARS-CoV-2 date: 2021-03-05 journal: J Pharm Sci DOI: 10.1016/j.xphs.2021.03.004 sha: 07eae29488d950e6d7ca183a2f0b5cb0e10929b7 doc_id: 790256 cord_uid: kxvmoz5k The novel coronavirus (SARS-CoV-2) outbreak has started taking away the millions of lives worldwide. Identification of known and approved drugs against novel coronavirus disease (COVID-19) seems to be an urgent need for the repurposing of the existing drugs. So, here we examined a safe strategy of using approved drugs of SuperDRUG2 database against modeled membrane protein (M-protein) of SARS-CoV-2 which is essential for virus assembly by using molecular docking-based virtual screening. A total of 3639 drugs from SuperDRUG2 database and additionally 14 potential drugs reported against COVID-19 proteins were selected. Molecular docking analyses revealed that nine drugs can bind the active site of M-protein with desirable molecular interactions. We therefore applied molecular dynamics simulations and binding free energy calculation using MM-PBSA to analyze the stability of the compounds. The complexes of M-protein with the selected drugs were simulated for 50 ns and ranked according to their binding free energies. The binding mode of the drugs with M-protein was analyzed and it was observed that Colchicine, Remdesivir, Bafilomycin A1 from COVID-19 suggested drugs and Temozolomide from SuperDRUG2 database displayed desirable molecular interactions and higher binding affinity towards M-protein. Interestingly, Colchicine was found as the top most binder among tested drugs against M-protein. We therefore additionally identified four Colchicine derivatives which can bind efficiently with M-protein and have better pharmacokinetic properties. We recommend that these drugs can be tested further through in vitro studies against SARS-CoV-2 M-protein. . The SARS-CoV-2 was identified as the member of the family Coronaviridae which is subdivided into two subfamilies and have four genera: α, β, γ and Δ coronaviruses. The SARS-CoV-2 belongs to β-coronavirus together with SARS-CoV and MERS-CoV viruses 6 . The virus have largest single stranded RNA genome of size 27-34 kb 7, 8 . The current therapeutic options under investigation for treating COVID-19 were antiviral, convalescent plasma and hyper immune immunoglobulin 9, 10 . There is still insufficient clinical data to recommend the use of these agents for the treatment. The urgent need of developing potential diagnostic, therapeutic, and preventive strategies till the vaccination arrives is drug repurposing. The main viral therapeutic proteins identified are spike protein (S-protein), 3C-like protease (3CL pro ), papain like protease (PL pro ), RNA-dependent RNA polymerase (RdRp), nucleocapsid protein (N-protein), transmembrane protease serine-2, envelope protein (E-protein) and the membrane (M) protein 11 . The spike protein is responsible for the interaction of virus to host cell receptor angiotensinconverting enzyme 2 (ACE2) and after the entry of the virus into the host cell, viral polyproteins are processed by 3CL pro and PL pro resulting in the release of nonstructural proteins (NSPs). The NSPs further forms a replication-transcriptase complex which is then assembled by RdRp and helicase leading to the production of mRNA. Simultaneously, sub genomic proteins translate structural and other accessory proteins S, M, N and E protein [12] [13] [14] . As stated earlier, S-protein helps in the entry of the virus inside the host cell while E-protein is an integral membrane protein responsible for envelop formation and assembly of the virus 15 . The M-protein is present in greater amounts in coronaviruses and conserved among β-coronaviruses 15 . The Mprotein from SARS-CoV-2 shares over 98% sequence identity with Bat and Pangolin 16 . Mprotein plays an important role in maintaining the shape of the virus envelop and also interacts with E-protein to form virions 12 . Furthermore, M-protein also appears to affect the immune responses by inhibiting nuclear factor kappa B (NF-κB) therefore resulting in the proliferation of the virus 17 . Additionally, M-protein inhibits the interaction of 3-phosphoinositide-dependant protein kinase 1 (PDK1) and protein kinase B (PKB) which resulted in release of caspases which eventually causes cell death 18 . This literature survey revealed that M-protein can be a potential target for limiting and targeting the formation of virions and preventing inflammation in host cells 11, 19 . Recent studies have repurposed numerous drug-like candidates against well-known targets of SARS-CoV-2 viz. S-protein, 3CL pro , PL pro , RdRp and helicase 9, 20 . Few of the drugs such as Umifenovir, Lopinavir, Ritonavir, Hydroxychloroquine, Remdesivir and Favipiravir are under clinical trials against COVID-19 21 . Only Remdesivir, a RdRp inhibitor has been found most effective till date and therefore approved by FDA for emergency use [22] [23] [24] . Researchers around the world are still testing and searching for effective solutions against COVID-19. Apart from the well-known protein targets (3CL pro , PL pro , RdRp), researchers are now targeting other structural (E, N and M-protein) and accessory proteins of the virus [25] [26] [27] . Among these proteins the M-protein whose role could be vital for viral entry, replication, assembly and maintenance of the virus envelop along with N, E and S proteins can be a potential targeting strategy for COVID-19 further seeks our attention 11, 12, 19 . Unfortunately, the 3 dimensional (3D) crystal structure of M-protein is not reported till date in protein data bank (PDB). Therefore, a homology modelled structure of SARS-CoV-2 M-protein was obtained from DeepMind Homology modeled three-dimensional (3D) structure of M-protein was downloaded from DeepMind algorithm AlphaFold system (https://deepmind.com/). The AlphaFold system uses convolutional neural network for protein structure prediction 31 . In brief, this neural networks approach uses protein sequence to predict distances and angles between chemical bonds which connects amino acids. Predicted properties were then combined into a score and these scores were then used to screen proteins database to find structures that match the prediction 31 The drug repurposing strategy was performed by using 3639 drugs from SuperDRUG2 database 28, 29 . We additionally selected 13 drugs (Lopinavir, Ritonavir, Hydroxychloroquine, Chloroquine, we therefore also included this drug in our dataset for binding mode analysis 37 . The drugs were saved in a single coordinated SDF format file. For each drug molecule, different possible conformers were generated and energy minimization was performed by using universal force field of PyRx software (https://pyrx.sourceforge.io/). To carry out the molecular docking studies, virtual screening software PyRx was employed 38, 39 . PyRx works based on empirical-based free energy scoring function and Lamarckian Genetic Algorithm. Molecular docking was performed in the grid box generated based on the binding site information provided by the CASTp 3.0 online server 35 . The binding site residues inside the grid box with X, Y and Z axis and dimensions were adjusted to 13.49 Å × 0.41 Å × 2.45 Å with an exhaustiveness of 8, and the calculations were conducted in such a manner that only lowest energy pose was obtained as an output. Molecular dynamics simulation technique is widely used in drug discovery to study the behavior of protein-ligand complexes at atomic level 40, 41 . In the present investigation, top ranked compounds retrieved from the molecular docking calculations were subjected to molecular dynamics simulations with M-protein. Topology parameters of the potential drug candidates were generated by using SwissParam server 42 . Thereafter, these complexes were simulated for 50 ns using GROningen MAchine for Chemical Simulations (GROMACS v5.1.5) 43 , following the same protocol as described in our previous report 44 . Protein-ligand complexes obtained via molecular dynamics simulations were further subjected to Molecular Mechanics-based Poisson-Boltzmann Surface Area (MM-PBSA) analysis for the calculation of their binding free energies 45 . Based on the RMSD plots, last 10 ns simulation trajectories were selected and a total of 40 snapshots were taken at a regular interval. The g_mmpbsa tool for GROMACS was employed to calculate the different parameters of binding free energies with the methodologies described in previous reports [46] [47] [48] [49] . The present study provides a comprehensive details on targeting the M-protein of novel coronavirus using homology modelling, molecular docking-based virtual screening, binding mode analyses using molecular dynamics simulations with SARS-CoV-2 M-protein and free energy calculations. The schematic representation of the workflow has been described ( Figure 1 ). Homology modeled structure of M-protein was downloaded from DeepMind algorithm AlphaFold system's project for COVID-19 structure prediction. The server uses deep neural network learning algorithm AlphaFold system (https://deepmind.com/). The server uses SASR-CoV-2 M-protein sequence (UniProtKB id VME1_SARS2) was used as input for the construction of 3D model. The obtained modeled structure was first aligned with SARS-CoV sequence (UniProtKB id Q19QW6_SARS). It was observed that the obtained model has 87.1% sequence identity and 93.1% sequence similarity with the SARS-CoV M-protein ( Figure 2a ). This analysis is parallel with the reported sequence identity between both the viruses SARS-CoV and SARS-CoV-2 16, 50 . The model was then submitted to SAVES server for the generation of Ramachandran plot to predict the distribution of residues 51, 52 . The analysis revealed that 92.5% residues are in the allowed region (Figure 2c ). In addition, the model was also analyzed by ERRAT web server and the analysis revealed that the quality factor of our model was 92.04%. Generally, models with quality factor more that 50% is of acceptable quality 50, 53 . The above detailed analysis of selected model using sequence alignment, PROCHECK and ERRAT revealed that the model is of reliable quality and can be used in further studies (Figure 2b ). The energy minimized drug database was used for docking-based virtual screening using PyRx software. PyRx works on empirical-based free energy scoring function and Lamarckian Genetic Algorithm. Molecular docking was performed in the grid box generated as stated above based on the binding site information provided by the CASTp 3.0 online server and the docking results were analyzed on the basis of binding energy scores. A total of 13 drugs from SuperDRUG2 database displayed acceptable binding energy score lower than -7.0 kcal/mol 38, 54, 55 . Additionally the drugs were also docked on two more possible sites predicted by CASTp 3.0 server. The results confirms that drugs have high binding affinity toward previously selected Top1binding site Table S2 . The molecular interactions of selected drugs with were Top1 binding site were further analyzed and it was observed that five drugs displayed better molecular interactions with the active site residues of M-protein. The selected potential drugs formed hydrogen bonds with the active site residues Ala40, Asn41, Arg44, Asn113, and Glu115 of Mprotein (Table S1 ). It has been observed that the drug Temozolomide from SuperDRUG2 database displayed highest binding affinity of -8.9 kcal/mol among selected drugs. The three drugs (Colchicine, Remdesivir and Bafilomycin A1) were also selected from the 14 COVID-19 reported drugs suggested on the basis of binding energy scores and molecular interactions (Table S1 ). The selected drug were additionally, docked with known drug targets. It was observed that drugs displayed comparable binding affinity towards SASR-CoV-2 M-protein complete details were provided in Table S3 . The selected drugs from molecular docking were subjected for molecular dynamics simulation for 50 ns run using GROMACS. The drugs were then analyzed for RMSD, potential energy, RMSF and formation of hydrogen bonds with M-protein. Furthermore, the binding free energies for selected drugs were calculated using MM-PBSA tool and drugs were ranked accordingly. Drugs which displayed acceptable binding were considered for detailed analyses (Table S1 ). To Additionally, stability of the simulated complexes was also analyzed by potential energy plots 61, 62 . The selected protein-ligand complexes attained equilibrium and were found to be stabilized throughout the period of simulation ( Figure S2a) . (Table 1) . Additionally, all the above mentioned parameters were compared with SARS-CoV-RdRp-Remedesivir and were found satisfactory ( Figure S3 ) The MM-PBSA method was used to estimate the binding affinity of ligands from the last 10 ns simulation trajectories (Table S1 ). Previous studies confirmed that binding free energy values lower than -30 kJ/mol can be considered for binding, however lower binding free energy values more favorable for interactions 63 65 . The binding affinity of the drug Remedesivir was also analyzed with SARS-CoV-RdRp ( Figure S3 ). As expected, Remedesivir displayed better binding affinity for its known target RdRp (-104kJ/mol) but interestingly, showed considerable binding affinity toward M-protein (-81.17kJ/mol) confirms the ability of the drug to target multi-targets. The binding site of the M-protein was identified in between the conserved C-terminal and transmembrane domain (Figure 2b ). The average structure for all the selected complexes were calculated from the last 10 ns of stable MD trajectories and superimposed. All the selected drugs were observed to bind inside the defined binding site (Figure 4) . A deep insight on molecular interaction pattern revealed that Colchicine formed hydrogen bond interaction with the amino-acid Met109, Remdesivir showed hydrogen bonding with the amino-acids Ala40 and Arg131, Temozolomide displayed hydrogen bonding with amino-acids Ala40, Asn41 and Asn43 and Bafilomycin A1 showed hydrogen bonding with the amino-acid residues Asn41, Asn113 and Glu115 of SARS-CoV-2 M-protein ( Figure 5 a, b, c and d) . On further noticing the bonding distance between the mentioned drugs and their respective interacting amino-acids, it is clearly visible that the drugs have shown the bonding in less than 3.5 Å distance (Table 3) Table 3) . According to binding free energy and binding mode analyses, out of the 14 COVID-19 reported drugs, Colchicine was found ranked on the top ( (Table 4) . Interestingly, the compound with PubChem ID 6711380 was found better among all selected derivatives in terms of pharmacokinetic properties predicted. The membrane glycoprotein is conserved across the β-coronaviruses. The multiple sequence alignment shows a remarkable similarity over 96% Table 4 . Pharmacokinetic properties analyses for Colchicine substructures. 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