SpikeScape: A Tool for Analyzing Structural Diversity in Experimental Structures of the SARS-CoV-2 Spike Glycoprotein (preprint)

In this application note we describe a tool which we developed to help structural biologists who study the SARS-CoV-2 spike glycoprotein. There are more than 500 structures of this protein available in the Protein Data Bank. These structures are available in different flavors: wild type spike, different variants, 2P substitutions, structures with bound antibodies, structures with Receptor Binding Domains in closed or open conformation, etc. Understanding differences between these structures could provide insight to how the spike structure changes in different variants or upon interaction with different molecules such as receptors or antibodies. However, inconsistencies among deposited structures, such as different chain or sequence numbering, hamper a straightforward comparison of all structures. The tool described in this note fixes those inconsistencies and calculates the distribution of the requested distance between any two atoms across all SARS-CoV-2 spike structures available in the Protein Data Bank, with the option to filter by various selections. The tool provides a histogram and cumulative frequency of the calculated distribution, as the ability to download the results and corresponding PDB IDs.

Cryo-EM structure and B-factor refinement with ensemble representation (preprint)

Cryo-EM experiments produce images of macromolecular assemblies that are combined to produce three-dimensional density maps. It is common to fit atomic models of the contained molecules to interpret those maps, followed by a density-guided refinement. Here, we propose TEMPy-REFF, a novel method for atomic structure refinement in cryo-EM density maps. By representing the atomic positions as components of a mixture model, their variances as B-factors, and a model ensemble description, we significantly improve the fit to the map compared to what is currently achievable with state-of-the-art methods. We validate our method on a large benchmark of 366 cryo-EM maps from EMDB at 1.8-7.1Å resolution and their corresponding PDB assembly models. We also show that our approach can provide newly-modelled regions in EMDB deposited maps by combining it with AlphaFold-Multimer. Finally, our method provides a natural interpretation of maps into components, allowing us to accurately create composite maps.

A computational evaluation of FDA medicines' ability to inhibit Hypoxia-inducible factor prolyl hydroxylase-2 (PHD-2) for Acute Respiratory Distress Syndrome. (preprint)

COVID-19 infection is associated with a significant fatality rate in individuals suffering from severe Acute Respiratory Distress Syndrome (ARDS). Among the several possibilities, inhibition of Hypoxia-inducible factor prolyl hydroxylase-2 or prolyl hydroxylase domain-containing protein 2 (PHD2) in a hypoxia independent way is a prospective therapeutic target for the treatment of ARDS. Vadadustat, Roxadustat, Daprodustat, Desidustat, and Enarudustat are the available clinical trial inhibitors. This study is proposed to focus on the repurposing of FDA approved drugs as effective PHD2 inhibitors. This computational study utilizes e-pharmacophore hypothesis generation from the native ligand-protein complex (PDB ID: 5OX6) based on XP Visualizer information. The hypothesis containing five essential features (AAANR) was incorporated for FDA database screening, followed by Glide XP molecular docking and Prime MM-GBSA binding free energy calculations. Top scored ligands were investigated and Fenbufen was identified as an effective PHD-2 inhibitor by comparing with the native co-crystal ligand (Vadadustat). The manual lead optimization of Fenbufen structure was adopted to improve inhibitory potency, by increasing the binding affinity and protein-ligand stability. The newly designed compounds B and C showed additional binding interactions, excellent docking scores, binding free energy, and an acceptable range of ADME properties. Also, Fenbufen and Compound C owned preferable protein-ligand stability during MD simulation when compared with the co-crystallized clinical trial ligand. Based on our findings we deduce that Fenbufen can be proposed as an effective repurposable candidate as its structural modification showed a remarkable improvement in PHD2 inhibition.

Potential Drug Leads for SARS-CoV2 from Phytochemicals of Aerva lanata: An in silico approach (preprint)

COVID-19 outbreak is the recently reported worldwide pandemic threat. As part of our interventions with molecular simulation approaches, we report the inhibitory effect of thirty compounds reported from the sacred plant Aerva lanata and compare their activity with the one of the present medication, hydroxy chloroquine, on the main protease (PDB:6YB7) of SARS-CoV-2. Our studies pointed out the effectiveness of the plant with twenty seven compounds having potential activity against the main protease compared to the reference HCQ. The robustness of some of the phytochemicals such as ervoside, which is only present in Aerva lanata computed to have very high anticoronavirus activity. The results are indicative of potential natural antivirus source, which subsidizes in thwarting the invasion of coronavirus into the human body. Many phytochemicals which are computed to be effective towards SARS-CoV-2 in this study are used as drugs for various other diseases. Perhaps these compounds could be attractive for the management of COVID-19, but clinical trials must be performed in order to validate this observation.

Working Mechanism of Remdesivir and Discovering New Potential Inhibitors to SARS-CoV-2 RNA-Dependent RNA Polymerase (RdRp) using High-Throughput Virtual Screening and Molecular Dynamics Simulations . (preprint)

RNA-dependent RNA polymerase (RdRp), is an enzyme essential component in the RNA replication within the life cycle of the severely acute respiratory coronavirus-2 (SARS-CoV-2), causing the deadly respiratory induced sickness COVID-19. Remdesivir is a prodrug that has seen some success in inhibiting this enzyme, however there is still the pressing need for effective alternatives. In this study, we present the discovery of four non-nucleoside small molecules that bind favorably to RdRp over adenosine-triphosphate (ATP) and active-form remdesivir-triphosphate (RTP) using high-throughput virtual screening (HTVS) coupled with extensive (total 4800 ns) molecular dynamics (MD) simulations with using the ZINC compounds database against SARS-CoV-2 RdRp (PDB: 7BV2). We found that the simulations with both ATP and RTP remained stable for the duration of their trajectories, and it was revealed that the phosphate tail of RTP was stabilized by a positive amino acid pocket near the entry channel of RTP and magnesium ions containing residues K551, R553, R555 and K621. It was also found that residues D623, D760, and N691 further stabilized the ribose portion of RTP with U10 on the template RNA strand forming hydrogen pairs with the adenosine motif. Using these models of RdRp, we employed them to screen the ZINC database of ~17 million molecules. Using docking and drug properties scoring, we narrowed down our selection to fourteen candidates. These were subjected to 200 ns simulations each underwent free energy calculations. We identified four hit compounds from the ZINC database that have similar binding poses to RTP while possessing lower overall binding free energies, with ZINC097971592 having a binding free energy two times lower than RTP.

Structural and Spectral characterization, Topological study and activity prediction of antiviral (E)-5-(diethylamino)-2-((3,5-dinitrophenylimino)methyl)phenol against SARS-CoV-2 Main Protease (preprint)

The (E)-5-(diethylamino)-2-((3,5-dinitrophenylimino)methyl)phenol was deliberated by Ultraviolet-Visible (UV-Vis) and Fourier Transform Infrared (FT-IR) spectroscopy techniques. Hirshfeld surface analysis was performed to analyze the contribution of intermolecular contacts in crystal structure of the studied Schiff base ligand. The plots of the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), and the electrostatic potential (ESP) were used to research the nucleophilic and electrophilic attack sites for the Schiff base ligand. According to the ESP analysis, the electrophilic reactivity was mainly found in nitro and hydroxy groups attached to phenyl rings, while the nucleophilic attack was more concentrated on hydrogen atoms. Major contributions from molecular orbitals to the electronic transitions computed according to the time-dependent DFT (TD-DFT) method were investigated using chloroform as a solvent. The topological parameters; electron density, Laplacian of electron density, kinetic energy, electron potential energy density, total electron energy density, ellipticity, hydrogen bond energy at the bond critical points have been evaluated. The compound was docked into the severe acute respiratory syndrome coronavirus 2, including a 3-chymotrypsin-like protease receptor's active site with the PDB ID 6LU7. The Schiff base ligand exhibited a good activity against coronavirus main protease receptor with 6LU7 protein as compared with Chloroquine, Favipiravir, and Remdesivir with −4.68 kcal/mol value of the binding energy.

New Potential Drug Candidates Against SARS-CoV-2 Using Generative Model (preprint)

Since known approved drugs like liponavir and ritonavir failed to cure SARS-CoV-2 infected patients, it is high time to generate new chemical entities against this virus. 3CL main protease acts as key enzyme for the growth of a virus which acts as biocatalyst and helps to break protein and ultimately in the growth of coronavirus. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) along with variational autoencoder with KL annealing and circular annealing for generating potential lead compounds targeting SARS-CoV-2 3CLpro. Structure-based optimization policy (SBOP) is used in reinforcement learning. The reason for using variational autoencoders is that it does not deviate much from actual inhibitors, but since VAE suffers from KL diminishing we have used KL annealing and circular annealing to address this issue. Researchers can use this compound as potential drugs against SARS-CoV-2

[Cu(Dipicolinoylamide)(NO 3)(H 2O)] as Anti-COVID-19 and Antibacterial Drug Candidate: Design, Synthesis, Crystal Structure, DFT and Molecular Docking (preprint)

For first time the new N-picolinoypicolinlamide was obtained as in situ ligand during the reaction of 2,4,6-ris(2-pyridyl)-,3,5-triazine with aqueous solution of CuNO 3 .3H 2 O and formed the corresponding complex [Cu(dipicolinoylamide)(NO3)(H2O)]. The crystal structure of the obtained complex was determined by x-ray structure. The complex crystallizes in space group P21/n, a = 10.2782(9) Å, b =7.5173(6) Å, c = 17.738(2) Å, α = 90.00º, β = 91.368(1)º, γ = 90.00º, V = 1370.1(2) Å3, Z=4. The copper center has a distorted octahedral geometry. DFT calculations show good agreement between theoretical and X-ray data. The Molecular docking studies were executed to consider the nature of binding and binding affinity of the synthesized compounds with the receptor of COVID-19 main protease viral protein (PDB ID: 6lu7), the receptor of gram –ve bacteria (Escherichia coli, PDB ID: 1fj4) and the receptor of gram +ve bacteria (Staphylococcus aureus, PDB ID: 3q8u and Proteus PDB ID: 5i39).

An Efficient Synthesis Towards the Core of Crinipellin and Alliacol-B Along With Their Docking Studies (preprint)

In this present work, we are reporting a novel route for the synthesis of the tetracyclic ring systems, which is a common core of crinipellin via oxidative dearomatization, cycloaddition and oxa- di-pi-methane rearrangement. We considered to exploring a route to tetracyclic core (1e) of Crinipellin and tricyclic core (1g) of Allicaol B through intermolecular diels alder reaction and photochemically 1,2 acyl shift. Moreover, docking study of compound 13 and 16has been investigated against AcrB multidrug efflux pump of Escherichia coli (PDB ID: 1T9U), main protease of SARS COV-2 (PDB ID: 6W63), DNA gyrase of Streptococcus pneumonia (PDB ID: 4Z2C), human estrogen receptor alpha (PDB ID: 3ERT), human lanosterol 14-alpha-demethylase (CYP51)(PDB ID: 3JUS) and cyclooxygenase-2 (Prostaglandin Synthase-2) (PDB ID: 1CX2). The obtained results herein are important for the exploitation of the therapeutic potential of these derivatives as antimicrobial, antiviral, anticancer, antifungal or anti-inflammatory agents.

Screening of World Approved Drugs against Highly Dynamical Spike Glycoprotein SARS-CoV-2 using CaverDock and Machine Learning (preprint)

The new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes pathological pulmonary symptoms. Most efforts to develop vaccines and drugs against this virus target the spike glycoprotein, particularly its S1 subunit, which is recognised by angiotensin-converting enzyme 2. Here we use the in-house developed tool CaverDock to perform virtual screening against spike glycoprotein using a cryogenic electron microscopy structure (PDB-ID: 6VXX) and the representative structures of five most populated clusters from a previously published molecular dynamics simulations. The dataset of ligands was obtained from the ZINC database and consists of drugs approved for clinical use worldwide. Trajectories for the passage of individual drugs through the tunnel of the spike glycoprotein homotrimer, their binding energies within the tunnel, and the duration of their contacts with the trimer’s three subunits were computed for the full dataset. Multivariate statistical methods were then used to establish structure-activity relationships and select top candidate molecules. This new protocol for rapid screening of globally approved drugs (4359 ligands) in a multi-state protein structure (6 states) required a total of 26,148 calculations and showed high robustness. The protocol is universal and can be applied to any target protein with an experimental tertiary structure containing protein tunnels or channels. The protocol will be implemented in the next version of CaverWeb (https://loschmidt.chemi.muni.cz/caverweb/) to make it accessible to the wider scientific community

Ultra-Large-Scale Ab Initio Quantum Chemical Computation of Bio-Molecular Systems: The Case of Spike Protein of SARS-CoV-2 Virus (preprint)

The COVID-19 pandemic poses a severe threat to human health with an unprecedented social and economic disruption. Spike (S) glycoprotein of the SARS-CoV-2 virus is pivotal in understanding the virus anatomy, since it initiates the first contact with the ACE2 receptor in the human cell. We report results of ab initio computation of the spike protein, the largest ab initio quantum chemical computation to date on any bio-molecular system, using a divide and conquer strategy by focusing on individual structural domains. In this approach we divided the S-protein into seven structural domains: N-terminal domain (NTD), receptor binding domain (RBD), subdomain 1 (SD1), subdomain 2 (SD2), fusion peptide (FP), heptad repeat 1 with central helix (HR1-CH) and connector domain (CD). The entire Chain A has 14,488 atoms including the hydrogen atoms but excluding the amino acids with missing coordinates based on the PDB data (ID: 6VSB). The results include structural refinement, ab initio calculation of intra-molecular bonding mechanism, 3- dimensional non-local inter-amino acid interaction with implications for the inter-domain interaction. Details of the electronic structure, interatomic bonding, partial charge distribution and the role played by hydrogen bond network are discussed. Extension of such calculation to the interface between the S-protein binding domain and ACE2 receptor can provide a pathway for computational understanding of mutations and the design of therapeutic drugs to combat the COVID-19 pandemic.

Comprehensive Virtual Screening of the Antiviral Potentialities of Marine Polycyclic Guanidine Alkaloids against SARS-CoV-2 (Covid-19) (preprint)

A comprehensive in silico binding affinity of fifteen guanidine alkaloids against five different proteins of SARS-CoV-2 has been investigated. The investigated proteins are COVID-19 main protease (M pro ) (PDB ID: 6lu7), spike glycoprotein (PDB ID: 6VYB), nucleocapsid phosphoprotein (PDB ID: 6VYO), membrane glycoprotein (PDB ID: 6M17), and non-structural protein (nsp10) (PDB ID: 6W4H). The binding energies for all tested compounds indicated promising binding affinities. A noticeable superiority for the pentacyclic alkaloids particularly, crambescidin 786 ( 5 ) and crambescidin 826 ( 13 ) have been observed. Compound 5 exhibited very good binding affinities against M pro (ΔG = -8.05 kcal/mol), nucleocapsid phosphoprotein (ΔG = -6.49 kcal/mol), and nsp10 (ΔG = -9.06 kcal/mol). Compound 13 showed promising binding affinities against M pro (ΔG = -7.99 kcal/mol), spike glycoproteins (ΔG = -6.95 kcal/mol), and nucleocapsid phosphoprotein (ΔG = -8.01 kcal/mol). Such promising activities might be attributed to the long ω-fatty acid chain, which may play a vital role in binding within the active sites. The ADMET studies were carried out in silico for the 15 compounds, all examined compounds (except compounds 8 and 15 ) have low or very low BBB penetration levels. Compounds 1 , 5 , 6 , 9 , 12 and 13 showed optimal range levels of ADMET aqueous solubility. Compounds 1 , 2 , 3 , 8 , and 15 were predicted to have good intestinal absorption levels, while compounds 4 , 7 , 9 , 10 , and 14 showed moderate absorption levels. All examined alkaloids (except the bicyclic compound 8 ) were predicted not to be inhibitors of CYP2D6, non-hepatotoxic, and bind plasma protein with a percentage less than 90%. The toxicity of the tested compounds was screened in silico against five models (FDA rodent carcinogenicity, carcinogenic potency TD 50 , rat maximum tolerated dose, rat oral LD 50 and rat chronic LOAEL). All compounds showed expected low toxicity against the tested models.

Interaction Analyses on SARS-CoV-2 Spike Protein Based on Large-Scale Correlated Fragment Molecular Orbital Calculations (preprint)

At the stage of SARS-CoV-2 infection to human cell, the spike protein consisting of three chains, A, B and C, with a total of 3.3 thousand residues plays the key role, and thus its nature have attracted considerable interests. Here, we report interaction analyses on the spike protein of both closed (PDB-ID: 6VXX) and open (6VYB) structures, based on large-scale fragment molecular orbital (FMO) calculations at the level of up to the fourth-order Møller-Plesset perturbation with singles, doubles and quadruples (MP4(SDQ)). Inter-chain interaction energies were evaluated for both structures, and mutual comparison indicated considerable losses of stabilization energies in the open structure, especially in the receptor binding domain (RBD) of chain-B. By two separate calculations for the RBD complexes with angiotensin converting enzyme 2 (ACE2) (6M0J) and B38 Fab antibody (7BZ5), it was found that this stabilization loss of RBD was partially compensated by the binding with ACE2 or antibody.

Automated In Silico Identification of Drug Candidates for Coronavirus Through a Novel Programmatic Tool and Extensive Computational (MD, DFT) Studies of Select Drug Candidates (preprint)

The work is composed of python based programmatic tool that automates the dry lab drug discovery workflow for coronavirus. Firstly, the python program is written to automate the process of data mining PubChem database to collect data required to perform a machine learning based AutoQSAR algorithm through which drug leads for coronavirus are generated. The data acquisition from PubChem was carried out through python web scrapping techniques. The workflow of the machine learning based AutoQSAR involves feature learning and descriptor selection, QSAR modelling, validation and prediction. The drug leads generated by the program are required to satisfy the Lipinski’s drug likeness criteria as compounds that satisfy Lipinski’s criteria are likely to be an orally active drug in humans. Drug leads generated by the program are fed as programmatic inputs to an In Silico modelling package to computer model the interaction of the compounds generated as drug leads and the coronaviral drug target identified with their PDB ID : 6Y84. The results are stored in the working folder of the user. The program also generates protein-ligand interaction profiling and stores the visualized images in the working folder of the user. Select drug leads were further studied extensively using Molecular Dynamics Simulations and best binders and their reactive profiles were analysed using Molecular Dynamics and Density Functional Theory calculations. Thus our programmatic tool ushers in a new age of automatic ease in drug identification for coronavirus. The program is hosted, maintained and supported at the GitHub repository link given below https://github.com/bengeof/Programmatic-tool-to-automate-the-drug-discovery-workflow-for-coronavirus

Potential Docking Affinity of Three Approved Drugs Against SARS-CoV-2 for COVID-19 Treatment (preprint)

Potential Docking Affinity of three Approved Drugs against SARS-CoV-2 for COVID-19 treatment. Venkata Sambasiva Rao Rachakulla 1 , Hemanjali Devi Rachakulla 2 1 Department of Math, Greene County High School, Greensboro, GA, 30642 USA. 2 Department of Science, Jonesboro High School, Jonesboro, GA, 30236, USA. 1 Author for correspondence email: rachakullav@gmail.com 2 Author email: hemanjali27@gmail.com Abstract Objectives: The availability of a safe and effective drug for COVID-19 is well-recognized as an additional tool to contribute to the control of the pandemic. At the same time, the challenges and efforts needed to rapidly develop, evaluate, and produce this at scale are enormous. It is vital that we evaluate as many vaccines as possible as we cannot predict how many will turn out to be viable. Methods: In this study, we have measured the virtual interaction of crystal data structures of protein downloaded from protein data bank (PDB ID 7BRP) with corticosteroid drug candidates approved by FDA for other medical purposes which have less side effects. The results are analyzed in contrast some drugs candidates currently using for the treatment of COVID-19. Results: The binding energies in kilocalories/mole obtained from the docking of 7BRP protease with ligands under investigation Betamethasone Phosphate (-6.9), Fluticasone (-6.1) and Dexamethasone (-5.9) and also with currently using drug candidates Remdesivir(-6.5), Lopinavir (-6.0), Baceprivir(-5.7), Rabavirin(-6), Ritinovir(-5.3), Hydroxyquinoline(-5.0), Chloroquine (-4.7), Oseltamivir(-4.6), Favipiravir(-3.9). Discussion: The docking results suggest a higher binding affinity of the drug molecules under investigation against SARS-CoV-2 in contrast with other drug candidates currently being used for the treatment of COVID-19. We have analyzed bond interactions of protein-ligand from images in 10 modes of investigated drugs in contrast with Remdesivir and also discussed the advantages of inhalation methods of drug fluticasone. Conclusion: From this study, it can be suggested that these drugs are promising candidates for antiviral treatment with high potential to fight against SARS-CoV-2 strain keeping in view various ways of administration of drugs currently practicing.

In Silico Study of 1,5-Bis(4-Hydroxy-3-Methoxyphenyl)-1,4-Pentadiene-3-One (Deketene Curcumin) on Crystallized Protein Structures of SARS-CoV-2 (preprint)

World Health Organization (WHO) reveals total number of coronavirus cases are 5,684,802 and 352,225 deaths till today worldwide. Coronavirus instances are nevertheless surging due to its speedy spreading through infected patients. Therefore, in order to find potent vaccine almost every researcher is doing hard work to find it. However, until today there is not any availability of effective vaccine or drug for the treatment of COVID-19. In this case, the computational approach is the good choice to identify effective drugs and could be very useful due to its low cost, less error and less time consumption. Here, Deketene curcumin has taken for docking study because of its lots of biological applications such as antiviral, antimicrobial, anti-inflammatory, antioxidant, antibiotic, and to a name of few, it is a derivative of curcumin. In this study, five main protease crystallized COVID-19 structures (PDB ID: 6LU7, 5R7Z, 5R7Y, 5R80, 5R81) have been taken for simulation against deketene curcumin. Required procedure for this in silico study done through Molegro virtual docker (MVD) and Molegro Molecular Viewer (MMV) used for visualization. The results showed H-bonding and steric interaction between Deketene Curcumin with COVID-19 (PDB ID: 6LU7, 5R7Z, 5R7Y, 5R80, 5R81). Moldock scores of Deketene Curcumin Observed -134.198 kcal/mol, -151.972 kcal/mol, -109.224 kcal/mol, -140.741 kcal/mol and -126.562 kcal/mol with PDB Id 6LU7, 5R7Z, 5R7Y, 5R80 and 5R81 respectively. As per our results, it can be say that Deketene Curcumin has effective as a lead compound to find new antiviral drug candidates against COVID-19 for possible medicinal agent.

Direct ACE2- Spike RBD Binding Disruption with Small Molecules: A Strategy for COVID-19 Treatment (preprint)

ACE2 is a key receptor for SARS-CoV-2 cell entry. Binding of SARS-Cov-2 to ACE2 involves the viral Spike protein. The molecular interaction between ACE2 and Spike has been resolved. Interfering with this interaction might be used in treating patients with COVID-19. Inhibition of this interaction can be attained via multiple routes: here we focus on identifying small molecules that would prevent the interaction. Specifically we focus on small molecules and peptides that have the capacity to effectively bind the ACE2: RBD contact domain to prevent and reduce SARS-CoV-2 entry into the cell. We aim to identify molecules that prevent the docking of viral spike protein (mediated by RBD) onto cells expressing ACE2, without inhibiting the activity of ACE2. We utilize the most recent ACE2-RBD crystallography resolved model (PDB-ID:6LZG). Based on animal susceptibility data we narrowed down our interest to the location of amino acid 34 (Histidine) located on ACE2. We performed an in silico screen of a chemical library of compounds with several thousand small molecules including FDA approved compounds. All compounds were tested for binding to the proximal binding site located close to histidine 34 on ACE2. We report a list of four potential small molecules that potentially have the capacity to bind target residue: AY-NH2, a selective PAR4 receptor agonist peptide (CAS number: 352017-71-1), NAD+ (CAS number: 53-84-9), Reproterol, a short-acting β2 adrenoreceptor agonist used in the treatment of asthma (CAS number: 54063-54-6), and Thymopentin, a synthetic immune-stimulant which enhances production of thymic T cells (CAS number: 69558-55-0). The focus is on a High Throughput Screen Assay (HTSA), or in silico screen, delineating small molecules that are selectively binding/masking the crucial interface residue on ACE2 at His34. Consequently, inhibiting SARS-CoV-2 binding to host ACE2 and viral entry is a potent strategy to reduce cellular entry of the virus. We suggest that this anti-viral nature of this interaction is a viable strategy for COVID19 whereas the small molecules including peptides warrant further in vitro screens.

Intermolecular Interaction Among Remdesivir, RNA and RNA-Dependent RNA Polymerase of SARS-CoV-2 Analyzed by Fragment Molecular Orbital Calculation (preprint)

COVID-19, a disease caused by a new strain of coronavirus (SARS-CoV-2) originating from Wuhan, China, has now spread around the world, triggering a global pandemic, leaving the public eagerly awaiting the development of a specific medicine and vaccine. In response, aggressive efforts are underway around the world to overcome COVID-19. In this study, referencing the data published on the Protein Data Bank (PDB ID: 7BV2) on April 22, we conducted a detailed analysis of the interaction between the complex structures of the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 and Remdesivir, an antiviral drug, from the quantum chemical perspective based on the fragment molecular orbital (FMO) method. In addition to the hydrogen bonding and intra-strand stacking between complementary strands as seen in normal base pairs, Remdesivir bound to the terminus of an primer-RNA strand was further stabilized by diagonal π-π stacking with the -1A base of the complementary strand and an additional hydrogen bond with an intra-strand base, due to the effect of chemically modified functional group. Moreover, stable OH/π interaction is also formed with Thr687 of the RdRp. We quantitatively revealed the exhaustive interaction within the complex among Remdesivir, template-primer-RNA, RdRp and co-factors, and published the results in the FMODB database.

In silico Proteome analysis of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (preprint)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (2019-nCoV), is a positive-sense, single-stranded RNA coronavirus. The virus is the causative agent of coronavirus disease 2019 (COVID-19) and is contagious through human-to-human transmission. The present study reports sequence analysis, complete coordinate tertiary structure prediction and in silico sequence-based and structure-based functional characterization of full SARS-CoV-2 proteome based on the NCBI reference sequence NC_045512 (29903 bp ss-RNA) which is identical to GenBank entry MN908947 and MT415321. The proteome includes 12 major proteins namely orf1ab polyprotein (includes 15 proteins), surface glycoprotein, ORF3a protein, envelope protein, membrane glycoprotein, ORF6 protein, ORF7a protein, orf7b, ORF8, Nucleocapsid phosphoprotein and ORF10 protein. Each protein of orf1ab polyprotein group has been studied separately. A total of 25 polypeptides have been analyzed out of which 15 proteins are not yet having experimental structures and only 10 are having experimental structures with known PDB IDs. Out of 15 newly predicted structures six (6) were predicted using comparative modeling and nine (09) proteins having no significant similarity with so far available PDB structures were modeled using ab-initio modeling. Structure verification using recent tools QMEANDisCo 4.0.0 and ProQ3 for global and local (per-residue) quality estimates indicate that the all-atom model of tertiary structure of high quality and may be useful for structure-based drug designing targets. The study has identified nine major targets (spike protein, envelop protein, membrane protein, nucleocapsid protein, 2-O-ribose methyltransferase, endoRNAse, 3-to-5 exonuclease, RNA-dependent RNA polymerase and helicase) for which drug design targets could be considered. There are other 16 nonstructural proteins (NSPs), which may also be percieved from the drug design angle. The protein structures have been deposited to ModelArchive. Tunnel analysis revealed the presence of large number of tunnels in NSP3, ORF 6 protein and membrane glycoprotein indicating a large number of transport pathways for small ligands influencing their reactivity.

Computational Screening of Phytochemicals from Medicinal plants as COVID-19 Inhibitors (preprint)

In this research a dataset of plant based bioactive compound was developed. A total of 101 phytochemicals were selected, virtually designed and its binding affinity with ACE enzyme was studied by molecular docking. Human ACE related carboxypeptidase and complex (PDB ID: 1R42) and (PDB ID: 6CS2) were selected for molecular docking studies as corona virus binds to ACE2 to enter into the host cell. Docking score results revealed that almost all selected phytochemicals binds to the pocket of the human ACE protein with high binding affinity and the scores were compared with chloroquine and hydroxychloroquine. The drug likeliness and ADMET analysis of all the screened compounds were performed. Two potential compound 6-α-acetoxygedunin and echitamine exhibited optimum binding with both the receptor.These phytochemicals can serve as lead molecule for further optimization and drug development against COVID-19. Therefore, it is predicted that the insights in the present study could be regarded valuable towards development of natural inhibitor of this virus.