Identifying the natural compound Catechin from tropical mangrove plants as a potential lead candidate against 3CLpro from SARS-CoV-2: An integrated in silico approach.

SARS-CoV-2, a member of beta coronaviruses, is a single-stranded, positive-sense RNA virus responsible for the COVID-19 pandemic. With global fatalities of the pandemic exceeding 4.57 million, it becomes crucial to identify effective therapeutics against the virus. A protease, 3CLpro, is responsible for the proteolysis of viral polypeptides into functional proteins, which is essential for viral pathogenesis. This indispensable activity of 3CLpro makes it an attractive target for inhibition studies. The current study aimed to identify potential lead molecules against 3CLpro of SARS-CoV-2 using a manually curated in-house library of antiviral compounds from mangrove plants. This study employed the structure-based virtual screening technique to evaluate an in-house library of antiviral compounds against 3CLpro of SARS-CoV-2. The library was comprised of thirty-three experimentally proven antiviral molecules extracted from different species of tropical mangrove plants. The molecules in the library were virtually screened using AutoDock Vina, and subsequently, the top five promising 3CLpro-ligand complexes along with 3CLpro-N3 (control molecule) complex were subjected to MD simulations to comprehend their dynamic behaviour and structural stabilities. Finally, the MM/PBSA approach was used to calculate the binding free energies of 3CLpro complexes. Among all the studied compounds, Catechin achieved the most significant binding free energy (-40.3 ± 3.1 kcal/mol), and was closest to the control molecule (-42.8 ± 5.1 kcal/mol), and its complex with 3CLpro exhibited the highest structural stability. Through extensive computational investigations, we propose Catechin as a potential therapeutic agent against SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

Identification of promising antiviral drug candidates against non-structural protein 15 (NSP15) from SARS-CoV-2: an in silico assisted drug-repurposing study.

The recent COVID-19 pandemic caused by SARS-CoV-2 has recorded a high number of infected people across the globe. The virulent nature of the virus makes it necessary for us to identify promising therapeutic agents in a time-sensitive manner. The current study utilises an in silico based drug repurposing approach to identify potential anti-viral drug candidates targeting non-structural protein 15 (NSP15), i.e. a uridylate specific endoribonuclease of SARS-CoV-2 which plays an indispensable role in RNA processing and viral immune evasion from the host immune system. The NSP15 protein was screened against an in-house library of 123 antiviral drugs obtained from the DrugBank database from which three promising drug candidates were identified based on their estimated binding affinities (ΔG), estimated inhibition constants (Ki), the orientation of drug molecules in the active site and the key interacting residues of NSP15. Molecular dynamics (MD) simulations were performed for the screened drug candidates in complex with NSP15 as well as the apo form of NSP15 to mimic their physiological states. Based on the stable MD simulation trajectories, the binding free energies of the screened NSP15-drug complexes were calculated using the MM/PBSA approach. Two candidate drugs, Simeprevir and Paritaprevir, achieved the lowest binding free energies for NSP15, with a value of -259.522 ± 17.579 and -154.051 ± 33.628 kJ/mol, respectively. In addition, their complexes with NSP15 also exhibited the strongest structural stabilities. Taken together, we propose that Simeprevir and Paritaprevir are promising drug candidates to inhibit NSP15 and may act as potential therapeutic agents against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Targeting SARS-CoV-2: a systematic drug repurposing approach to identify promising inhibitors against 3C-like proteinase and 2'-O-ribose methyltransferase.

The recent pandemic associated with SARS-CoV-2, a virus of the Coronaviridae family, has resulted in an unprecedented number of infected people. The highly contagious nature of this virus makes it imperative for us to identify promising inhibitors from pre-existing antiviral drugs. Two druggable targets, namely 3C-like proteinase (3CLpro) and 2'-O-ribose methyltransferase (2'-O-MTase) were selected in this study due to their indispensable nature in the viral life cycle. 3CLpro is a cysteine protease responsible for the proteolysis of replicase polyproteins resulting in the formation of various functional proteins, whereas 2'-O-MTase methylates the ribose 2'-O position of the first and second nucleotide of viral mRNA, which sequesters it from the host immune system. The selected drug target proteins were screened against an in-house library of 123 antiviral drugs. Two promising drug molecules were identified for each protein based on their estimated free energy of binding (ΔG), the orientation of drug molecules in the active site and the interacting residues. The selected protein-drug complexes were then subjected to MD simulation, which consists of various structural parameters to equivalently reflect their physiological state. From the virtual screening results, two drug molecules were selected for each drug target protein [Paritaprevir (ΔG = -9.8 kcal/mol) & Raltegravir (ΔG = -7.8 kcal/mol) for 3CLpro and Dolutegravir (ΔG = -9.4 kcal/mol) and Bictegravir (ΔG = -8.4 kcal/mol) for 2'-OMTase]. After the extensive computational analysis, we proposed that Raltegravir, Paritaprevir, Bictegravir and Dolutegravir are excellent lead candidates for these crucial proteins and they could become potential therapeutic drugs against SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

A Comprehensive In Silico Study Towards Understanding the Inhibitory Mechanism of Lactoperoxidase by Dapsone and Propofol.

INTRODUCTION: Lactoperoxidase (LPO) is a member of the mammalian heme peroxidase family and is an enzyme of the innate immune system. It possesses a covalently linked heme prosthetic group (a derivative of protoporphyrin IX) in its active site. LPO catalyzes the oxidation of halides and pseudohalides in the presence of hydrogen peroxide (H2O2) and shows a broad range of the antimicrobial activity. METHODS: In this study, we have used two pharmaceutically important drug molecules, namely dapsone and propofol, which were earlier reported as potent inhibitors of LPO. At the same time, the stereochemistry and mode of binding of dapsone and propofol to LPO are still not known because of the lack of the crystal structures of LPO with these two drugs. In order to fill this gap, we utilized molecular docking and molecular dynamics (MD) simulation studies of LPO in its native and complex forms with dapsone and propofol. RESULTS: From the docking results, the estimated binding free energies (ΔG) of -9.25 kcal/mol (Ki = 0.16 µM) and -7.05 kcal/mol (Ki = 6.79 µM) were observed for dapsone, and propofol, respectively. The standard error of the Auto Dock program is 2.5 kcal/mol; therefore, molecular docking results alone were inconclusive. CONCLUSION: To further validate the docking results, we performed MD simulation on unbound, and two drugs bounded LPO structures. Interestingly, MD simulations results explained that the structural stability of LPO-Propofol complex was higher than LPO-Dapsone complex. The results obtained from this study establish the mode of binding and interaction pattern of the dapsone and propofol to LPO as inhibitors.

A comprehensive review on promising anti-viral therapeutic candidates identified against main protease from SARS-CoV-2 through various computational methods.

BACKGROUND: The COVID-19 pandemic caused by SARS-CoV-2 has shown an exponential trend of infected people across the planet. Crediting its virulent nature, it becomes imperative to identify potential therapeutic agents against the deadly virus. The 3-chymotrypsin-like protease (3CLpro) is a cysteine protease which causes the proteolysis of the replicase polyproteins to generate functional proteins, which is a crucial step for viral replication and infection. Computational methods have been applied in recent studies to identify promising inhibitors against 3CLpro to inhibit the viral activity. This review provides an overview of promising drug/lead candidates identified so far against 3CLpro through various in silico approaches such as structure-based virtual screening (SBVS), ligand-based virtual screening (LBVS) and drug-repurposing/drug-reprofiling/drug-retasking. Further, the drugs have been classified according to their chemical structures or biological activity into flavonoids, peptides, terpenes, quinolines, nucleoside and nucleotide analogues, protease inhibitors, phenalene and antibiotic derivatives. These are then individually discussed based on the various structural parameters namely estimated free energy of binding (ΔG), key interacting residues, types of intermolecular interactions and structural stability of 3CLpro-ligand complexes obtained from the results of molecular dynamics (MD) simulations. CONCLUSION: The review provides comprehensive information of potential inhibitors identified through several computational methods thus far against 3CLpro from SARS-CoV-2 and provides a better understanding of their interaction patterns and dynamic states of free and ligand-bound 3CLpro structures.

Identification of promising molecules against MurD ligase from Acinetobacter baumannii: insights from comparative protein modelling, virtual screening, molecular dynamics simulations and MM/PBSA analysis.

Acinetobacter baumannii, an opportunistic bacterium of the multidrug-resistant (MDR) ESKAPE family of pathogens, is responsible for 2-10% infections associated with all gram-negative bacteria. The hospital-acquired nosocomial infections caused by A.baumannii include deadly diseases like ventilator-associated pneumonia, bacteremia, septicemia and urinary tract infections (UTI). Over the last 3 years, it has evolved into multiple strains demonstrating high antibiotic resistance against a wide array of antibiotics. Hence, it becomes imperative to identify novel drug-like molecules to treat such infections effectively. UDP-N-acetylmuramoyl-L-alanine-D-glutamate ligase (MurD) is an essential enzyme of the Mur family which is responsible for peptidoglycan biosynthesis, making it a unique and ideal drug target. Initially, a homology modelling approach was employed to predict the three-dimensional model of MurD from A. baumannii using MurD from Escherichia coli (PDB ID: 4UAG) as a suitable structural template. Subsequently, an optimised model of MurD was subjected to virtual high-throughput screening (vHTS) against a ZINC library of ~ 642,759 commercially available molecules to identify promising lead compounds demonstrating high binding affinities towards it. From the screening process, four promising molecules were identified based on the estimated binding affinities (ΔG), estimated inhibition constants (Ki), catalytic residue interactions and drug-like properties, which were then subjected to molecular dynamics (MD) simulation studies to reflect the physiological state of protein molecules in vivo equivalently. The binding free energies of the selected MurD-ligand complexes were also calculated using MM/PBSA (molecular mechanics with Poisson-Boltzmann and surface area solvation) approach. Finally, the global dynamics along with binding free energy analysis suggested that ZINC19221101 (ΔG = - 62.6 ± 5.6 kcal/mol) and ZINC12454357 (ΔG = - 46.1 ± 2.6 kcal/mol) could act as most promising candidates for inhibiting the function of MurD ligase and aid in drug discovery and development against A.baumannii. Graphical abstract.

Prioritization of Mur family drug targets against A. baumannii and identification of their homologous proteins through molecular phylogeny, primary sequence, and structural analysis.

BACKGROUND: The World Health Organization (WHO) report stated that Acinetobacter baumannii had been classified as one of the most important pathogenic bacteria causing nosocomial infection in hospital patients due to multi-drug resistance (MDR). It is vital to find out new bacterial drug targets and annotated their structure and function for the exploration of new anti-bacterial agents. The present study utilized a systematic route to prioritize the potential drug targets that belong to Mur family of Acinetobacter baumannii and identify their homologous proteins using a computational approach such as sequence similarity search, multiple sequence alignment, phylogenetic analysis, protein sequence, and protein structure analysis. RESULTS: From the results of protein sequence analysis of eight Mur family proteins, they divided into three main enzymatic classes namely transferases (MurG, MurA and MraY), ligases (MurC, MurD, MurE, and MurF), and oxidoreductase (MurB). Based on the results of intra-comparative protein sequence analysis and enzymatic classification, we have chosen MurB, MurE, and MurG as the prioritized drug targets from A. baumannii and subjected them for further detailed studies of inter-species comparison. This inter-species comparison help us to explore the sequential and structural properties of homologous proteins in other species and hence, opens a gateway for new target identification and using common inhibitor for different bacterial species caused by various diseases. The pairwise sequence alignment results between A. baumannii's MurB with A. calcoaceticus's MurB, A. baumannii's MurE with A. seifertii's MurE, and A. baumannii's MurG with A. pittii's MurG showed that every group of the proteins are highly similar with each other and they showed sequence identity of 95.7% and sequence similarity of 97.2%. CONCLUSION: Together with the results of secondary and three-dimensional structure predictions explained that three selected proteins (MurB, MurE, and MurG) from A. baumannii and their related proteins (AcMurB, AsMurE, and ApMurG) belong to mixed αß class and they are very similar.

Structure based drug designing and discovery of promising lead molecules against UDP-N-acetylenolpyruvoylglucosamine reductase (MurB): A potential drug target in multi-drug resistant Acinetobacter baumannii.

According to the world health organization (WHO) reports, Acinetobacter baumannii was considered as one of the significant and first-line priority pathogens, which causes hospital-acquired nosocomial infections in human. The enzymes involved in the peptidoglycan biosynthetic pathway are critical for the survival of this bacterium. Therefore, these enzymes are ideal drug target since they are conserved among most of the species and non-homologous to human. Here, we utilized the structure-based virtual screening (SBVS) technique to identify the promising lead molecules against MurB (UDP-N-acetylenolpyruvoylglucosamine reductase) protein using computational approaches. Initially, the three-dimensional structure of MurB was predicted based on MurB from P. aeruginosa (PDB ID: 4JAY), which is used as a structural template for homology modeling. During the High-throughput Virtual screening (HTVS) analysis, we started with 30,792 molecules against MurB model, among these; only 5238 molecules could be considered suitable for further step. Finally, only twenty molecules were able to pass Lipinski's and ADMET properties. After a thorough examination of interaction analysis, higher ΔG and Ki values, we had chosen five promising molecules (ZINC IDs: ZINC12530134, ZINC15675540, ZINC15675762, ZINC15675624 and ZINC15707270) and three control molecules (PubChem IDs: 54682555, 729933 and 39964628) for Molecular dynamics (MD) simulation to understand the effect of ligands towards the structural stability, structural integrity and structural compactness of MurB protein. Further, the MM/PBSA binding free energy analysis was performed for eight ligands bound MurB structures. Together the results obtained from global dynamics, essential dynamics and MM-PBSA binding free energy analysis, we concluded that apart from the control molecules, ZINC12530134 should be considered as one of the most promising ones and it could be the potent inhibitor against A baumannii and provide valuable insight for further experimental studies.

Computer aided ligand based screening for identification of promising molecules against enzymes involved in peptidoglycan biosynthetic pathway from Acinetobacter baumannii.

A. baumannii has been considered as Priority-I as suggested by the World Health Organization (WHO) and the most critical pathogenic microorganism for causing nosocomial infection in imunno-compromised hospital-acquired patients due to multi-drug resistance (MDR). In the current study, we utilized "Computer-aided ligand-based virtual screening approach" for identification of promising molecules against Mur family proteins based on the known inhibitor (Naphthyl Tetronic Acids ((5Z)-3-(4-chlorophenyl)-4-hydroxy-5-(1-naphthylmethylene) furan-2(5H)-one)) of MurB from E. coli. The in-house library was prepared using a similarity search of a known inhibitor (Drug Bank ID: DB07296) against several relevant chemical databases. The molecules obtained from virtual screening of Naphthyl Tetronic Acids in-house library were successively subjected to physicochemical and ADMET screening. After this, the molecules which passed all the filters, subsequently subjected into interaction analysis with the drug target proteins (MurB, MurD, MurE and MurG) of A. baumanni and the results explained that four molecules were promising (CHEMBL468144, DB07296, Enamine_T5956969 and 54723243) for further molecular dynamics simulations. The free and ligand bounded proteins that undergone MD simulation are listed as follows: MurB, MurB-CHEMBL468144, MurB-DB07296, MurE, MurE-54723243, MurE-DB07296, MurD, MurD-Enamine_T5956969, MurD-DB07296, MurG, MurG-CHEMBL468144, and MurG-DB07296. Based on global and essential dynamics analysis, the stability order of molecules towards MurB (CHEMBL468144 > DB07296); MurD (Enamine_T5956969 > DB07296); MurE (54723243 > DB07296) and MurG (CHEMBL468144 > DB07296) indicates that the newly identified molecules are more promising one in comparison with the existing inhibitor. Based on all the docking and MD simulation results, the stability order of the free and ligand bounded protein are as follows; MurB and MurB-ligand complexes > MurD and MurD-ligand complexes > MurG and MurG-ligand complexes > MurE and MurE-ligand complexes. Finally, the selected compounds would be recommended for further experimental investigations and used as promising inhibitors of the infection caused by A. baumannii.

Screening of promising molecules against MurG as drug target in multi-drug-resistant-Acinetobacter baumannii - insights from comparative protein modeling, molecular docking and molecular dynamics simulation.

The UDP-N-acetylglucosamine-N-acetylmuramyl-(pentapeptide) pyrophosphoryl-undecaprenol N-acetylglucosamine transferase (MurG) is located in plasma membrane which plays a crucial role for peptidoglycan biosynthesis in Gram-negative bacteria. Recently, this protein is considered as an important and unique drug target in Acinetobacter baumannii since it plays a key role during the synthesis of peptidoglycan as well as which is not found in Homo sapiens. In this study, initially we performed comparative protein modeling approach to predict the three-dimensional model of MurG based on crystal structure of UDP-N-acetylglucosamine-N-acetylmuramyl-(pentapeptide) pyrophosphoryl-undecaprenol N-acetylglucosamine transferase (PDB ID: 1F0K) from E.coli K12. MurG model has two important functional domains located in N and C- terminus which are separated by a deep cleft. Active site residues are located between two domains and they are Gly20, Arg170, Gly200, Ser201, Gln227, Phe254, Leu275, Thr276, and Glu279 which play essential role for the function of MurG. In order to inhibit the function of MurG, we employed the High Throughput Virtual Screening (HTVS) and docking techniques to identify the promising molecules which will further subjected into screening for computing their drug like and pharmacokinetic properties. From the HTVS, we identified 5279 molecules, among these, 12 were passed the drug-like and pharmacokinetic screening analysis. Based on the interaction analysis in terms of binding affinity, inhibition constant and intermolecular interactions, we selected four molecules for further MD simulation to understand the structural stability of protein-ligand complexes. All the analysis of MD simulation suggested that ZINC09186673 and ZINC09956120 are identified as most promising putative inhibitors for MurG protein in A. baumannii.Communicated by Ramaswamy H. Sarma.