Evaluation of the inhibitory potential of Valproic acid against histone deacetylase of Leishmania donovani and computational studies of Valproic acid derivatives.

Valproic acid (VA) is a proven inhibitor of human histone deacetylases (HDACs). The homogenous HDAC has been associated with all major human parasitic pathogens and hence, it has been considered an attractive drug target for anti-leishmanial therapy. To assist in drug design endeavors for HDACs, an in-vitro study has been presented to investigate the VA inhibition on Leishmania donovani HDAC (LdHDAC). The regression analysis of VA by 24 hrs viability assay confirmed its activity against LdHDAC. Moreover, the toxicity of VA is also well documented. Thus, the in-silico experiments were also conducted to screen the non-toxic VA derivatives as anti-leishmanial drug candidates having potential as inhibitors of LdHDAC. For in-silico study, the 3D structure of target LdHDAC was developed by homology modeling. Based on their in-silico activity, we shortlisted 13 VA derivatives having maximum affinity for LdHDAC and identified four potential derivatives that can specifically bind to this protein. After that, these ligands were subjected to molecular dynamics simulation. These derivatives may be effective against L. donovani promastigotes since they followed Lipinski's RO5 and were non-toxic. Thus, screened derivatives can be considered as lead ligands for targeting LdHDAC and may be used as possible drug candidates to treat leishmaniasis and overcome the limitation of anti-leishmanial drugs. This is the first report of antileishmanial potential of VA and its derivatives targeting LdHDAC. Hence, the current investigation presents a search for novel target specific drugs to aid the anti-leishmanial drug development.Communicated by Ramaswamy H. Sarma.

Identification of essential oil phytocompounds as natural inhibitors of Odorant-binding protein to prevent malaria through in silico approach.

Malaria predominantly affects millions annually in the African and Asian tropical and subtropical countries. With no effective vaccine, malaria prevention is exclusively dependent on preventing human-vector interaction. Anopheles gambiae, the main vector of the malaria parasite Plasmodium falciparum contains Odorant Binding proteins (OBPs) which are considered an attractive drug target for anti-malarial therapy. To identify a potential anti-malarial compound, we performed a structure-based screening of 876 phytocompounds derived from essential oils against the OBP4 by molecular docking. The compounds having better docking scores were assessed for drug-likeness, toxicity, and molecular interaction analysis. As per the results, strong affinities and high stability were demonstrated by two phytocompounds viz. Alpha-cyperone (-8.1 kcal mol-1) and Humulene oxide (-8.1 kcal mol-1) with OBP4. The hydrophobic interactions involve Phe123, Ala106, Thr57, Ala52, Thr69, and Ile64 within the binding cavities, which may block the OBP4 receptor resulting in disorientation. After that, the potential compounds were subjected to molecular dynamics (MD) simulation to evaluate their structural stability and dynamics at the active site of OBP4. The MM-PBSA result revealed that Alpha-cyperone and Humulene oxide had binding free energy of -92.44 kJ mol-1 and -113.25 kJ mol-1, respectively. Simulation outcomes demonstrate that these phytocompounds displayed considerable significant structural and pharmacological properties. The LD50 value of Alpha-cyperone and Humulene oxide also suggested that both are safe and suitable for use in natural repellent development. We suggest that the use of these compounds can minimize the treatment period and the various side effects associated with the currently available anti-malarial drugs.Communicated by Ramaswamy H. Sarma.

Molecular basis for the repurposing of histamine H2-receptor antagonist to treat COVID-19.

With the world threatened by a second surge in the number of Coronavirus cases, there is an urgent need for the development of effective treatment for the novel coronavirus (COVID-19). Recently, global attention has turned to preliminary reports on the promising anti-COVID-19 effect of histamine H2-receptor antagonists (H2RAs), most especially Famotidine. Therefore, this study was designed to exploit a possible molecular basis for the efficacy of H2RAs against coronavirus. Molecular docking was performed between four H2RAs, Cimetidine, Famotidine, Nizatidine, Ranitidine, and three non-structural proteins viz. NSP3, NSP7/8 complex, and NSP9. Thereafter, a 100 ns molecular dynamics simulation was carried out with the most outstanding ligands to determine the stability. Thereafter, Famotidine and Cimetidine were subjected to gene target prediction analysis using HitPickV2 and eXpression2Kinases server to determine the possible network of genes associated with their anti-COVID activities. Results obtained from molecular docking showed the superiority of Famotidine and Cimetidine compared to other H2RAs with a higher binding affinity to all selected targets. Molecular dynamic simulation and MMPBSA results revealed that Famotidine as well as Cimetidine bind to non-structural proteins more efficiently with high stability over 100 ns. Results obtained suggest that Famotidine and Cimetidine could be a viable option to treat COVID-19 with a mechanism of action that involves the inhibition of viral replication through the inhibition of non-structural proteins. Therefore, Famotidineand Cimetidine qualify for further study as a potential treatment for COVID-19.

Identification of natural inhibitors against Mpro of SARS-CoV-2 by molecular docking, molecular dynamics simulation, and MM/PBSA methods.

The recent outbreak of SARS-CoV-2 disease, also known as COVID-19, has emerged as a pandemic. The unavailability of specific therapeutic drugs and vaccines urgently demands sincere efforts for drug discovery against COVID-19. The main protease (Mpro) of SARS-CoV-2 is a critical drug target as it plays an essential role in virus replication. Therefore for the identification of potential inhibitors of SARS-CoV-2 Mpro, we applied a structure-based virtual screening approach followed by molecular dynamics (MD) study. A library of 686 phytochemicals was subjected to virtual screening which resulted in 28 phytochemicals based on binding energy. These phytochemicals were further subjected to drug-likeness and toxicity analysis, which resulted in seven drug-like hits. Out of seven, five phytochemicals viz., Mpro-Dehydrtectol (-10.3 kcal/mol), Epsilon-viniferin (-8.6 kcal/mol), Peimisine (-8.6 kcal/mol), Gmelanone (-8.4 kcal/mol), and Isocolumbin (-8.4 kcal/mol) were non-toxic. Consequently, these phytochemicals are subjected to MD, post MD analysis, and MM/PBSA calculations. The results of 100 ns MD simulation, RMSF, SASA, Rg, and MM/PBSA show that Epsilon-viniferin (-29.240 kJ/mol), Mpro-Peimisine (-43.031 kJ/mol) and Gmelanone (-13.093 kJ/mol) form a stable complex with Mpro and could be used as potential inhibitors of SARS-CoV-2 Mpro. However, further investigation of these inhibitors against Mpro receptor of COVID-19 is needed to validate their candidacy for clinical trials. Communicated by Ramaswamy H. Sarma.

A dynamic simulation study of FDA drug from zinc database against COVID-19 main protease receptor.

The sudden outbreak of COVID-19 has been responsible for several deaths across the globe. Due to its high contagious nature, it spreads from one human to another very quickly. Now it becomes a global public health threat with no approved treatments. In silico techniques can accelerate the drug development process. Our research aimed to identify the novel drugs for inhibition of Main protease (Mpro) enzyme of COVID-19 by performing in silico approach. In this context, a library consisting of 3180 FDA-approved drugs from 'the ZINC database' was used to identify novel drug candidates against 'the Mpro' of SARS-CoV-2. Initially, the top 10 drugs out of 3180 drugs were selected by molecular docking according to their binding score. Among 10 selected drugs; seven drugs that showed binding with Mpro enzyme residue Glu166 were subjected to100 ns Molecular dynamics (MD) simulation. Out of seven compounds, four namely, ZINC03831201, ZINC08101052, ZINC01482077, and ZINC03830817 were found significant based on MD simulation results. Furthermore, RMSD, RMSF, RG, SASA, PCA, MMPBSA (for last 40 ns) were calculated for the 100 ns trajectory period. Currently, the world needs potent drugs in a short period and this work suggests that these four drugs could be used as novel drugs against COVID-19 and it also provides new lead compounds for further in vitro, in vivo, and ongoing clinical studies against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Deep-learning based repurposing of FDA-approved drugs against Candida albicans dihydrofolate reductase and molecular dynamics study.

Candida albicans causes the fatal fungal bloodstream infection in humans called Candidiasis. Most of the Candida species are resistant to the antifungals used to treat them. Drug-resistant C. albicans poses very serious public health issues. To overcome this, the development of effective drugs with novel mechanism(s) of action is requisite. Drug repurposing is considered a viable alternative approach to overcome the above issue. In the present study, we have attempted to identify drugs that could target the essential enzyme, dihydrofolate reductase of C. albicans (CaDHFR) to find out potent and selective antifungal antifolates. FDA-approved-drug-library from the Selleck database containing 1930 drugs was screened against CaDHFR using deep-learning, molecular docking, X-score and similarity search methods. The screened compounds showing better binding with CaDHFR were subjected to molecular dynamics simulation (MDS). The results of post-MDS analysis like RMSD, RMSF, RG, SASA, the number of hydrogen bonds and PCA suggest that Paritaprevir, Lumacaftor and Rifampin can make good interaction with CaDHFR. Furthermore, analysis of binding free energy corroborated the stability of interactions as they had binding energy of -114.91 kJ mol-1, -79.22 kJ mol-1 and -78.52 kJ mol-1 for Paritaprevir, Lumacaftor and Rifampin respectively as compared to the reference (-63.10 kJ mol-1). From the results, we conclude that these drugs have great potential to inhibit CaDHFR and would add to the drug discovery against candidiasis, and hence these drugs for repurposing should be explored further.

Identification of SARS-CoV-2 RNA dependent RNA polymerase inhibitors using pharmacophore modelling, molecular docking and molecular dynamics simulation approaches.

The RNA-dependent RNA polymerase (RdRp) is one of the crucial enzymes in severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) catalysing the replication of RNA, therefore acts as a potential target for antiviral drug design. The fixation of a ligand in the active site of RdRp may alter the SARS-CoV-2 life cycle. Present work aimed at identifying novel inhibitors of the SARS-CoV-2 RdRp enzyme by performing pharmacophore-based virtual screening, molecular docking and molecular dynamics simulation (MDS). Initially, the pharmacophore model of SARS-CoV-2 RdRp was constructed and the resulting model was used to screen compounds from ChEMBL, ZINC and PubChem databases. During the investigation, 180 compounds were screened using the above model and subjected to molecular docking with RdRp. Two compounds viz. ChEMBL1276156 and PubChem135548348 showed a strong binding affinity with RdRp than its standard inhibitor, Remdesivir. Toxicity prediction of these two compounds reveals their non-toxic nature. These compounds were further subjected to MDS for 100 ns to check their stability after binding with RdRp. The MDS of RdRp-ChEMBL1276156 and RdRp-PubChem135548348 complexes show enhanced stability in comparison to the RdRp-Remdesivir complex. The average interaction energy calculated after 100 ns of MDS was -146.56 and -172.68 KJ mol-1 for RdRp-CHEMBL1276156 complex and RdRp-PubChem135548348 complex, respectively, while -59.90 KJ mol-1 for RdRp-Remdesivir complex. The current investigation reveals that these two compounds are potent inhibitors of SARS-CoV-2 RdRp and they could be tested in the experimental condition to evaluate their efficacy against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Identification of Berbamine, Oxyacanthine and Rutin from Berberis asiatica as anti-SARS-CoV-2 compounds: An in silico study.

Owing to the shortage of specific medicines, the global pandemic of COVID-19 caused by SARS-CoV-2 has been the greatest challenge for the science community. Researchers from all over the world developed some drugs which failed to completely suppress the contiguous disease. SARS-CoV-2 main protease (Mpro), an important component in viral pathogenesis, is considered as a prospective drug target to stop SARS-CoV-2 infection. Since identification of phytochemicals with anti-Mpro activity has been carried out to develop the potential drugs against SARS-CoV-2. Therefore, the present study was conducted to screen phytochemicals of Berberis asiatica for anti-SARS-CoV-2 activity. Through text mining, thirty phytochemicals were reported from B. asiatica, of which, three phytochemicals (Berbamine, Oxyacanthine, and Rutin) show high affinity with the SARS-CoV-2 Mpro and exhibited favorable intermolecular interactions with the catalytic residues (His41 and Cys145) and other essential residues. The molecular dynamics simulation showed that Mpro-phytochemical complexes are more stable, less fluctuating, more compact, and moderately extended than the Mpro-X77 (Reference) complex. The number of H-bonds and MMPBSA results also demonstrates that Berbamine, Oxyacanthine, and Rutin are potent Mpro inhibitors having free energy of -20.79, -33.35, and -31.12 kcal mol-1 respectively. The toxicity risk prediction supports all phytochemicals for drug-like and non-toxic nature. From the result, we propose that binding of these phytochemicals could hamper the function of Mpro. This work suggests that selected phytochemicals could be used as novel anti-COVID-19 drug candidates, and might act as novel compounds for in vitro and in vivo study.

Structure-based screening of novel lichen compounds against SARS Coronavirus main protease (Mpro) as potentials inhibitors of COVID-19.

The outbreak of SARS-CoV-2 and deaths caused by it all over the world have imposed great concern on the scientific community to develop potential drugs to combat Coronavirus disease-19 (COVID-19). In this regard, lichen metabolites may offer a vast reservoir for the discovery of antiviral drug candidates. Therefore, to find novel compounds against COVID-19, we created a library of 412 lichen compounds and subjected to virtual screening against the SARS-CoV-2 Main protease (Mpro). All the ligands were virtually screened, and 27 compounds were found to have high affinity with Mpro. These compounds were assessed for drug-likeness analysis where two compounds were found to fit well for redocking studies. Molecular docking, drug-likeness, X-Score, and toxicity analysis resulting in two lichen compounds, Calycin and Rhizocarpic acid with Mpro-inhibiting activity. These compounds were finally subjected to molecular dynamics simulation to compare the dynamics behavior and stability of the Mpro after ligand binding. The binding energy was calculated by MM-PBSA method to determine the intermolecular protein-ligand interactions. Our results showed that two compounds; Calycin and Rhizocarpic acid had the binding free energy of - 42.42 kJ mol/1 and - 57.85 kJ mol/1 respectively as compared to reference X77 (- 91.78 kJ mol/1). We concluded that Calycin and Rhizocarpic acid show considerable structural and pharmacological properties and they can be used as hit compounds to develop potential antiviral agents against SARS-CoV-2. These lichen compounds may be a suitable candidate for further experimental analysis.

In silico identification of natural fungicide from Melia azedarach against isocitrate lyase of Fusarium graminearum.

Isocitrate Lyase (ICL) is a crucial enzyme involved in the Glyoxylate pathway, essential for the virulence of several fungal pathogens including Fusarium graminearum. ICL is a novel target for the discovery of antifungal compounds and F. graminearum ICL inhibitors can be used to control the growth of this fungus. Although, several inhibitors of ICL have been identified, however, most of these inhibitors are not environment-friendly. Hence there is still a need to discover natural inhibitors of ICL that can be more effective. To identify a potential antifungal compound, we performed a structure-based screening of phytochemicals of Melia azedarach against the FgICL structure by molecular docking and 104 ligands were found to have a better docking score as compared to the reference molecule. These compounds were assessed for drug-likeness and ADMET prediction. After molecular docking, drug-likeness and toxicity analysis, six potential compounds (Melianoninol (-6.6 kcal/mol), Nimbinene (-7.7 kcal/mol), Vilasinin (-8.1 kcal/mol), Fraxinellone (-6.7 kcal/mol), Gedunin (-7.8 kcal/mol), and Meldenin (-7.8 kcal/mol)) were subjected for rescoring by X-Score. The structural stability and dynamics of screened compounds at the active site of FgICL were examined using MD simulation and MM-PBSA analysis. The result of MM-PBSA revealed that four phytochemicals viz. Melianoninol, Nimbinene, Vilasinin, and Fraxinellone had binding free energy of -17.25 kcal/mol, -59.35 kcal/mol, -64.79 kcal/mol, and -29.86 kcal/mol, respectively. Molecular dynamics simulation and MM-PBSA demonstrated that these four phytochemicals displayed considerable significant structural and pharmacological properties and could be probable antifungal drug candidates against F. graminearum. These phyotchemicals of M. azedarach may be suitable candidates for further experimental analysis. [Formula: see text]Communicated by Ramaswamy H. Sarma.

Using Chou's 5-steps rule to study pharmacophore-based virtual screening of SARS-CoV-2 Mpro inhibitors.

Recently emerged SARS-CoV-2 is the cause of the ongoing outbreak of COVID-19. It is responsible for the deaths of millions of people and has caused global economic and social disruption. The numbers of COVID-19 cases are increasing exponentially across the world. Control of this pandemic disease is challenging because there is no effective drug or vaccine available against this virus and this situation demands an urgent need for the development of anti-SARS-CoV-2 potential medicines. In this regard, the main protease (Mpro) has emerged as an essential drug target as it plays a vital role in virus replication and transcription. In this research, we have identified two novel potent inhibitors of the Mpro (PubChem3408741 and PubChem4167619) from PubChem database by pharmacophore-based high-throughput virtual screening. The molecular docking, toxicity, and pharmacophore analysis indicate that these compounds may act as potential anti-viral candidates. The molecular dynamic simulation along with the binding free energy calculation by MMPBSA showed that these compounds bind to Mpro enzyme with high stability over 50 ns. Our results showed that two compounds: PubChem3408741 and PubChem4167619 had the binding free energy of - 94.02 kJ mol-1 and - 122.75 kJ mol-1, respectively, as compared to reference X77 (- 76.48 kJ mol-1). Based on our work's findings, we propose that these compounds can be considered as lead molecules for targeting Mpro enzyme and they can be potential SARS-CoV-2 inhibitors. These inhibitors could be tested in vitro and explored for effective drug development against COVID-19.

Predictive modeling by deep learning, virtual screening and molecular dynamics study of natural compounds against SARS-CoV-2 main protease.

The whole world is facing a great challenging time due to Coronavirus disease (COVID-19) caused by SARS-CoV-2. Globally, more than 14.6 M people have been diagnosed and more than 595 K deaths are reported. Currently, no effective vaccine or drugs are available to combat COVID-19. Therefore, the whole world is looking for new drug candidates that can treat the COVID-19. In this study, we conducted a virtual screening of natural compounds using a deep-learning method. A deep-learning algorithm was used for the predictive modeling of a CHEMBL3927 dataset of inhibitors of Main protease (Mpro). Several predictive models were developed and evaluated based on R2, MAE MSE, RMSE, and Loss. The best model with R2=0.83, MAE = 1.06, MSE = 1.5, RMSE = 1.2, and loss = 1.5 was deployed on the Selleck database containing 1611 natural compounds for virtual screening. The model predicted 500 hits showing the value score between 6.9 and 3.8. The screened compounds were further enriched by molecular docking resulting in 39 compounds based on comparison with the reference (X77). Out of them, only four compounds were found to be drug-like and three were non-toxic. The complexes of compounds and Mpro were finally subjected to Molecular dynamic (MD) simulation for 100 ns. The MMPBSA result showed that two compounds Palmatine and Sauchinone formed very stable complex with Mpro and had free energy of -71.47 kJ mol-1 and -71.68 kJ mol-1 respectively as compared to X77 (-69.58 kJ mol-1). From this study, we can suggest that the identified natural compounds may be considered for therapeutic development against the SARS-CoV-2.Communicated by Ramaswamy H. Sarma.