Computational investigation of drug bank compounds against 3C-like protease (3CLpro) of SARS-CoV-2 using deep learning and molecular dynamics simulation.

Blocking the main replicating enzyme, 3 Chymotrypsin-like protease (3CLpro) is the most promising drug development strategy against the SARS-CoV-2 virus, responsible for the current COVID-19 pandemic. In the present work, 9101 drugs obtained from the drug bank database were screened against SARS-CoV-2 3CLpro prosing deep learning, molecular docking, and molecular dynamics simulation techniques. In the initial stage, 500 drug-screened by deep learning regression model and subjected to molecular docking that resulted in 10 screened compounds with strong binding affinity. Further, five compounds were checked for their binding potential by analyzing molecular dynamics simulation for 100 ns at 300 K. In the final stage, two compounds {4-[(2s,4e)-2-(1,3-Benzothiazol-2-Yl)-2-(1h-1,2,3-Benzotriazol-1-Yl)-5-Phenylpent-4-Enyl]Phenyl}(Difluoro)Methylphosphonic Acid and 1-(3-(2,4-dimethylthiazol-5-yl)-4-oxo-2,4-dihydroindeno[1,2-c]pyrazol-5-yl)-3-(4-methylpiperazin-1-yl)urea were screened as potential hits by analyzing several parameters like RMSD, Rg, RMSF, MMPBSA, and SASA. Thus, our study suggests two potential drugs that can be tested in the experimental conditions to evaluate the efficacy against SARS-CoV-2. Further, such drugs could be modified to develop more potent drugs against COVID-19.

Integrated Machine Learning and Chemoinformatics-Based Screening of Mycotic Compounds against Kinesin Spindle ProteinEg5 for Lung Cancer Therapy.

Among the various types of cancer, lung cancer is the second most-diagnosed cancer worldwide. The kinesin spindle protein, Eg5, is a vital protein behind bipolar mitotic spindle establishment and maintenance during mitosis. Eg5 has been reported to contribute to cancer cell migration and angiogenesis impairment and has no role in resting, non-dividing cells. Thus, it could be considered as a vital target against several cancers, such as renal cancer, lung cancer, urothelial carcinoma, prostate cancer, squamous cell carcinoma, etc. In recent years, fungal secondary metabolites from the Indian Himalayan Region (IHR) have been identified as an important lead source in the drug development pipeline. Therefore, the present study aims to identify potential mycotic secondary metabolites against the Eg5 protein by applying integrated machine learning, chemoinformatics based in silico-screening methods and molecular dynamic simulation targeting lung cancer. Initially, a library of 1830 mycotic secondary metabolites was screened by a predictive machine-learning model developed based on the random forest algorithm with high sensitivity (1) and an ROC area of 0.99. Further, 319 out of 1830 compounds screened with active potential by the model were evaluated for their drug-likeness properties by applying four filters simultaneously, viz., Lipinski's rule, CMC-50 like rule, Veber rule, and Ghose filter. A total of 13 compounds passed from all the above filters were considered for molecular docking, functional group analysis, and cell line cytotoxicity prediction. Finally, four hit mycotic secondary metabolites found in fungi from the IHR were screened viz., (-)-Cochlactone-A, Phelligridin C, Sterenin E, and Cyathusal A. All compounds have efficient binding potential with Eg5, containing functional groups like aromatic rings, rings, carboxylic acid esters, and carbonyl and with cell line cytotoxicity against lung cancer cell lines, namely, MCF-7, NCI-H226, NCI-H522, A549, and NCI H187. Further, the molecular dynamics simulation study confirms the docked complex rigidity and stability by exploring root mean square deviations, root mean square fluctuations, and radius of gyration analysis from 100 ns simulation trajectories. The screened compounds could be used further to develop effective drugs against lung and other types of cancer.

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.

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.

Identifying Novel Therapeutics for the Resistant Mutant "F533L" in PBP3 of Pseudomonas aeruginosa Using ML Techniques.

Pseudomonas aeruginosa (P. aeruginosa) is a highly infectious and antibiotic-resistant bacterium, which causes acute and chronic nosocomial infections. P. aeruginosa exhibits multidrug resistance due to the emergence of resistant mutants. The bacterium takes advantage of intrinsic and acquired resistance mechanisms to resist almost every antibiotic. To overcome the drug-resistance problem, there is a need to develop effective drugs against antibiotic-resistant mutants. Therefore, in this study, we selected the F533L mutation in PBP3 (penicillin-binding protein 3) because of its important role in ß-lactam recognition. To target this mutation, we screened 147 antibacterial compounds from PubChem through a machine-learning model developed based on the decision stump algorithm with 75.75% accuracy and filtered out 55 compounds. Subsequently, out of 55 compounds, 47 compounds were filtered based on their drug-like activity. These 47 compounds were subjected to virtual screening to obtain binding affinity compounds. The binding affinity range of all 47 compounds was -11.3 to -4.6 kcal mol-1. The top 10 compounds were examined according to their binding with the mutation point. A molecular dynamic simulation of the top 8 compounds was conducted to understand the stability of the compounds containing the mutated PBP3. Out of 8 compounds, 3 compounds, namely, macozinone, antibacterial agent 71, and antibacterial agent 123, showed good stability and were validated by RMSD, RMSF, and binding-free analysis. The findings of this study revealed promising antibacterial compounds against the F533L mutant PBP3. Furthermore, developments in these compounds may pave the way for novel therapeutic interventions.

In silico screening of chalcone derivatives as promising EGFR-TK inhibitors for the clinical treatment of cancer.

Epidermal growth factor receptor (EGFR) promotes tumorigenic characteristics and activates cancer-associated signaling pathways such as Wnt/-catenin, transforming growth factor (TGF-ß), and phosphoinositide-3-kinase (PI3K). Several inhibitors have been reported to suppress the activity of EGFR and are being used in cancer treatment. However, patients in the malignant stage of cancer show resistance to those inhibitors, opening a wide space for research to discover novel inhibitors. Therefore, we carried out machine learning and virtual screening to discover novel inhibitors with high affinity against EGFR-TK. Initially, a library of 2640 chalcones were screened out using a machine-learning model developed based on the random forest algorithm, exhibiting high sensitivity and a Receiver Operating Characteristic curve (ROC area) of 0.99. Furthermore, out of the initial 2640 screened compounds, 412 compounds exhibiting potential activity are subjected to evaluation for drug-likeness properties through different filters: Blood-brain barrier penetration, Lipinski's rule, CMC-50 like rule, Veber rule, and Ghose filter, alongside Cell Line Cytotoxicity Prediction. A total of 30 compounds that successfully pass through all these filters are selected for molecular docking. Of these, 6 compounds display substantial binding affinity and closer interaction with the conserved catalytic residues of the target EGFR-TK compared to the reference molecule (erlotinib). Furthermore, molecular dynamics simulation studies were conducted on four compounds (CID-375861, CID-375862, CID-23636403, and CID-259166) to confirm the stability of the docked complexes over a 100 ns simulation trajectory. Additionally, the binding free energy calculations by MMPBSA reveal that these four chalcone compounds exhibit strong affinity towards the EGFR-TK enzyme, with binding free energies of - 65.421 kJ/mol, - 94.266 kJ/mol, - 80.044 kJ/mol, and - 79.734 kJ/mol, respectively. The findings from this investigation highlight a set of promising chalcone compounds that have the potential to be developed into effective drugs for the treatment of various cancers. Further research and development on these compounds could pave the way for novel therapeutic interventions. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03858-8.

Genomic assortment and interactive insights of the chromosomal encoded control of cell death (ccd) toxin-antitoxin (TA) module in Xenorhabdus nematophila.

In the present circumstances, toxin-antitoxin (TA) modules have a great consideration due to their elusive role in bacterial physiology. TA modules consist of a toxic part and a counteracting antitoxin part and these are abundant genetic loci harbored on bacterial plasmids and chromosomes. The control of cell death (ccd) TA locus was the first identified TA module and its unitary function (such as plasmid maintenance) has been described, however, the function of its chromosomal counterparts is still ambiguous. Here, we are exploring the genomic assortment, structural and functional association of chromosomally encoded ccdAB TA homolog (ccdABXn1) in the genome of an entomopathogenic bacterium Xenorhabdus nematophila. This bacterium is a symbiotic model with the nematode Steinernema carpocapsae that infects and kills the host insect. By genomic assortment analysis, our observations suggested that CcdA antitoxin homologs are not more closely related than CcdB toxin homologs. Further results suggest that the ccdABXn1 TA homolog has sulphonamide (such as 4C6, for CcdA homolog) and peptide (such as gyrase, for CcdB homolog) ligand partners with a typical TA interaction network that may affect essential cellular metabolism of the X. nematophila. Collectively, our results improve the knowledge and conception of the metabolic interactive role of ccdAB TA homologs in X. nematophila physiology.Communicated by Ramaswamy H. Sarma.

In silico screening of natural compounds to inhibit interaction of human ACE2 receptor and spike protein of SARS-CoV-2 for the prevention of COVID-19.

A computational investigation was carried out to find out potential phytochemicals that could inhibit the binding of human angiotensin-converting enzyme-2 (ACE2) receptors to spike protein of SARS-CoV-2 which is an essential step to gain entry inside human cells and onset of viral infection known as Coronavirus disease (COVID-19). A library of phytochemicals was screened by virtual screening against ACE2 receptors resulting in twenty phytochemicals out of 686 which had binding energy (-11.8 to -6.9 kcal/mol). Drug-likeness gave five hits, but ADMET analysis yielded 4 nontoxic hit phytochemicals. Molecular dynamics simulation of four-hit compounds resulted in acceptable stability and good dynamics behavior. These phytochemicals are Hinokinin, Gmelanone, Isocolumbin, and Tinocordioside, from Vitis vinifera, Gmelina arborea, and Tinospora cordifolia. The above-mentioned phytochemicals may be promising ACE2 inhibitors and can prevent infection of SARS-CoV-2 by inhibiting the entry of the virus into host cells.Communicated by Ramaswamy H. Sarma.

Potent multi-target natural inhibitors against SARS-CoV-2 from medicinal plants of the Himalaya: a discovery from hybrid machine learning, chemoinformatics, and simulation assisted screening.

The emergence and immune evasion ability of SARS-CoV-2 Omicron strains, mainly BA.5.2 and BF.7 and other variants of concern have raised global apprehensions. With this context, the discovery of multitarget inhibitors may be proven more comprehensive paradigm than its one-drug-to-one target counterpart. In the current study, a library of 271 phytochemicals from 25 medicinal plants from the Indian Himalayan Region has been virtually screened against SARS-CoV-2 by targeting nine virus proteins, viz., papain-like protease, main protease, nsp12, helicase, nsp14, nsp15, nsp16, envelope, and nucleocapsid for screening of a multi-target inhibitor against the viral replication. Initially, 94 phytochemicals were screened by a hybrid machine learning model constructed by combining 6 confirmatory bioassays against SARS-CoV-2 replication using an instance-based learner lazy k-nearest neighbour classifier. Further, 25 screened compounds with excellent drug-like properties were subjected to molecular docking. The phytochemical Cepharadione A from the plant Piper longum showed binding potential against four proteins with the highest binding energy of -10.90 kcal/mol. The compound has acceptable absorption, distribution, metabolism, excretion, and toxicity properties and exhibits stable binding behaviour in terms of root mean square deviation (0.068 ± 0.05 nm), root-mean-square fluctuation, hydrogen bonds, solvent accessible surface area (83.88-161.89 nm2), and molecular mechanics Poisson-Boltzmann surface area during molecular dynamics simulation of 200 ns with selected target proteins. Concerning the utility of natural compounds in the therapeutics formulation, Cepharadione A could be further investigated as a remarkable lead candidate for the development of therapeutic drugs against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Assessment of activity of chalcone compounds as inhibitors of 3-chymotrypsin like protease (3CLPro) of SARS-CoV-2: in silico study.

The COVID-19 is still pandemic due to emerging of various variant of concern of SARS-CoV2. Hence, it is devastating the world, causing significant economic as well as social chaos. This needs great effort to search and develop effective alternatives along with vaccination. Therefore, to continue drug discovery endeavors, we used chalcone derivatives to find an effective drug candidate against SARS-CoV2. Chalcone is a common simple scaffold that exists in many diets as well as in traditional medicine. Natural as well as synthetic chalcones have shown numerous interesting biological activities and are also effective in fighting various diseases. Hence, various computational methods were applied to find out potential inhibitors of 3CLPro using a library of 3000 compounds of chalcones. Firstly, the screening by structure-based pharmacophore model yielded 84 hits that were subjected to molecular docking. The top 10 docked compounds were characterized for stability by using 100 ns molecular dynamic (MD) simulation approach. Further, the binding free energy calculation by MMPBSA showed that four compounds bind to 3CLPro enzyme with high affinity, i.e., - 87.962 (kJ/mol), - 66.125 (kJ/mol), - 59.589 (kJ/mol), and - 66.728 (kJ/mol), respectively. Since chalcone is a common simple scaffold that is present in many diets as well as in traditional medicine, we suggest that screened compounds may emerge as promising drug candidates for SARS-CoV-2. These compounds may be investigated in vitro to evaluate the efficacy against SARS-CoV-2. Supplementary Information: The online version contains supplementary material available at 10.1007/s11224-022-01887-2.

In silico identification of antidiabetic target for phytochemicals of A. marmelos and mechanistic insights by molecular dynamics simulations.

The leaves and fruits of Aegle marmelos (L.) have antidiabetic activity. However, the mode of action and molecules having antidiabetic activity are not known. Hence, we conducted molecular docking of phytochemicals with various molecular antidiabetic targets to find the same. Docking prioritized Dipeptidyl peptidase-4 (DPP-4) as the main target for phytochemicals of Aegle marmelos. DPP-4 inactivates intestinal peptides, glucagon-like peptide-1 (GLP-1), and Gastric inhibitory polypeptide (GIP). GLP-1 and GIP stimulate a decline in blood glucose levels, but DPP-4 inhibits their functions resulting high level of glucose. Hence inhibiting the activity of DPP-4 is a well-known strategy to treat Type 2 diabetes. Therefore, to find a mechanism that may be involved to act as a natural inhibitor of DPP-4, we screened five phytochemicals out of seventy-three based on Virtual Screening, ADMET Drug-likeness analysis, and PAINS filtering. Further, all five phytochemicals, i.e. Aegeline, Citral, Marmesinin, Auraptene, ß-Bisabolene, and reference compound subjected MDS for analyzing the stability of docked complexes to assess the fluctuation and conformational changes during protein-ligand interaction. The values of RMSD, RG, RMSF, SASA, and Gibbs energy revealed the good stability of these phytochemicals in the active site pocket of DPP-4 in comparison to reference. Additionally, we have done the pharmacophore analysis, which revealed many common pharmacophore features between screened phytochemicals of A. marmelos and reference molecule. Our results show that these phytochemicals are potential antidiabetic candidates and can be further modified and evaluated to develop more effective antidiabetic drugs against DPP-4 to treat Type 2 Diabetes. Communicated by Ramaswamy H. Sarma.

Identification of Putative Elicitors From Plant Root Exudates Responsible for PsoR Activation in Plant-Beneficial Pseudomonas spp. by Docking and Molecular Dynamics Simulation Approaches to Decipher Plant-Microbe Interaction.

Plants and rhizobacteria are coexisting since the beginning, but the exact mechanism of communication between them remains enigmatic. The PsoR protein of plant-beneficial Pseudomonas spp., a group of root-associated bacteria, is known to produce a range of antifungal and insecticidal secondary metabolites like 2,4-diacetyl phloroglucinol (DAPG), pyrrolnitrin, and chitinase making them great biocontrol agents and thus helping in plant growth promotion. To better understand the inter-kingdom signaling between plants and plant growth-promoting rhizobacteria (PGPR), the interaction of PsoR with various root exudates was investigated computationally. For this, we first modeled the PsoR protein and confirmed it using the Ramachandran plot. A total of 59 different low molecular weight phytochemicals, secreted as root exudates by plants, were identified by extensive text mining. They were virtually screened with the PsoR protein by molecular docking. Based on the lowest binding energy, ranging from -7.1 to -6.3 kcal mol-1, the top five exudates were chosen. To analyze the stability of the docked protein-ligand complex, a molecular dynamics (MD) simulation of 100 nanoseconds was done. Two root exudates, saponarin and 2-benzoxazolinone (BOA), showed suitable binding with PsoR by forming hydrogen, hydrophobic, and Van der Waals interactions. To confirm the MD simulation results, RMSF, RG, SASA, and interaction energy were calculated. This computational study first time reports that saponarin and 2-BOA, predominantly present in the root exudates of barley and wheat, respectively, demonstrate effective binding with the modeled PsoR protein and are likely of showing cross-kingdom interactions.

Repurposing of FDA approved drugs against Salmonella enteric serovar Typhi by targeting dihydrofolate reductase: an in silico study.

Drug-resistant Salmonella enteric serovar Typhi (S. Typhi) poses a vital public health issue. To overcome drug resistance issues, the development of effective drugs with novel mechanism(s) of action is required. In this regard, drug repurposing is a viable alternative approach to find novel drugs to overcome drug resistance. Therefore, a FDA-approved-drug-library containing 1930 drugs was analyzed against the dihydrofolate reductase (DHFR) of S. Typhi using deep learning regression algorithms. Initially, a total of 500 compounds were screened, followed by rescreening by molecular docking. Further, from screened compounds by molecular docking, the top eight compounds were subjected to molecular dynamics (MD) simulation. Analysis of MD simulation resulted in four potential compounds, namely; Duvelisib, Amenamevir, Lifitegrast and Nilotinib against the DHFR enzyme. During the MD simulation, these four drugs achieved good stability during the 100 ns trajectory period at 300 K. Further to know the insights of the complex's stability, we calculated RMSF, RG, SASA and interaction energy for the last 60 ns trajectory period because all complexes showed the stability after 40 ns trajectory period. MM-PBSA analysis of the last 10 ns of MD trajectories showed the stability of the complexes. From our results, we conclude that these drugs can also be useful for treating typhoid fever and can inhibit S. Typhi by interfering with the function of the DHFR enzyme. Communicated by Ramaswamy H. Sarma.

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.

Screening of potential bio-molecules from Moringa olifera against SARS-CoV-2 main protease using computational approaches.

COVID-19 caused by SARS-CoV-2 is responsible for the deaths of millions of people worldwide. It is having devastating effects on the people of all countries. In this regard, the phytochemicals of medicinal plants could be explored to prevent this disease. M. oleifera is a miracle plant with antibacterial, antiviral, and antioxidant properties because of its high content of flavonoids, glucosides and glucosinolates. Therefore, we constructed a library of 294 phytochemicals of M. oleifera and filtered it through the FAF-Drugs4. Further, molecular docking studies of filtered phytochemicals were performed with Mpro enzyme to investigate the binding interactions. Drug likeness properties, ADMET prediction were analyzed to determine the therapeutic aspect of these compounds. Based on the binding energy score of the top 4 compounds, the results indicate that Vicenin-2 has the highest binding affinity (-8.6 kcal mol-1) as compared to the reference molecule (-8.4 kcal mol-1). ADMET result reveals that all top four compounds have minimal toxic effects and good absorption. Further, 500 ns molecular dynamics simulation of the top four compounds showed that Kaempferol-3-O-rutinoside and Vitexin have good stability with Mpro. These two compounds were then subjected for MMPBSA (last 50 ns) calculation to analyze the protein-ligand stability and dynamic behavior. Kaempferol-3-O-rutinoside and Vitexin showed very good binding free energy i.e. -40.136 kJ mol-1 and -26.784 kJ mol-1, respectively. Promising outcomes from MD simulations evidence the worth of these compounds for future drug development to combat coronavirus disease.Communicated by Ramaswamy H. Sarma.

Identification of Zinc-Binding Inhibitors of Matrix Metalloproteinase-9 to Prevent Cancer Through Deep Learning and Molecular Dynamics Simulation Approach.

The overexpression of matrix metalloproteinase-9 (MMP-9) is associated with tumor development and angiogenesis, and hence, it has been considered an attractive drug target for anticancer therapy. To assist in drug design endeavors for MMP-9 targets, an in silico study was presented to investigate whether our compounds inhibit MMP-9 by binding to the catalytic domain, similar to their inhibitor or not. For that, in the initial stage, a deep-learning algorithm was used for the predictive modeling of the CHEMBL321 dataset of MMP-9 inhibitors. Several regression models were built and evaluated based on R2, MAE MSE, RMSE, and Loss. The best model was utilized to screen the drug bank database containing 9,102 compounds to seek novel compounds as MMP-9 inhibitors. Then top high score compounds were selected for molecular docking based on the comparison between the score of the reference molecule. Furthermore, molecules having the highest docking scores were selected, and interaction mechanisms with respect to S1 pocket and catalytic zinc ion of these compounds were also discussed. Those compounds, involving binding to the catalytic zinc ion and the S1 pocket of MMP-9, were considered preferentially for molecular dynamics studies (100 ns) and an MM-PBSA (last 30 ns) analysis. Based on the results, we proposed several novel compounds as potential candidates for MMP-9 inhibition and investigated their binding properties with MMP-9. The findings suggested that these compounds may be useful in the design and development of MMP-9 inhibitors in the future.

Molecular docking and molecular dynamics simulation approaches for evaluation of laccase-mediated biodegradation of various industrial dyes.

Dyes are being increasingly utilized across the globe, but there is no appropriate method of bioremediation for their full mineralization from the environment. Laccases are key enzymes that help microbes to degrade dyes as well as their intermediate metabolites. Various dyes have been reported to be degraded by bacteria, but it is still unclear how these enzymes function during dye degradation. To effectively eradicate toxic dyes from the system, it is essential to understand the molecular function of enzymes. As a result, the interaction of laccase with different toxic dyes was investigated using molecular docking. Based on the highest binding energy we have screened ten dyes with positive interaction with laccase. Evaluating the MD simulation results, three out of ten dyes were more stable as potential targets for degradation by laccase of Bacillus subtilis. As a result, subsequent research focused solely on the results of three substrates: pigment red, fuchsin base, and Sudan IV. Analysis of MD simulation revealed that pigments red 23, fuchsin base, and Sudan IV form hydrogen and hydrophobic bond as well as Vander Waals interactions with the active site of laccase to keep it stable in aqueous solution. The conformation of laccase is greatly altered by the inclusion of all three substrates in the active site. The MD simulation findings show that laccase complexes remain stable throughout the catalytic reaction. Therefore, this research provides a molecular understanding of laccase expression and its role in the bioremediation of the pigments red 23, fuchsin base, and Sudan IV.Communicated by Ramaswamy H. Sarma.

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.