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.

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.

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.

Identification of luteolin -7-glucoside and epicatechin gallate from Vernoniacinerea, as novel EGFR L858R kinase inhibitors against lung cancer: Docking and simulation-based study.

Lung cancer ranks number one among the all cancer types in the world, out of which 85% are non-small cell lung cancer (NSCLC). In case of NSCLC, a substitution mutation of Leu 858 Arg (L858R) in the gene of Epidermal Growth Factor Receptor (EGFR) has been reported. Hence, targeting EGFR containing L858R mutation using inhibitors is well reported strategy to discover potential drugs against NSCLC. The present work aims to identify the potent inhibitors against EGFR L858R from Vernonia cinerea plant. A library of 45 phytochemicals was subjected to virtual screening using rigid and flexible docking. 12 potential phytochemicals were screened by molecular docking with high binding energy (between -8.0 and -9.7 kcal mol-1). Two compounds viz., luteolin -7-glucoside and epicatechin gallate showed interaction with Met793 of EGFR-L858R which was similar to the reference inhibitor PD168393. To analyze the stability of the luteolin -7-glucoside and epicatechin gallate with EGFR L858R, molecular dynamics simulations were conducted in explicit water conditions using 60 nanosecond. The results of hydrogen bonding patterns, radius of gyration, deviations in conformational elements, fluctuations in the residual components, and solvent accessible surface area revealed better stability of luteolin -7-glucoside and epicatechin gallate with EGFR-L858R as compared to PD168393. Therefore, we conclude that luteolin -7-glucoside and epicatechin gallate have excellent inhibition properties thus they can be used further to develop effective drugs against lung cancer having EGFR-L858R mutation.Communicated by Ramaswamy H. Sarma.

Identification of potent Antigen 85C inhibitors of Mycobacterium tuberculosis via in-house lichen library and binding free energy studies Part-II.

Tuberculosis remains the cause of mortality throughout the world. Currently, the available anti-tubercular drugs are not effective because of the existence of Multi-Drug resistant tuberculosis (MDR-TB) and Extensively-Drug resistant tuberculosis (XDR-TB). It has, therefore, become necessary to develop novel drugs that inhibit the activity of drug-resistant Mycobacterium tuberculosis. Due to the existence of MDR and XDR-TB, Mtb Ag85C has risen out as a propitious molecular drug target as it has importance in the synthesis of main components of the Mtb cell envelope which are essential for the virulence and survival of Mtb. In a previous paper, we studied a potential drug target by virtual high throughput screening of compounds and in continuation of the study on Mtb Ag85C, we further studied the role of lichen compounds in the inhibition of Ag85C. In the current research work, virtual screening of a lichen compounds library was performed against Ag85C. Further, ADMET analysis was employed to filter out the screened lichen compounds. Bioactivity score and toxicity prediction finalized four lichen compounds i.e. Portentol, Aspicilin, Parietinic acid and Polyporic acid as potential inhibitors of Ag85C. The stability and dynamic behavior of four compounds were analyzed by using Molecular dynamics simulation which indicated that they may be potential inhibitors of Ag85C. Therefore, based on the above results, Portentol, Aspicilin, Parietinic acid and Polyporic acid may be potential drug candidates against Mtb. We suggest that the use of these compounds can minimize the treatment time-period and the various side effects associated with the currently available anti-tubercular drugs.

Antimicrobial activity of methanolic extracts of Vernonia cinerea against Xanthomonas oryzae and identification of their compounds using in silico techniques.

Bacterial Leaf Blight (BLB) disease is an extremely ruinous disease in rice, caused by Xanthomonas oryzae pv. oryzae (Xoo). Although various chemicals are available to manage BLB, they are toxic to the environment as well as humans. Hence there is a need to develop new pesticides as alternatives to hazardous chemicals. Therefore, a study was carried out to discover new potent natural pesticides against Xoo from different solvent extracts of Vernonia cinerea. Among all the fractions, the methanolic extract showed the highest inhibition zone. Further, to gain mechanistic insight of inhibitory action, 40 molecules of methanolic extracts were subjected for in silico study against two enzymes D-alanine-D-alanine ligase (Ddl) and Peptide deformylase (PDF). In silico study showed Rutin and Methanone, [1,4-dimethyl-7-(1- methylethyl)-2- azulenyl]phenyl have a good binding affinity with Ddl while Phenol, 2,4-bis(1-phenylethyl)- and 1,2-Benzenedicarboxylic acid, diisooctyl ester showed an excellent binding affinity to PDF. Finally, the system biology approach was applied to understand the agrochemical's effect in the cell system of bacteria against both the enzymes. Conclusively, these four-hit compounds may have strong potential against Xoo and can be used as biopesticides in the future.

Dayara bugyal restoration model in the alpine and subalpine region of the Central Himalaya: a step toward minimizing the impacts.

Eco-restoration initiative work in the high altitude Dayara pastureland (3501 m) from the Indian Himalayan Region has been considered to be one of the successful field demonstration against both natural and anthropogenic degradation. The present study therefore attempts to assess the implications of entire eco-restoration model as practiced by Department of Forest, Government of Uttarakhand in 2019. Its assessment was done by calculating restoration success index by way of considering three categories, viz., direct management measure (M), environmental desirability (E) and socio-economic feasibility (SE) considering 22 individual variables. 'M' comprised both biotic and abiotic pressures. Grazing and tourism were biotic, while abiotic pressure was considered mainly soil erosion in alpine area due to topographic fragility. Above ground vegetation profile and below ground soil nutrient profile (N, P, K, pH and water holding capacity) were analyzed in 'E' component. In the last but not least, 'SE' was analyzed to assess the social acceptability of the local communities and stakeholders who are supposed to be ultimate beneficiary of alike interventions. Direct management measure was found with the variable index score of 0.8 indicating the higher score as compared to environmental desirability (0.56). Under direct management measure, grazing and tourists' carrying capacity of the area was analyzed with high management needs to call the region sustainable in terms of availability of bio-resources. The ecosystem index score was evaluated for the reference (81.94), treated (64.5) and untreated zones (52.03), wherein increasing profile of these values were found. The outcomes like improved vegetation profile in terms of total herb density, soil nutrient profile of the restored area along with soil pH (4.96) and water holding capacity (49.85%) were found to be restored significantly along with controlling 169.64 tonne year-1 soil erosion from draining. The assessment of grazing pattern of 118 migratory Cow Unit (CU) (76 horse/mule and 18 sheep/goat, already controlled), 318 local CU (30 horse/mule and 187 sheep/goat) were calculated and recommended to be controlled. Tourists' carrying capacity of 274 tourists per day and manual removal of Rumex nepalensis at the shepherd camping site were found to be worth to apply in the area. Use of biodegradable but locally sourced material and engaging local villagers in this endeavor were also found to be in harmony with SDG Goal 1 (no poverty). Therefore, the restoration and its evaluation model could have its future prospects to prove as a successful restoration practice. This restoration practice could not only be worth in high altitude degraded alpine pastures of the Indian Himalayan Region but also to other mountain alpine and sub-alpine ecosystems.

Identification of potential Mycolyltransferase Ag85C inhibitors of Mycobacterium tuberculosis H37Rv via Virtual High Throughput Screening and Binding free energy studies.

Tuberculosis (TB) is a deadly disease which causes millions of death annually worldwide. Although TB is treatable but the rise of cases of multidrug-resistant and totally drug-resistant strains of Mycobacterium tuberculosis (Mtb) poses a great challenge to cure TB completely and this situation demands an urgent need for development of potential anti-tubercular drugs. In this regard, the antigen 85C (Ag85C) has emerged as an essential mycobacterial drug target as it plays a central role in synthesizing major components of the inner and outer layers of outer membrane of Mtb. In this research, we have identified four novel potential inhibitors as a potent inhibitor of the Mtb Ag85C from CHEMBL24, MolPort, Zinc and PubChem library by High Throughput Virtual Screening. The results of molecular dynamics show that these compounds bind to Ag85C protein with high stability. The ADMET profiling and pharmacophore analysis indicate that these compounds may act as potential anti-mycobacterial candidates. On the basis of findings our work, we propose that these compounds are novel potential inhibitors of Mtb Ag85C with similar or better properties than the classic inhibitor and they can potentially shorten the treatment duration and may have anti-mycobacterial activity against drug-resistant Mtb strains.

Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis.

COVID-19 caused by the SARS-CoV-2 is a current global challenge and urgent discovery of potential drugs to combat this pandemic is a need of the hour. 3-chymotrypsin-like cysteine protease (3CLpro) enzyme is the vital molecular target against the SARS-CoV-2. Therefore, in the present study, 1528 anti-HIV1compounds were screened by sequence alignment between 3CLpro of SARS-CoV-2 and avian infectious bronchitis virus (avian coronavirus) followed by machine learning predictive model, drug-likeness screening and molecular docking, which resulted in 41 screened compounds. These 41 compounds were re-screened by deep learning model constructed considering the IC50 values of known inhibitors which resulted in 22 hit compounds. Further, screening was done by structural activity relationship mapping which resulted in two structural clefts. Thereafter, functional group analysis was also done, where cluster 2 showed the presence of several essential functional groups having pharmacological importance. In the final stage, Cluster 2 compounds were re-docked with four different PDB structures of 3CLpro, and their depth interaction profile was analyzed followed by molecular dynamics simulation at 100 ns. Conclusively, 2 out of 1528 compounds were screened as potential hits against 3CLpro which could be further treated as an excellent drug against SARS-CoV-2.

Virtual screening of natural compounds as inhibitors of EGFR 696-1022 T790M associated with non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the most dominating and lethal type of lung cancer triggering more than 1.3 million deaths per year. The most effective line of treatment against NSCLC is to target epidermal growth factor receptor (EGFR) activating mutation. The present study aims to identify the novel anti-lung cancer compounds form nature against EGFR 696-1022 T790M by using in silico approaches. A library of 419 compounds from several natural resources was subjected to pre-screen through machine learning model using Random Forest classifier resulting 63 screened molecules with active potential. These molecules were further screened by molecular docking against the active site of EGFR 696-1022 T790M protein using AutoDock Vina followed by rescoring using X-Score. As a result 4 compounds were finally screened namely Granulatimide, Danorubicin, Penicinoline and Austocystin D with lowest binding energy which were -6.5 kcal/mol, -6.1 kcal/mol, -6.3 kcal/mol and -7.1 kcal/mol respectively. The drug likeness of the screened compounds was evaluated using FaF-Drug3 server. Finally toxicity of the hit compounds was predicted in cell line using the CLC-Pred server where their cytotoxic ability against various lung cancer cell lines was confirmed. We have shown 4 potential compounds, which could be further exploited as efficient drug candidates against lung cancer.