Designing novel possible kinase inhibitor derivatives as therapeutics against Mycobacterium tuberculosis: An in silico study

Inhibition of Monkeypox Virus DNA Polymerase Using Moringa oleifera Phytochemicals: Computational Studies of Drug-Likeness, Molecular Docking, Molecular Dynamics Simulation and Density Functional Theory

The emergence of zoonotic monkeypox (MPX) disease, caused by the double-stranded DNA monkeypox virus (MPXV), has become a global threat. Due to unavailability of a specific small molecule drug for MPX, this study investigated Moringa oleifera phytochemicals to find potent and safe inhibitors of DNA Polymerase (DNA Pol), a poxvirus drug target due to its role in the viral life cycle. For that, 146 phytochemicals were screened through drug-likeness and molecular docking analyses. Among these, 136 compounds exhibited drug-like properties, with Gossypetin showing the highest binding affinity (- 7.8 kcal/mol), followed by Riboflavin (- 7.6 kcal/mol) and Ellagic acid (- 7.6 kcal/mol). In comparison, the control drugs Cidofovir and Brincidofovir displayed lower binding affinities, with binding energies of - 6.0 kcal/mol and - 5.1 kcal/mol, respectively. Hydrogen bonds, electrostatic and hydrophobic interactions were the main non-bond interactions between inhibitors and protein active site. The identified compounds were further evaluated using molecular dynamics simulation, density functional theory analysis and ADMET analysis. Molecular dynamics simulations conducted over 200 ns revealed that DNA Pol-Gossypetin complex was not stable, however, Riboflavin and Ellagic acid complexes showed excellent stability indicating them as better DNA Pol inhibitors. The density functional theory analysis exhibited the chemical reactivity of these inhibitor compounds. The ADMET analysis suggested that the top phytochemicals were safe and showed no toxicity. In conclusion, this study has identified Riboflavin and Ellagic acid as potential DNA Pol inhibitors to control MPXV. Further experimental assays and clinical trials are needed to confirm their activity against the disease.

Graphical Abstract

Design, synthesis, and evaluation of 1,4-benzothiazine-3-one containing bisamide derivatives as dual inhibitors of Staphylococcus aureus with plausible application in a urinary catheter

In this study, 1,4-benzothiazine-based bisamide derivatives, a new class of antibacterial agents targeting bacterial peptide deformylase (PDF), were designed and synthesized to combat Staphylococcus aureus infection. Molecular modeling of the designed molecules showed better docking scores compared to the natural product actinonin. Bioactivity assessment identified two derivatives with promising antibacterial activity in vitro. The stability of the most active molecule, 8bE, was assessed using molecular dynamics (MD) simulation. Significantly, compound 8bE could also inhibit the S. aureus biofilm at low concentrations. Furthermore, the capability of the synthesized molecule to inhibit S. aureus biofilm formation on medical devices like urinary catheters is also demonstrated.

In Silico Design of Potential Small-Molecule Antibiotic Adjuvants against Salmonella typhimurium Ortho Acetyl Sulphydrylase Synthase to Address Antimicrobial Resistance

The inhibition of O-acetyl sulphydrylase synthase isoforms has been reported to represent a promising approach for the development of antibiotic adjuvants. This occurs via the organism developing an unpaired oxidative stress response, causing a reduction in antibiotic resistance in vegetative and swarm cell populations. This consequently increases the effectiveness of conventional antibiotics at lower doses. This study aimed to predict potential inhibitors of Salmonella typhimurium ortho acetyl sulphydrylase synthase (StOASS), which has lower binding energy than the cocrystalized ligand pyridoxal 5 phosphate (PLP), using a computer-aided drug design approach including pharmacophore modeling, virtual screening, and in silico ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) evaluation. The screening and molecular docking of 4254 compounds obtained from the PubChem database were carried out using AutoDock vina, while a post-screening analysis was carried out using Discovery Studio. The best three hits were compounds with the PubChem IDs 118614633, 135715279, and 155773276, possessing binding affinities of -9.1, -8.9, and -8.8 kcal/mol, respectively. The in silico ADMET prediction showed that the pharmacokinetic properties of the best hits were relatively good. The optimization of the best three hits via scaffold hopping gave rise to 187 compounds, and they were docked against StOASS; this revealed that lead compound 1 had the lowest binding energy (-9.3 kcal/mol) and performed better than its parent compound 155773276. Lead compound 1, with the best binding affinity, has a hydroxyl group in its structure and a change in the core heterocycle of its parent compound to benzimidazole, and pyrimidine introduces a synergistic effect and consequently increases the binding energy. The stability of the best hit and optimized compound at the StOASS active site was determined using RMSD, RMSF, radius of gyration, and SASA plots generated from a molecular dynamics simulation. The MD simulation results were also used to monitor how the introduction of new functional groups of optimized compounds contributes to the stability of ligands at the target active site. The improved binding affinity of these compounds compared to PLP and their toxicity profile, which is predicted to be mild, highlights them as good inhibitors of StOASS, and hence, possible antimicrobial adjuvants.

Computational investigation of Moringa oleifera phytochemicals targeting EGFR: molecular docking, molecular dynamics simulation and density functional theory studies

Epidermal growth factor receptor (EGFR) is a prominent target for anticancer therapy due to its role in activating several cell signaling cascades. Clinically approved EGFR inhibitors are reported to show treatment resistance and toxicity, this study, therefore, investigates Moringa oleifera phytochemicals to find potent and safe anti-EGFR compounds. For that, phytochemicals were screened based on drug-likeness and molecular docking analysis followed by molecular dynamics simulation, density functional theory analysis and ADMET analysis to identify the effective inhibitors of EGFR tyrosine kinase (EGFR-TK) domain. Known EGFR-TK inhibitors (1-4 generations) were used as control. Among 146 phytochemicals, 136 compounds showed drug-likeness, of which Delta 7-Avenasterol was the most potential EGFR-TK inhibitor with a binding energy of -9.2 kcal/mol followed by 24-Methylenecholesterol (-9.1 kcal/mol), Campesterol (-9.0 kcal/mol) and Ellagic acid (-9.0 kcal/mol). In comparison, the highest binding affinity from control drugs was displayed by Rociletinib (-9.0 kcal/mol). The molecular dynamics simulation (100 ns) exhibited the structural stability of native EGFR-TK and protein-inhibitor complexes. Further, MM/PBSA computed the binding free energies of protein complex with Delta 7-Avenasterol, 24-Methylenecholesterol, Campesterol and Ellagic acid as -154.559 ± 18.591 kJ/mol, -139.176 ± 19.236 kJ/mol, -136.212 ± 17.598 kJ/mol and -139.513 ± 23.832 kJ/mol, respectively. Non-polar interactions were the major contributors to these energies. The density functional theory analysis also established the stability of these inhibitor compounds. ADMET analysis depicted acceptable outcomes for all top phytochemicals without displaying any toxicity. In conclusion, this report has identified promising EGFR-TK inhibitors to treat several cancers that can be further investigated through laboratory and clinical tests.

A novel microbial and hepatic biotransformation-integrated network pharmacology strategy explores the therapeutic mechanisms of bioactive herbal products in neurological diseases: the effects of Astragaloside IV on intracerebral hemorrhage as an example

BACKGROUND

The oral bioavailability and blood-brain barrier permeability of many herbal products are too low to explain the significant efficacy fully. Gut microbiota and liver can metabolize herbal ingredients to more absorbable forms. The current study aims to evaluate the ability of a novel biotransformation-integrated network pharmacology strategy to discover the therapeutic mechanisms of low-bioavailability herbal products in neurological diseases.

METHODS

A study on the mechanisms of Astragaloside IV (ASIV) in treating intracerebral hemorrhage (ICH) was selected as an example. Firstly, the absorbed ASIV metabolites were collected by a literature search. Next, the ADMET properties and the ICH-associated targets of ASIV and its metabolites were compared. Finally, the biotransformation-increased targets and biological processes were screened out and verified by molecular docking, molecular dynamics simulation, and cell and animal experiments.

RESULTS

The metabolites (3-epi-cycloastragenol and cycloastragenol) showed higher bioavailability and blood-brain barrier permeability than ASIV. Biotransformation added the targets ASIV in ICH, including PTK2, CDC42, CSF1R, and TNF. The increased targets were primarily enriched in microglia and involved in cell migration, proliferation, and inflammation. The computer simulations revealed that 3-epi-cycloastragenol bound CSF1R and cycloastragenol bound PTK2 and CDC42 stably. The In vivo and in vitro studies confirmed that the ASIV-derived metabolites suppressed CDC42 and CSF1R expression and inhibited microglia migration, proliferation, and TNF-α secretion.

CONCLUSION

ASIV inhibits post-ICH microglia/macrophage proliferation and migration, probably through its transformed products to bind CDC42, PTK2, and CSF1R. The integrated strategy can be used to discover novel mechanisms of herbal products or traditional Chinses medicine in treating diseases.

Advances in computational frameworks in the fight against TB: The way forward

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

Study on the molecular mechanism of anti-liver cancer effect of Evodiae fructus by network pharmacology and QSAR model

Introduction: Evodiae Fructus (EF) is the dried, near ripe fruit of Euodia rutaecarpa (Juss.) Benth in Rutaceae. Numerous studies have demonstrated its anti-liver cancer properties. However, the molecular mechanism of Evodiae fructus against liver cancer and its structure-activity connection still require clarification. Methods: We utilized network pharmacology and a QSAR (2- and 3-dimensional) model to study the anti-liver cancer effect of Evodiae fructus. First, by using network pharmacology to screen the active substances and targets of Evodiae fructus, we investigated the signaling pathways involved in the anti-liver cancer actions of Evodiae fructus. The 2D-QSAR pharmacophore model was then used to predict the pIC50 values of compounds. The hiphop method was used to create an ideal 3D-QSAR pharmacophore model for the prediction of Evodiae fructus compounds. Finally, molecular docking was used to validate the rationality of the pharmacophore, and molecular dynamics was used to disclose the stability of the compounds by assessing the trajectories in 10 ns using RMSD, RMSF, Rg, and hydrogen bonding metrics. Results: In total, 27 compounds were acquired from the TCMSP and TCM-ID databases, and 45 intersection targets were compiled using Venn diagrams. Network integration analysis was used in this study to identify SRC as a primary target. Key pathways were discovered by KEGG pathway analysis, including PD-L1 expression and PD-1 checkpoint pathway, EGFR tyrosine kinase inhibitor resistance, and ErbB signaling pathway. Using a 2D-QSAR pharmacophore model and the MLR approach to predict chemical activity, ten highly active compounds were found. Two hydrophobic features and one hydrogen bond acceptor feature in the 3D-QSAR pharmacophore model were validated by training set chemicals. The results of molecular docking revealed that 10 active compounds had better docking scores with SRC and were linked to residues via hydrogen and hydrophobic bonds. Molecular dynamics was used to show the structural stability of obacunone, beta-sitosterol, and sitosterol. Conclusion:Pharmacophore 01 has high selectivity and the ability to distinguish active and inactive compounds, which is the optimal model for this study. Obacunone has the optimal binding ability with SRC. The pharmacophore model proposed in this study provides theoretical support for further screening effective anti-cancer Chinese herbal compounds and optimizing the compound structure.

Computational analysis of natural product B-Raf inhibitors

B-Raf protein is a serine-threonine kinase and an important signal transduction molecule of the MAPK signaling pathway that mediates signals from RAS to MEK, ultimately promoting various essential cellular functions. The B-Raf kinase domain is divided into two subdomains: a small N-terminal lobe and a large C-terminal lobe, with a deep catalytic cleft between them. The N-terminal lobe contains a phosphate-binding loop (P-loop) and nucleotide-binding pocket, while the C-terminal lobe binds the protein substrates and contains the catalytic loop. The ligand pharmacophore was generated by using 17 different natural products and the receptor pharmacophore was generated by using protein structures. The reported natural product B-Raf inhibitors were analyzed according to the pharmacophore analysis (HipHop fit), virtual screening tools by Lipinski's rule of five. Thirteen out of seventeen molecules share the best ligand based pharmacophoric model (HipHop_5). The best receptor based pharmacophoric model came as AADHR. The compounds were docked against the B-Raf receptors (PDB ID: 3OG7, 4XV2, 5C9C). The compound DHSilB with cDOCKER interaction energy of -62.7 kcal/mol, -83.3 kcal/mol, -73.6 kcal/mol as well as the compound DHSilA with cDOCKER interaction energy of -63.9 kcal/mol, -63.2 kcal/mol, -74.7 kcal/mol showed satisfactory interaction with the respective receptors. Finally, the MD simulation was run for 100 ns for the top docked compounds DHSilA and DHSilB with the B-Raf proteins (PDB ID: 3OG7, 4XV2 and 5C9C). After the MD simulation run for 100 ns, the ligand 2,3-dehydrosilybin A (DHSilA) was found to be more stable in terms of the trajectories of RMSD, RMSF, Rg and H-bonds.

Discovery of potential Aurora-A kinase inhibitors by 3D QSAR pharmacophore modeling, virtual screening, docking, and MD simulation studies

The Aurora-kinase family comprises of cell cycle-regulated serine/threonine kinases playing a vital role during mitosis. Aurora-A kinase is involved in multiple mitotic events in cell cycle and is a major regulator of centrosome function during mitosis. Aurora-A is overexpressed in breast, lung, colon, ovarian, glial, and pancreatic cancer. Hence, Aurora-A kinase is a promising target in cancer therapy. In our current study, a four-point 3D QSAR pharmacophore model has been generated using substituted pyrimidine class of Aurora-A kinase inhibitors. It had a fixed cost value 88.7429. The model mapped well to the external test set comprising of clinically active molecules, with a correlation coefficient r = 0.99. From the mapping, it was found that the hydrophobic features (HY) of a molecule play an important role for Aurora-A kinase inhibitory activity, whereas the ring aromatic feature provides geometric constraint for spatial alignment of different functional group. The hypothesis, with one hydrogen bond acceptor, two ring aromatic features, and one hydrophobic feature, was selected to screen miniMaybridge database. The screened ligands were filtered on the basis of activity, shape, and drug likeliness. This led to the identification of five top hits. These identified potential leads were further subjected to docking with the ATP-binding site of Aurora-A kinase. The molecular dynamic simulation studies of top lead molecules having diverse scaffolds endorsed that the identified molecules had distinctive ability to inhibit Aurora-A kinase. Thus, this study may facilitate the medicinal chemists to design promising ligands with various scaffolds to inhibit Aurora-A kinase. Communicated by Ramaswamy H. Sarma.

Fluorinated dihydropyridines as candidates to block L-type voltage-dependent calcium channels

Voltage-gated calcium (Ca_v) channels malfunction may lead to Alzheimer's and cardiovascular disorders, thus a critical protein target for drug development and treatment against several diseases. Indeed, dihydropyridines (DHPs) as nifedipine and amlodipine are top-selling pharmaceuticals and, respectively, the 121st and 5th most prescribed drugs in the United States that have been used as successful selective blockers for L-type Ca2+ channels (LCC) and may be helpful model structures to compare with new DHP analogs. In this context, we have performed a structure-based drug design (SBDD) study of several fluorinated DHPs by using homology modeling, molecular docking, quantitative structure activity relationship (QSAR) and molecular dynamics calculations. Such approaches combined with molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) interaction energy results and screening of ADMET (absorption, distribution, metabolism, excretion and toxicity) properties indicate that all ligands in this study are potential new candidates to be tested experimentally for inhibition of LCC and may have higher affinities than the commonly used drugs, being convenient synthetic routes proposed for 11-16, which are among the ligands that showed the best theoretical results concerning LCC inhibition. Furthermore, the ligand interactions with the binding site were carefully examined using the topological noncovalent interactions (NCI) method, which highlighted specifically responsible amino acid residues that increase the spontaneity of the new proposed DHP ligands.Communicated by Ramaswamy H. Sarma.

Identification of 1H-purine-2,6-dione derivative as a potential SARS-CoV-2 main protease inhibitor: molecular docking, dynamic simulations, and energy calculations

The rapid spread of the coronavirus since its first appearance in 2019 has taken the world by surprise, challenging the global economy, and putting pressure on healthcare systems across the world. The introduction of preventive vaccines only managed to slow the rising death rates worldwide, illuminating the pressing need for developing effective antiviral therapeutics. The traditional route of drug discovery has been known to require years which the world does not currently have. In silico approaches in drug design have shown promising results over the last decade, helping to decrease the required time for drug development. One of the vital non-structural proteins that are essential to viral replication and transcription is the SARS-CoV-2 main protease (Mpro). Herein, using a test set of recently identified COVID-19 inhibitors, a pharmacophore was developed to screen 20 million drug-like compounds obtained from a freely accessible Zinc database. The generated hits were ranked using a structure based virtual screening technique (SBVS), and the top hits were subjected to in-depth molecular docking studies and MM-GBSA calculations over SARS-COV-2 Mpro. Finally, the most promising hit, compound (1), and the potent standard (III) were subjected to 100 ns molecular dynamics (MD) simulations and in silico ADME study. The result of the MD analysis as well as the in silico pharmacokinetic study reveal compound 1 to be a promising SARS-Cov-2 MPro inhibitor suitable for further development.

Evaluating the Performance of PPE44, HSPX, ESAT-6 and CFP-10 Factors in Tuberculosis Subunit Vaccines

Mycobacterium tuberculosis (M. tuberculosis) is an intracellular pathogen causing long-term infection in humans that mainly attacks macrophages and can escape from the immune system with the various mechanisms. The only FDA-approved vaccine against M. tuberculosis (MTB) is Mycobacterium bovis bacillus Calmette-Guérin (BCG). The protection of this vaccine typically lasts 10–15 years. Due to the increasing number of people becoming ill with MTB each year worldwide, the need to develop a new effective treatment against the disease has been increased. During the past two decades, the research budget for TB vaccine has quadrupled to over half a billion dollars. Most of these research projects were based on amplifying and stimulating the response of T-cells and developing the subunit vaccines. Additionally, these studies have demonstrated that secretory and immunogenic proteins of MTB play a key role in the pathogenesis of the bacteria. Therefore, these proteins were used to develop the new subunit vaccines. In this review, based on the use of these proteins in the successful new subunit vaccines, the PPE44, HSPX, CFP-10 and ESAT-6 antigens were selected and the role of these antigens in designing and developing new subunit vaccines against TB and for the prevention of TB were investigated.

An Oral 3D Printed PLGA-Tocopherol PEG Succinate Nanocomposite Hydrogel for High-Dose Methotrexate Delivery in Maintenance Chemotherapy

High-dose methotrexate (HDMTX) is one of the chemotherapeutic agents used to treat a variety of cancers in both adults and children. However, the toxicity associated with HDMTX has resulted in the spread of infections and treatment interruption. Further, poor bioavailability due to efflux pump activities mediated by P-glycoprotein has also been linked to poor therapeutic effects of methotrexate following oral administrations. D-α-Tocopheryl poly-ethylene glycol 1000 succinate (TPGS) is known to improve the bioavailability of poorly soluble drugs by inhibiting P-gp efflux activities, thus enhancing cellular uptake. Therefore, to achieve improved bioavailability for MTX, this study aimed to design and develop a novel drug delivery system employing TPGS and a biodegradable polymer, i.e., PLGA, to construct methotrexate-loaded nanoparticles fixated in alginate-gelatine 3D printable hydrogel ink to form a solid 3D printed tablet for oral delivery. The results indicated that high accuracy (>95%) of the 3D printed tablets was achieved using a 25 G needle. In vitro, drug release profiles were investigated at pH 1.2 and pH 7.4 to simulate the gastrointestinal environment. The in vitro release profile displayed a controlled and prolonged release of methotrexate over 24 h. The in silico modeling study displayed P-gp ATPase inhibition, suggesting enhanced MTX absorption from the gastrointestinal site. The 3D-printed hydrogel-based tablet has the potential to overcome the chemotherapeutic challenges that are experienced with conventional therapies.

Structure-guided pharmacophore based virtual screening, docking, and molecular dynamics to discover repurposed drugs as novel inhibitors against endoribonuclease Nsp15 of SARS-CoV-2

COVID-19 (Corona Virus Disease of 2019) caused by the novel 'Severe Acute Respiratory Syndrome Coronavirus-2' (SARS-CoV-2) has wreaked havoc on human health and the global economy. As a result, for new medication development, it's critical to investigate possible therapeutic targets against the novel virus. 'Non-structural protein 15' (Nsp15) endonuclease is one of the crucial targets which helps in the replication of virus and virulence in the host immune system. Here, in the current study, we developed the structure-based pharmacophore model based on Nsp15-UMP interactions and virtually screened several databases against the selected model. To validate the screening process, we docked the top hits obtained after secondary filtering (Lipinski's rule of five, ADMET & Topkat) followed by 100 ns molecular dynamics (MD) simulations. Next, to revalidate the MD simulation studies, we have calculated the binding free energy of each complex using the MM-PBSA procedure. The discovered repurposed drugs can aid the rational design of novel inhibitors for Nsp15 of the SARS-CoV-2 enzyme and may be considered for immediate drug development.

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.

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 identification of novel allosteric inhibitors of dengue virus NS2B/NS3 serine protease

Although dengue is a disease that affects more than 100 countries and puts almost 400 million lives at risk each year, there is no approved antiviral in the treatment of this pathology. In this context, proteases are potential biological targets since they are essential in the replication process of this virus. In this study, a library of more than 3,000 structures was used to explore the allosteric inhibition of the NS2B/NS3 protease complex using Consensual Docking techniques. The results show four best ranked structures that were selected for molecular dynamics and free energy simulations. Our analysis corroborate with other studies (experimental and theoretical) presented in the literature. Thus, the computational approach used here proved to be useful for planning new inhibitors in the combat against Dengue disease.

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 (3CL^pro) 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 3CL^pro 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.

Targeting the Mycobacterium tuberculosis Stringent Response as a Strategy for Shortening Tuberculosis Treatment

The stringent response is well conserved across bacterial species and is a key pathway involved both in bacterial survival and virulence and in the induction of antibiotic tolerance in Mycobacteria. It is mediated by the alarmone (p)ppGpp and the regulatory molecule inorganic polyphosphate in response to stress conditions such as nutrient starvation. Efforts to pharmacologically target various components of the stringent response have shown promise in modulating mycobacterial virulence and antibiotic tolerance. In this review, we summarize the current understanding of the stringent response and its role in virulence and tolerance in Mycobacteria, including evidence that targeting this pathway could have therapeutic benefit.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Exploring the inhibitory potentials of Momordica charantia bioactive compounds against Keap1-Kelch protein using computational approaches

The search for Keap1 inhibitors as potential Nrf2 activator is a way of increasing the antioxidant status of the human cellular environ. In this research, we used in silico methods to investigate Keap1-kelch inhibitory potential of Momordica charantia's bioactive compounds in order to predict their Nrf2 activating potential. ADMET profiling, physicochemical properties, molecular docking, molecular dynamics, and Molecular Mechanics-Poisson Boltzmann Surface Area (g_MMPBSA) free energy calculation studies were executed to drive home our aim. Out of all the bioactive compounds of Momordica charantia, catechin (CAT) and chlorogenic acid (CGA) were selected based on their ADMET profile, physicochemical properties, and molecular docking analysis. Molecular docking studies of CAT and CGA to Keap1 kelch domain showed that they have - 9.2 kJ/mol and - 9.1 kJ/mol binding energies respectively with CAT having four hydrogen bond interactions with Keap1 while CGA had three. Analysis after the 30 ns molecular dynamics simulation revealed that CAT and CGA were both stable, although with minimal conformational alterations at the kelch pocket of Keap1. Finally, MMPBSA calculation of the Gibbs free energy of each amino acid interaction with CAT and CGA revealed that CAT had a higher total binding energy than CGA. Therefore, the Keap1 inhibitory capacities and the molecular dynamic characters of CAT and CGA at the Kelch domain of Keap1 suggest a putative Nrf2 signaling activating prowess.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-021-00100-2.

In silico virtual screening of potent inhibitor to hamper the interaction between HIV-1 integrase and LEDGF/p75 interaction using E-pharmacophore modeling, molecular docking, and dynamics simulations

The rapid increase of HIV-1 infection throughout the globe has a high demand for a superior drug with lesser side effects. LEDGF/p75, the human Lens Epithelium-Derived Growth Factor is identified as a promising cellular cofactor with integrase in facilitating the viral replication in an early stage by acting as a tethering factor in the pre-integration to the chromatin. Therefore, the present study was designed to identify a potent inhibitor by applying an E-pharmacophore based virtual screening, molecular docking, and dynamics simulation approaches. Finally, ZINC22077550 and ZINC32124441 were best identified potent molecules with the efficient binding affinity, strong hydrogen bonding, and acceptable pharmacological properties to hamper the interaction between integrase and LEDGF/p75. Further, the DFT and MDS studies were also analyzed, and shown a favorable energetic state and dynamic stability then reference compound. In conclusion, we suggest that these findings could be novel therapeutics in the future and may increase the lifespan of individuals suffering from viral infection.

Molecular dynamics, quantum mechanics and docking studies of some Keap1 inhibitors - An insight into the atomistic mechanisms of their antioxidant potential

Inhibitors of Keap1 would disrupt the covalent interaction between Keap1 and Nrf2 to unleash Nrf2 transcriptional machinery that orchestrates its cellular antioxidant, cytoprotective and detoxification processes thereby, protecting the cells against oxidative stress mediated diseases. In this in silico research, we investigated the Keap1 inhibiting potential of fifty (50) antioxidants using pharmacokinetic ADMET profiling, bioactivity assessment, physicochemical studies, molecular docking investigation, molecular dynamics and Quantum mechanical-based Density Functional Theory (DFT) studies using Keap1 as the apoprotein control. Out of these 50 antioxidants, Maslinic acid (MASA), 18-alpha-glycyrrhetinic acid (18-AGA) and resveratrol stand out by passing the RO5 (Lipinski rule of 5) for the physicochemical properties and ADMET studies. These three compounds also show high binding affinity of -10.6 kJ/mol, -10.4 kJ/mol and -7.8 kJ/mol at the kelch pocket of Keap1 respectively. Analysis of the 20ns trajectories using RMSD, RMSF, ROG and h-bond parameters revealed the stability of these compounds after comparing them with Keap1 apoprotein. Furthermore, the electron donating and accepting potentials of these compounds was used to investigate their reactivity using Density Functional Theory (HOMO and LUMO) and it was revealed that resveratrol had the highest stability based on its low energy gap. Our results predict that the three compounds are potential drug candidates with domiciled therapeutic functions against oxidative stress-mediated diseases. However, resveratrol stands out as the compound with the best stability and therefore, could be the best candidate with the best therapeutic efficacy.

Docking and molecular dynamics studies of potential new leads against DBL3x derived from chondroitin sulfate A (CSA): a new approach for the treatment of malaria

In this work the DBL3x domain of the erythrocyte membrane protein from Plasmodium Falciparum (PfEMP1), was revisited as a potential molecular target for the development of new drugs against malaria. This protein interacts with chondroitin sulfate A (CSA), a glycosaminoglycan present in the substance fundamental for connective tissues of vertebrates and is implicated in malaria complications in pregnant women. We performed molecular docking and molecular dynamic studies of DBL3x complexed with CSA and five analogues, where the sulfate group was replaced by phosphate, in order to evaluate if the better electrostatic interactions provided by phosphate groups could afford better binders capable of preventing the binding of CSA to DBL3x. Results suggest that all proposed compounds have high affinity towards DBL3x and could bind better to the DBL3x domain of PfEMP1 than CSA, qualifying as potential inhibitors of this protein and, therefore, new potential leads for the drug design against malaria.Communicated by Ramaswamy H. Sarma.

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.

In silico approach of naringin as potent phosphatase and tensin homolog (PTEN) protein agonist against prostate cancer

Prostate cancer (PC) is one of the major impediments affecting men, which leads approximately 31,620 deaths in both developing and developed countries. Although some chemotherapy drugs have been reported for prostate cancer, they are not effective due to the lack of safety, efficacy and low selectivity. Hence, the novel alternative anticancer agents with remarkable effect are highly appreciable. Natural plants contain several bio-active compounds which have been traditionally used for the various medical treatments. Particularly, naringin is a natural bio-active compound commonly found in the citrus fruits, which have shown numerous biological activities. Phosphatase and tensin homolog (PTEN) is a tumor suppressor gene, which activates both lipid phosphates and protein phosphates. The PTEN gene is negative regulator of PI3K/AKT/mTOR pathways, since, this signaling pathway play an essential role in the cell survival, proliferation and migration. In the present in silico investigation, structure based virtual screening, molecular docking, molecular dynamics simulation and Adsorption, Distribution, Metabolism, Excretion (ADME) prediction were employed to determine the binding affinity, stability and drug likeness properties of top ranked screened compounds and naringin, respectively. The results revealed that the complex has good molecular interactions, binding stability (peak between 0.3 and 0.4 nm) and no violations in the Lipinski Rule of 5 in naringin, but the screened compounds violated the drug likeness properties. From the in silico analyses, it is identified that naringin compound might assist in the development of novel therapeutic candidate against prostate cancer.Communicated by Ramaswamy H. Sarma.

Identification of luteolin -7-glucoside and epicatechin gallate from Vernonia cinerea, 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.

Cellular and Molecular Targets of Waterbuck Repellent Blend Odors in Antennae of Glossina fuscipes fuscipes Newstead, 1910

Insects that transmit many of the world's deadliest animal diseases, for instance trypanosomosis, find their suitable hosts and avoid non-preferred hosts mostly through olfactory cues. The waterbuck repellent blend (WRB) comprising geranylacetone, guaiacol, pentanoic acid, and δ-octalactone derived from waterbuck skin odor is a repellent to some savannah-adapted tsetse flies and reduces trap catches of riverine species. However, the cellular and molecular mechanisms associated with detection and coding of the repellent odors remain to be elucidated. Here, we demonstrated that WRB inhibited blood feeding in both Glossina pallidipes Austen, 1903 and Glossina fuscipes fuscipes Newstead, 1910. Using the DREAM (Deorphanization of Receptors based on Expression Alterations in odorant receptor mRNA levels) technique, combined with ortholog comparison and molecular docking, we predicted the putative odorant receptors (ORs) for the WRB in G. f. fuscipes, a non-model insect. We show that exposure of G. f. fuscipes in vivo to WRB odorant resulted in up- and downregulation of mRNA transcript of several ORs. The WRB component with strong feeding inhibition altered mRNA transcript differently as compared to an attractant odor, showing these two odors of opposing valence already segregate at the cellular and molecular levels. Furthermore, molecular dynamics simulations demonstrated that the predicted ligand-OR binding pockets consisted mostly of hydrophobic residues with a few hydrogen bonds but a stable interaction. Finally, our electrophysiological response showed the olfactory sensory neurons of G. f. fuscipes tuned to the tsetse repellent components in different sensitivity and selectivity.

Computer-aided synthesis of dapsone-phytochemical conjugates against dapsone-resistant Mycobacterium leprae

Leprosy continues to be the belligerent public health hazard for the causation of high disability and eventual morbidity cases with stable prevalence rates, even with treatment by the on-going multidrug therapy (MDT). Today, dapsone (DDS) resistance has led to fear of leprosy in more unfortunate people of certain developing countries. Herein, DDS was chemically conjugated with five phytochemicals independently as dapsone-phytochemical conjugates (DPCs) based on azo-coupling reaction. Possible biological activities were verified with computational chemistry and quantum mechanics by molecular dynamics simulation program before chemical synthesis and spectral characterizations viz., proton-HNMR, FTIR, UV and LC-MS. The in vivo antileprosy activity was monitored using the 'mouse-foot-pad propagation method', with WHO recommended concentration 0.01% mg/kg each DPC for 12 weeks, and the host-toxicity testing of the active DPC4 was seen in cultured-human-lymphocytes in vitro. One-log bacilli cells in DDS-resistant infected mice footpads decreased by the DPC4, and no bacilli were found in the DDS-sensitive mice hind pads. Additionally, the in vitro host toxicity study also confirmed that the DCP4 up to 5,000 mg/L level was safety for oral administration, since a minor number of dead cells were found in red color under a fluorescent microscope. Several advanced bioinformatics tools could help locate the potential chemical entity, thereby reducing the time and resources required for in vitro and in vitro tests. DPC4 could be used in place of DDS in MDT, evidenced from in vivo antileprosy activity and in vitro host toxicity study.

Extensive immunoinformatics study for the prediction of novel peptide-based epitope vaccine with docking confirmation against envelope protein of Chikungunya virus: a computational biology approach

Chikungunya virus (CHIKV) instigating Chikungunya fever is a global infective menace resulting in high fever, weakened joint-muscle pain, and brain inflammation. Inaccessibility and unavailability of effective drugs have led us to an uncertain arena when it comes to providing proper medical treatment to the affected people. In this study, authentic encroachment has been made concerning the peptide-based epitope vaccine designing against CHIKV. A Proteome-wide search was performed to locate a conserved portion among the accessible viral outer membrane proteins which showcase a remarkable immune response using specific immunoinformatics and docking simulation tools. Primarily, the most probable immunogenic envelope glycoproteins E1 and E2 were identified from the UniProt database depending on their antigenicity scores. Subsequently, we selected two distinctive sequences "SEDVYANTQLVLQRP" and "IMLLYPDHPTLLSYR" in both E1 and E2 glycoproteins respectively. These two sequences identified as the most potent T and B cell epitope-based peptides as they interacted with 6 and 7 HLA-I and 5 HLA-II molecules with an extremely low IC50 score that was verified by molecular docking. Moreover, the sequences possess no allergenicity and are certainly located outside the transmembrane region. In addition, the sequences exhibited 88.46% and 100.00% Conservancy, covering high population coverage of 89.49% to 94.74% and 60.51% to 88.87% respectively in endemic countries. The identified peptide SEDVYANTQLVLQRP and IMLLYPDHPTLLSYR can be utilized next for the development of peptide-based epitope vaccine contrary to CHIKV, so further documentations and experimentations like Antigen testing, Antigen production, Clinical trials are needed to prove the validity of it. Communicated by Ramaswamy H. Sarma.

Molecular docking and molecular dynamics simulation approach to screen natural compounds for inhibition of Xanthomonas oryzae pv. Oryzae by targeting peptide deformylase

Xanthomonas oryzae pv. Oryzae (Xoo) causes bacterial leaf blight (BLB) of rice which results in a huge loss in production. Many chemicals are used to control BLB disease. However, these chemicals are toxic to the environments, animals and human beings. Thus, there is a demand to discover potential and safe natural pesticides to manage BLB disease successfully. Therefore, we screened a library of phytochemicals of different plants having antibacterial activity by targeting Peptide Deformylase (PDF) of Xoo using in silico techniques. A library of 318 phytochemicals was prepared and subjected to rigid and flexible molecular docking against PDF followed by molecular dynamics simulation and free energy analysis of protein-ligand complexes. The results of virtual screening showed that 14 compounds from different plants have good binding energy as compare to reference molecule (3 R)-2,3-dihydro[1,3] thiazolo [3,2 a]benzimidazol-3-ol) (-7.7 kcal mol^-1). Out of 14 hit compounds, eight compounds that were selected based on binding energy were analyzed by Molecular dynamic (MD) simulation. Analysis of MD simulation revealed that eight compounds namely; Bisdemethoxycurcumin, Rosmarinic acid, Piperanine, Dihydropiperlonguminine, Piperdardine, Dihydrocurcumin and Lonhumosides B achieved good stability during the 80 ns MD simulation at 300 K in term of the RMSD. Further, we calculated RMSF, RG, SASA, and interaction energy after 40 ns due to showing the stability of complexes. From our results, we conclude that these natural compounds could inhibit Xoo by targeting PDF receptor and can be used as potential bactericidal candidates against BLB disease of rice against Xoo and other bacteria. Communicated by Ramaswamy H. Sarma.