Identification of DprE1 inhibitors for tuberculosis through integrated in-silico approaches.

Decaprenylphosphoryl-ß-D-ribose-2'-epimerase (DprE1), a crucial enzyme in the process of arabinogalactan and lipoarabinomannan biosynthesis, has become the target of choice for anti-TB drug discovery in the recent past. The current study aims to find the potential DprE1 inhibitors through in-silico approaches. Here, we built the pharmacophore and 3D-QSAR model using the reported 40 azaindole derivatives of DprE1 inhibitors. The best pharmacophore hypothesis (ADRRR_1) was employed for the virtual screening of the chEMBL database. To identify prospective hits, molecules with good phase scores (> 2.000) were further evaluated by molecular docking studies for their ability to bind to the DprE1 enzyme (PDB: 4KW5). Based on their binding affinities (< - 9.0 kcal/mole), the best hits were subjected to the calculation of free-binding energies (Prime/MM-GBSA), pharmacokinetic, and druglikeness evaluations. The top 10 hits retrieved from these results were selected to predict their inhibitory activities via the developed 3D-QSAR model with a regression coefficient (R2) value of 0.9608 and predictive coefficient (Q2) value of 0.7313. The induced fit docking (IFD) studies and in-silico prediction of anti-TB sensitivity for these top 10 hits were also implemented. Molecular dynamics simulations (MDS) were performed for the top 5 hit molecules for 200 ns to check the stability of the hits with DprE1. Based on their conformational stability throughout the 200 ns simulation, hit 2 (chEMBL_SDF:357100) was identified as the best hit against DprE1 with an accepted safety profile. The MD results were also in accordance with the docking score, MM-GBSA value, and 3D-QSAR predicted activity. The hit 2 molecule, (N-(3-((2-(((1r,4r)-4-(dimethylamino)cyclohexyl)amino)-9-isopropyl-9H-purin-6-yl)amino)phenyl)acrylamide) could serve as a lead for the discovery of a novel DprE1 inhibiting anti-TB drug.

An in silico drug repurposing approach to identify HDAC1 inhibitors against glioblastoma.

Despite considerable improvement in therapy and diagnosis, brain tumors remain a global public health concern. Among all brain tumors, 80% are due to Glioblastoma. The average survival rate of a patient once diagnosed with glioblastoma is 15 months. Lately, the role of peptidase enzymes, especially Neprilysin, a neutral endopeptidase, is gaining attention for its role in tumor growth regulation. Neprilysin expressions are positively correlated with several tumors including GBM and reduced expression of NEP protein is associated with the pathogenesis of multiple tumors. One of the main reasons for NEP protein downregulation is the action of Histone deacetylase (HDAC) enzymes, especially HDAC1. Additionally, studies have reported that increased levels of HDAC1 are responsible for downregulating NEP gene expression. Hence, HDAC1 inhibition can be a good target to elevate NEP levels, which can be a good therapeutic approach to GBM. This study utilizes the computational drug repurposing tool, Schrodinger Maestro to identify HDAC1 inhibitors from the ZINC15 database.1379 FDA-approved drugs from the ZINC15 database were screened through molecular docking. Based on docking score and ligand-protein interaction, the top ten molecules were selected which were then subjected to binding energy calculation and molecular dynamics (MD) simulations. The three most active drugs from the MD simulations- ZINC22010649 (Panobinostat), ZINC4392649 (Tasimelteon) and ZINC1673 (Melphalan), were tested on C6 and U87 MG glioblastoma cells for cytotoxicity and HDAC1 protein levels using western blot analysis. Among the three drugs, Panobinostat exhibited potent cytotoxic action and showed a significant reduction in the HDAC1 protein levels.Communicated by Ramaswamy H. Sarma.

Design of PI3K-mTOR Dual Inhibitors for Ovarian Cancer: Are We on the Right Track?

Ovarian cancer is one of the most familiar kinds of gynecological cancer seen in women. Though it is not as familiar as breast cancer, the survival rate for ovarian cancer is very low when compared with breast cancer. Even after being one among the familiar types, to date, there are no proper treatments available for ovarian cancer. All the treatments that are present currently show a high rate of recurrence after the treatment. Therefore, treating this silent killer from the roots is the need of the hour. PI3K/AKT/m- TOR pathway is one of the pathways that get altered during ovarian cancer. Studies are already going on for the inhibition of PI3K and mTOR separately. Efforts have been made to inhibit either PI3K or mTOR separately earlier. However, due to its side effects and resistance to the treatments available, current studies are based on the inhibition of PI3K and mTOR together. Inhibition of PI3K and mTOR simultaneously reduces the chances of negative feedback, thus decreasing the toxicity. This review contains the evolution of PI3K and mTOR drugs that are approved by the FDA and are in the trials for different cancer types, including Ovarian cancer. In this article, how a molecular targeted therapy can be made successful and free from toxicity for treating ovarian cancer is discussed. Therefore, this review paves the way for finding an effective scaffold rather than the clinical part. The scaffold thus selected can be further modified and synthesized in the future as dual PI3K/mTOR inhibitors specifically for OC.

Structure-activity relationship mediated molecular insights of DprE1 inhibitors: A Comprehensive Review.

Emerging threats of multi-drug resistant (MDR), extensively drug-resistant (XDR), and totally drug-resistant (TDR) tuberculosis led to the discovery of a novel target which was entitled Decaprenylphosphoryl-ß-D-ribose 2'-epimerase (DprE1) enzyme. DprE1 is composed of two isoforms, decaprenylphosphoryl-ß-D-ribose oxidase (DprE1) and decaprenylphosphoryl-D-2-keto erythro pentose reductase (DprE2). The enzymes, DprE1 and DprE2, regulate the two-step epimerization process to form DPA (Decaprenylphosphoryl arabinose) from DPX (Decaprenylphosphoryl-D-ribose), which is the sole precursor in the cell wall synthesis of arabinogalactan (AG) and lipoarabinomannan (LAM). Target-based and whole-cell-based screening played an imperative role in the identification of the druggable target, DprE1, whereas the druggability of the DprE2 enzyme is not proved yet. To date, diverse scaffolds of heterocyclic and aromatic ring systems have been reported as DprE1 inhibitors based on their interaction mode, i.e. covalent, and non-covalent inhibitors. This review describes the structure-activity relationship (SAR) of reported covalent and non-covalent inhibitors to enlighten about the crucial pharmacophoric features required for DprE1 inhibition, along with in-silico studies which characterize the amino acid residues responsible for covalent and non-covalent interactions.Communicated by Ramaswamy H. Sarma.

Identification of potential Akt activators: a ligand and structure-based computational approach.

The Akt pathway plays a significant role in various diseases like Alzheimer's, Parkinson's, and Diabetes. Akt is the central protein whose phosphorylation controls many downstream pathways. Binding of small molecules to the PH domain of Akt facilitates its phosphorylation in the cytoplasm and upregulates the Akt pathway. In the current study, to identify Akt activators, ligand-based approaches like 2D QSAR, shape, and pharmacophore-based screening were used, followed by structure-based approaches such as docking, MM-GBSA, ADME prediction, and MD simulation. The top twenty-five molecules from the Asinex gold platinum database found to be active in most 2D QSAR models were used for shape and pharmacophore-based screening. Later docking was performed using the PH domain of Akt1 (PDB: 1UNQ), and 197105, 261126, 253878, 256085, and 123435 were selected based on docking score and interaction with key residues, which were druggable and formed a stable protein-ligand complex. MD simulations of 261126 and 123435 showed better stability and interactions with key residues. To further investigate the SAR of 261126 and 123435, derivatives were downloaded from PubChem, and structure-based approaches were employed. MD simulation of derivatives 12289533, 12785801, 83824832, 102479045, and 6972939 was performed, in which 83824832 and 12289533 showed interaction with key residues for a longer duration of time, proving that they may act as Akt activators.

Computational design of a broad-spectrum multi-epitope vaccine candidate against seven strains of human coronaviruses.

Spike (S) proteins are an attractive target as it mediates the binding of the SARS-CoV-2 to the host through ACE-2 receptors. We hypothesize that the screening of the S protein sequences of all the seven known HCoVs would result in the identification of potential multi-epitope vaccine candidates capable of conferring immunity against various HCoVs. In the present study, several machine learning-based in-silico tools were employed to design a broad-spectrum multi-epitope vaccine candidate targeting the S protein of seven known strains of human coronaviruses. Herein, multiple B-cell epitopes and T-cell epitopes (CTL and HTL) were predicted from the S protein sequences of all seven known HCoVs. Post-prediction they were linked together with an adjuvant to construct a potential broad-spectrum vaccine candidate. Secondary and tertiary structures were predicted and validated, and the refined 3D-model was docked with an immune receptor. The vaccine candidate was evaluated for antigenicity, allergenicity, solubility, and its ability to achieve high-level expression in bacterial hosts. Finally, the immune simulation was carried out to evaluate the immune response after three vaccine doses. The designed vaccine is antigenic (with or without the adjuvant), non-allergenic, binds well with TLR-3 receptor and might elicit a diverse and strong immune response. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03286-0.

Computer-aided identification of human carbonic anhydrase isoenzyme VII inhibitors as potential antiepileptic agents.

Human carbonic anhydrase (hCA) belongs to a superfamily of metalloenzymes that reversibly catalyse the hydration of carbon dioxide to give bicarbonate (HCO3-) and proton (H+). As HCO3- ions play an important role in neuronal signalling hence, hCA enzymes are an attractive target for antiepileptic drugs. Out of all the isoforms, hCA VII is predominantly expressed in the brain cortex and hippocampus region, which are the most affected area during seizure activity. Hence, we have identified some hCA VII inhibitors employing computational tools like atom-based 3D quantitative structure-activity relationship (QSAR), auto-QSAR, pharmacophore-based virtual screening, molecular docking, and molecular dynamics (MD) simulations. Atom-based 3D QSAR modelling outperformed auto-QSAR with an R2 and Q2 value of 0.9634 and 0.9646, respectively. A four-feature pharmacophore model (AADR_1) was developed and a focussed library of around 3,00,000 compounds was screened. Compounds with a phase screen score >2.40 were selected for docking studies. The activity of the selected hits was predicted employing the developed 3D QSAR model. Finally, three compounds were taken up for the MD simulation studies which also suggest that the identified hits might form a stable complex with hCA VII enzyme. A comparative docking study was also done with other hCA isoforms like I, II, IV, IX, and XII to examine the selectivity of the identified hits towards hCA VII. Based on these studies, three hits have been identified as potential hCA VII inhibitor which is drug-like molecules. Further, in vitro studies are required to develop leads from these identified hits.Communicated by Ramaswamy H. Sarma.

A Recent Appraisal of Artificial Intelligence and In Silico ADMET Prediction in the Early Stages of Drug Discovery.

In silico ADMET models have progressed significantly over the past ~4 decades, but still, the pharmaceutical industry is vexed by the late-stage toxicity failure of lead molecules. This problem of late-stage attrition of the drug candidates because of adverse ADMET profile motivated us to analyze the current role and status of different in silico tools along with the rise of machine learning (ML) based program for ADMET prediction. In this review, we have differentiated AI from traditional in silico tools because, unlike traditional in silico tools where the final decision is made manually, AI automates the decision-making prerogative of humans. Due to the large volume of literature in this field, we have considered the publications in the last two years for our review. Overall, from the literature reviewed, deep neural networks (DNN) algorithm or deep learning seems to be the future of ML-based prediction models. DNNs have shown the ability to learn from more complex data and this gives DNN an edge over other ML algorithms to be applied for ADMET prediction. Our result also suggests that we need closer collaboration between the ADMET data generators and those who are employing ML-based tools on this generated data to build predictive models, so that more accurate models could be developed. Overall, our study concludes that ML is still a work in progress and its appetite for data has not been sated yet. It needs loads of more quality data and still some time to prove its real worth in predicting ADMET.

Pharmacophore-guided fragment-based design of novel mammalian target of rapamycin inhibitors: extra precision docking, fingerprint-based 2D and atom-based 3D-QSAR modelling.

Rapamycin and their derivatives known as rapalogs were the first-generation mTOR inhibitors which interacted with mTORC1 but not with the mTORC2 protein. Second-generation inhibitors could bind with both and showed excellent anti-proliferative activity. Our aim was to design novel mTOR inhibitors which could bind at both the allosteric and the kinase site. The FRB domain is present in both the mTORC1 and mTORC2 protein complexes. We have employed e-pharmacophore-guided fragment-based design to develop novel mTOR inhibitors. The affinity of designed molecules at both the sites was analysed using molecular docking in extra precision mode. The atom-based 3D-QSAR model was developed to predict the activity while the fingerprint-based 2D-QSAR model was employed to refine an identified hit as potent dual mTOR inhibitor. Ligand ASK23 showed a docking score of -15.452 kcal/mol at the allosteric site (PDB ID 5GPG) while ASK38 showed a docking score of -11.535 kcal/mol at the kinase site (PDB ID 4JT6). Ligand ASK12 showed binding energy of -106.23 kcal/mol at the allosteric site. Refined molecule ASK12a from ASK12 showed the highest predicted activity (pIC50: 6.512). The stability of the best hits and receptor complex was analysed using molecular dynamics simulation studies. Herein we report five potential mTOR dual inhibitors based on the predicted activity, drug-likeness analysis and off-target effects. To the best of our knowledge, this is the first report on pharmacophore-guided fragment-based drug design for mTOR inhibitors. This design strategy can be used for the rational drug design against other proteins for which only apo-structures are available. Communicated by Ramaswamy H. Sarma.

Repurposing of existing FDA approved drugs for Neprilysin inhibition: An in-silico study.

Neprilysin (NEP) is a neutral endopeptidase with diverse physiological roles in the body. NEP's role in degradation of diverse classes of peptides such as amyloid beta, natriuretic peptide, substance P, angiotensin, endothelins, etc., is associated with pathologies of alzheimer's, kidney and heart diseases, obesity, diabetes and certain malignancies. Hence, the functional inhibition of NEP in the above systems can be a good therapeutic target. In the present study, in-silico drug repurposing approach was used to identify NEP inhibitors. Molecular docking was carried out using GLIDE tool. 2934 drugs from the ZINC12 database were screened using high throughput virtual screening (HTVS) followed by standard precision (SP) and extra precision (XP) docking. Based on the XP docking score and ligand interaction, the top 8 hits were subjected to free ligand binding energy calculation, to filter out 4 hits (ZINC000000001427, ZINC000001533877, ZINC000000601283, and ZINC000003831594). Further, induced fit docking-standard precision (IFD-SP) and molecular dynamics (MD) studies were performed. The results obtained from MD studies suggest that ZINC000000601283-NEP and ZINC000003831594-NEP complexes were most stable for 20ns simulation period as compared to ZINC000001533877-NEP and ZINC000000001427-NEP complexes. Interestingly, ZINC000000601283 and ZINC000003831594 showed similarity in binding with the reported NEP inhibitor sacubitrilat. Findings from this study suggest that ZINC000000601283 and ZINC000003831594 may act as NEP inhibitors. In future studies, the role of ZINC000000601283 and ZINC000003831594 in NEP inhibition should be tested in biological systems to evaluate therapeutic effect in NEP associated pathological conditions.

Exploration of small-molecule entry disruptors for chikungunya virus by targeting matrix remodelling associated protein.

BACKGROUND AND PURPOSE: A genome-wide clustered regularly interspaced short palindromic repeats- associated protein 9-based screen has revealed that the cell adhesion molecule matrix remodelling associated protein 8 (Mxra8) acts as an entry mediator for many alphaviruses including chikungunya virus. The first X-ray crystal structure reported for Mxra8 a few months ago has a low-resolution of 3.49Å. EXPERIMENTAL APPROACH: Homology modelling of Mxra8 protein was done employing the SWISS-MODEL and PRIME module of Maestro. To design novel Mxra8 inhibitors pharmacophore guided fragment-based drug design and structure-based virtual screening of Food and Drug Administration approved drug libraries were undertaken. Molecular docking and molecular dynamics (MD) simulations study were carried out to validate the findings. FINDINGS / RESULTS: The molecule H1a (dock score: -6.137, binding energy: -48.95 kcal/mol, and PHASE screen score: 1.528816) was identified as the best hit among the fragment-based designed ligands. Structure- based virtual screening suggested histamine, epinephrine, and capreomycin as potential hits which could be repurposed as Mxra8 inhibitor. MD simulations study suggested that only small molecules like histamine could be a potential inhibitor of Mxra8. H-bond interaction with Arg58 and Glu200 amino acid residues seems to be crucial for effective binding. CONCLUSION AND IMPLICATIONS: To the best of our knowledge, this is the first report on the design of novel inhibitors against Mxra8 protein to tackle the menace of alphaviruses infections. This design strategy could be used for structure-based drug design against other apo-proteins. This study also advances the application of in silico tools in the field of drug repurposing.

Computational approaches in efflux pump inhibitors: current status and prospects.

Treatment of bacterial infections is currently threatened by the development of antibiotic resistance and a poor pipeline of new antibiotics. Efflux pumps (EPs) are an integral part of the defense machinery of bacteria, preventing the entry of molecules, such as antibiotics, into the intracellular environment and resulting in antibiotic resistance. Therefore, research has focused on the discovery of novel EP inhibitors (EPIs), such as PAßN, D13-9001, and MBX2319. however, there are still no US Food and Drug Administration (FDA)-approved drugs targeting EPs because of the inadequate assimilation of the inhibitors. Here, we discuss the use of computational approaches for molecular mechanistic studies of EPIs to help direct future research.

Is viral E6 oncoprotein a viable target? A critical analysis in the context of cervical cancer.

An understanding of the pathology of cervical cancer (CC) mediated by E6/E7 oncoproteins of high-risk human papillomavirus (HPV) was developed by late 80's. But if we look at the present scenario, not a single drug could be developed to inhibit these oncoproteins and in turn, be used specifically for the treatment of CC. The readers are advised not to presume the "viability of E6 protein" as mentioned in the title relates to just druggability of E6. The viability aspect will cover almost everything a researcher should know to develop E6 inhibitors until the preclinical stage. Herein, we have analysed the achievements and shortcomings of the scientific community in the last four decades in targeting HPV E6 against CC. Role of all HPV proteins has been briefly described for better perspective with a little detailed discussion of the role of E6. We have reviewed the articles from 1985 onward, reporting in vitro inhibition of E6. Recently, many computational studies have reported potent E6 inhibitors and these have also been reviewed. Subsequently, a critical analysis has been reported to cover the in vitro assay protocols and in vivo models to develop E6 inhibitors. A paragraph has been devoted to the role of public policy to fight CC employing vaccines and whether the vaccine against HPV has quenched the zeal to develop drugs against it. The review concludes with the challenges and the way forward.

Identification of potential tumour-associated carbonic anhydrase isozyme IX inhibitors: atom-based 3D-QSAR modelling, pharmacophore-based virtual screening and molecular docking studies.

Tumour hypoxia results in dramatic changes in the gene expression, proliferation and survival of tumour cells. The tumour cells shift towards anaerobic glycolysis which results in change of pH in their microenvironment. In response to this stress, over expression of carbonic anhydrase IX (CA IX) genes is observed in many solid tumours. So, selective inhibition of CA IX can be a promising target for anti-cancer drugs. In this work in silico tools like atom-based 3D-QSAR modelling, pharmacophore-based virtual screening and molecular docking were used to identify potential CA IX inhibitors. Based on the training set used in the QSAR model, twenty pharmacophore models were generated. Out of these, HHHR_1, AHHR_1, DHHHR_1, AHHHR_1 model was used to screen a database of 1,50,000 compounds retrieved from ZINC 15 database. R2 and Q2 was 0.9864 and 0.8799, respectively, for the developed QSAR model. 163 compounds showed a phase screen score above 2.4 in which ZINC02260669 was the highest ranked (screen score, 2.852058) compound in all the four models. Built QSAR model was used to predict the activity of all these 163 compounds and ZINC72370966 showed the highest predicted activity with pKi value of 7.649. These compounds were docked against CA IX (human) protein (PDB ID 5FL6) and molecular docking results showed favourable binding interactions for the best ten identified hits. This work gives design insights and some potential scaffolds which can be developed as CA IX inhibitors.Communicated by Ramaswamy H. Sarma.

A Review on Anticancer Potentials of Benzothiazole Derivatives.

Benzothiazole is an organic compound bearing a heterocyclic nucleus (thiazole) which imparts a broad spectrum of biological activities to it. The significant and potent activity of benzothiazole moiety influenced distinctively by nature and position of substitutions. This review summarizes the effect of various substituents in recent trends and approaches to design and develop novel benzothiazole derivatives for anticancer potential in different cell lines by interpreting the Structure- Activity Relationship (SAR) and mechanism of action of a wide range of derivatives. The list of derivatives is categorized into different groups and reviewed for their anticancer activity. The structure-activity relationship for the various derivatives revealed an excellent understanding of benzothiazole moiety in the field of cancer therapy against different cancer cell line. Data obtained from the various articles showed the potential effect of benzothiazole moiety and its derivatives to produce the peculiar and significant lead compound. The important anticancer mechanisms found are tyrosine kinase inhibition, topoisomerase inhibition and induction of apoptosis by Reactive Oxygen Species (ROS) activation. Therefore, the design and development of novel benzothiazole have broad scope in cancer chemotherapy.

Molecular dynamics guided insight, binding free energy calculations and pharmacophore-based virtual screening for the identification of potential VEGFR2 inhibitors.

Vascular endothelial growth factor-A (VEGF-A) is a crucial member of the Vascular endothelial growth factor (VEGF) family which mediates the metastasis of tumor by 'angiogenic switch'. Therefore, targeting a VEGF-A mediated VEGFR2 signaling pathway is the most promising approach to repress the angiogenesis of tumor cells. VEGFR2 inhibitors are two types: Type I and Type II. Type II inhibitors have more chemical space to exploit and have better selectivity because of allosteric binding pocket over type I inhibitors. Hence, The present study encompasses identification of potential type II VEGFR2 inhibitors employing pharmacophore based virtual screnning. In this study, ten five featured pharmacophore model were generated from a dataset of 39 biaryl urea analogs.Out of all, ADDRR_1 pharmacophore model were used to screen the library of 5.2 million compounds retrieved from NCI, Maybridge, Asinex and Zinc databases. 7000 hits were filtered out from the pharmacophore-based virtual screening based on the phase fitness score. Among all best ten hits were identified employing extra precision mode of GLIDE module. ZINC00759038 and 211246 were chosen as top hits based on docking score, free binding energy, and ADME profile. They were subjected to molecular-dynamic studies to assess the hits-VEGFR2 binding stability. It suggests that ZINC00759038-VEGFR2 and 211246-VEGFR2 complexes are quite stable for the 20 ns simulation period. The strength of hit-protein complexes were further assessed by thermodynamic analysis of MD simulation studies by MMGBSA. Interestingly, these hits retains 90% similarity with standard VEGFR2 inhibitor (Sorafenib). Hence, these identified hits may led to new lead compounda as VEGFR2 inhibitors.

Potentials of Diphenyl Ether Scaffold as a Therapeutic Agent: A Review.

Diphenyl ethers (DPE) and its analogs have exhibited excellent potential for therapeutic and industrial applications. Since the 19th century, intensive research is perpetuating on the synthetic routes and biological properties of DPEs. Few well-known DPEs are Nimesulide, Fenclofenac, Triclosan, Sorafenib, MK-4965, and MK-1439 which have shown the potential of this moiety as a lead scaffold for different pharmacological properties. In this review, we recapitulate the diverse synthetic route of DPE moiety inclusive of merits and demerits over the classical synthetic route and how this moiety sparked an interest in researchers to discern the SAR (Structure Activity Relationship) for the development of diversified biological properties of DPEs such as antimicrobial, antifungal, antiinflammatory & antiviral activities.