ADMET profiling of geographically diverse phytochemical using chemoinformatic tools

From docking to dynamics: Unveiling the potential non-peptide and non-covalent inhibitors of Mpro from natural products

MOTIVATION

This study aims to investigate non-covalent and non-peptide inhibitors of Mpro, a crucial protein target, by employing a comprehensive approach that integrates molecular docking, molecular dynamics simulations, and top-hits activity predictions. The focus is on elucidating the non-covalent and non-peptide binding modes of potential inhibitors with Mpro.

METHODS

We employed a semi-flexible molecular docking methodology, binding score and ADME screening, which are based on structure, to screen compounds from CMNPD and HERB in silico. These methodologies allowed us to find potential candidates depending on their binding values and interactions with the binding site of main protease. To further evaluate the stability of these interactions, we conducted molecular dynamics simulations and calculated binding energies. Ultimately, a top-hits activity prediction method was employed to prioritize compounds based on their predicted inhibitory potential.

RESULTS

Through a combination of binding energy calculations and activity predictions, we identified six potential inhibitor molecules exhibiting promising activity against Mpro. These compounds demonstrated favorable binding interactions and stability profiles, making them attractive candidates for further experimental validation and drug development efforts targeting Mpro.

C ucumis melo compounds: A new avenue for ALR-2 inhibition in diabetes mellitus

Diabetes mellitus (DM) is a prominent contributor to morbidity and mortality in developed nations, primarily attributable to vascular complications such as atherothrombosis occurring in the coronary arteries. Aldose reductase (ALR2), the main enzyme in the polyol pathway, catalyzes the conversion of glucose to sorbitol, leading to a significant buildup of reactive oxygen species in different tissues. It is therefore a prime candidate for therapeutic targeting, and extensive study is currently underway to discover novel natural compounds that can inhibit it. Cucumis melo (C. melo) has a long history as a lipid-lowering ethanopharmaceutical plant. In this study, compounds derived from C. melo were computationally evaluated as possible lead candidates. Various computational filtering methods were employed to assess the drug-like properties and ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles of the compounds. The compounds were subsequently addressed to analysis of their interactions, molecular docking, and molecular dynamics simulation studies. When compared to the conventional therapeutic compounds, three compounds exhibited enhanced binding affinity and intra-molecular residue interactions, resulting in increased stability and specificity. Consequently, four potent inhibitors, namely PubChem CIDs 119205, 65373, 6184, and 332427, have been identified. These inhibitors exhibit promising potential as pharmacological targets for the advancement of novel ALR-2 inhibitors.

Identification of Bile Acid-Derived Chemical Chaperone(s) Targeting E46K-Mutated Alpha-Synuclein Protein to Treat Parkinson's Disease: Molecular Modelling, Docking, ADME, and Simulation Studies

Aggregated α-synuclein (α-syn) present inside small cytoplasmic inclusions in the substantia nigra region marks the major pathological hallmark of Parkinson's disease (PD) and makes it an attractive target for the drug development process. Certain small-molecule chaperones (such as DCA, UDCA, TUDCA) presented the ability to prevent misfolding and aggregation of α-syn as well as to disentangle mature α-syn amyloid fibrils. However, due to toxicity constraints, these small molecules could not be translated into clinical settings. Computational biology methods and bioinformatics approaches allow virtual screening of a large number of molecules, with reduced side effects and better efficacy. In the present study, a library of 10,928 derivatives was generated using DCA, UDCA, and TUDCA bile acid scaffolds and analysed for their binding affinity, pharmacokinetic properties, and drug likeliness profile, to come up with promising compounds with reduced toxicity and better chaperone ability. Molecular docking revealed that with respect to their free binding energy, C1-C25 have the lowest binding energy and bind significantly to recombinantly assembled E46K α-syn fibrils (PDB ID-6UFR). In silico ADME predictions revealed that all these compounds had minimal toxic effects and had good absorption as well as solubility characteristics. Simulation studies further showed that the imidazole ring-based TUDCA derivatives interacted better with the protein in comparison to the others. The proposed study has identified potent chemical chaperones (C2 and C3) as effective therapeutic agents for Parkinson's disease, and further in vitro and in vivo testing will be undertaken to substantiate their potential as novel drugs.

Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches

Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules.

Chemical Characterization, DNA-Damage Protection, Antiproliferative Activity and in Silico Studies of the Essential Oils from Perralderia coronopifolia Coss

In this study, for the first time, we analyzed the chemical composition of essential oils (EOs) steam-distilled from the flowers and leaves of Perralderia coronopifolia by GC-FID/MS. The objective was to explore new anticancer and antioxidant bioactive substances and understand their mechanisms of action through the use of plant-derived natural products. The major chemical components characterizing the EOs were cis-chrysanthenyl acetate 1, 6-oxocyclonerolidol 2, cis-8-acetoxychrysanthenyl acetate 3, and 6α-hydroxycyclonerolidol 4, respectively. Furthermore, the EOs inhibited cell proliferation in HeLa (human cervix carcinoma) and PC3 (human prostate cancer) cells and protected plasmid DNA from oxidative damage caused by UV-photolyzed H2 O2 . Employing a molecular docking study, we elucidated the main compounds' inhibition mechanisms. Consequently, the antitumor activity could be related to the inhibitory property of compound 3 against CDC25B phosphatase. The evaluation of ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties and the density functional theory (DFT) calculations of the major compounds, especially compound 3, offer potential insights for designing and developing new cancer drug candidates. In conclusion, our study provides a framework for future research and development in the field by establishing a scientific foundation for the use of Perralderia coronopifolia essential oils as a prospective source of antioxidant and anticancer agents.

Neomangiferin, a Naturally Occurring Mangiferin Congener, Inhibits Sodium-Glucose Co-transporter-2: An In silico Approach

Type 2 diabetes is a major health concern contributing to most of diabetic cases worldwide. Mangiferin and its congeners are known for their diverse pharmacological properties. This study sought to investigate the inhibitory property of naturally occurring mangiferin congeners on sodium-glucose co-transporter 2 protein (SGLT-2) using comprehensive computational methods. The naturally occurring mangiferin congeners were subjected to molecular docking, molecular dynamics (MDs) simulation (100 ns), molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) binding free energy, density functional theory calculations (B3LYP 6-31G basis set), and ADMET approaches to identify potential SGLT-2 inhibitor. The molecular docking studies revealed neomangiferin (-9.0 kcal/mol) as the hit molecule compared with dapagliflozin (-8.3 kcal/mol). Root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) plots from the MD simulations established that neomangiferin stabilizes SGLT-2 better than the dapagliflozin, a standard drug. The MM-PBSA binding free energy calculations showed that neomangiferin (-26.05 kcal/mol) elicited better binding affinity than dapagliflozin (-17.42 kcal/mol). The electronic studies showed that neomangiferin (3.48 eV) elicited high electrophilicity index compared with mangiferin (3.31 eV) and dapagliflozin (2.11 eV). Also, the ADMET properties showed that the hit molecule is safe when administered to diabetic subjects. The current in silico studies suggest that neomangiferin could emerge as a promising lead molecule as a SGLT-2 inhibitor.

In-silico and in-vitro functional validation of imidazole derivatives as potential sirtuin inhibitor

Introduction

Epigenetic enzymes can interact with a wide range of genes that actively participate in the progression or repression of a diseased condition, as they are involved in maintaining cellular homeostasis. Sirtuins are a family of Class III epigenetic modifying enzymes that regulate cellular processes by removing acetyl groups from proteins. They rely on NAD+ as a coenzyme in contrast to classical histone deacetylases (HDACs) (Class I, II, and IV) that depend on Zn+ for their activation, linking their function to cellular energy levels. There are seven mammalian sirtuin isoforms (Sirt1-7), each located in different subcellular compartments. Sirtuins have emerged as a promising target, given that inhibitors of natural and synthetic sources are highly warranted. Imidazole derivatives are often investigated as sirtuin regulators due to their ability to interact with the binding site and modulate their activity. Imidazole bestows many possible substitutions on its ring and neighboring atoms to design and synthesize derivatives with specific target selectivity and improved pharmacokinetic properties, optimizing drug development.

Materials and methods

Ligand preparation, protein preparation, molecular docking, molecular dynamics, density function theory (DFT) analysis, and absorption, distribution, metabolism, and excretion (ADME) analysis were performed to understand the interacting potential and effective stability of the ligand with the protein. RT-PCR and Western blot analyses were performed to understand the impact of ligands on the gene and protein expression of Class III HDAC enzymes.

Results and discussion

We evaluated the sirtuin inhibition activity of our in-house compound comprised of imidazole derivatives by docking the molecules with the protein data bank. ADME properties of all the compounds used in the study were evaluated, and it was found that all fall within the favorable range of being a potential drug. The molecule with the highest docking score was analyzed using DFT, and the specific compound was used to treat the non-small cell lung cancer (NSCLC) cell lines A549 and NCI-H460. The gene and protein expression data support the in-silico finding that the compound Ethyl 2-[5-(4-chlorophenyl)-2-methyl-1-H-Imidazole-4-yl) acetate has an inhibitory effect on nuclear sirtuins. In conclusion, targeting sirtuins is an emerging strategy to combat carcinogenesis. In this study, we establish that Ethyl 2-[5-(4-chlorophenyl)-2-methyl-1-H-Imidazole-4-yl) acetate possesses a strong inhibitory effect on nuclear sirtuins in NSCLC cell lines.

WGS-based screening of the co-chaperone protein DjlA-induced curved DNA binding protein A (CbpA) from a new multidrug-resistant zoonotic mastitis-causing Klebsiella pneumoniae strain: a novel molecular target of selective flavonoids

The research aimed to establish a multidrug-resistant Klebsiella pneumoniae-induced genetic model for mastitis considering the alternative mechanisms of the DjlA-mediated CbpA protein regulation. The Whole Genome Sequencing of the newly isolated K. pneumoniae strain was conducted to annotate the frequently occurring antibiotic resistance and virulence factors following PCR and MALDI-TOF mass-spectrophotometry. Co-chaperon DjlA was identified and extracted via restriction digestion on PAGE. Based on the molecular string property analysis of different DnaJ and DnaK type genes, CbpA was identified to be regulated most by the DjlA protein during mastitis. Based on the quantum tunnel-cluster profiles, CbpA was modeled as a novel target for diversified biosynthetic, and chemosynthetic compounds. Pharmacokinetic and pharmacodynamic analyses were conducted to determine the maximal point-specificity of selective flavonoids in complexing with the CbpA macromolecule at molecular docking. The molecular dynamic simulation (100 ns) of each of the flavonoid-protein complexes was studied regarding the parameters RMSD, RMSF, Rg, SASA, MMGBSA, and intramolecular hydrogen bonds; where all of them resulted significantly. To ratify all the molecular dynamic simulation outputs, the potential stability of the flavonoids in complexing with CbpA can be remarked as Quercetin > Biochanin A > Kaempherol > Myricetin, which were all significant in comparison to the control Galangin. Finally, a comprehensive drug-gene interaction pathway for each of the flavonoids was developed to determine the simultaneous and quantitative-synergistic effects of different operons belonging to the DnaJ-type proteins on the metabolism of the tested pharmacophores in CbpA. Considering all the in vitro and in silico parameters, DjlA-mediated CbpA can be a novel target for the tested flavonoids as the potential therapeutics of mastitis as futuristic drugs.

L protein characterization and in silico screening of putative broad range target molecules for pathogenic mammarenaviruses from South America

The genus Mammarenavirus belonging to the family Arenaviridae encompasses pathogenic viral species capable of triggering severe diseases in humans, causing concern for the health system due to the high fatality rate associated with them. Currently, there is a dearth of specific therapies against pathogens of the genus. Natural products isolated from plants have impacted the development of drugs against several diseases. The Núcleo de Bioensaios, Biossíntese e Ecofisiologia de Produtos Naturais (NuBBE) database offers several natural compounds with antimicrobial activities that can be used in the development of new antiviral drugs. In this context, here we modeled the arenavirus L protein, multifunctional machinery essential for the viral replicative cycle, making this enzyme a potential candidate for targeting the development of antivirals against genus pathogens. Using the modeled L protein, a virtual screening was performed, which suggested eleven molecules from the NuBBE database that binds to the active site of the L protein, which was promising in the in silico predictions of absorption and toxicity analysis. The NuBBE 1642 molecule proved to be the best candidate for four of the five species evaluated, acting as a possible broad-spectrum molecule. Additionally, our results showed that the L protein is highly conserved among species of the genus, as well as presenting close phylogenetic relationships between many of the species studied, strengthening its candidacy as a therapeutic target. The data presented here demonstrate that some NuBBE molecules are potential ligands for the L protein of arenaviruses, which may help to contain possible outbreaks.Communicated by Ramaswamy H. Sarma.

Structure-based virtual screening of natural compounds as inhibitors of HCV using molecular docking and molecular dynamics simulation studies

The hepatitis C virus (HCV), which causes hepatitis C, is a viral infection that damages the liver and causes inflammation in the liver. New potentially effective antiviral drugs are required for its treatment owing to various issues associated with the existing medications, including moderate to severe adverse effects, higher costs, and the emergence of drug-resistant strains. The objective of the current study was to utilize computational techniques to assess the anti-HCV efficacy of certain phytochemicals against tetraspanin (CD81) and claudin 1 (CLDN1) entry proteins. A 200-nanosecond molecular dynamics (MD) simulation was employed to examine the stability of the lead-protein complexes. Free binding energy and molecular docking calculations were conducted utilizing MM/GBSA method, and the selectivity of hit compounds for CD81 and CLDN1 was determined. Five significant CD81 and CLDN1 inhibitors were identified: Petasiphenone, Silibinin, Tanshinone IIA, Taxifolin, and Topaquinone. The MM/GBSA analysis of the compounds revealed high free binding energies. All the identified compounds were stable within the CD81 and CLDN1 binding pockets. This study indicated the promising inhibitory potential of the identified compounds against CD81 and CLDN1 receptors and might develop into potential viral entry inhibitors. However, to validate the chemotherapeutic capabilities of the discovered leads extensive preclinical research is required.Communicated by Ramaswamy H. Sarma.

Lipophilic Studies and In Silico ADME Profiling of Biologically Active 2-Aminothiazol-4(5H)-one Derivatives

Pseudothiohydantoin derivatives have a wide range of biological activities and are widely used in the development of new pharmaceuticals. Lipophilicity is a basic, but very important parameter in the design of potential drugs, as it determines solubility in lipids, nonpolar solvents, and makes it possible to predict the ADME profile. The aim of this study was to evaluate the lipophilicity of 28 pseudothiohydantoin derivatives showing the inhibition of 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) using chromatographic methods. Experimentally, lipophilicity was determined by reverse phase thin layer chromatography (RP-TLC) and reverse phase high-performance liquid chromatography (RP-HPLC). In both methods, methanol was used as the organic modifier of the mobile phase. For each 2-aminothiazol-4(5H)-one derivative, a relationship was observed between the structure of the compound and the values of the lipophilicity parameters (log kw, RM0). Experimental lipophilicity values were compared with computer calculated partition coefficient (logP) values. A total of 27 of the 28 tested compounds had a lipophilicity value < 5, which therefore met the condition of Lipinski's rule. In addition, the in silico ADME assay showed favorable absorption, distribution, metabolism, and excretion parameters for most of the pseudothiohydantoin derivatives tested. The study of lipophilicity and the ADME analysis indicate that the tested compounds are good potential drug candidates.

Molecular docking and dynamics simulation of Orthosiphon stamineus against SGLT1 and SGLT2

Orthosiphon stamineus Benth a traditional medicine used in the treatment of diabetes and kidney diseases. Sodium-glucose co-transporter (SGLT1 and SGLT2) inhibitors are the novel group of drugs used to treat patients with type 2 diabetes mellitus. In this study 20 phytochemical compounds from Orthosiphon stamineus Benth were obtained from 3 databases viz Dr.Duke's phytochemical, Ethno botanical database and IMPPAT. They were subjected to physiochemical, drug likeliness, and ADMET and toxicity predictions. Homology modeling and molecular docking against SGLT1 and SGLT2 were performed and the stability of the selected drug molecule was validated by molecular dynamic (MD) simulation for 200 ns. Among the 20 compounds, 14-Dexo-14-O-acetylorthosiphol Y alone showed higher binding affinity with SGLT1 and SGLT2 protein with the binding energy of -9.6 and -11.4 Kcal/mol respectively and had highest affinity towards SGLT2 inhibitor. This compound also satisfied Lipinski rule of 5 and had a good ADMET profile. The compound is non-toxic to marine organisms and to normal cell lines and non-mutagenic. The RMSD value attained equilibrium at 150 ns with the stability around 4.8 Å and no significant deviation was reported from 160 to 200 ns for SGLT2. Our study suggests that 14-Dexo-14-O-acetylorthosiphol Y showed promising results against the SGLT2 and could be considered as a potent anti-diabetic drug.Communicated by Ramaswamy H. Sarma.

Cyclohexane-1,3-dione Derivatives as Future Therapeutic Agents for NSCLC: QSAR Modeling, In Silico ADME-Tox Properties, and Structure-Based Drug Designing Approach

The abnormal expression of the c-Met tyrosine kinase has been linked to the proliferation of several human cancer cell lines, including non-small-cell lung cancer (NSCLC). In this context, the identification of new c-Met inhibitors based on heterocyclic small molecules could pave the way for the development of a new cancer therapeutic pathway. Using multiple linear regression (MLR)-quantitative structure-activity relationship (QSAR) and artificial neural network (ANN)-QSAR modeling techniques, we look at the quantitative relationship between the biological inhibitory activity of 40 small molecules derived from cyclohexane-1,3-dione and their topological, physicochemical, and electronic properties against NSCLC cells. In this regard, screening methods based on QSAR modeling with density-functional theory (DFT) computations, in silico pharmacokinetic/pharmacodynamic (ADME-Tox) modeling, and molecular docking with molecular electrostatic potential (MEP) and molecular mechanics-generalized Born surface area (MM-GBSA) computations were used. Using physicochemical (stretch-bend, hydrogen bond acceptor, Connolly molecular area, polar surface area, total connectivity) and electronic (total energy, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels) molecular descriptors, compound 6d is identified as the optimal scaffold for drug design based on in silico screening tests. The computer-aided modeling developed in this study allowed us to design, optimize, and screen a new class of 36 small molecules based on cyclohexane-1,3-dione as potential c-Met inhibitors against NSCLC cell growth. The in silico rational drug design approach used in this study led to the identification of nine lead compounds for NSCLC therapy via c-Met protein targeting. Finally, the findings are validated using a 100 ns series of molecular dynamics simulations in an aqueous environment on c-Met free and complexed with samples of the proposed lead compounds and Foretinib drug.

A Computational Study of Carbazole Alkaloids from Murraya koenigii as Potential SARS-CoV-2 Main Protease Inhibitors

Despite COVID-19 vaccination, immune escape of new SARS-CoV-2 variants has created an urgent priority to identify additional antiviral drugs. Targeting main protease (Mpro) expressed by SARS-CoV-2 is a therapeutic strategy for drug development due to its prominent role in viral replication cycle. Leaves of Murraya koenigii are used in various traditional medicinal applications and this plant is known as a rich source of carbazole alkaloids. Thus, this computational study was designed to investigate the inhibitory potential of carbazole alkaloids from Murraya koenigii against Mpro. Molecular docking was initially used to determine the binding affinity and molecular interactions of carbazole alkaloids and the reference inhibitor (3WL) in the active site of SARS-CoV-2 Mpro (PDB ID: 6M2N).The top scoring compounds were further assessed for protein structure flexibility, physicochemical properties and drug-likeness, pharmacokinetic and toxicity (ADME/T) properties, antiviral activity, and pharmacophore modeling. Five carbazole alkaloids (koenigicine, mukonicine, o-methylmurrayamine A, koenine, and girinimbine) displayed a unique binding mechanism that shielded the catalytic dyad of Mpro with stronger binding affinities and molecular interactions than 3WL. Furthermore, the compounds with high affinity displayed favorable physicochemical and ADME/T properties that satisfied the criteria for oral bioavailability and druggability. The pharmacophore modeling study shows shared pharmacophoric features of those compounds for their biological interaction with Mpro. During the molecular dynamics simulation, the top docking complexes demonstrated precise stability except koenigicine. Therefore, mukonicine, o-methylmurrayamine A, koenine, and girinimbine may have the potential to restrict SARS-CoV-2 replication by inactivating the Mpro catalytic activity.

Identification of Active Compounds against Melanoma Growth by Virtual Screening for Non-Classical Human DHFR Inhibitors

Antifolates such as methotrexate (MTX) have been largely known as anticancer agents because of their role in blocking nucleic acid synthesis and cell proliferation. Their mechanism of action lies in their ability to inhibit enzymes involved in the folic acid cycle, especially human dihydrofolate reductase (hDHFR). However, most of them have a classical structure that has proven ineffective against melanoma, and, therefore, inhibitors with a non-classical lipophilic structure are increasingly becoming an attractive alternative to circumvent this clinical resistance. In this study, we conducted a protocol combining virtual screening (VS) and cell-based assays to identify new potential non-classical hDHFR inhibitors. Among 173 hit compounds identified (average logP = 3.68; average MW = 378.34 Da), two-herein, called C1 and C2-exhibited activity against melanoma cell lines B16 and A375 by MTT and Trypan-Blue assays. C1 showed cell growth arrest (39% and 56%) and C2 showed potent cytotoxic activity (77% and 51%) in a dose-dependent manner. The effects of C2 on A375 cell viability were greater than MTX (98% vs 60%) at equivalent concentrations and times. Our results indicate that the integrated in silico/in vitro approach provided a benchmark to identify novel promising non-classical DHFR inhibitors showing activity against melanoma cells.

Progress and Impact of Latin American Natural Product Databases

Natural products (NPs) are a rich source of structurally novel molecules, and the chemical space they encompass is far from being fully explored. Over history, NPs have represented a significant source of bioactive molecules and have served as a source of inspiration for developing many drugs on the market. On the other hand, computer-aided drug design (CADD) has contributed to drug discovery research, mitigating costs and time. In this sense, compound databases represent a fundamental element of CADD. This work reviews the progress toward developing compound databases of natural origin, and it surveys computational methods, emphasizing chemoinformatic approaches to profile natural product databases. Furthermore, it reviews the present state of the art in developing Latin American NP databases and their practical applications to the drug discovery area.

Identification of Putative Plant-Based ALR-2 Inhibitors to Treat Diabetic Peripheral Neuropathy

Diabetic peripheral neuropathy (DPN) is a common diabetes complication (DM). Aldose reductase -2 (ALR-2) is an oxidoreductase enzyme that is most extensively studied therapeutic target for diabetes-related complications that can be inhibited by epalrestat, which has severe adverse effects; hence the discovery of potent natural inhibitors is desired. In response, a pharmacophore model based on the properties of eplarestat was generated. The specified pharmacophore model searched the NuBBE_DB database of natural compounds for prospective lead candidates. To assess the drug-likeness and ADMET profile of the compounds, a series of in silico filtering procedures were applied. The compounds were then put through molecular docking and interaction analysis. In comparison to the reference drug, four compounds showed increased binding affinity and demonstrated critical residue interactions with greater stability and specificity. As a result, we have identified four potent inhibitors: ZINC000002895847, ZINC000002566593, ZINC000012447255, and ZINC000065074786, that could be used as pharmacological niches to develop novel ALR-2 inhibitors.

Computational identification of natural product inhibitors against EGFR double mutant (T790M/L858R) by integrating ADMET, machine learning, molecular docking and a dynamics approach

Double mutated epidermal growth factor receptor is a clinically important target for addressing drug resistance in lung cancer treatment. Therefore, discovering new inhibitors against the T790M/L858R (TMLR) resistant mutation is ongoing globally. In the present study, nearly 150 000 molecules from various natural product libraries were screened by employing different ligand and structure-based techniques. Initially, the library was filtered to identify drug-like molecules, which were subjected to a machine learning based classification model to identify molecules with a higher probability of having anti-cancer activity. Simultaneously, rules for constrained docking were derived from three-dimensional protein-ligand complexes and thereafter, constrained docking was undertaken, followed by HYDE binding affinity assessment. As a result, three molecules that resemble interactions similar to the co-crystallized complex were selected and subjected to 100 ns molecular dynamics simulation for stability analysis. The interaction analysis for the 100 ns simulation period showed that the leads exhibit the conserved hydrogen bond interaction with Gln791 and Met793 as in the co-crystal ligand. Also, the study indicated that Y-shaped molecules are preferred in the binding pocket as it enables them to occupy both pockets. The MMGBSA binding energy calculations revealed that the molecules have comparable binding energy to the native ligand. The present study has enabled the identification of a few ADMET adherent leads from natural products that exhibit the potential to inhibit the double mutated drug-resistant EGFR.

Exploring the inhibitory potential of novel bioactive compounds from mangrove actinomycetes against nsp10 the major activator of SARS-CoV-2 replication

The current study reveals the inhibitory potential of novel bioactive compounds of mangrove actinomycetes against nsp10 of SARS-CoV-2. A total of fifty (50) novel bioactive (antibacterial, antitumor, antiviral, antioxidant, and anti-inflammatory) compounds of mangrove actinomycetes from different chemical classes such as alkaloids, dilactones, sesquiterpenes, macrolides, and benzene derivatives are used for interaction analysis against nsp10 of SARS-CoV-2. The six antiviral agents sespenine, xiamycin c, xiamycin d, xiamycin e, xiamycin methyl ester, and xiamycin A (obeyed RO5 rule) are selected based on higher binding energy, low inhibition constant values, and better-docked positions. The effective hydrogen and hydrophobic (alkyl, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi$$\end{document}π–sigma, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi$$\end{document}π–\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi$$\end{document}π T shaped and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi$$\end{document}π-alkyl) interaction analysis reveals the four antivirals sespenine, xiamycin C, xiamycin methyl ester, and xiamycin A are supposed to be the most auspicious inhibitors against nsp10 of SARS-CoV-2. Quantum chemistry methods such as frontier molecular orbitals and molecular electrostatic potential are used to explain the thermal stability and chemical reactivity of ligands. The toxicity profile shows that selected ligands are safe by absorption, distribution, metabolism, excretion, and toxicity profiling and also effective for inhibition of nsp10 protein of SARS-CoV-2. The molecular dynamic simulation investigation of apo and halo forms of nsp10 done by RMSD of C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α atoms of nsp10, all amino acid residues RMSF, count total number of hydrogen bonds and radius of gyration (R_g). MD simulations reveal the complexes are stable and increase the structural compactness of nsp10 in the binding pocket. The lead antiviral compounds sespenine, xiamycin C, xiamycin methyl ester, and xiamycin A are recommended as the most promising inhibitors against nsp10 of SARS-CoV-2 pathogenicity.

Investigation of Antidepressant Properties of Yohimbine by Employing Structure-Based Computational Assessments

The use of pharmaceuticals to treat Major Depressive Disorder (MDD) has several drawbacks, including severe side effects. Natural compounds with great efficacy and few side effects are in high demand due to the global rise in MDD and ineffective treatment. Yohimbine, a natural compound, has been used to treat various ailments, including neurological conditions, since ancient times. Serotonergic neurotransmission plays a crucial role in the pathogenesis of depression; thus, serotonergic receptor agonist/antagonistic drugs are promising anti-depressants. Yohimbine was investigated in this study to determine its antidepressant activity using molecular docking and pharmacokinetic analyses. Additionally, the in silico mutational study was carried out to understand the increase in therapeutic efficiency using site-directed mutagenesis. Conformational changes and fluctuations occurring during wild type and mutant serotonergic receptor, 5-hydroxytryptamine receptors 1A (5HT1A) and yohimbine were assessed by molecular dynamics MD simulation studies. Yohimbine was found to satisfy all the parameters for drug-likeness and pharmacokinetics analysis. It was found to possess a good dock score and hydrogen-bond interactions with wild type 5HT1A structure. Our findings elaborate the substantial efficacy of yohimbine against MDD; however, further bench work studies may be carried out to prove the same.

Molecular Docking and Simulation Studies of Antidiabetic Agents Devised from Hypoglycemic Polypeptide-P of Momordica charantia

Diabetes mellitus termed as metabolic disorder is a collection of interlinked diseases and mainly body's inability to manage glucose level which leads to cardiovascular diseases, renal failure, neurological disorders, and many others. The drugs contemporarily used for diabetes have many inevitable side effects, and many of them have become less responsive to this multifactorial disorder. Momordica charantia commonly known as bitter gourd has many bioactive compounds with antidiabetic properties. The current study was designed to use computational methods to discover the best antidiabetic peptides devised from hypoglycemic polypeptide-P of M. charantia. The binding affinity and interaction patterns of peptides were evaluated against four receptor proteins (i.e., as agonists of insulin receptor and inhibitors of sodium-glucose cotransporter 1, dipeptidyl peptidase-IV, and glucose transporter 2) using molecular docking approach. A total of thirty-seven peptides were docked against these receptors. Out of which, top five peptides against each receptor were shortlisted based on their S-scores and binding affinities. Finally, the eight best ligands (i.e., LIVA, TSEP, EKAI, LKHA, EALF, VAEK, DFGAS, and EPGGGG) were selected as these ligands strictly followed Lipinski's rule of five and exhibited good ADMET profiling. One peptide EPGGGG showed activity towards insulin and SGLT1 receptor proteins. The top complex for both these targets was subjected to 50 ns of molecular dynamics simulations and MM-GBSA binding energy test that concluded both complexes as highly stable, and the intermolecular interactions were dominated by van der Waals and electrostatic energies. Overall, the selected ligands strongly fulfilled the drug-like evaluation criterion and proved to have good antidiabetic properties.

Molecular docking and simulation studies of natural compounds of Vitex negundo L. against papain-like protease (PLpro) of SARS CoV-2 (coronavirus) to conquer the pandemic situation in the world

The severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) is β-coronavirus that is responsible for the pandemic coronavirus disease 2019 (COVID-19) all over the world. The rapid spread of the novel SARS CoV-2 worldwide is raising a significant global public health issue with nearly 61.86 million people infected and 1.4 million deaths. To date, no specific drugs are available for the treatment of COVID-19. The inhibition of proteases essential for the proteolytic treatment of viral polyproteins is a conventional therapeutic strategy for conquering viral infections. In the study, molecular docking approach was used to screen potential drug compounds among the phytochemicals of Vitex negundo L. against COVID-19 infection. Molecular docking analysis showed that oleanolic acid forms a stable complex and other phyto-compounds ursolic acid, 3β-acetoxyolean-12-en-27-oic acid and isovitexin of V. negundo natural compounds form a less-stable complex. When compared with the control the synergistic interaction of these compounds shows inhibitory activity against papain-like protease (PL^pro) of SARS CoV-2 (COVID-19). The molecular dynamics (MD) simulation (50 ns) were performed on the complexes of PL^pro and the phyto-compounds viz. oleanolic acid, ursolic acid, 3β-acetoxyolean-12-en-27-oic acid and isovitexin followed by the binding free energy calculations using MM-GBSA and these molecules have stable interactions with PL^pro protein binding site. The MD simulation study provides more insight into the functional properties of the protein-ligand complex and suggests that these molecules can be considered as a potential drug molecule against COVID-19. In this pandemic situation, these herbal compounds provide a rich resource to produce new antivirals against COVID-19.Communicated by Ramaswamy H. Sarma.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

Identification of Persuasive Antiviral Natural Compounds for COVID-19 by Targeting Endoribonuclease NSP15: A Structural-Bioinformatics Approach

SARS-CoV-2 is a positive-stranded RNA virus that bundles its genomic material as messenger-sense RNA in infectious virions and replicates these genomes through RNA intermediates. Several virus-encoded nonstructural proteins play a key role during the viral life cycle. Endoribonuclease NSP15 is vital for the replication and life cycle of the virus, and is thus considered a compelling druggable target. Here, we performed a combination of multiscoring virtual screening and molecular docking of a library of 1624 natural compounds (Nuclei of Bioassays, Ecophysiology and Biosynthesis of Natural Products (NuBBE) database) on the active sites of NSP15 (PDB:6VWW). After sequential high-throughput screening by LibDock and GOLD, docking optimization by CDOCKER, and final scoring by calculating binding energies, top-ranked compounds NuBBE-1970 and NuBBE-242 were further investigated via an indepth molecular-docking and molecular-dynamics simulation of 60 ns, which revealed that the binding of these two compounds with active site residues of NSP15 was sufficiently strong and stable. The findings strongly suggest that further optimization and clinical investigations of these potent compounds may lead to effective SARS-CoV-2 treatment.

Structure-based discovery of neoandrographolide as a novel inhibitor of Rab5 to suppress cancer growth

Rab5 is a small GTPase that plays a crucial role in oncogenic signal transduction, which was considered as an attractive target for cancer therapy. Rapid GDP/GTP exchange in the packet of Rab5 sustains its high activity for promoting cancer progression. However, Rab5 currently remains undruggable due to the lack of specific inhibitor. Herein, we reported the discovery of a novel Rab5 inhibitor, neoandrographolide (NAP), by using high-throughput virtual screening with a natural product library containing 7459 compounds, which can occupy the surface groove of Rab5, competing with GDP/GTP for the binding. Ser34 is the most important residue in the groove of Rab5, as it forms most hydrogen-bond interactions with GDP/GTP or NAP, and in silico mutation of Ser34 decreased the stabilization of Rab5. Moreover, fluorescence titration experiment and isothermal titration calorimetry (ITC) assay revealed a direct binding between NAP and Rab5. Biochemical and cell-based assays showed that NAP treatment not only diminished the activity of Rab5, but also suppressed cell growth of cancer cell. This finding firstly identifies NAP as a novel inhibitor of Rab5, which directly binds with Rab5 by occupying the GDP/GTP binding groove to suppress its functions, highlighting a great potential of NAP to be developed as a chemotherapeutic agent in cancer therapy.

Cheminformatics to Characterize Pharmacologically Active Natural Products

Natural products have a significant role in drug discovery. Natural products have distinctive chemical structures that have contributed to identifying and developing drugs for different therapeutic areas. Moreover, natural products are significant sources of inspiration or starting points to develop new therapeutic agents. Natural products such as peptides and macrocycles, and other compounds with unique features represent attractive sources to address complex diseases. Computational approaches that use chemoinformatics and molecular modeling methods contribute to speed up natural product-based drug discovery. Several research groups have recently used computational methodologies to organize data, interpret results, generate and test hypotheses, filter large chemical databases before the experimental screening, and design experiments. This review discusses a broad range of chemoinformatics applications to support natural product-based drug discovery. We emphasize profiling natural product data sets in terms of diversity; complexity; acid/base; absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties; and fragment analysis. Novel techniques for the visual representation of the chemical space are also discussed.

In Silico ADME/Tox Profiling of Natural Products: A Focus on BIOFACQUIM

Natural products continue to be major sources of bioactive compounds and drug candidates not only because of their unique chemical structures but also because of their overall favorable metabolism and pharmacokinetic properties. The number of publicly accessible natural product databases has increased significantly in the past few years. However, the systematic ADME/Tox profile has been reported on a limited basis. For instance, BIOFACQUIM was recently published as a public database of natural products from Mexico, a country with a rich source of biomolecules. However, its ADME/Tox profile has not been reported. Herein, we discuss the results of an in-depth in silico ADME/Tox profile of natural products in BIOFACQUIM and other large public collections of natural products. It was concluded that the absorption and distribution profiles of compounds in BIOFACQUIM are similar to those of approved drugs, while the metabolism profile is comparable to that in the other natural product databases. The excretion profile of compounds in BIOFACQUIM is different from that of the approved drugs, but their predicted toxicity profile is comparable. This work further contributes to the deeper characterization of natural product collections as major sources of bioactive compounds with therapeutic potential.

Towards a unified Latin American Natural Products Database: LANaPD

Around the world, the number of compound databases of natural products in the public domain is rising. This is in line with the increasing synergistic combination of natural product research and chemoinformatics. Toward this global endeavor, countries in Latin America are assembling, curating, and analyzing the contents and diversity of natural products available in their geographical regions. In this manuscript we collect and analyze the efforts that countries in Latin America have made so far to build natural product databases. We further encourage the scientific community in particular in Latin America, to continue their efforts to building quality natural product databases and, whenever possible, to make them publicly accessible. It is proposed that all compound collections could be assembled into a unified resource called LANaPD: Latin American Natural Products Database. Opportunities and challenges to build, distribute and maintain LANaPD are also discussed.