Database on pharmacophore analysis of active principles, from medicinal plants

Plants continue to be a major source of medicines, as they have been throughout human history. In the present days, drug discovery from plants involves a multidisciplinary approach combining ethnobotanical, phytochemical and biological techniques to provide us new chemical compounds (lead molecules) for the development of drugs against various pharmacological targets, including cancer, diabetes and its secondary complications. In view of this need in current drug discovery from medicinal plants, here we describe another web database containing the information of pharmacophore analysis of active principles possessing antidiabetic, antimicrobial, anticancerous and antioxidant properties from medicinal plants. The database provides the botanical, taxonomic classification, biochemical as well as pharmacological properties of medicinal plants. Data on antidiabetic, antimicrobial, anti oxidative, anti tumor and anti inflammatory compounds, and their physicochemical properties, SMILES Notation, Lipinski's properties are included in our database. One of the proposed features in the database is the predicted ADMET values and the interaction of bioactive compounds to the target protein. The database alphabetically lists the compound name and also provides tabs separating for anti microbial, antitumor, antidiabetic, and antioxidative compounds. AVAILABILITY: http://www.hccbif.info /     

A computational approach to explore and identify potential herbal inhibitors for the p21-activated kinase 1 (PAK1)

Abstract

The oncogenic kinase PAK1 (p21-activated kinase 1) is involved in developing many diseases including cancers, neurofibromatosis, Alzheimer's disease, diabetes (type 2), and hypertension. Thus, it is thought to be a prominent therapeutic target, and its selective inhibitors have a huge market potential. Recently, herbal PAK1 inhibitors have gained immense interest over synthetic ones mainly due to their non-toxic effects. Till date, many herbal compounds have been suggested to inhibit PAK1, but their information on selectivity, bioavailability, ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, and molecular interactions with PAK1 has not been explored. Hence, this study was designed with computational approaches to explore and identify the best herbal PAK1-blockers showing good ADMET properties, druggable features and binding affinity with PAK1. Herbal inhibitors reported here were initially filtered with Lipinski’s rule of five (RO5). Then, molecular docking between these inhibitors and PAK1 catalytic sites was performed using AutoDock Vina and GOLD suite to determine the binding affinity and interactions. Finally, 200?ns molecular dynamics (MD) simulations on three top-ranked inhibitors including cucurbitacin I (C-I), nymphaeol A (NA), and staurosporine (SPN) were carried out. The binding free energies and interactions revealed that NA can strongly bind with the PAK1 catalytic cleft. PASS prediction and ADMET profiling supported that NA is appeared to be a more selective and safer inhibitor than C-I and SPN. These results conform to the previous experimental evidences, and therefore, NA from Okinawa propolis could be a promising inhibitor for treating PAK1-dependent illnesses.

Communicated by Ramaswamy H. Sarma     

Molecular docking based screening of GABA (A) receptor inhibitors from plant derivatives

The present antipsychotic drugs have known to show serious concerns like extra pyramidal side effects therefore, pursuit for novel antipsychotic GABAnergic drugs has lately focused on the folkloric medicine from plant derivatives as better treatment option of schizophrenia. The present study centers to identify potential inhibitors of plant origin for GABA receptor through in silico approaches. Three compound datasets were undertaken in the study. The first set consisted of seven compounds which included Magnolol, Honokiol and other plant derivatives. The second set consisted of 16 derivatives of N-diarylalkenyl-piperidinecarboxylic acid synthesized by Zheng et al., 2006. The third dataset had thirty two compounds which were Magnolol and Honokiol analogues synthesized by Fuchs et al., 2014. All the compounds were docked at the allosteric site of the GABA (A) receptor. The compounds were further tested for ADMET and biological activity. We observed Honokiol and its derivatives demonstrated superior druglike properties than any compound undertaken in the study. Further, compound 61 [2-(4-methoxyphenyl)-4-propylphenol] of dataset three - a synthetic derivative of honokiol had better profile than its parent compound. In a possible attempt to identify compound with even better efficacious compound than 61, virtual screening was performed, 135 compounds akin to compound 61 were retrieved. Interestingly none of the 135 compounds showed better druggable properties than compound 61. Our in silico pharmacological profiling of compounds is in coherence and is complemented by the findings of Fuchs et al, which also revealed compound 61 to be the good potentiator of GABA receptor.

Abbreviations

GABA (A) R - Gamma Amino Butyric Acid Receptor, subtype A, GPCR - G Protein Coupled Receptor, OPLS - Optimized Potentials for Liquid Simulations, PDB - Protein Data Bank, PLP - Piece wise Linear Potential, T.E.S.T - Toxicity Estimation Software Tool, TCM - Traditional Chinese Medicine.     

Potential human cholesterol esterase inhibitor design: benefits from the molecular dynamics simulations and pharmacophore modeling studies

Human pancreatic cholesterol esterase (hCEase) is one of the lipases found to involve in the digestion of large and broad spectrum of substrates including triglycerides, phospholipids, cholesteryl esters, etc. The presence of bile salts is found to be very important for the activation of hCEase. Molecular dynamic simulations were performed for the apoform and bile salt complexed form of hCEase using the co-ordinates of two bile salts from bovine CEase. The stability of the systems throughout the simulation time was checked and two representative structures from the highly populated regions were selected using cluster analysis. These two representative structures were used in pharmacophore model generation. The generated pharmacophore models were validated and used in database screening. The screened hits were refined for their drug-like properties based on Lipinski's rule of five and ADMET properties. The drug-like compounds were further refined by molecular docking simulation using GOLD program based on the GOLD fitness score, mode of binding, and molecular interactions with the active site amino acids. Finally, three hits of novel scaffolds were selected as potential leads to be used in novel and potent hCEase inhibitor design. The stability of binding modes and molecular interactions of these final hits were re-assured by molecular dynamics simulations.     

Chemoinformatic identification of novel inhibitors against Mycobacterium tuberculosis L-aspartate α-decarboxylase

L-aspartate α-decarboxylase (ADC) belongs to a class of pyruvoyl dependent enzymes and catalyzes the conversion of aspartate to β-alanine in the pantothenate pathway, which is critical for the growth of several micro-organisms, including Mycobacterium tuberculosis (Mtb). Its presence only in micro-organisms, fungi and plants and its absence in animals, particularly human, make it a promising drug target. We have followed a chemoinformatics-based approach to identify potential drug-like inhibitors against Mycobacterium tuberculosis L-aspartate α-decarboxylase (MtbADC). The structure-based high throughput virtual screening (HTVS) mode of the Glide program was used to screen 333,761 molecules of the Maybridge, National Cancer Institute (NCI) and Food and Drug Administration (FDA) approved drugs databases. Ligands were rejected if they cross-reacted with S-adenosylmethionine (SAM) decarboxylase, a human pyruvoyl dependent enzyme. The lead molecules were further analyzed for physicochemical and pharmacokinetic parameters, based on Lipinski's rule of five, and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties. This analysis resulted in eight small potential drug-like inhibitors that are in agreement with the binding poses of the crystallographic ADC:fumarate and ADC:isoasparagine complex structures and whose backbone scaffolds seem to be suitable for further experimental studies in therapeutic development against tuberculosis.     

化合物ADMET性质预测平台的构建

在药物研发早期阶段对化合物成药性和安全性进行评估,对于提高药物研发成功率、降低研发成本具有十分重要的意义。为了能够帮助药物研究工作者快速准确地判断候选化合物的成药性与安全性,开发了一个基于计算机方法的化合物ADMET性质预测平台。首先,通过文本挖掘的方法收集了化合物药代动力学性质和毒性(ADMET)的高质量实验数据。然后,根据原始文献复原了13个预测模型,同时采用支持向量机方法自建了15个具有较高预测能力的计算模型。最后,基于分布式架构,结合高性能计算集群优势,开发了化合物ADMET性质预测平台(http://www.vslead.com/?r=admet/index),用于预测28种重要的化合物ADMET性质。研究者可以使用这一平台快捷方便地对药物研究中比较重要的ADMET性质进行预测,在药物研发早期对候选化合物进行成药性评价和风险评估,有助于提高药物研的成功率,节省研发时间和经费的投入。     

Computational Fragment-Based Design of Phytochemical Derivatives as EGFR Inhibitors

Epidermal growth factor receptor (EGFR) is a potential target with disease modifying benefits against Alzheimer's disease (AD). Repurposing of FDA approved drugs against EGFR have shown beneficial effect against AD but are confined to quinazoline, quinoline and aminopyrimidines. Futuristically, the possibility of acquiring drug resistance mutation as seen in the case of cancer could also hamper AD treatment. To identify novel chemical scaffolds, we rooted on phytochemicals identified from plants such as Acorus calamus, Bacopa monnieri, Convolvulus pluricaulis, Tinospora cordifloia, and Withania somnifera that have well-established records in the treatment of brain disorders. The rationale was to mimic the biosynthetic metabolite extension process observed in the plants for synthesizing new phytochemical derivates. Thus, novel compounds were designed computationally by fragment-based method followed by extensive in silico analysis to pick potential phytochemical derivates. PCD1, 8 and 10 were predicted to have better blood brain barrier permeability. ADMET and SoM analysis suggested that these PCDs exhibited druglike properties. Further simulation studies showed that PCD1 and PCD8 stably interact with EGFR and have the potential to be used even in cases of drug-resistance mutations. With further experimental evidence, these PCDs could be leveraged as potential inhibitors of EGFR.     

Predictive ADMET studies, the challenges and the opportunities

Predictive ADMET is the new 'hip' area in drug discovery. The aim is to use large databases of ADMET data associated with structures to build computational models that link structural changes with changes in response, from which compounds with improved properties can be designed and predicted. These databases also provide the means to enable predictions of human ADMET properties to be made from human in vitro and animal in vivo ADMET measurements. Both methods are limited by the amount of data available to build such predictive models, the limitations of modelling methods and our understanding of the systems we wish to model. The current failures, successes and opportunities are reviewed.     

Development of a robust QSAR model to predict the affinity of pyrrolidine analogs for dipeptidyl peptidase IV (DPP- IV)

QSAR analysis using multiple linear regression and partial least squares methods were conducted on a data set of 47 pyrrolidine analogs acting as DPP IV inhibitors. The QSAR models generated (both MLR and PLS) were robust with statistically significant s, F, r, r(2) and r(2) (CV) values. The analysis helped to ascertain the role of shape flexibility index, Ipso atom E-state index and electrostatic parameters like dipole moment, in determining the activity of DPP IV inhibitors. In addition to QSAR modeling, Lipinski's rule of five was also employed to check the pharmacokinetic profile of DPP IV inhibitors. Since none of the compounds violated the Lipinski's rule of five indicating that the DPP IV inhibitors reported herein have sound pharmacokinetic profile and can be considered as potential drug candidates for diabetes mellitus Type II.     

Computational selection of flavonoid compounds as inhibitors against SARS-CoV-2 main protease,RNA-dependent RNA polymerase and spike proteins: A molecular docking study

An outbreak of Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 has been recognized as a global health concern. Since, no specific antiviral drug is proven effective for treatment against COVID-19, identification of new therapeutics is an urgent need. In this study, flavonoid compounds were analyzed for its inhibitory potential against important protein targets of SARS-CoV-2 using computational approaches. Virtual docking was performed for screening of flavonoid compounds retrieved from PubChem against the main protease of SARS-CoV-2 using COVID-19 docking server. The cut off of dock score was set to >?9 kcal/mol and screened compounds were individually docked against main protease, RNA-dependent RNA polymerase, and spike proteins using AutoDock 4.1 software. Finally, lead flavonoid compounds were subjected to ADMET analysis. A total of 458 flavonoid compounds were virtually screened against main protease target and 36 compounds were selected based on the interaction energy value >?9 kcal/mol. Furthermore, these compounds were individually docked against protein targets and top 10 lead compounds were identified. Among the lead compounds, agathisflavone showed highest binding energy value of ?8.4 kcal/mol against main protease, Albireodelphin showed highest dock score of ?9.8 kcal/mol and ?11.2 kcal/mol against RdRp, and spike proteins, respectively. Based on the high dock score and ADMET properties, top 5 lead molecules such as Albireodelphin, Apigenin 7-(6″-malonylglucoside), Cyanidin-3-(p-coumaroyl)-rutinoside-5-glucoside, Delphinidin 3-O-beta-D-glucoside 5-O-(6-coumaroyl-beta-D-glucoside) and (-)-Maackiain-3-O-glucosyl-6″-O-malonate were identified as potent inhibitors against main protease, RdRp, and spike protein targets of SARS-CoV-2. These all compounds are having non-carcinogenic and non-mutagenic properties. This study finding suggests that the screened compounds include Albireodelphin, Apigenin 7-(6″-malonylglucoside), Cyanidin-3-(p-coumaroyl)-rutinoside-5-glucoside, Delphinidin 3-O-beta-D-glucoside 5-O-(6-coumaroyl-beta-D-glucoside) and (-)-Maackiain-3-O-glucosyl-6″-O-malonate could be the potent inhibitors of SARS-CoV-2 targets.     

Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates

MOTIVATION: To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process. RESULTS: We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. These methods are discussed, and some possible future directions in this rapidly developing field are described.     

Virtual screening of natural inhibitors targeting ornithine decarboxylase with pharmacophore scaffolding of DFMO and validation by molecular dynamics simulation studies

Ornithine decarboxylase (ODC) is an enzyme that initiates polyamine synthesis in human. Polyamines play key roles in cell–cell adhesion, cell motility and cell cycle regulation. Higher synthesis of polyamines also occurs in rapidly proliferating cancer cells are mediated by ODC. As per earlier studies, di-flouro-methyl-orninthine (DFMO) is a proven efficient inhibitor ODC targeting the catalytic activity, however, its usage is limited due to side effects. Targeting ODC is considered as a potential therapeutic modality in the treatment of cancer. In this study, it is attempted to use DFMO scaffold to build a ligand-based pharmocophore query using MOE to screen similar active compounds from Universal Natural Products Database with better ADMET properties. The identified compounds were virtually screened against the active cavity of ODC using Glide. Further, potential natural hits targeting ODC were shortlisted based on Molecular Mechanics/Generalized-Born/Surface Area (MM-GBSA) score. Finally, molecular dynamics simulations were performed for the natural molecule hit and DFMO in complex with ODC using Desmond. Among the hits shortlisted, 2-amino-5, 9, 13, 17-tetramethyloctadeca-8, 16-diene-1, 3, 14-triol (UNPD208110) was found to be highly potential, as it showed a higher binding affinity in terms of interactions with key active cavity residues, and also showed better ADMET property, HUMO–LUMO gap energy and more stable complex formation with ODC compared to DFMO. Hence, the proposed molecule (UNPD208110) shall be favourably considered as a potential natural inhibitor targeting ODC-mediated disease conditions.     

Synthesis and Computational Exploration of Morpholine Bearing Halogenated Sulfonamides as Potential Tyrosinase Inhibitors

In the presented work, a new series of three different 4-((3,5-dichloro-2-[(2/4-halobenzyl)oxy]phenyl)sulfonyl)morpholines was synthesized and the structure of these compounds were corroborated by ¹H-NMR & ¹³C-NMR studies. The in vitro results established all the three compounds as potent tyrosinase inhibitors relative to the standard. The Kinetics mechanism plots established that compound 8 inhibited the enzyme non-competitively. The inhibition constants K_i calculated from Dixon plots for this compound was 0.0025 μM. Additionally, computational techniques were used to explore electronic structures of synthesized compounds. Fully optimized geometries were further docked with tyrosinase enzyme for inhibition studies. Reasonably good binding/interaction energies and intermolecular interactions were obtained. Finally, drug likeness was also predicted using the rule of five (RO5) and Chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET) characteristics. It is anticipated that current experimental and computational investigations will evoke the scientific interest of the research community for the above-entitled compounds.     

Alkyloxy carbonyl modified hexapeptides as a high affinity compounds for Wnt5A protein in the treatment of psoriasis

Psoriasis is one of the most prevalent chronic inflammatory diseases of the skin. The Wnt pathways have been documented to play essential role in stem cell self-renewal and keratinocyte differentiation in the skin. Antagonizing the Wnt5a protein would emerge as a novel therapeutics in psoriasis treatment. In this view, we have developed and characterized series of compounds by attaching varied tertiary alkyloxy carbonyl groups at the N-terminal end of the hexapeptide (Met-Asp-Gly-Cys-Glu-Leu) bestowed to inhibit Wnt/Ca2+ signaling in psoriasis. Hexapeptide compound with 1,1-diphenylethoxy carbonyl group attached to N-terminal end of hexapeptide demonstrated highest binding affinity amongst all the evaluated compounds. The compound identified in the study can be subjected further for in vitro and in vivo studies for ADMET properties.     

Identification of putative antiviral bioactive compounds derived from family Asteraceae: An in silico approach

This computational study investigates 21 bioactive compounds from the Asteraceae family as potential inhibitors targeting the Spike protein (S protein) of SARS-CoV-2. Employing in silico methods and simulations, particularly CDOCKER and MM-GBSA, the study identifies two standout compounds, pterodontic acid and cichoric acid, demonstrating robust binding affinities (−46.1973 and −39.4265 kcal/mol) against the S protein. Comparative analysis with Favipiravir underscores their potential as promising inhibitors. Remarkably, these bioactives exhibit favorable ADMET properties, suggesting safety and efficacy. Molecular dynamics simulations validate their stability and interactions, signifying their potential as effective SARS-CoV-2 inhibitors.     

Synthesis and pharmacological evaluation of the stereoisomers of 3-carba cyclic-phosphatidic acid

Cyclic phosphatidic acid (CPA) is a naturally occurring analog of lysophosphatidic acid (LPA) in which the sn-2 hydroxy group forms a five-membered ring with the sn-3 phosphate. Here, we describe the synthesis of R-3-CCPA and S-3-CCPA along with their pharmacological properties as inhibitors of lysophospholipase D/autotaxin, agonists of the LPA(5) GPCR, and blockers of lung metastasis of B16-F10 melanoma cells in a C57BL/6 mouse model. S-3CCPA was significantly more efficacious in the activation of LPA(5) compared to the R-stereoisomer. In contrast, no stereoselective differences were found between the two isomers toward the inhibition of autotaxin or lung metastasis of B16-F10 melanoma cells in vivo. These results extend the potential utility of these compounds as potential lead compounds warranting evaluation as cancer therapeutics.     

Molecular docking analysis of an isoflavone derivative with the protein phosphatase 1 from Leishmania donovani

Leishmaniasis is one of the most neglected diseases with high morbidity and mortality rate. Severe side effects with existing drug and lack of proper vaccine encouraged us to design alternative models to combat the disease. We showed that PP1 of Leishmania donovani mediates immunomodulation in host macrophages needed for parasite survival. Therefore, it is of interest to report the molecular docking analysis of 512 isoflavone derivatives with the phosphatase 1 protein from Leishmania donovani to highlight compound 362 (5-hydroxy-5-{9-[2-methoxy-2-(2-methylfuran-3-yl) ethyl]-1H, 3H, 4H, 10bH-pyrano[4,3-c]chromen-3-yl}pentanoic acid) having good binding features and acceptable ADMET properties for further consideration.     

Molecular epidemiology of antibiotic-resistant genes and potent inhibitors against TEM,CTX-M-14, CTX-M-15, and SHV-1 proteins of Escherichia coli in district Peshawar,Pakistan

The Extended Spectrum Beta-Lactamases (ESBLs) producing bacteria is an issue of concern for clinicians resulting in minimize the treatment options. To overcome resistance mechanisms, novel inhibitors with good Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties must inhibit the ESBLs resistant genes. The current study aimed to identify the antibiotic resistance genes of ESBLs producing E. coli and a single inhibitor was designed to inhibit all the resistant proteins. The results showed that 42.9% ESBL producers had CTX-M (69.9%), TEM (63.4%), SHV (34.5%) and CTX-M-14 (17.5%) genes. The ESBLs producing isolates were resistant to cephalosporins, quinolones, and sulfonamide with Minimum Inhibitory Concentration (MICs) ranging from 64 to >256 μg/ml. To design multi inhibitory ligands, RECAP synthesis was used for the de-novo discovery of 1000 inhibitors database. Protein crystal structures were retrieved from Protein Data Base (PDB). Lipinski’s rules of five were applied to the novel inhibitors database to improve the ADMET properties. The novel inhibitors database was selected for docking simulations. Placement of the ligand was used by the London dG algorithm implemented in Molecular Operating Environment (MOE), while GBVI/WSA dG algorithm was used for final refinement. Based on docking score, visual inspection of ligands interaction with key residues, binding affinity, and binding energy of ligands with proteins, ten compounds were selected for ESBLs proteins with best ADMET properties, binding energy, and binding affinity the reported ones. These hits compounds have unique scaffolds and are predicted to be a starting point for developing potent inhibitors against antibiotic-resistant proteins.     

Rational design,synthesis and characterization of potent,non-peptidic Smac mimics/XIAP inhibitors as proapoptotic agents for cancer therapy

Novel proapoptotic Smac mimics/IAPs inhibitors have been designed, synthesized and characterized. Computational models and structural studies (crystallography, NMR) have elucidated the SAR of this class of inhibitors, and have permitted further optimization of their properties. In vitro characterization (XIAP BIR3 and linker-BIR2–BIR3 binding, cytotox assays, early ADMET profiling) of the compounds has been performed, identifying one lead for further in vitro and in vivo evaluation.