scholarly journals Estimation of drug-likeness properties of GC–MS separated bioactive compounds in rare medicinal Pleione maculata using molecular docking technique and SwissADME in silico tools

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Hakani D. Sympli
Keyword(s):  
Molecular Docking ◽  
Bioactive Compounds ◽  
In Silico ◽  
In Silico Tools

scholarly journals Characterization of α-Glucosidase Inhibitors from Psychotria malayana Jack Leaves Extract Using LC-MS-Based Multivariate Data Analysis and In-Silico Molecular Docking

Molecules ◽  
2020 ◽  
Vol 25 (24) ◽  
pp. 5885
Author(s):  
Tanzina Sharmin Nipun ◽  
Alfi Khatib ◽  
Zalikha Ibrahim ◽  
Qamar Uddin Ahmed ◽  
Irna Elina Redzwan ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Data Analysis ◽  
Binding Affinity ◽  
Bioactive Compounds ◽  
In Silico ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Partial Least Square ◽  
Protein Data Bank Code ◽  
Glucosidase Inhibitors

Psychotria malayana Jack has traditionally been used to treat diabetes. Despite its potential, the scientific proof in relation to this plant is still lacking. Thus, the present study aimed to investigate the α-glucosidase inhibitors in P.malayana leaf extracts using a metabolomics approach and to elucidate the ligand–protein interactions through in silico techniques. The plant leaves were extracted with methanol and water at five various ratios (100, 75, 50, 25 and 0% v/v; water–methanol). Each extract was tested for α-glucosidase inhibition, followed by analysis using liquid chromatography tandem to mass spectrometry. The data were further subjected to multivariate data analysis by means of an orthogonal partial least square in order to correlate the chemical profile and the bioactivity. The loading plots revealed that the m/z signals correspond to the activity of α-glucosidase inhibitors, which led to the identification of three putative bioactive compounds, namely 5′-hydroxymethyl-1′-(1, 2, 3, 9-tetrahydro-pyrrolo (2, 1-b) quinazolin-1-yl)-heptan-1′-one (1), α-terpinyl-β-glucoside (2), and machaeridiol-A (3). Molecular docking of the identified inhibitors was performed using Auto Dock Vina software against the crystal structure of Saccharomyces cerevisiae isomaltase (Protein Data Bank code: 3A4A). Four hydrogen bonds were detected in the docked complex, involving several residues, namely ASP352, ARG213, ARG442, GLU277, GLN279, HIE280, and GLU411. Compound 1, 2, and 3 showed binding affinity values of −8.3, −7.6, and −10.0 kcal/mol, respectively, which indicate the good binding ability of the compounds towards the enzyme when compared to that of quercetin, a known α-glucosidase inhibitor. The three identified compounds that showed potential binding affinity towards the enzymatic protein in molecular docking interactions could be the bioactive compounds associated with the traditional use of this plant.

scholarly journals Open access in silico tools to predict the ADMET profiling and PASS (Prediction of Activity Spectra for Substances of Bioactive compounds of Garlic (Allium sativum L.)

2021 ◽  
Author(s):  
Ivan Vito Ferrari
Keyword(s):  
Open Access ◽  
Bioactive Compounds ◽  
In Silico ◽  
Allium Sativum ◽  
Bioactive Components ◽  
Bioinformatics Tool ◽  
Prediction Tools ◽  
Open Access Database ◽  
Database Servers ◽  
In Silico Tools

Background: Garlic (Allium sativum L.) is a common spice with many health benefits, mainly due to its diverse bioactive compounds, (see below) such as organic sulphides, saponins, phenolic compounds, and polysaccharides. Several studies have demonstrated its functions such as anti-inflammatory, antibacterial, and antiviral, antioxidant, cardiovascular protective and anticancer property. In this work we have investigated the main bioactive components of garlic through a bioinformatics approach. Indeed, we are in an era of bioinformatics where we can predict data in the fields of medicine. Approaches with open access in silico tools have revolutionized disease management due to early prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically designed and eco-friendly next-generation drugs. Methods: This paper encompasses the fundamental functions of open access in silico prediction tools, as PASS database (Prediction of Activity Spectra for Substances) that it estimates the probable biological activity profiles for compounds. This paper also aims to help support new researchers in the field of drug design and to investigate best bioactive compounds in garlic. Results: Screening through each of pharmacokinetic criteria resulted in identification of Garlic compounds that adhere to all the ADMET properties. Conclusions: It was established an open-access database (PASS database, available bioinformatics tool SwissADME, PreADMET pkCSM database) servers were employed to determine the ADMET (metabolism, distribution, excretion, absorption, and toxicity) attributes of garlic molecules and to enable identification of promising molecules that follow ADMET properties.

scholarly journals STUDI MOLECULAR DOCKING SENYAWA GOLONGAN FLAVONOL SEBAGAI ANTIBAKTERI

2018 ◽  
Vol 1 (2) ◽  
pp. 20-27
Author(s):  
Isna Wardaniati ◽  
Muhammad Azhari Herli
Keyword(s):  
Molecular Docking ◽  
Binding Energy ◽  
Binding Affinity ◽  
Bioactive Compounds ◽  
In Silico ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Three Dimensional Structure ◽  
Receptor Interactions

In this paper we studied the bioactive compounds of Flavonol-D-alanil D-alanin dekarboksipeptidase receptor interactions In silico. First, prepared three dimensional structure of D-alanil D-alanin dekarboksipeptidase as receptor. Preparation of fourth bioactive compounds of flavonol which will be as ligands, klokasilin and D-alanil D-alanin as a comparison. The fourth bioactive compounds of flavonol, klokasilin and D-alanil D-alanin were docked with D-alanil D-alanin dekarboksipeptidase until energy values were obtained. The fourth bioactive compounds of flavonol had lesser binding energy values than D-alanil D-alanin, Quercitrine and rutin also predicted to have greater binding energy and binding affinity than klokasilin (antibiotic) and D-alanil D-alanin (nature ligand).

scholarly journals GC-MS- and NMR-Based Metabolomics and Molecular Docking Reveal the Potential Alpha-Glucosidase Inhibitors from Psychotria malayana Jack Leaves

2021 ◽  
Vol 14 (10) ◽  
pp. 978
Author(s):  
Tanzina Sharmin Nipun ◽  
Alfi Khatib ◽  
Zalikha Ibrahim ◽  
Qamar Uddin Ahmed ◽  
Irna Elina Redzwan ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Amino Acid ◽  
Bioactive Compounds ◽  
In Silico ◽  
Partial Least Square ◽  
Least Square ◽  
Amino Acid Residues ◽  
Phytochemical Study ◽  
Glucosidase Inhibitors ◽  
Alpha Glucosidase

Psychotria malayana Jack leaf, known in Indonesia as “daun salung”, is traditionally used for the treatment of diabetes and other diseases. Despite its potential, the phytochemical study related to its anti-diabetic activity is still lacking. Thus, this study aimed to identify putative inhibitors of α-glucosidase, a prominent enzyme contributing to diabetes type 2 in P. malayana leaf extract using gas chromatography-mass spectrometry (GC-MS)- and nuclear magnetic resonance (NMR)-based metabolomics, and to investigate the molecular interaction between those inhibitors and the enzyme through in silico approach. Twenty samples were extracted with different solvent ratios of methanol–water (0, 25, 50, 75, and 100% v/v). All extracts were tested on the alpha-glucosidase inhibition (AGI) assay and analyzed using GC-MS and NMR. Multivariate data analysis through a partial least square (PLS) and orthogonal partial square (OPLS) models were developed in order to correlate the metabolite profile and the bioactivity leading to the annotation of the putative bioactive compounds in the plant extracts. A total of ten putative bioactive compounds were identified and some of them reported in this plant for the first time, namely 1,3,5-benzenetriol (1); palmitic acid (2); cholesta-7,9(11)-diene-3-ol (3); 1-monopalmitin (4); β-tocopherol (5); α-tocopherol (6); 24-epicampesterol (7); stigmast-5-ene (8); 4-hydroxyphenylpyruvic acid (10); and glutamine (11). For the evaluation of the potential binding modes between the inhibitors and protein, the in silico study via molecular docking was performed where the crystal structure of Saccharomyces cerevisiae isomaltase (PDB code: 3A4A) was used. Ten amino acid residues, namely ASP352, HIE351, GLN182, ARG442, ASH215, SER311, ARG213, GLH277, GLN279, and PRO312 established hydrogen bond in the docked complex, as well as hydrophobic interaction of other amino acid residues with the putative compounds. The α-glucosidase inhibitors showed moderate to high binding affinities (−5.5 to −9.4 kcal/mol) towards the active site of the enzymatic protein, where compounds 3, 5, and 8 showed higher binding affinity compared to both quercetin and control ligand.

scholarly journals Epitope-Based Peptide Vaccine Design Against Fructose Bisphosphate Aldolase of Candida Glabrata: An Immunomics Approach

2020 ◽  
Author(s):  
Elamin Elhasan LM ◽  
Mohamed B. Hassan ◽  
Reham M. Elhassan ◽  
Fatima A. Abdelrhman ◽  
Essam A. Salih ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Mhc Class Ii ◽  
In Silico ◽  
Candida Glabrata ◽  
Fungal Infections ◽  
Peptide Vaccine ◽  
Class Ii ◽  
Fructose Bisphosphate ◽  
Fructose Bisphosphate Aldolase ◽  
In Silico Tools

AbstractBackgroundCandida glabrata is a human opportunistic pathogen that can cause life-threatening systemic infections. Although, there are multiple effective vaccines against fungal infections, and some of these vaccines were engaged in different stages of clinical trials, none of them yet approved by (FDA).AimTo predict the most conserved and immunogenic B- and T-cell epitopes from the Fructose Bisphosphate aldolase (Fba1) protein of C. glabrata.Materials and Methods13 C. glabrata Fructose bisphosphate aldolase protein sequences (361amino acid) were retrieved from NCBI and several in silico tools presented in the IEDB server for predicting peptides were used and homology modeling and molecular docking were performed.ResultThe promising B-cell Epitopes were AYFKPH, VDKESLYTK, and HVDKESLYTK. While, promising peptides which have the high affinity to MHC I binding were: AVHEALAPI, KYFKRMAAM, QTSNGGAAY, RMAAMNQWL and YFKEHGEPL. Two peptides (LFSSHMLDL and YIRSIAPAY) were noted to have the highest affinity to MHC class II that interact with 9 MHC class II alleles. The molecular Docking revealed the epitopes QTSNGGAAY and LFSSHMLDL have the high binding energy to MHC moleculesConclusionThe epitope-based vaccines predicted by using immunoinformatics tools have remarkable advantages over the conventional vaccines that they are more specific, less time consuming, safe, less allergic and more antigenic. Further in vivo and in vitro experiments are needed to prove the effectiveness of the best candidates epitopes (QTSNGGAAY and LFSSHMLDL). To the best of our knowledge, this is the first study that has predicted B- and T-cells epitopes from Fba1 protein by using in silico tools in order to design an effective epitope-based vaccine against C. galabrata.

scholarly journals In Silico Approach of Potential Phytochemical Inhibitor from Moringa oleifera, Cocos nucifera, Allium cepa, Psidium guajava, and Eucalyptus globulus for the treatment of COVID-19 by Molecular Docking

2020 ◽  
Author(s):  
Ika Nur Fitriani ◽  
Wiji Utami ◽  
Adi Tiara Zikri ◽  
Pugoh Santoso
Keyword(s):  
Molecular Docking ◽  
Medicinal Plants ◽  
Oleanolic Acid ◽  
Allium Cepa ◽  
Bioactive Compounds ◽  
In Silico ◽  
Eucalyptus Globulus ◽  
Moringa Oleifera ◽  
Cocos Nucifera ◽  
Psidium Guajava

Abstract Background Coronavirus disease 2019 (COVID-19) is caused by infection with severe acute respiratory syndrome coronavirus 2. COVID-19 has devastating effects on people in all countries and getting worse. We aim to investigate an in-silico docking analysis of phytochemical compounds from medicinal plants that used to combat inhibition of the COVID-19 pathway. There are several phytochemicals in medicinal plants, however, the mechanism of bioactive compounds remains unclear. These results are obtained from in silico research provide further information to support the inhibition of several phytochemicals. Methods Molecular docking used to determine the best potential COVID-19 M pro inhibitor from several bioactive compounds in Moringa oleifera, Allium cepa, Cocos nucifera, Psidium guajava, and Eucalyptus globulus. Molecular docking was conducted and scored by comparison with standard drugs remdesivir. ADME properties of selected ligands were evaluated using the Lipinski Rule. The interaction mechanism of the most recommended compound predicted using the STITCH database. Results There was no recommended compound in Moringa oleifera as a potential inhibitor for COVID-19. Oleanolic acid in Allium cepa, α-tocotrienol in Cocos nucifera, asiatic acid in Psidium guajava and culinoside in Eucalyptus globulus were the most recommended compound in each medicinal plant. Oleanolic acid was reported to exhibit anti-COVID-19 activity with binding energy was − 9.20 kcal/mol. This score was better than remdesivir as standard drug. Oleanolic acid interacted through the hydrogen bond with HIS41, THR25, CYS44, GLU166. Oleanolic acid binding with CASP-3, CASP-9, and XIAP signaling pathway. Conclusions Oleanolic acid in Allium cepa found as a potential inhibitor of COVID-19 M-pro that should be examined in future studies. These results suggest that oleanolic acid may be useful in COVID-19 treatment.

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