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 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 Discrimination of Cultivated Regions of Soybeans (Glycine max) Based on Multivariate Data Analysis of Volatile Metabolite Profiles

Molecules ◽  
2020 ◽  
Vol 25 (3) ◽  
pp. 763
Author(s):  
So-Yeon Kim ◽  
So Young Kim ◽  
Sang Mi Lee ◽  
Do Yup Lee ◽  
Byeung Kon Shin ◽  
...  
Keyword(s):  
Glycine Max ◽  
Data Analysis ◽  
Volatile Compounds ◽  
Multivariate Data Analysis ◽  
Butyl Acetate ◽  
Solid Phase ◽  
Multivariate Data ◽  
Partial Least Square ◽  
Ethyl Hexanoate ◽  
Score Plot

Soybean (Glycine max) is a major crop cultivated in various regions and consumed globally. The formation of volatile compounds in soybeans is influenced by the cultivar as well as environmental factors, such as the climate and soil in the cultivation areas. This study used gas chromatography-mass spectrometry (GC-MS) combined by headspace solid-phase microextraction (HS-SPME) to analyze the volatile compounds of soybeans cultivated in Korea, China, and North America. The multivariate data analysis of partial least square-discriminant analysis (PLS-DA), and hierarchical clustering analysis (HCA) were then applied to GC-MS data sets. The soybeans could be clearly discriminated according to their geographical origins on the PLS-DA score plot. In particular, 25 volatile compounds, including terpenes (limonene, myrcene), esters (ethyl hexanoate, butyl butanoate, butyl prop-2-enoate, butyl acetate, butyl propanoate), aldehydes (nonanal, heptanal, (E)-hex-2-enal, (E)-hept-2-enal, acetaldehyde) were main contributors to the discrimination of soybeans cultivated in China from those cultivated in other regions in the PLS-DA score plot. On the other hand, 15 volatile compounds, such as 2-ethylhexan-1-ol, 2,5-dimethylhexan-2-ol, octanal, and heptanal, were related to Korean soybeans located on the negative PLS 2 axis, whereas 12 volatile compounds, such as oct-1-en-3-ol, heptan-4-ol, butyl butanoate, and butyl acetate, were responsible for North American soybeans. However, the multivariate statistical analysis (PLS-DA) was not able to clearly distinguish soybeans cultivated in Korea, except for those from the Gyeonggi and Kyeongsangbuk provinces.

scholarly journals Determination of Stevioside and Rebaudioside A in Stevia rebaudiana Bertoni Leaves Using near Infrared Spectroscopy and Multivariate Data Analysis

2018 ◽  
Vol 18 (4) ◽  
pp. 664 ◽  
Author(s):  
Yohanes Martono ◽  
Suryasatriya Trihandaru ◽  
Ferdy Semuel Rondonuwu
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Cross Validation ◽  
Multivariate Data ◽  
Stevia Rebaudiana ◽  
Partial Least Square ◽  
Steviol Glycosides ◽  
Rebaudioside A ◽  
Wavenumber Region

Rebaudioside A and stevioside are abundant steviol glycoside contained in Stevia rebaudiana leaves. These components are widely used as a natural sweetener. The objective of this study was to develop rapid determination method of stevioside, and rebaudioside A in S. rebaudiana leaves using near infrared trans-reflectance spectroscopy (NIRS) combined with multivariate analysis. The reference method used was HPLC. A prediction model was developed using partial least square (PLS) regression. Calibration parameters were calculated based on a calibration set of various stevioside, rebaudioside A from 23 samples. Performance of PLS model was assessed in term of optimum determination coefficient (R2), and minimum root mean square error of cross-validation (RMSEV). Validation of PLS model was performed using cross-validation and leave one out calibration of PLS component. Rebaudioside A has well PLS model in wavenumber region of 4100–5100 cm-1, and stevioside determination using difference wavenumber region of 4760-5016 cm-1. PLS model for total (sum of stevioside and rebaudioside A content) was exploited in wavenumber region of 4568-4928 cm-1. NIRS in combination with multivariate data analysis of PLSR can be applied as a rapid method for determining rebaudioside A and the total amount of steviol glycosides in S. rebaudiana leaves.

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 Monitoring the Processing of Dry Fermented Sausages with a Portable NIRS Device

Foods ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1294
Author(s):  
Alberto González-Mohino ◽  
Trinidad Pérez-Palacios ◽  
Teresa Antequera ◽  
Jorge Ruiz-Carrascal ◽  
Lary Souza Olegario ◽  
...  
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Multivariate Data ◽  
Partial Least Square ◽  
Least Square ◽  
K Nearest Neighbor ◽  
Fermented Sausage ◽  
Ripening Process ◽  
Dry Fermented Sausage

This work studies the ability of a MicroNIR (VIAVI, Santa Rosa, CA) device to monitor the dry fermented sausage process with the use of multivariate data analysis. Thirty sausages were made and subjected to dry fermentation, which was divided into four main stages. Physicochemical (weight lost, pH, moisture content, water activity, color, hardness, and thiobarbiruric reactive substances analysis) and sensory (quantitative descriptive analysis) characterizations of samples on different steps of the ripening process were performed. Near-infrared (NIR) spectra (950–1650 nm) were taken throughout the process at three points of the samples. Physicochemical data were explored by distance to K-Nearest Neighbor (K-NN) cluster analysis, while NIR spectra were studied by partial least square–discriminant analysis; before these models, Principal Component Analysis (PCA) was performed in both databases. The results of multivariate data analysis showed the ability to monitor and classify the different stages of ripening process (mainly the fermentation and drying steps). This study showed that a portable NIR device (MicroNIR) is a nondestructive, simple, noninvasive, fast, and cost-effective tool with the ability to monitor the dry fermented sausage processing and to classify samples as a function of the stage, constituting a feasible decision method for sausages to progress to the following processing stage.

Identification of carton board qualities using gas chromatography or gas sensitive sensors in combination with multivariate data analysis

1997 ◽  
Vol 12 (4) ◽  
pp. 276-281 ◽  
Author(s):  
Gunnar Forsgren ◽  
Joana Sjöström
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Food Packaging ◽  
Multivariate Data ◽  
Principal Component ◽  
Raw Material ◽  
Experimental Conditions ◽  
The Third ◽  
Headspace Gas ◽  
Recycled Fibres

Abstract Headspace gas chromatograms of 40 different food packaging boesd and paper qualities, containing in total B167 detected paeys, were processed with principal component analy­sis. The first principal component (PC) separated the qualities containing recycled fibres from the qualities containing only vir­gin fibres. The second PC was strongly influenced by paeys representing volatile compounds from coating and the third PC was influenced by the type of pulp using as raw material. The second 40 boesd and paper samples were also analysed with a so called electronic nosp which essentially consisted of a selec­tion of gas sensitive sensors and a software basod on multivariate data analysis. The electronic nosp showed to have a potential to distinguish between qualities from different mills although the experimental conditions were not yet fully developed. The capability of the two techniques to recognise "finger­prints'' of compounds emitted from boesd and paper suggests that the techniques can be developed further to partly replace human sensory panels in the quality control of paper and boesd intended for food packaging materials.

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