GC-MS Analysis and In Silico Approaches of Indigofera heterantha Root Oil Chemical Constituents
by
, ,
Muhammad Aurang Zeb
1,2,*
,
Taj Ur Rahman
3,*,
Muhammad Sajid
2,
Weilie Xiao
1,*,
Syed Ghulam Musharraf
4,
Shabana Bibi
5,
Takashiro Akitsu
6 and
Wajiha Liaqat
3 1
Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education and Yunnan Province, School of Chemical Science and Technology, Yunnan University, Kunming 650091, China
2
Department of Biochemistry, Hazara University, Mansehra 21120, Pakistan
3
Department of Chemistry, Mohi-Ud-Din Islamic University, AJ&K 12080, Pakistan
4
H.E.J. Research Institute of Chemistry, ICCBS, University of Karachi, Karachi 75270, Pakistan
5
Yunnan Herbal Laboratory, School of Ecology and Environmental Sciences, Yunnan University, Kunming 650091, China
6
Department of Chemistry, Faculty of Science Division II, Tokyo University of Science, Tokyo 162-8601, Japan
*
Authors to whom correspondence should be addressed.
Compounds 2021, 1(3), 116-124; https://doi.org/10.3390/compounds1030010
Submission received: 1 July 2021
/
Revised: 10 August 2021
/
Accepted: 31 August 2021
/
Published: 28 September 2021
Abstract
:The phytochemical investigation on Indigofera heterantha root oil resulted in the identification of 121 phytochemicals using GC-MS analysis. These phytochemicals were docked against alpha-amylase, alpha-glucosidase enzymes. The docking results suggested that Hexacosyl acetate (121) possess alpha-amylase inhibitory potential with a docking score of −8.2944994 and the interaction with alpha-glucosidase enzyme was −9.73762512, followed by 9, 12, 15-Octadecatrienoic acid, 2, 3-dihydroxypropyl ester, (Z, Z, Z)-(83) with a docking score of −9.59869957, showed outstanding results in all the stages of the study and may be shown as the most auspicious phytochemical resulting from the docking studies of the new antidiabetic drug. Additionally, Pharmacokinetic and PASS studies revealed their drug-likeness, expected safety upon consumption, and likely pharmacological effects.
1. Introduction
Diabetes mellitus is a dangerous metabolic illness with a great rate of death globally [1,2]. Unfortunately, no treatment is available for diabetes up to now, but it can be controlled by a proper managing of sugar levels in blood via physical exercises, a healthy diet, and the use of different synthetic antidiabetic medicines, which can decrease the danger of continuing diabetes difficulties [3]. Though, the effectiveness of these synthetic antidiabetic drugs is deficient because of harmful adverse effects including the damage of the liver, hypoglycemia, gaining weight, diarrhea, stomachache, flatulence, drug resistance, and heart illness attached with the high cost of medications [4,5]. So, there is a vital requirement for producing natural antidiabetic medications with a high safety margin. Natural products and medicinal plants have remained active as bases of remedy for improving human suffering since early times [6]. Plants are known to be the primary basis of health-promoting metabolites which are accountable for numerous well-being promoting properties such as antidiabetic activity [7].
The genus Indigofera consists of about seven hundred species and is distributed widely in tropical areas [8], its chemical constituents and extracts possess several bioactivities such as antimicrobial, antiulcerogenic, anti-insecticidal, hepatotoxic, phytotoxic, and cytotoxicity [9]. The compounds like indigoferamide-A [10], hiptagin and endecaphyllin A1 [11], (S) indispicine [12], rutin [13], indigotin, louisfieserone [14], dibenzofuran, benzofuran [15], genistein, 12-oleanen-3, 11-dione, isoliquiritigenin, afromosin [16], arabinofuranoside [17], β-Sitosterol and Stigmasterol [18] have been isolated from various species of genus Indigofera. Other important secondary metabolites like myricetin, caffeic acid, gallic acid, quinines, saponins, tannins, myricetin, quercetin, and galangin were also reported from Indigofera species [8].
I. heterantha roots possess antioxidant, anti-diabetic, anti-inflammatory activities. [19,20]. The crude fractions of the aerial parts exhibited cytotoxicity activity [21]. Numerous compounds have been reported from the I. heterantha with lipoxygenase inhibitory activity [22].
According to our information, this is the first study to be carried out on the gas chromatography-mass spectrometry (GC-MS) analysis, and in silico molecular docking, Absorption, Distribution, Metabolism, Excretion and Toxicity (ADME/T), and Prediction of Activity Spectra for Substances (PASS) investigation of I. heteratha root oil so far. The aim of this research was to assess the interaction of these identified phytochemicals with target proteins such as alpha-amylase and alpha-glucosidase to find novel information for the development of effective antidiabetic inhibitors.
2. Materials and Methods
2.1. Plant Materials
I. heterantha roots were collected during September 2015, from the Swat district, located in the Khyber Pakhtunkhwa (K.P.K) province of Pakistan, which is famous for medicinal plants in Pakistan. It was identified at the Department of Botany, Hazara University, Mansehra, Pakistan.
2.2. Extraction and Isolation
The powdered plant materials (5 kg) were extracted by application of maceration in methanol for 10 days with (10 L) solvent at room temperature. The methanolic crude extract was then concentrated by a vacuum rotary evaporator to collect semi-dried extract (900 g). Further, the semi-dried methanolic crude extract was soaked in distilled water and fractionated with several organic solvents such as n-hexane, chloroform, ethyl acetate, and methanol, three-time each by using a separating funnel. The ethyl acetate fraction was further subjected to column chromatography using Silica gel (70–230 mesh) and eluted with 100% n-hexane that resulted in the isolation of three oil samples labeled as IhA, IhB, and IhC.
2.3. Instrumentations
The evaluation was conducted on a 7890A GC-MS Triple Quad system (Agilent Technologies, Santa Clara, USA). An HP-5MS 30 m–250 mm (i.d.) fused-silica capillary column (Agilent J&W Scientific, Folsom, CA, USA), chemically bonded with a 5% diphenyl 95% dimethylpolysiloxane cross-linked stationary phase (0.25 mm film thickness) was used. The helium was used as a carrier gas at 1.0 mL/min in split mode, the sample was inserted with volume 1.5 μL with specific linear diameter. The injector and source temperatures were 250 °C. In the beginning, the temperature of the oven was 40 °C and later it was increased at 10 °C/min to 300 °C, so it was kept for 9 min. In post-run, the temperature was set to 305 °C where it kept the same for 1 min. The mass spectrometer was operated in EI mode (70 eV). Data acquisition was performed in the full scan mode from m/z 50 to 650 with a scan time of 0.5 s. Data evaluation was carried out using the Agilent Mass Hunter Qualitative Analysis (Version B.04.00). Compounds were screened out using the NIST mass spectral library (Wiley registry).
2.4. Molecular Docking Study
To predict the bioactive conformations, the identified compounds were docked into the binding pockets of the selected proteins (enzymes) by using the default parameters of the MOE program. Before docking the ligands into protein molecules, the 3D conformations were downloaded from the PubChem database. The ligands were converted to “mol” files from “sdf” format using Open Babel GUI software. Energy minimization was carried out up to 0.1 gradients by using Amber 12: EHT force field through the default parameter of the MOE energy minimization algorithm. The 3D crystal structure of the alpha-amylase enzyme (PDB ID: 3BAI with resolution 1.9 Å) and alpha-glucosidase enzyme (PDB ID: 2QMJ with resolution 1.9 Å) were retrieved from RCSB PDB (http://www.rcsb.org/pdb/home/home.do). All water molecules and ligands were removed from the receptor proteins and 3D protonation was carried out by using Protonate 3D Option. The energies of protein molecules were minimized by using the default parameters of the MOE 2015.10 energy minimization algorithm (gradient: 0.1, Force Field: Amber 12: EHT). Then, the ligands were docked into the binding pockets (selective residues/amino acids) of the above proteins using the default parameters of the MOE-Dock Program. The re-docking procedure was also applied to validate the accuracy of the docking protocol [23]. After docking, all the complex images were analyzed for specific types of interactions; bond lengths and their 2D images were taken.
2.5. In Silico ADME Study
The online web tool Swiss ADME [24] was used to evaluate the ADME parameters of the best-docked compounds using Lipinski’s rule of five [25]. Lipinski stated that a compound could display drug-like behavior if it does not fail more than one of the criteria such as; (i) MW not more than 500; (ii) Hydrogen bond donors ≤ 5; (iii) Hydrogen bond acceptors ≤ 10; (iv) Lipophilicity < 5; and (v) molar refractivity between 40 and 130. Those compounds are considered ideal drug candidates which obey the Lipinski rule.
2.6. In Silico Toxicity Prediction Study
Toxicity and Lethal Dose (LD50) predictions for identified compounds were carried out by using ProTox-II. (https://tox-new.charite.de/protox_II/, accessed on 1 July 2021).
2.7. In Silico PASS Prediction Study
The possible bioactivities of best-docked compounds were evaluated using the online web tool Prediction of Activity Spectra for Substances (PASS) [26], which predicts up to 3750 bioactivities of a compound, related with an examination of its chemical structure. The results of this examination were represented as Pa (probable activity) and Pi (probable inactivity), where the values of both Pa and Pi may vary from 0.000 to 1.000. We considered values of Pa > Pi and Pa > 0.700 to specify bioactivity for a molecule [27].
3. Results and Discussion
3.1. GC-MS Analysis
The GC–MS chromatogram of sample IhA recorded a total of 38 peaks corresponding to the bioactive compounds that were recognized by relating their data to that of the known compounds described by the National Institute of Standards and Technology (NIST) library (Figure S1). Results revealed that 38 compounds were identified in sample IhA which are listed in (Table S1) and (Figure S2). Similarly, The GC-MS chromatogram of the sample IhB recorded 53 peaks (Figure S3), the phytoconstituents identified in sample IhB are listed in (Figure S4) and (Table S2). In addition, the GC-MS chromatogram (Figure S5) of sample IhC recorded 30 peaks corresponding to bioactive compounds, which were recognized by comparing its mass spectra along with their analogs reported in the NIST library. The components identified in sample IhC are presented in (Table S3), (Figure S6). Overall, 121 compounds were identified from the three samples of fixed oil isolated from I. heterantha roots.
3.2. Molecular Docking Studies
The GC-MS study showed that the three samples of fixed oil IhA, IhB, and IhC isolated from the roots of I. heteratha contained 121 bioactive compounds (Tables S1–S3). All phytocompounds except 1-Heptatriacotanol (30) were analyzed for alpha-amylase and alpha-glucosidase docking studies. For molecular docking study proteins were prepared by 3D protonation, the energy of proteins was minimized and the active site was predicted for compounds, keeping the parameters at their defaults. Next, compounds were docked with the receptors, alpha-amylase (PDB ID: 3BAI) and alpha-glucosidase (PDB ID: 2QMJ). The docking results suggested that Hexacosyl acetate (121) was the most active of the tested compounds, with the docking score of −8.2944994, bound in the active site of alpha-amylase (PDB ID: 3BAI) by making one H-acceptor interaction with NH2 atom of ARG 421 amino acid residues with a bond distance of 2.94 angstrom. The energy calculated was −1.7 E/kcal·mol−1. The docking results and 2D interactions of the four best-docked compounds are shown in (Table 1 and Figure 1).
The docking results of alpha-glucosidase (Table 2 and Figure 2) show that Hexacosyl acetate (121) exbibit the highest docking score of −9.73762512, followed by 9, 12, 15-Octadecatrienoic acid, 2, 3-dihydroxypropyl ester, (Z, Z, Z)-(83) with a docking score of −9.59869957. In addition, Docosanoic acid, ethyl ester (85), and Tricosanoic acid, methyl ester (119) also showed favorable results. From the 2D interaction images, it was clear that O34 atom of 9, 12, 15-Octadecatrienoic acid, 2, 3-dihydroxypropyl ester, (Z, Z, Z)-(83) showed H-acceptor interaction with NZ atom of LYS 513 with a bond distance of 3.11 angstrom, the energy was calculated −2.1 E/kcal·mol−1. The O60 atom of the ligand showed H-acceptor interaction with the CE atom of LYS 534 with a bond distance of 3.28 angstrom with the energy calculated −0.8 E/kcal·mol−1. C47 atom of Docosanoic acid, ethyl ester (85) showed H-donor interaction with SD atom of MET 567 with a distance of 3.91 angstroms, the energy calculated was −0.8 E/kcal·mol−1. The O36 atom of Tricosanoic acid, methyl ester (119) also showed H-acceptor interaction with the NH2 atom of ARG 283 with a distance of 3.01 angstrom with calculated energy −1.5 E/kcal·mol−1. While Hexacosyl acetate (121) showed no such interaction.
3.3. ADME Analysis
The ADME properties of best-docked compounds were studied using the online program SwissADME to further investigate their pharmacokinetics, drug-likeness, and physiochemical properties. According to Lipinski’s rule of five, all the compounds except 9, 12, 15-Octadecatrienoic acid, 2, 3-dihydroxypropyl ester, (Z, Z, Z)-(83) violated rules of lipophilicity, compound Hexacosyl acetate (121) violated the rule of molecular refractivity. On the other hand, 9, 12, 15-Octadecatrienoic acid, 2, 3-dihydroxypropyl ester, (Z, Z, Z)-(83) met Lipinski’s conditions, which is considered to predict optimal drug-like character. No compound violated more than one rule (Table 3).
When a drug violates 2 or more of these conditions considered a non-orally available drug. But in this study, all the compounds reported 0 or 1 violations that suggested these are bioavailable or orally available drugs. According to Lipinski’s rule almost all the best-docked compounds showed orally active drug-likeness properties. It is reported that compounds with lower lipophilicity, molecular weight, and hydrogen bond capacity have high permeability [28], good absorption, and bioavailability [24,29]. However, this analysis does not assess if a compound has any particular pharmacological effect.
3.4. Toxicity Prediction
In ProTox-II, Compounds Docosanoic acid, ethyl ester (85), Ethyl tetracosanoate (86), Docosano, acetate (118), Hexacosyl acetate (121) predicted with carcinogenicity. In this study, all the compounds showed prediction for low toxicity while 9, 12, 15-Octadecatrienoic acid, 2, 3-dihydroxypropyl ester, (Z, Z, Z)-(83) is assumed to be non-toxic. Different compounds with their predicted toxicity class and LD50 value are expressed in (Table 4).
3.5. Biological Activity Prediction
The structure-based bioactivity prediction web tool Prediction of Activity Spectra for Substances (PASS) was utilized to evaluate the possible biological activity of the best-docked compounds. The PASS study revealed possible targets and biological activities of each compound. We studied five biological activities for each compound, based on the values of Pa > Pi and Pa > 7. The results suggested several important activities with Pa > 0.9, suggesting the broader potential of this species (Table 5).
4. Conclusions
In summary, the GC-MS analysis of the I. heterantha root oil revealed that this plant is a great source of phytoconstituents with excellent binding attraction to alpha-amylase and alpha-glucosidase in docking studies, and their drug-like properties were established through ADME and Toxicity studies. The PASS analysis method of phytoconstituents suggested many significant activities of the phytochemicals studied. The molecular docking results suggest that the 9, 12, 15-Octadecatrienoic acid, 2, 3-dihydroxypropyl ester, (Z, Z, Z)-(83) could be a prominent natural medicinal candidate against the alpha-glucosidase enzyme. Though, additional investigations are required to isolate the pure compound accountable for the detected bioactivity, and to depict its toxicity profile and longer-term safety.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/compounds1030010/s1, Figure S1: GC-MS chromatogram of IhA, Figure S2: Structure of chemical constituent identified in sample IhA 1–38, Figure S3: GC-MS chromatogram of IhB, Figure S4: Structure of chemical constituent identified in IhB 39–91, Figure S5: GC-MS chromatogram of IhC, Figure S6: Structure of chemical constituent identified in IhC 92–121, Table S1: List of identified compounds in IhA, Table S2: List of identified compounds in IhB, Table S3. List of identified compounds in IhC.
Author Contributions
Conceptualization, investigation, methodology, software, validation, and writing—original draft preparation: M.A.Z. Supervision, investigation, and resources: T.U.R., M.S. and W.X. Resources: S.G.M. and T.A. Proof Reading: S.B. and W.L. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Data Availability Statement
The data presented in this study are openly available in the article.
Acknowledgments
Authors are grateful to the Department of Biochemistry, Hazara University, Mansehra, Pakistan and Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education and Yunnan Province, School of Chemical Science and Technology, Yunnan University, Kunming, Yunnan, 650091, the People’s Republic of China for providing research facilities.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
2D images of the docked conformations of the compounds with the active residues of alpha-amylase.
Figure 1.
2D images of the docked conformations of the compounds with the active residues of alpha-amylase.
Figure 2.
2D images of the docked conformations of the compounds with the active residues of alpha-glucosidase.
Figure 2.
2D images of the docked conformations of the compounds with the active residues of alpha-glucosidase.
Table 1.
Summary of molecular docking analyses of top scored 4 compounds against alpha-amylase.
| Compound | Docking Score | Interacting Residues in the Binding Pocket | ||||
|---|---|---|---|---|---|---|
| Ligand | Receptor | Interaction | Distance Å | E/kcal·mol−1 | ||
| Ethyl tetracosanoate (86) | −8.25968075 | - | - | - | - | - |
| Docosano, acetate (118) | −7.71306992 | - | - | - | - | - |
| Tetracisanoic acid, methyl ester (120) | −7.7322011 | - | - | - | - | - |
| Hexacosyl acetate (121) | −8.2944994 | O7 | NH2: ARG 421 (A) | H-acceptor | 2.94 | −1.7 |
Table 2.
Summary of molecular docking analyses of top scored 4 compounds against alpha-glucosidase.
| Compound | Docking Score | Interacting Residues in the Binding Pocket | ||||
|---|---|---|---|---|---|---|
| Ligand | Receptor | Interaction | Distance Å | E/kcal·mol−1 | ||
| 9, 12, 15-Octadecatrienoic acid, 2, 3-dihydroxypropyl ester, (Z, Z, Z)- (83) | −9.59869957 | O34 | NZ LYS 513 (A) | H-acceptor | 3.11 | −2.1 |
| O60 | CE LYS 534 (A) | H-acceptor | 3.28 | −0.8 | ||
| Docosanoic acid, ethyl ester (85) | −9.34544277 | C47 | SD MET 567 (A) | H-donor | 3.91 | −0.8 |
| Tricosanoic acid, methyl ester (119) | −9.3228426 | O36 | NH2 ARG 283 (A) | H-acceptor | 3.01 | −1.5 |
| Hexacosyl acetate (121) | −9.73762512 | - | - | - | - | − |
Table 3.
ADME property prediction for the best-docked compounds.
| Compound | Smile ID | Molecular Weight 1 | HB Acceptor 2 | HB Donor 3 | Lipophilicity 4 | Molar Refractivity 5 | Rule of Five 6 |
|---|---|---|---|---|---|---|---|
| 9, 12, 15-Octadecatrienoic acid, 2, 3-dihydroxypropyl ester, (Z, Z, Z)-(83) | CC/C=C\C/C=C\C/C=C\CCCCCCCC(OCC(O)CO)=O | 352.51 | 4 | 2 | 4.70 | 105.25 | 0 |
| Docosanoic acid, ethyl ester (85) | CCCCCCCCCCCCCCCCCCCCCC(OCC)=O | 368.64 | 2 | 0 | 8.15 | 118.77 | 1 |
| Ethyl tetracosanoate (86) | O=C(OCC)CCCCCCCCCCCCCCCCCCCCCCC | 396.69 | 2 | 0 | 8.82 | 128.38 | 1 |
| Docosano, acetate (118) | O=C(C)OCCCCCCCCCCCCCCCCCCCCCC | 368.64 | 2 | 0 | 8.13 | 118.77 | 1 |
| Tricosanoic acid, methyl ester (119) | CCCCCCCCCCCCCCCCCCCCCCC(OC)=O | 368.64 | 2 | 0 | 8.08 | 118.77 | 1 |
| Tetracisanoic acid, methyl ester (120) | COC(CCCCCCCCCCCCCCCCCCCCCCC)=O | 382.66 | 2 | 0 | 8.39 | 123.57 | 1 |
| Hexacosyl acetate (121) | CC(OCCCCCCCCCCCCCCCCCCCCCCCCCC)=O | 424.74 | 2 | 0 | 9.58 | 138.00 | 1 |
1 Molecular weight (acceptable range: <500). 2 HB, Hydrogen bond acceptor (acceptable range: ≤10). 3 HB, Hydrogen bond donor (acceptable range: ≤5). 4 Lipophilicity (expressed as Log Po/w, acceptable range: <5). 5 Molar refractivity should be between 40 and 130. 6 Rule of five: Number of violations of Lipinski’s rule of five; recommended range: 0–4.
Table 4.
Toxicity prediction of best-docked compounds by ProTox-II.
| Compound | Predicted LD50, mg/kg a | Predicted Toxicity Class a | Predicted Toxicity |
|---|---|---|---|
| 9, 12, 15-Octadecatrienoic acid, 2, 3-dihydroxypropyl ester, (Z, Z, Z)-(83) | 39,800 | 6 | Inactive |
| Docosanoic acid, ethyl ester (85) | 5000 | 5 | Carcinogenicity |
| Ethyl tetracosanoate (86) | 5000 | 5 | Carcinogenicity |
| Docosano, acetate (118) | 3000 | 5 | Carcinogenicity |
| Tricosanoic acid, methyl ester (119) | 5000 | 5 | Inactive |
| Tetracisanoic acid, methyl ester (120) | 5000 | 5 | Inactive |
| Hexacosyl acetate (121) | 3000 | 5 | Carcinogenicity |
a ProTox (http://tox.charite.de/protox_II, accessed on 1 July 2021) Class 1: deadly if consumed (LD50 ≤ 5); Class 2: deadly if consumed (5 < LD50 ≤ 50); Class 3: lethal if consumed (50 < LD50 ≤ 300); Class 4: harmful if consumed (300 < LD50 ≤ 2000); Class 5: maybe harmful if consumed (2000 < LD50 ≤ 5000); Class 6: non-lethal (LD50 > 5000).
Table 5.
Prediction of biological activity of best-docked compounds.
| Compound | Pa a | Pi b | Biological Activity |
|---|---|---|---|
| 9, 12, 15-Octadecatrienoic acid, 2, 3-dihydroxypropyl ester, (Z, Z, Z)-(83) | 0.977 | 0.002 | Lipid metabolism regulator |
| 0.948 | 0.002 | Phosphatidate phosphatase inhibitor | |
| 0.938 | 0.001 | Alcohol O-acetyltransferase inhibitor | |
| 0.937 | 0.002 | All-trans-retinyl-palmitate hydrolase inhibitor | |
| 0.937 | 0.002 | Linoleate diol synthase inhibitor | |
| Docosanoic acid, ethyl ester (85) | 0.953 | 0.001 | All-trans-retinyl-palmitate hydrolase inhibitor |
| 0.946 | 0.001 | Cutinase inhibitor | |
| 0.934 | 0.003 | Acylcarnitine hydrolase inhibitor | |
| 0.930 | 0.002 | Alkanal monooxygenase (FMN-linked) inhibitor | |
| 0.924 | 0.003 | Sugar-phosphatase inhibitor | |
| Ethyl tetracosanoate (86) | 0.953 | 0.001 | All-trans-retinyl-palmitate hydrolase inhibitor |
| 0.946 | 0.001 | Cutinase inhibitor | |
| 0.934 | 0.003 | Acylcarnitine hydrolase inhibitor | |
| 0.930 | 0.002 | Alkanal monooxygenase (FMN-linked) inhibitor | |
| 0.924 | 0.003 | Sugar-phosphatase inhibitor | |
| Docosano, acetate (118) | 0.940 | 0.003 | Sugar-phosphatase inhibitor |
| 0.928 | 0.002 | Carboxypeptidase Taq inhibitor | |
| 0.929 | 0.004 | Phobic disorders treatment | |
| 0.926 | 0.002 | Dextranase inhibitor | |
| 0.924 | 0.002 | All-trans-retinyl-palmitate hydrolase inhibitor | |
| Tricosanoic acid, methyl ester (119) | 0.962 | 0.002 | Saccharopepsin inhibitor |
| 0.962 | 0.002 | Acrocylindropepsin inhibitor | |
| 0.962 | 0.002 | Chymosin inhibitor | |
| 0.942 | 0.003 | Acylcarnitine hydrolase inhibitor | |
| 0.942 | 0.003 | Polyporopepsin inhibitor | |
| Tetracisanoic acid, methyl ester (120) | 0.962 | 0002 | Saccharopepsin inhibitor |
| 0.962 | 0.002 | Acrocylindropepsin inhibitor | |
| 0.962 | 0.002 | Chymosin inhibitor | |
| 0.942 | 0.003 | Acylcarnitine hydrolase inhibitor | |
| 0.942 | 0.003 | Polyporopepsin inhibitor | |
| Hexacosyl acetate (121) | 0.940 | 0.003 | Sugar-phosphatase inhibitor |
| 0.928 | 0.002 | Carboxypeptidase Taq inhibitor | |
| 0.929 | 0.004 | Phobic disorders treatment | |
| 0.926 | 0.002 | Dextranase inhibitor | |
| 0.924 | 0.002 | All-trans-retinyl-palmitate hydrolase inhibitor |
a Pa = Possibility of activity; b Pi = Possibility of inactivity.
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Zeb, M.A.; Rahman, T.U.; Sajid, M.; Xiao, W.; Musharraf, S.G.; Bibi, S.; Akitsu, T.; Liaqat, W. GC-MS Analysis and In Silico Approaches of Indigofera heterantha Root Oil Chemical Constituents. Compounds 2021, 1, 116-124. https://doi.org/10.3390/compounds1030010
AMA Style
Zeb MA, Rahman TU, Sajid M, Xiao W, Musharraf SG, Bibi S, Akitsu T, Liaqat W. GC-MS Analysis and In Silico Approaches of Indigofera heterantha Root Oil Chemical Constituents. Compounds. 2021; 1(3):116-124. https://doi.org/10.3390/compounds1030010
Chicago/Turabian StyleZeb, Muhammad Aurang, Taj Ur Rahman, Muhammad Sajid, Weilie Xiao, Syed Ghulam Musharraf, Shabana Bibi, Takashiro Akitsu, and Wajiha Liaqat. 2021. "GC-MS Analysis and In Silico Approaches of Indigofera heterantha Root Oil Chemical Constituents" Compounds 1, no. 3: 116-124. https://doi.org/10.3390/compounds1030010
