Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure–activity relationship-based artificial intelligence and molecular docking of hit compounds

Hermansyah, Oky; Bustamam, Alhadi; Yanuar, Arry

doi:10.1016/j.compbiolchem.2021.107597

Cited by 15 publications

(7 citation statements)

References 55 publications

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“…Taken to gather, one promising compound (ZINC000003015356) established strong hydrogen bonds and hydrophobic interactions with important residues, Arg125, Asp545, Tyr547, His740, Glu206, and Ser 630 at the binding site of the DPP4 enzyme which is essential for selectivity 19 , 32 . ZINC000003015356 had better docking scores than that of the FDA-approved drugs for diabetes, Sitagliptin.…”

Section: Resultsmentioning

confidence: 99%

“…During decades much research was performed to find the DPP4 inhibitors, for example, Tanwar et al introduced hydrazine analogs by using virtual screening workflow (VSW) and molecular dynamics simulation for DPP4 inhibitory activity selective against DPP8 and DPP9 18 . In addition, Hermansyah et al found CH0002 as a potent DPP4 agent with low selectivity for DPP8 and DPP9 receptors using the VS in combination with the QSAR approach and artificial intelligence 19 . By the VS, the new non-peptides were found and evaluated as DPP4 inhibitors by Alonso et al 20 .…”

Section: Introductionmentioning

confidence: 99%

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A combination of virtual screening, molecular dynamics simulation, MM/PBSA, ADMET, and DFT calculations to identify a potential DPP4 inhibitor

Zare,

Ataollahi,

Mardaneh

et al. 2024

Sci Rep

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DPP4 inhibitors can control glucose homeostasis by increasing the level of GLP-1 incretins hormone due to dipeptidase mimicking. Despite the potent effects of DPP4 inhibitors, these compounds cause unwanted toxicity attributable to their effect on other enzymes. As a result, it seems essential to find novel and DPP4 selective compounds. In this study, we introduce a potent and selective DPP4 inhibitor via structure-based virtual screening, molecular docking, molecular dynamics simulation, MM/PBSA calculations, DFT analysis, and ADMET profile. The screened compounds based on similarity with FDA-approved DPP4 inhibitors were docked towards the DPP4 enzyme. The compound with the highest docking score, ZINC000003015356, was selected. For further considerations, molecular docking studies were performed on selected ligands and FDA-approved drugs for DPP8 and DPP9 enzymes. Molecular dynamics simulation was run during 200 ns and the analysis of RMSD, RMSF, Rg, PCA, and hydrogen bonding were performed. The MD outputs showed stability of the ligand–protein complex compared to available drugs in the market. The total free binding energy obtained for the proposed DPP4 inhibitor was more negative than its co-crystal ligand (N7F). ZINC000003015356 confirmed the role of the five Lipinski rule and also, have low toxicity parameter according to properties. Finally, DFT calculations indicated that this compound is sufficiently soft.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A combination of virtual screening, molecular dynamics simulation, MM/PBSA, ADMET, and DFT calculations to identify a potential DPP4 inhibitor

Zare,

Ataollahi,

Mardaneh

et al. 2024

Sci Rep

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“…DPP4 has gained importance as a key therapeutic target over the last few years due to its primary role in increasing endogenous GLP-1 levels, which is found to be important for improved glycemic control and systemic glucose homeostasis. Though there are several approved DPP4 inhibitors, they are relatively expensive and are associated with some side effects such as pancreatitis, joint pain and a possible increased risk of cancer (Mohanty et al, 2019;Kalhotra et al, 2018;Hermansyah et al, 2021;Shirakawa and Terauchi, 2020). Hence there is an increasing interest in identifying new compounds that are cheap, safe and The present study is an attempt to provide transdisciplinary understanding for the antidiabetic action of a clinically established formulation called NK with a special focus on DPP4 inhibition activity.…”

Section: Discussionmentioning

confidence: 99%

In vitroandin silicoanalysis proving DPP4 inhibition and diabetes associated gene network modulation by a polyherbal formulation –Nisakathakadi Kashaya

Thottappillil

Sahoo

Chakraborty

et al. 2022

Preprint

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Frontiers of disease biology started recognizing the importance of systems and network medicine approach for managing chronic disease like diabetes. Polyherbal preparations like Ayurveda formulations are known to exert a multicomponent-multitargeted mode of action that makes them an ideal tool for delineating new biological insights into this systemic mechanism of disease manifestation and management. Additionally, these formulations are rich repository for identifying novel ligands that interact with drug targets having systemic effects. The current study aims at identifying DPP4 inhibitory potential and modulation of diabetes associated gene network by a clinically established Ayurvedic anti-diabetic formulation Nisakathakadi Kashaya (NK) using in vitro and in silico methods. DPP4 inhibitory potential of NK was evaluated by standard enzyme inhibition assay. Bioinformatics and computational biology tools were used to identify the potential bioactives responsible for the DPP4 inhibitory activity of NK. Molecular docking and dynamics studies of the identified compounds provided insights about the molecular interactions involved in DPP4 inhibition. Target mapping of phytochemicals using network pharmacology tools viz. STITCH, CHEMBL and BindingDB databases was used to depict the multi-targeted interaction of the formulation and a sub network for diabetes related genes and their relationship with other diabetes associated comorbidities were depicted with the help of EnrichR. NK demonstrated a dose dependent DPP4 inhibition with an IC50 of 2.06 μg GAE/mL. Further, the in silico studies identified three compounds namely Terchebin, Locaracemoside B and 1,2,4,6 Tetra o Galloyl Beta D Glucose showing stable interactions with DPP4 when compared to the standard drug Vildagliptin. Network pharmacology studies with the phytochemicals identified from NK revealed a number of genes like TNF, TGFβ, SOD1, SOD2, AKT1, DPP4 and GLP1R in its protein-protein interaction network which are vital to diabetic progression and complications. This suggests that a polyherbal formulation like NK can exert its action through various phytomolecules present. The present work demonstrated that the polyherbal formulation NK has DPP4 inhibition potential and modulates a large number of diabetes related genes and pathways. The approach adopted in the current study by combining in vitro and in silico methods allowed us to understand the mechanism of DPP4 inhibition by the formulation and also the possible pharmacological networking through which the formulation modulate diverse disease related targets.

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“…A total of 1,149,497 published papers were assembled in the Web of Science Core Collection (WoSCC) until June 2023 on the topics of "diabetes", 930,263 of which have been published after the year 2000 The scientific community's interest in the management of this disease has grown considerably (about 40%), observing a significant increase in scientific publications from approximately 17,000 in the year 2000 to approximately 70,000 in the year 2022 (Figure 1). So, in the early stage of drug design, in silico requirements have been managed by using various computational approaches, such as pharmacophore modeling [28][29][30][31], quantitative structure-activity relationships (QSAR) [32][33][34][35], molecular docking [36][37][38][39], molecular dynamics simulation [40][41][42], DFT simulation [35,[43][44][45][46], etc. These techniques have generated notable interest by reducing the time required for experimental trials, as well as human and resource costs.…”

Section: Introductionmentioning

confidence: 99%

“…Investigating the binding interactions of DPP-4 inhibitors at the binding site is essential for gaining insights into their effectiveness and for providing guidance in the exploration of new drug candidates. The crystal structure of DPP-4 displays a homodimeric configuration, with two chains, chain A and chain B, and it consists of four domains (a cytoplasmic domain (1-6), a transmembrane domain (TMD) , a flexible stalk segment (29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39), and the extracellular domain (40-766) with five subsites: S1 (SER630, VAL656, TRP659, TYR662, TYR666, ASN710, VAL711), S2 (ARG125, GLU205, GLU206, PHE357, ARG358, ARG669), S1 (PHE357, TYR547, PRO550, SER630, TYR631, TYR666), S2 (TYR547, TRP629, SER630, HIS740), and S2 extensive (VAL207, SER209, PHE357, ARG358) [52,53]. The mandatory ligand's interactions for DPP-4 inhibition with S1 and S2 subunits were observed both for alogliptin within the 3GB0 binding site, linagliptin within the 2RGU binding site, and sitagliptin within the 1 × 70 binding site.…”

Section: Introductionmentioning

confidence: 99%

Dipeptidyl Peptidase 4 Inhibitors in Type 2 Diabetes Mellitus Management: Pharmacophore Virtual Screening, Molecular Docking, Pharmacokinetic Evaluations, and Conceptual DFT Analysis

Istrate,

Crisan

2023

Processes

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Dipeptidyl Peptidase 4 (DPP-4) expressed on the surface of many different cells is a promising target to develop new candidates for Type 2 diabetes mellitus (T2DM) management. In this light, we performed a computer-aided simulation involving 3-D pharmacophore screening, molecular docking, and drug-likeness assessment to identify novel potential DPP-4 inhibitors with an improved physicochemical profile to treat T2DM. In addition, global reactivity descriptors, including HOMO and LUMO energies, HOMO-LUMO gaps, and Fukui indices, were computed to confirm the essential structural features to achieve DPP-4 activity. The gathered outcomes recommend that eight out of 240 million compounds collected from eight pre-built databases (Molport, Chembl30, ChemDiv, ChemSpace, Mcule, Mcule-ultimate, LabNetwork, and ZINC) are drug-like and nontoxic, and may serve as starting points for designing novel, selective, and potent DPP-4 inhibitors. Furthermore, the success of the current workflow to identify DPP-4-potential inhibitors strengthens its potential efficiency to also predict natural compounds as novel adjutants or main therapy for T2DM or discover hit compounds of other targets.

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Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure–activity relationship-based artificial intelligence and molecular docking of hit compounds

Cited by 15 publications

References 55 publications

A combination of virtual screening, molecular dynamics simulation, MM/PBSA, ADMET, and DFT calculations to identify a potential DPP4 inhibitor

A combination of virtual screening, molecular dynamics simulation, MM/PBSA, ADMET, and DFT calculations to identify a potential DPP4 inhibitor

In vitroandin silicoanalysis proving DPP4 inhibition and diabetes associated gene network modulation by a polyherbal formulation –Nisakathakadi Kashaya

Dipeptidyl Peptidase 4 Inhibitors in Type 2 Diabetes Mellitus Management: Pharmacophore Virtual Screening, Molecular Docking, Pharmacokinetic Evaluations, and Conceptual DFT Analysis

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