Anti-diabetic potentials of L-prolinamides: a computational study

L-Prolinamides are emerging as promising scaffolds in medicinal chemistry due to their favorable electronic properties and structural versatility. This study employed a comprehensive computational approach, including molecular docking, density functional theory (DFT), molecular dynamics (MD) simulations, and binding free energy calculations, to evaluate a series of L-prolinamides as potential multi-target inhibitors against key Type 2 diabetes (T2D) enzymes: α-amylase, α-glucosidase, and dipeptidyl peptidase-4 (DPP-4). Out of 27 screened compounds, six (Q10, Q14, Q19, Q22, Q24, and Q26) demonstrated superior binding profiles relative to standard inhibitors. Binding free energy calculations revealed that Q14 and Q10 interacted effectively with α-amylase, with ΔGbind values of − 49.51 and − 45.18 kcal/mol, respectively, closely approaching that of acarbose (− 55.20 kcal/mol). Against α-glucosidase, Q24 emerged as the most potent (− 55.79 kcal/mol), outperforming miglitol (− 48.05 kcal/mol), while Q22, Q26, and Q19 also showed competitive binding. Quantum chemical analysis computed at the B3LYP/6–311 + + G(d,p) level of theory indicated Q10 had the lowest LUMO energy (− 2.43 eV), enhancing electron acceptance, while Q24 had the highest HOMO value (− 5.98 eV), promoting electron donation. MD simulations further confirmed the structural stability of the top ligand enzyme complexes. In silico pharmacokinetic assessments supported their drug-likeness and oral bioavailability. Collectively, these findings highlight L-prolinamides, particularly Q10 and Q24, as promising multi-target candidates for T2D therapy and warrant further experimental exploration.

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Publication Details

Journal
Discover Chemistry.
Published
2025-09-18
DOI
https://doi.org/10.1007/s44371-025-00281-1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Anti-diabetic potentials of L-prolinamides: a computational study

Vincent A. Obakachi, Oluwole B. Familoni, Adejoke Deborah Osinubi, Oluwaseun H. Anselm et al.
Discover Chemistry.
Computational Drug Discovery Methods
article

Anti-diabetic potentials of L-prolinamides: a computational study

Vincent A. Obakachi, Oluwole B. Familoni, Adejoke Deborah Osinubi, Oluwaseun H. Anselm, Olayinka T. Asekun, Samuel Oluwadare Olalekan, Segun D. Oladipo, Abosede Adejoke Badeji
article en

Abstract

L-Prolinamides are emerging as promising scaffolds in medicinal chemistry due to their favorable electronic properties and structural versatility. This study employed a comprehensive computational approach, including molecular docking, density functional theory (DFT), molecular dynamics (MD) simulations, and binding free energy calculations, to evaluate a series of L-prolinamides as potential multi-target inhibitors against key Type 2 diabetes (T2D) enzymes: α-amylase, α-glucosidase, and dipeptidyl peptidase-4 (DPP-4). Out of 27 screened compounds, six (Q10, Q14, Q19, Q22, Q24, and Q26) demonstrated superior binding profiles relative to standard inhibitors. Binding free energy calculations revealed that Q14 and Q10 interacted effectively with α-amylase, with ΔGbind values of − 49.51 and − 45.18 kcal/mol, respectively, closely approaching that of acarbose (− 55.20 kcal/mol). Against α-glucosidase, Q24 emerged as the most potent (− 55.79 kcal/mol), outperforming miglitol (− 48.05 kcal/mol), while Q22, Q26, and Q19 also showed competitive binding. Quantum chemical analysis computed at the B3LYP/6–311 + + G(d,p) level of theory indicated Q10 had the lowest LUMO energy (− 2.43 eV), enhancing electron acceptance, while Q24 had the highest HOMO value (− 5.98 eV), promoting electron donation. MD simulations further confirmed the structural stability of the top ligand enzyme complexes. In silico pharmacokinetic assessments supported their drug-likeness and oral bioavailability. Collectively, these findings highlight L-prolinamides, particularly Q10 and Q24, as promising multi-target candidates for T2D therapy and warrant further experimental exploration.

Discover Chemistry.Vol. 2(1)
University of Lagos (NG), University of Johannesburg (ZA), Stellenbosch University (ZA), Olabisi Onabanjo University (NG), Tai Solarin University of Education (NG)
Center for High Performance Computing
Openalex Percentile: Top 73%
Computational Drug Discovery Methods
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