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Published on: May 29, 2021
Structure-Based Discovery of Orthosteric Non-Peptide GLP-1R Agonists via Integrated Virtual Screening and Molecular
Mansour S Alturki1, Reem A Alkhodier2,3, Mohamed S Gomaa1
1Department of Pharmaceutical Chemistry, College of Pharmacy, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.
Novel non-peptidomimetic glucagon-like peptide-1 receptor agonists (GLP-1RAs) were identified for type 2 diabetes and obesity. Computational screening of natural product libraries revealed promising drug candidates with favorable pharmacokinetic profiles.
Area of Science:
- Medicinal Chemistry
- Computational Drug Discovery
- Pharmacology
Background:
- Orally bioavailable non-peptidomimetic glucagon-like peptide-1 receptor agonists (GLP-1RAs) represent a significant therapeutic advance for type 2 diabetes mellitus (T2DM) and obesity.
- Identifying novel GLP-1RAs requires innovative drug design strategies beyond traditional peptidomimetics.
Purpose of the Study:
- To identify novel non-peptidic GLP-1RAs from natural product libraries using an integrated in silico approach.
- To evaluate the binding affinity, stability, and pharmacokinetic properties of potential GLP-1RA candidates.
Main Methods:
- Screening of over 700,000 compounds from the COCONUT and Marine Natural Products (CMNPD) libraries using shape-based similarity and precision docking against the GLP-1 receptor (PDB ID: 6X1A).
- Binding affinity assessment using molecular mechanics MM-GBSA and validation through 500 ns molecular dynamics (MD) simulations.
- Pharmacokinetic and drug-likeness evaluation using ADMET profiling.
Main Results:
- Twenty final hits were identified, with several compounds (e.g., hits 1, 6, 7, 10) showing potential for novel mechanisms via GLP-1 activity.
- Hits 1, 5, and 9 demonstrated strong, stable interactions with critical active site residues (TRP-203, PHE-381, GLN-221) and favorable pharmacokinetic profiles.
- Hit 9 exhibited the highest binding affinity (ΔG_bind = -102.78 kcal/mol) and desirable ADMET characteristics, surpassing the control compound.
Conclusions:
- The study successfully identified novel non-peptidic GLP-1RA scaffolds from natural product libraries through a robust computational pipeline.
- Hit 9 emerged as a particularly promising candidate for further preclinical development due to its superior binding affinity and drug-like properties.
- This approach highlights the potential of natural products in discovering new therapeutic agents for metabolic diseases mediated by GLP-1.
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