Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment.

Journal: Frontiers in chemistry
Published Date: Jan 9, 2025

Abstract

INTRODUCTION: Treatment of type 2 diabetes (T2D) remains a significant challenge because of its multifactorial nature and complex metabolic pathways. There is growing interest in finding new therapeutic targets that could lead to safer and more effective treatment options. Takeda G protein-coupled receptor 5 (TGR5) is a promising antidiabetic target that plays a key role in metabolic regulation, especially in glucose homeostasis and energy expenditure. TGR5 agonists are attractive candidates for T2D therapy because of their ability to improve glycemic control. This study used machine learning-based models (ML), molecular docking (MD), and molecular dynamics simulations (MDS) to explore novel small molecules as potential TGR5 agonists.

Authors

  • Ojochenemi A Enejoh
    Genetics, Genomics and Bioinformatics Department, National Biotechnology Research and Development Agency, Abuja, Nigeria.
  • Chinelo H Okonkwo
    Department of Pharmacy, National Hospital Abuja, Abuja, Nigeria.
  • Hector Nortey
    Department of Clinical Pathology, Noguchi Memorial Institute for Medical Research, College of Health Science, University of Ghana, Accra, Ghana.
  • Olalekan A Kemiki
    Molecular and Tissue Culture Laboratory, Babcock University, Ilisan-remo, Ogun State, Nigeria.
  • Ainembabazi Moses
    African Centers of Excellence in Bioinformatics and data intensive sciences, Department of Immunology and Microbiology, Makerere University, Makerere, Uganda.
  • Florence N Mbaoji
    Department of Pharmacology and Toxicology, Faculty of Pharmaceutical Sciences, University of Nigeria, Nsukka, Enugu, Nigeria.
  • Abdulrazak S Yusuf
    Department of Biochemistry, Faculty of Basic Health Science, Bayero University, Kano, Nigeria.
  • Olaitan I Awe
    African Society for Bioinformatics and Computational Biology, Cape Town, South Africa.

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