ArticleFrontiers in bioengineering and biotechnology2021
Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- Bridging Generative AI and Diffusion Models With Molecular Simulation to Design Anti-Quorum-Sensing De novo Peptides Targeting LasR of Pseudomonas aeruginosa.Probiotics and antimicrobial proteins · 2026Article
- De novo generation and in silico screening of anti-diabetic peptide candidates via a deep learning-attention framework with physicochemical feature fusion.Scientific reports · 2026Article
- Effect of a Low Electrostatic Environment on the Helical Structures of Peptides and Proteins Using Flexible Water Models: An In Silico Study.ACS omega · 2025Article
- Targeted Penetrating Motif Engineering of BH3 Mimetic: Harnessing Non-Canonical Amino Acids for Coinhibition of MCL-1 and BCL-xL in Acute Myeloid Leukemia.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- Influence of the Water Model on the Structure and Interactions of the GPR40 Protein with the Lipid Membrane and the Solvent: Rigid versus Flexible Water Models.Journal of chemical theory and computation · 2024Article
- Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- Design of a multi-target focused library for antidiabetic targets using a comprehensive set of chemical transformation rules.Frontiers in pharmacology · 2023Article
- Effects of Oral Glucose-Lowering Agents on Gut Microbiota and Microbial Metabolites.Frontiers in endocrinology · 2022Review
- Real-time determination of flowering period for field wheat based on improved YOLOv5s model.Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
G protein-coupled receptor 40 (GPR40), one of the G protein-coupled receptors that are available to sense glucose metabolism, is an attractive target for the treatment of type 2 diabetes mellitus (T2DM). Despite many efforts having been made to discover small-molecule agonists, there is limited research focus on developing peptides acting as GPR40 agonists to treat T2DM. Here, we propose a novel strategy for peptide design to generate and determine potential peptide agonists against GPR40 efficiently. A molecular fingerprint similarity (MFS) model combined with a deep neural network (DNN) and convolutional neural network was applied to predict the activity of peptides constructed by unnatural amino acids (UAAs). Site-directed mutagenesis (SDM) further optimized the peptides to form specific favorable interactions, and subsequent flexible docking showed the details of the binding mechanism between peptides and GPR40. Molecular dynamics (MD) simulations further verified the stability of the peptide-protein complex. The R-square of the machine learning model on the training set and the test set reached 0.87 and 0.75, respectively; and the three candidate peptides showed excellent performance. The strategy based on machine learning and SDM successfully searched for an optimal design with desirable activity comparable with the model agonist in phase III clinical trials.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.