ArticleJournal of chemical information and modeling2023
Activity Map and Transition Pathways of G Protein-Coupled Receptor Revealed by Machine Learning.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- Computer-aided structural modeling and drug discovery for G-protein-coupled receptors in the age of artificial intelligence.Current opinion in structural biology · 2026Review
- Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identification.Journal of chemical theory and computation · 2025Article
- Evolutionary scenarios for the specific recognition of nonhomologous endogenous peptides by G protein-coupled receptor paralogs.The Journal of biological chemistry · 2025Article
- Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties.Journal of chemical information and modeling · 2025Article
- Leveraging Artificial Intelligence in GPCR Activation Studies: Computational Prediction Methods as Key Drivers of Knowledge.Methods in molecular biology (Clifton, N.J.) · 2025Article
- IDP-Bert: Predicting Properties of Intrinsically Disordered Proteins Using Large Language Models.The journal of physical chemistry. B · 2024Article
- Interaction and dynamics of chemokine receptor CXCR4 binding with CXCL12 and hBD-3.Communications chemistry · 2024Article
- GradNav: Accelerated Exploration of Potential Energy Surfaces with Gradient-Based Navigation.Journal of chemical theory and computation · 2024Article
- GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models.Journal of chemical information and modeling · 2024Article
- Unveiling Switching Function of Amino Acids in Proteins Using a Machine Learning Approach.Journal of chemical theory and computation · 2023Article
- PeptideBERT: A Language Model Based on Transformers for Peptide Property Prediction.The journal of physical chemistry letters · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Approximately, one-third of all U.S. Food and Drug Administration approved drugs target G protein-coupled receptors (GPCRs). However, more knowledge of protein structure-activity correlation is required to improve the efficacy of the drugs targeting GPCRs. In this study, we developed a machine learning model to predict the activation state and activity level of the receptors with high prediction accuracy. Furthermore, we applied this model to thousands of molecular dynamics trajectories to correlate residue-level conformational changes of a GPCR to its activity level. Finally, the most probable transition pathway between activation states of a receptor can be identified using the state-activity information. In addition, with this model, we can associate the contribution of each amino acid to the activation process. Using this method, we can design drugs that mainly target principal amino acids driving the transition between activation states of GPCRs. Our advanced method is generalizable to all GPCR classes and provides mechanistic insight into the activation mechanism in the receptors.
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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.