Characterizing the Conformational States of G Protein Coupled Receptors Generated with AlphaFold
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866929729988198400 |
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| author | Chib, Garima Mollaei, Parisa Farimani, Amir Barati |
| author_facet | Chib, Garima Mollaei, Parisa Farimani, Amir Barati |
| contents | G-Protein Coupled Receptors (GPCRs) are integral to numerous physiological processes and are the target of approximately one-third of FDA-approved therapeutics. Despite their significance, only a limited subset of GPCRs has been successfully targeted, primarily due to challenges in accurately modeling their structures. AlphaFold, a state-of-the-art deep learning model, has demonstrated remarkable capability in predicting protein structures with high accuracy. This study conducts an evaluation of AlphaFold performance in predicting GPCR structures and their conformational states by comparing its predictions to experimentally determined structures using metrics such as average deformation between alpha carbon atoms and the Helix 3 - Helix 6 (H3-H6) distance. Our analysis reveals that both AlphaFold 2 (AF2) and AlphaFold 3 (AF3) produce more accurate predictions for GPCRs in inactive conformations, with lower activity levels correlating with smaller deformations. Conversely, higher activity levels are associated with increased variability in AlphaFold performance due to difficulties with accurately predicting conformational changes upon GPCR activation and ligand binding. Additionally, AlphaFold performance varies across different GPCR classes, influenced by the availability and quality of training data as well as the structural complexity and diversity of the receptors. These findings demonstrate the potential of AlphaFold in advancing drug discovery efforts, while also highlighting the necessity for continued refinement to enhance predictive accuracy for active conformations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17628 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Characterizing the Conformational States of G Protein Coupled Receptors Generated with AlphaFold Chib, Garima Mollaei, Parisa Farimani, Amir Barati Quantitative Methods Biomolecules G-Protein Coupled Receptors (GPCRs) are integral to numerous physiological processes and are the target of approximately one-third of FDA-approved therapeutics. Despite their significance, only a limited subset of GPCRs has been successfully targeted, primarily due to challenges in accurately modeling their structures. AlphaFold, a state-of-the-art deep learning model, has demonstrated remarkable capability in predicting protein structures with high accuracy. This study conducts an evaluation of AlphaFold performance in predicting GPCR structures and their conformational states by comparing its predictions to experimentally determined structures using metrics such as average deformation between alpha carbon atoms and the Helix 3 - Helix 6 (H3-H6) distance. Our analysis reveals that both AlphaFold 2 (AF2) and AlphaFold 3 (AF3) produce more accurate predictions for GPCRs in inactive conformations, with lower activity levels correlating with smaller deformations. Conversely, higher activity levels are associated with increased variability in AlphaFold performance due to difficulties with accurately predicting conformational changes upon GPCR activation and ligand binding. Additionally, AlphaFold performance varies across different GPCR classes, influenced by the availability and quality of training data as well as the structural complexity and diversity of the receptors. These findings demonstrate the potential of AlphaFold in advancing drug discovery efforts, while also highlighting the necessity for continued refinement to enhance predictive accuracy for active conformations. |
| title | Characterizing the Conformational States of G Protein Coupled Receptors Generated with AlphaFold |
| topic | Quantitative Methods Biomolecules |
| url | https://arxiv.org/abs/2502.17628 |