โก Quick Summary
This study compared experimental structures of the GPR101 receptor, linked to X-linked acrogigantism (X-LAG), with computational models generated through artificial intelligence methods. The findings highlight the superiority of AI-generated models, particularly the AlphaFold2 model, in accurately capturing complex structural features.
๐ Key Details
- ๐ฌ Focus: GPR101 receptor and its role in X-LAG
- ๐งช Methods: Comparison of experimental structures and computational models
- ๐ค AI Technology: AlphaFold2 and AlphaFold-Multistate
- ๐ Key Findings: AI models showed greater accuracy, especially in complex regions
๐ Key Takeaways
- ๐ GPR101 is implicated in the genetic condition X-LAG.
- ๐งฌ Experimental structures were solved using cryo-electron microscopy.
- ๐ค AI methods like AlphaFold2 demonstrated high fidelity in modeling.
- ๐ Homology models also performed well but were generally outperformed by AI.
- ๐ Molecular dynamics simulations had variable effects on model accuracy.
- ๐ Insights contribute to the understanding of GPCR modeling.
- ๐ Study published in the Journal of Molecular Graphics and Modelling.
- ๐๏ธ Citation: Costanzi S, et al. 2025; 140:109103.
๐ Background
G protein-coupled receptors (GPCRs) are crucial for various physiological processes and are often implicated in diseases. The GPR101 receptor, in particular, has been linked to X-linked acrogigantism, a condition characterized by excessive growth. Understanding the structure of GPR101 is essential for developing targeted therapies, and advancements in structural biology, particularly through AI, are paving the way for more accurate models.
๐๏ธ Study
The study aimed to compare the experimental structures of the GPR101 receptor obtained via cryo-electron microscopy with previously published computational models. These included both homology models and those generated using AI techniques like AlphaFold2. The researchers sought to evaluate the accuracy of these models in predicting the receptor’s structure and its interactions with G proteins.
๐ Results
The results indicated that the AlphaFold2 model excelled in capturing intricate structural features, particularly the challenging second extracellular loop. While the homology model based on the ฮฒ2-adrenergic receptor accurately predicted the G protein binding mode, it was the AI-generated models that generally provided superior accuracy. Notably, the molecular dynamics simulations did not consistently enhance model accuracy across all domains.
๐ Impact and Implications
This research underscores the transformative potential of artificial intelligence in structural biology, particularly for GPCRs where experimental structures are lacking. The findings suggest that AI methods can significantly improve our understanding of receptor structures, which is vital for drug discovery and therapeutic development. As we continue to explore these technologies, we may unlock new avenues for treating conditions like X-LAG and beyond.
๐ฎ Conclusion
The study highlights the importance of integrating AI in structural modeling of GPCRs. With AI-generated models demonstrating remarkable accuracy, researchers can better understand complex receptor structures, paving the way for innovative therapeutic strategies. The future of drug discovery looks promising as we harness the power of AI to enhance our knowledge of biological systems.
๐ฌ Your comments
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Comparing models and experimental structures of the GPR101 receptor: Artificial intelligence yields highly accurate models.
Abstract
Experimental structures solved through cryo-electron microscopy have recently been published for GPR101, a G protein-coupled receptor (GPCR) implicated in the genetic condition X-linked acrogigantism (X-LAG). Here, we compared these experimental structures with computational models that we previously published, including our internally developed homology models and third-party models generated through the AlphaFold2 and AlphaFold-Multistate artificial intelligence (AI) methods. Our analysis revealed considerable accuracy for both homology models and AI-generated models. However, it also revealed the general superiority of AI methods. Particularly noteworthy is the model generated by AlphaFold2, which captured with high fidelity various structural aspects, including the challenging second extracellular loop. Our previously published homology model of the GPR101-Gs protein complex, based on the ฮฒ2-adrenergic receptor, accurately predicted the binding mode of the G protein to the receptor. Moreover, this model predicted the structure of the sixth transmembrane domain (TM6) significantly more accurately than the others, including those built through AI methods, suggesting that homology modeling based on templates solved in complex with the G protein of interest might be the most reliable way of modeling this transmembrane domain. Lastly, our analysis revealed that our molecular dynamics simulations did not have a significant and consistent effect on the accuracy of the models, increasing the accuracy for some domains while decreasing it for others. This work provides insights into the relative strengths of different modeling approaches for our case study on GPR101. More broadly, when considered alongside other assessment studies, it contributes to the growing body of knowledge that can guide the modeling of GPCRs for which experimental structures are not yet available.
Author: [‘Costanzi S’, ‘Stahr LG’, ‘Trivellin G’, ‘Stratakis CA’]
Journal: J Mol Graph Model
Citation: Costanzi S, et al. Comparing models and experimental structures of the GPR101 receptor: Artificial intelligence yields highly accurate models. Comparing models and experimental structures of the GPR101 receptor: Artificial intelligence yields highly accurate models. 2025; 140:109103. doi: 10.1016/j.jmgm.2025.109103