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Structure prediction of protein-ligand complexes from sequence information with Umol
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Structure prediction of protein-ligand complexes from sequence information with Umol
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Journal Article
Structure prediction of protein-ligand complexes from sequence information with Umol
2024
Overview
Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure is required and often the protein is treated as fully or partially rigid. Here we develop an AI system that can predict the fully flexible all-atom structure of protein-ligand complexes directly from sequence information. We find that classical docking methods are still superior, but depend upon having crystal structures of the target protein. In addition to predicting flexible all-atom structures, predicted confidence metrics (plDDT) can be used to select accurate predictions as well as to distinguish between strong and weak binders. The advances presented here suggest that the goal of AI-based drug discovery is one step closer, but there is still a way to go to grasp the complexity of protein-ligand interactions fully. Umol is available at:
https://github.com/patrickbryant1/Umol
.
Here the authors report the AI system Umol that predicts flexible all-atom structures of protein-ligand complexes from sequence information, advancing AI-driven drug discovery: accurate structures and affinity can be selected from predicted confidence metrics (plDDT).
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
