Structure prediction of protein-ligand complexes from sequence information with Umol

Bryant, P.; Kelkar, A.; Guljas, A.; Clementi, C.; Noe, F.

doi:10.1101/2023.11.03.565471

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bioRxiv · 10.1101/2023.11.03.565471

Structure prediction of protein-ligand complexes from sequence information with Umol

Bryant, P.·Kelkar, A.·Guljas, A.·Clementi, C.·Noe, F.

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Abstract

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, given a multiple sequence alignment representation of the protein and a SMILES string representing the ligand. At a high accuracy threshold, unseen protein-ligand complexes can be predicted more accurately than for RoseTTAFold-AA, and at medium accuracy even classical docking methods that use known protein structures as input are surpassed. The high accuracy presented here suggests that the goal of AI-based drug discovery is one step closer, but there is still a way to go to fully grasp the complexity of protein-ligand interactions. Umol is available at: https://github.com/patrickbryant1/Umol

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Bryant, P., Kelkar, A., Guljas, A., Clementi, C., Noe, F.. 2023-11-05. Structure prediction of protein-ligand complexes from sequence information with Umol. https://doi.org/10.1101/2023.11.03.565471

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