Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery

By Felix Wong, Aarti Krishnan, Erica J. Zheng, Hannes Stärk, A. Manson, A. Earl, Tommi S. Jaakkola, James J. Collins. 2022

218 citations recorded by the source corpus.

Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein‐ligand interactions between 296 proteins spanning Escherichia coli's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning‐based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true‐positive rate to false‐positive rate. This work indicates that advances in modeling protein‐ligand interactions, particularly using machine learning‐based approaches, are needed to better harness AlphaFold2 for drug discovery.

Subfields: Computational Theory and Mathematics, Organic Chemistry, Pharmacology

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Citations in the corpus

AlphaFold2 and its applications in the fields of biology and medicine (2023, background): “Wong et al. 102 combined molecular docking simulations with AF2 for protein-ligand interactions prediction.”
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking (2022, methodology): “The top-ranked ligand pose and the associated confidence are then taken as D IFF D OCK ’s top-1 prediction and confidence score.”
How accurately can one predict drug binding modes using AlphaFold models? (2023, unknown): “, 2021)—for tasks related to drug design, with mixed results (Díaz-Rovira et al., 2023; He et al., 2022; Heo & Feig, 2022; C. Lee et al., 2022; S. Lee et al., 2022; Liang et al., 2022; Qiao et al., 2022; Scardino et al., 2022; Wong et al., 2022).”
Computational approaches streamlining drug discovery (2023, background): “…targets lacking experimental structures, especially for target classes with less obvious structural homologies in the ligand-binding pocket, the performance of AlphaFold2 models in small-molecule docking showed disappointing results in recent assessments for GPCR and antibacterial targets 107,108 .”