Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery. Issue 9 (6th September 2022)

• Record Type: Journal Article
• Authors: Wong, Felix; Krishnan, Aarti; Zheng, Erica J; Stärk, Hannes; Manson, Abigail L; Earl, Ashlee M; Jaakkola, Tommi; Collins, James J
• Is Part Of: Molecular systems biology. Volume 18:Issue 9(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-06
• Subjects: AlphaFold2 -- enzymatic activity -- machine learning -- molecular docking -- protein‐ligand interactions; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.202211081
• Languages: English
• ISSNs: 1744-4292

Abstract

Abstract: 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. Synopsis: Assessing molecular docking simulations based on AlphaFold2‐predicted structures with high‐throughput measurements of protein‐ligand interactions reveals weak model performance. Machine learning‐based approaches improve performance and better harness AlphaFold2 for drug discovery. AlphaFold2‐based molecular docking predictions for 296 Escherichia coli proteins, 218 active antibacterial compounds and 100 inactive compounds predict widespread promiscuity and similar distributions of binding affinities between active and inactive compounds. Quantitative enzymatic inhibition assays for 12 essential E. coli proteins treated with each of the 218 antibacterial compounds confirm extensive promiscuity. The enzymatic inhibition dataset reveals that the performance of the molecular docking model is weak. Rescoring of docking poses using machine learning‐based scoring functions improves model performance. Abstract : Assessing molecular docking simulations based on AlphaFold2‐predicted structures with high‐throughput measurements of protein‐ligand interactions reveals weak model performance. Machine learning‐based approaches improve performance and better harness AlphaFold2 for drug discovery.