Benchmarking AlphaFold ‐enabled molecular docking predictions for antibiotic discovery
Name
Molecular Systems Biology - 2022 - Wong - Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic.pdf
Description
Published version
Size
2.23 MB
Format
Adobe PDF
Checksum (MD5)
10e1a2898f3440fa7e5ccc64f9d04e9a
Author(s)
Wong, Felix
Krishnan, Aarti
Zheng, Erica J
Stärk, Hannes
Manson, Abigail L
Earl, Ashlee M
Jaakkola, Tommi
Collins, James J
Date Issued
2022
Journal
Molecular Systems Biology
Publisher
EMBO
Citation
Wong, Felix, Krishnan, Aarti, Zheng, Erica J, Stärk, Hannes, Manson, Abigail L et al. 2022. "Benchmarking AlphaFold ‐enabled molecular docking predictions for antibiotic discovery." Molecular Systems Biology, 18 (9).
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.15252/MSB.202211081
