Cited by 284Computational Drug Discovery MethodsProtein Structure and Dynamicsvaccines and immunoinformatics approaches

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Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery

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Wong, Felix, Krishnan, Aarti, Zheng, Erica J, Stärk, Hannes, Manson, Abigail L, Earl, Ashlee M, Jaakkola, Tommi, Collins, James J·2022·Molecular Systems Biology

DOI 10.15252/msb.202211081PMC PMC9446081

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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.

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UCSF Chimera—A visualization system for exploratory research and analysis
Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20084 · Cited by 45,882 · cited 3×
“We used a Python script that employs the DockPrep function from UCSF Chimera (Pettersen et al , 2004; Allen, 2018) to add hydrogens and partial charges.”
Method
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · cited 8×
“This is consistent with previous assessments of AlphaFold’s fidelity to experimentally determined protein structures (Jumper et al , 2021) and suggests that the weak performance of our model arises from the docking method, and not the quality of protein structures.”
BackgroundMethodResultInfluential
The Protein Data Bank
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · cited 9×
“As positive controls for our docking simulations, we re-docked 11 experimentally evidenced protein-ligand complexes from the RCSB Protein Data Bank (Dataset EV2).”
MethodInfluential
AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings
Eberhardt, J., Santos‐Martins, D., Tillack, A. F., & Forli, S. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00203 · Cited by 8,292 · cited 5×
“Different software for performing molecular docking are widely available and commonly used platforms include AutoDock Vina (Eberhardt et al , 2021 ) and DOCK (Allen et al , 2015 ).”
MethodInfluential
The complete genome sequence of Escherichia coli K-12.
Blattner, F., Plunkett, G., Bloch, C., Perna, N., Burland, V., Riley, M., Collado-Vides, J., Glasner, J. D., Rode, C., Mayhew, G., et al. (1997) · Science · doi:10.1126/science.277.5331.1453 · Cited by 7,996 · cited 2×
“We shortlisted genes identified in at least two of these three studies, resulting in a total of 295 genes out of ∼ 4,000 total genes in E. coli (Blattner et al , 1997).”
Method
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1061 · Cited by 7,850 · cited 3×
“As shown in the workflow here, in order to leverage AlphaFold 2 for docking, we first downloaded all 296 AlphaFold 2 -predicted E. coli essential protein structures from the AlphaFold Protein Structure Database (Jumper et al , 2021 ; Varadi et al , 2022 ).”
Influential
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,549 · cited 2×
“The advent of AlphaFold2 and other deep neural networks for protein folding, such as RoseTTAFold (Baek et al , 2021; Humphreys et al , 2021), has been widely anticipated and celebrated in structural biology.”
Background
Docking and scoring in virtual screening for drug discovery: methods and applications
Kitchen, D. B., Decornez, H. Y., Furr, J. R., & Bajorath, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1549 · Cited by 4,354 · cited 1×
“Various approaches to identifying molecular drug targets have been developed, including those based on biochemical assays, genetic interactions, and molecular docking (Kitchen et al , 2004; Schenone et al , 2013).”
Background
Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery
Meng, X.-Y., Zhang, H.-X., Mezei, M., & Cui, M. (2011) · Current Computer Aided-Drug Design · doi:10.2174/157340911795677602 · Cited by 3,771 · cited 1×
“Model predictions based on the following rescoring functions were ensembled in sequence: RF-Score, NNScore, PLEC score, and RF-Score-VS.”
Method
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025

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“…(Jumper et al, 2021; Baek et al, 2021), there have been several successful attempts to accelerate and improve the docking process with the use of machine learning (Corso et al, 2022; Stärk et al, 2022; Wong et al, 2022; Méndez-Lucio et al, 2021; Lu et al, 2022; Zhou et al, 2023; Isert et al, 2023).”
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FAIR data for optical tweezers experiments
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“Several investigational drug compounds have been discovered using AlphaFold and require further validation (28,29), experimentally determined structures still have a higher success rate for predicting drug interactions with proteins (30,31).”
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Fluorescein diacetate (FDA) should not be used to study human carboxylesterase 2 (CES2) in complex biological systems without validation
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Exploring Novel Inhibitory Compounds Against Phosphatase Gamma 2: A Therapeutic Target for Male Contraceptives
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“In parallel, docking validation was performed by extracting co-crystalized ligands from a PDB ID: 4QDI and re-docked with the mentioned PDB MurF protein using the same procedure discussed above [28].”
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AlphaFold for Docking Screens
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“33 In our study, we utilized ESM-Fold and DiffDock2 to model protein structure and docking of antibiotic molecules, along with GNINA to refine docking poses and calculate binding affinity properties.”
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“Only the metabolites that met the cutoff RMSD between targeted and blind poses and had binding affinities � − 7.0 Kcal mol − 1 (Wong et al., 2022) were considered valid and further analyzed.”
Influential
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Authors: Wong, F., Krishnan, A., Zheng, E. J., Stärk, H., Manson, A. L., Earl, A. M., Jaakkola, T., & Collins, J. J.
Affiliations: Broad Institute (US)
MIT Computer Science and Artificial Intelligence Laboratory (US)
Corresponding author: Collins, James J
ORCID: Wong, Felix
Krishnan, Aarti
Stärk, Hannes
Manson, Abigail L
Earl, Ashlee M
Jaakkola, Tommi
Collins, James J
Venue: Molecular Systems Biology
Publisher: Springer Science and Business Media LLC
Published: 2022-09-01
Volume: 18
Issue: 9
Pages: e11081-e11081
ISSN: 1744-4292 (ISSN-L)
DOI: 10.15252/msb.202211081
PMC: PMC9446081
OpenAlex: W4294719209
Cited by: 284
References: 52
Citations by year: 2026: 61 · 2025: 85 · 2024: 70 · 2023: 60 · 2022: 8
FWCI: 30.16
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (tdm)
creativecommons.org/licenses/by/4.0/ (vor, 5 d)
Locations: Molecular Systems Biology (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
Funders: Broad Institute of MIT and Harvard
Flu Lab, LLC
HHS¦NIH¦National Institute of Allergy and Infectious Diseases (U19AI110818, R01‐AI146194)
HHS¦NIH¦National Institute of General Medical Sciences (P41‐GM103311)
James S. McDonnell Foundation
Rosamund Zander and Hansjorg Wyss
Sea Grape Foundation
Swiss National Science Foundation (SNSF_ 203071)
The Audacious Project
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords: AlphaFold2, molecular docking, protein-ligand interactions, antibiotic discovery, antibacterial agents, machine learning, drug target prediction, benchmarking
MeSH terms: Anti-Bacterial Agents, Ligands, Protein Binding, Proteins, Benchmarking, Molecular Docking Simulation

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Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery

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