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Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery.

Felix Wong^1,2,3, Aarti Krishnan^1,2,3, Erica J Zheng^3,4

¹Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA.

Molecular Systems Biology

|September 6, 2022

Summary

Predicting drug targets is hard. Combining AlphaFold2 with docking showed many interactions, but performance was weak until machine learning rescoring improved accuracy for antibacterial drug discovery.

Keywords:

AlphaFold2 enzymatic activity machine learning molecular docking protein-ligand interactions

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Area of Science:

Computational biology
Drug discovery
Structural bioinformatics

Background:

Identifying drug mechanisms of action (MoA) is a significant challenge in drug discovery.
Computational docking relies on protein structures, with recent advances from AlphaFold2 enabling more accurate predictions.
Understanding protein-ligand interactions is crucial for developing novel therapeutics.

Purpose of the Study:

To predict protein-ligand interactions using AlphaFold2 and molecular docking.
To evaluate the performance of computational models in identifying antibacterial compound targets.
To explore the potential of machine learning for enhancing prediction accuracy.

Main Methods:

Combined AlphaFold2 structural predictions with molecular docking simulations.
Screened 218 active and 100 inactive antibacterial compounds against 296 essential Escherichia coli proteins.
Benchmarked model performance using enzymatic activity assays for 12 essential proteins.
Applied machine learning-based rescoring to docking poses to improve prediction accuracy.

Main Results:

Identified widespread promiscuity between antibacterial compounds and Escherichia coli proteins.
Initial docking model performance was weak, with an average area under the receiver operating characteristic curve (auROC) of 0.48.
Machine learning rescoring significantly improved model performance, achieving average auROCs up to 0.63.
Ensembles of rescoring functions further enhanced prediction accuracy and the true-positive to false-positive rate ratio.

Conclusions:

While AlphaFold2 and docking show promise, current models exhibit limitations in predicting antibacterial compound targets.
Machine learning-based rescoring is a critical advancement for improving the accuracy of protein-ligand interaction predictions.
Further development of computational approaches, especially those incorporating machine learning, is necessary to effectively utilize structural data for drug discovery.