Benchmarking Reverse Docking through AlphaFold2 Human Proteome
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Abstract
Predicting binding of a small molecule to the human proteome by reverse docking methods, we can predict the target interactions of drug compounds in the human body, as well as further evaluate their potential off-target effects or toxic side effects. In this study, we constructed 11 pipelines to evaluate and benchmark thoroughly the predictive capabilities of these reverse docking pipelines. The pipelines were built using site prediction tools (PointSite and SiteMap) based on the AF2 human proteome, docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, OnionNet-SFCT). The results show that pipeline glide_sfct (PS) exhibited the best target prediction ability and successfully predicted the similar proteins of native targets. This finding provides important clues for understanding the promiscuity between the drug ligand and the whole human proteome. In general, our study has the potential to increase the success rate and reduce the development timeline of drug discovery, thereby saving costs.
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The Uniprot IDs of the proteins and the predicted binding sites by PointSite have been deposited at https://github.com/molu851-luo/Reverse-docking-benchmark.
It's great that this has been made available! It would be awesome if you could add documentation in the github repo about how others might be able to use this resource either using the workflows/pipelines you describe in the paper or with other modified pipelines, since it isn't really clear from the outset how to use this data for different purposes.
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those lacking well-defined ligand binding pockets, were removed
how was this determined?
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Introduction
This was a great overall summary of the approaches in the field and current challenges/caveats!
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