Benchmarking reverse docking through AlphaFold2 human proteome
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (Arcadia Science)
Abstract
Predicting the binding of ligands to the human proteome via reverse‐docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off‐target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet‐SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome. It achieved a success rate of 27.8% when considering the top 100 ranked prediction. This pipeline effectively narrows the range of potential targets within the human proteome, laying a foundation for drug target prediction, off‐target assessment, and toxicity prediction, ultimately boosting drug development. By facilitating these critical aspects of drug discovery and development, our work has the potential to ultimately accelerate the identification of new therapeutic agents and improve drug safety.
Article activity feed
-
-
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.
-
those lacking well-defined ligand binding pockets, were removed
how was this determined?
-
Introduction
This was a great overall summary of the approaches in the field and current challenges/caveats!
-
