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Updated: Apr 24, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Benchmarking reverse docking through AlphaFold2 human proteome.

Qing Luo¹, Sheng Wang², Hoi Yeung Li³

¹Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.

Protein Science : a Publication of the Protein Society

|September 14, 2024

Summary

This summary is machine-generated.

We developed 11 reverse docking pipelines to predict drug targets. The Glide_SFCT (PS) pipeline showed the best performance, improving drug discovery and safety assessments.

Keywords:

drug discovery drug–target interaction human proteome reverse docking target prediction

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Area of Science:

Computational chemistry
Pharmacology
Bioinformatics

Background:

Reverse docking predicts ligand-protein interactions for drug repositioning and safety evaluation.
Understanding off-target effects and toxicity is crucial for drug development.

Purpose of the Study:

To construct and benchmark 11 reverse docking pipelines for predicting ligand interactions with the human proteome.
To identify the most effective pipeline for accurate drug target prediction.

Main Methods:

Integrated site prediction tools (PointSite, SiteMap), docking programs (Glide, AutoDock Vina), and scoring functions (Glide, AutoDock Vina, RTMScore, DeepRMSD, OnionNet-SFCT).
Benchmarked 11 distinct reverse docking pipelines using AlphaFold2 human proteome models.
Evaluated pipeline performance based on target prediction accuracy.

Main Results:

The Glide_SFCT (PS) pipeline demonstrated superior performance in predicting potential drug targets.
Achieved a 27.8% success rate for top 100 ranked predictions against the human proteome.
Successfully narrowed down potential targets within the vast human proteome.

Conclusions:

The Glide_SFCT (PS) pipeline provides a robust foundation for drug target prediction, off-target assessment, and toxicity evaluation.
This approach accelerates drug discovery and development by improving efficiency and safety.
Facilitates the identification of novel therapeutic agents and enhances drug safety profiles.