This project is a part of the "Large-Scale Target Identification with Reverse Docking" project.

We employed a novel computational pipeline for assisting protein target discovery. The pipeline allows de novo identification of protein targets for herbal ingredients by simulating ligand-protein interactions against the user-designated protein-ligand collection, in an efficient and high throughput manner.

We evaluated the pipeline using three Traditional Chinese Medicine (TCM) ingredients such as acteoside, quercetin and epigallocatechin gallate (EGCG) respectively. Majority of the known targets of these ingredients were successfully identified by the pipeline. Structural comparative analyses confirmed many predicted ligand-target interactions use same binding pockets and the same binding modes as those of known ligand-target interactions. Furthermore, we illustrated the mechanism of actions (MOAs) of the TCM ingredients by network analysis based on the predicted target profiles. Through the paper, we insist that the novel computational pipeline provides an alternate option to explore the MOA of herb medicine within a reasonable computational time. It is also particularly valuable in aiding precise drug discovery and drug repurposing via an economical and efficient route.

Haiping Zhang et al. Large-Scale Target Identification of Herbal Medicine Using a Reverse Docking Approach. 2018.

Lesnik, S.; Stular, T.; Brus, B.; Knez, D.; Gobec, S.; Janezic, D.; Konc, J., LiSiCA: A Software for Ligand-Based Virtual Screening and Its Application for the Discovery of Butyrylcholinesterase Inhibitors. Journal of chemical information and modeling 2015, 55, 1521-8.

1. Define the center of mass of a receptor protein

2. Process the receptor and ligand molecules for Vina.

3. Pass the center of mass of a receptor and the input pdbqt files (of receptor and ligand) to vina

4. Rescore and sort the results

You could download the 3 examples to run in your local evironment. Extract the zip files in your working directory. Read the README.txt file in each example, run the tutorial according to the README.txt. If you encounter any errors or questions, please raise an issue.