Previously in our lab, Jiayan Wang et al. worked on a project related to the druggable genome. In her project, she evaluated the druggability of protein domains found in the human genome using protein structures bound to druglike ligands. The left panel in Figure 1 demonstrates her workflow to define ligand binding pockets. To build upon the previous project, we would like to extend our analysis to proteins in the human genome without structurally confirmed ligands in the protein data bank (PDB). The right panel in Figure 1 briefly captures my approach to defining potential protein binding pockets. But first, I would like to explain to you why we started this project in the first place.
Ligandability of the human genome is critical in drug development. A valid therapeutic target should have an essential role in a specific disease pathway. Additionally, it should also be ligandable (i.e., it should include a binding pocket where drug-like ligands can bind potently). Hence, we decided to map the ligandable proteins in the human genome computationally using structures from the PDB.
Many methods have been developed to predict the ligandability of proteins. [1, 2] These methods examine the physical properties of potential drug binding sites, which I will refer to as pockets from now on.  The pocket on your pant should be of a particular size to fit your wallet. Similarly, a protein pocket should have a specific volume, area, buriedness (the depth of the pocket), and hydrophobicity to fit a ligand. To gauge the pocket properties, I used the icmPocketFinder method in ICM package (Molsoft, San Diego). To help you visualize a protein pocket better, I provided an example in Figure 2. If you would like to read the detailed steps of my method, please refer to my Zenodo report here.
Figure 1. The workflow of finding binding pockets. The left panel demonstrates Jiayan Wang et al.’s approach to define ligand binding pockets. The right panel represents my approach to defining potential ligand binding pockets.
Figure 2. A representation of one of the potential binding pockets of the PDB structure 1QUQ and some of its properties using the icmPocketFinder method on the right side.
 Barril X. Druggability predictions: methods, limitations, and applications. Wiley Interdisciplinary Reviews: Computational Molecular Science 2012;3:327-338.
 Halgren T. Identifying and Characterizing Binding Sites and Assessing Druggability. Journal Of Chemical Information And Modeling 2009;49:377-389.
 Sheridan R, Maiorov V, Holloway M, Cornell W, Gao Y. Drug-like Density: A Method of Quantifying the “Bindability” of a Protein Target Based on a Very Large Set of Pockets and Drug-like Ligands from the Protein Data Bank. Journal Of Chemical Information And Modeling 2010;50:2029-2040.