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

• Published in: Journal of chemical information and modeling Vol. 50; no. 11; pp. 2029 - 2040
• Main Authors: Sheridan, Robert P, Maiorov, Vladimir N, Holloway, M. Katharine, Cornell, Wendy D, Gao, Ying-Duo
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 22.11.2010
• Subjects: Analytical, structural and metabolic biochemistry; Animals; Binding sites; Biochemistry; Biological and medical sciences; Cattle; Crystal structure; Databases, Protein; Drug Discovery - methods; Fundamental and applied biological sciences. Psychology; Humans; Hydrophobic and Hydrophilic Interactions; Interactions. Associations; Intermolecular phenomena; Ligands; Mice; Models, Molecular; Molecular biophysics; Molecules; Pharmaceutical Modeling; Pharmaceutical Preparations - chemistry; Pharmaceutical Preparations - metabolism; Protein Binding; Protein Conformation; Proteins; Proteins - chemistry; Proteins - metabolism; Structure in molecular biology; Tridimensional structure; Drug; Linear regression; Hydrophobic compound; Molecular interaction; Artefact; Binding site; Regression analysis; Hydrophobicity; Physical properties; Protein; Database; Metric; Crystalline structure
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/ci100312t

One approach to estimating the “chemical tractability” of a candidate protein target where we know the atomic resolution structure is to examine the physical properties of potential binding sites. A number of other workers have addressed this issue. We characterize ∼290 000 “pockets” from ∼42 000 protein crystal structures in terms of a three parameter “pocket space”: volume, buriedness, and hydrophobicity. A metric DLID (drug-like density) measures how likely a pocket is to bind a drug-like molecule. This is calculated from the count of other pockets in its local neighborhood in pocket space that contain drug-like cocrystallized ligands and the count of total pockets in the neighborhood. Surprisingly, despite being defined locally, a global trend in DLID can be predicted by a simple linear regression on log(volume), buriedness, and hydrophobicity. Two levels of simplification are necessary to relate the DLID of individual pockets to “targets”: taking the best DLID per Protein Data Bank (PDB) entry (because any given crystal structure can have many pockets), and taking the median DLID over all PDB entries for the same target (because different crystal structures of the same protein can vary because of artifacts and real conformational changes). We can show that median DLIDs for targets that are detectably homologous in sequence are reasonably similar and that median DLIDs correlate with the “druggability” estimate of Cheng et al. (Nature Biotechnology 2007, 25, 71−75).