Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method
Garrett A. Stevenson, Daniel A. Kirshner, Brian J. Bennion, Yue Cheng Yang, Xiaohua Zhang, Adam T Zemla, Marisa W. Torres, Aidan T. Epstein, Derek Jones, Hyojin Kim, William F. Bennett, Sergio Ernesto Wong, Jonathan Allen, Felice C. Lightstone · Journal of Chemical Information and Modeling · 2023
Protein-ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multitarget interactions are the first step in finding an effective therapeutic, while undesirable off-target interactions are the first step in assessing safety. In this work, we introduce a novel ligand-based featurization and mapping of human protein pockets to identify closely related protein targets and to project novel drugs into a hybrid protein-ligand feature space to identify their likely protein interactions. Using structure-based template matches from PDB, protein pockets are featured by the ligands that bind to their best co-complex template matches. The simplicity and interpretability of this approach provide a granular characterization of the human proteome at the protein-pocket level instead of the traditional protein-level characterization by family, function, or pathway. We demonstrate the power of this featurization method by clustering a subset of the human proteome and evaluating the predicted cluster associations of over 7000 compounds.