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. 2023 Nov 13;63(21):6655-6666.
doi: 10.1021/acs.jcim.3c00722. Epub 2023 Oct 17.

Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method

Garrett A Stevenson  1 , Dan Kirshner  2 , Brian J Bennion  2 , Yue Yang  2 , Xiaohua Zhang  2 , Adam Zemla  3 , Marisa W Torres  3 , Aidan Epstein  3 , Derek Jones  3   4 , Hyojin Kim  5 , W F Drew Bennett  2 , Sergio E Wong  2 , Jonathan E Allen  3 , Felice C Lightstone  2
Affiliations

Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method

Garrett A Stevenson et al. J Chem Inf Model. .

Abstract

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.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Protein pocket processing and featurization.
Figure 2
Figure 2
Preparing pocket featurizations for input to clustering.
Figure 3
Figure 3
Protein pocket cluster centers (core sample centroids) and 167 clusters visualized by their first three principal components and size.
Figure 4
Figure 4
Sample compounds (CID: PubChem compound identifier) that are not in clustering feature matrix B (panel 1); their pose in the protein structural model of the UniProt reference sequence included in clusters #58 and #140—the poses are derived by alignment of the PDB co-complex pockets that matched the structural model to the structural model of the UniProt sequence (panel 2); and the nearest—in feature space—ligands/structural models, again in poses derived by aligning the PDB co-complex to the structural model alignment (panel 3).
Figure 5
Figure 5
Compound 71520717 in cluster #117 with known and top-predicted activity.
See this image and copyright information in PMC

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