Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-based Featurization Method
Abstract Protein-ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multi-target interactions are a first step in finding an effective therapeutic; undesirable off-target interactions are a 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 featurized by the ligands which bind to their best co-complex template matches. The simplicity and interpretability of this approach provides 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 7,000 compounds..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 23. Apr. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Stevenson, Garrett A. [VerfasserIn] |
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| doi: |
10.1101/2023.05.11.538979 |
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| PPN (Katalog-ID): |
XBI039565157 |
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| 245 | 1 | 0 | |a Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-based Featurization Method |
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| 520 | |a Abstract Protein-ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multi-target interactions are a first step in finding an effective therapeutic; undesirable off-target interactions are a 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 featurized by the ligands which bind to their best co-complex template matches. The simplicity and interpretability of this approach provides 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 7,000 compounds. | ||
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| 700 | 1 | |a Kirshner, Dan |e verfasserin |4 aut | |
| 700 | 1 | |a Bennion, Brian J. |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Yue |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xiaohua |e verfasserin |4 aut | |
| 700 | 1 | |a Zemla, Adam |e verfasserin |4 aut | |
| 700 | 1 | |a Torres, Marisa W. |e verfasserin |4 aut | |
| 700 | 1 | |a Epstein, Aidan |e verfasserin |4 aut | |
| 700 | 1 | |a Jones, Derek |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Hyojin |e verfasserin |4 aut | |
| 700 | 1 | |a Bennett, W. F. D. |e verfasserin |4 aut | |
| 700 | 1 | |a Wong, Sergio E. |e verfasserin |4 aut | |
| 700 | 1 | |a Allen, Jonathan E. |e verfasserin |4 aut | |
| 700 | 1 | |a Lightstone, Felice C. |e verfasserin |4 aut | |
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