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Munson BP, Chen M, Bogosian A, Kreisberg JF, Licon K, Abagyan R, Kuenzi BM, Ideker T. De novo generation of multi-target compounds using deep generative chemistry. Nat Commun 2024; 15:3636. [PMID: 38710699 DOI: 10.1038/s41467-024-47120-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 03/18/2024] [Indexed: 05/08/2024] Open
Abstract
Polypharmacology drugs-compounds that inhibit multiple proteins-have many applications but are difficult to design. To address this challenge we have developed POLYGON, an approach to polypharmacology based on generative reinforcement learning. POLYGON embeds chemical space and iteratively samples it to generate new molecular structures; these are rewarded by the predicted ability to inhibit each of two protein targets and by drug-likeness and ease-of-synthesis. In binding data for >100,000 compounds, POLYGON correctly recognizes polypharmacology interactions with 82.5% accuracy. We subsequently generate de-novo compounds targeting ten pairs of proteins with documented co-dependency. Docking analysis indicates that top structures bind their two targets with low free energies and similar 3D orientations to canonical single-protein inhibitors. We synthesize 32 compounds targeting MEK1 and mTOR, with most yielding >50% reduction in each protein activity and in cell viability when dosed at 1-10 μM. These results support the potential of generative modeling for polypharmacology.
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Affiliation(s)
- Brenton P Munson
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Michael Chen
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Audrey Bogosian
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Jason F Kreisberg
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Katherine Licon
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Brent M Kuenzi
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Trey Ideker
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, 92093, USA.
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Van de Graaf MW, Eggertsen TG, Zeigler AC, Tan PM, Saucerman JJ. Benchmarking of protein interaction databases for integration with manually reconstructed signalling network models. J Physiol 2023:10.1113/JP284616. [PMID: 37199469 PMCID: PMC11073820 DOI: 10.1113/jp284616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 05/10/2023] [Indexed: 05/19/2023] Open
Abstract
Protein interaction databases are critical resources for network bioinformatics and integrating molecular experimental data. Interaction databases may also enable construction of predictive computational models of biological networks, although their fidelity for this purpose is not clear. Here, we benchmark protein interaction databases X2K, Reactome, Pathway Commons, Omnipath and Signor for their ability to recover manually curated edges from three logic-based network models of cardiac hypertrophy, mechano-signalling and fibrosis. Pathway Commons performed best at recovering interactions from manually reconstructed hypertrophy (137 of 193 interactions, 71%), mechano-signalling (85 of 125 interactions, 68%) and fibroblast networks (98 of 142 interactions, 69%). While protein interaction databases successfully recovered central, well-conserved pathways, they performed worse at recovering tissue-specific and transcriptional regulation. This highlights a knowledge gap where manual curation is critical. Finally, we tested the ability of Signor and Pathway Commons to identify new edges that improve model predictions, revealing important roles of protein kinase C autophosphorylation and Ca2+ /calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy. This study provides a platform for benchmarking protein interaction databases for their utility in network model construction, as well as providing new insights into cardiac hypertrophy signalling. KEY POINTS: Protein interaction databases are used to recover signalling interactions from previously developed network models. The five protein interaction databases benchmarked recovered well-conserved pathways, but did poorly at recovering tissue-specific pathways and transcriptional regulation, indicating the importance of manual curation. We identify new signalling interactions not previously used in the network models, including a role for Ca2+ /calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy.
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Affiliation(s)
- Matthew W. Van de Graaf
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Children’s National Hospital, Washington, District of Columbia, USA
| | - Taylor G. Eggertsen
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Angela C. Zeigler
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Philip M. Tan
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Jeffrey J. Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
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Zięba A, Stępnicki P, Matosiuk D, Kaczor AA. What are the challenges with multi-targeted drug design for complex diseases? Expert Opin Drug Discov 2022; 17:673-683. [PMID: 35549603 DOI: 10.1080/17460441.2022.2072827] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
INTRODUCTION Current findings on multifactorial diseases with a complex pathomechanism confirm that multi-target drugs are more efficient ways in treating them as opposed to single-target drugs. However, to design multi-target ligands, a number of factors and challenges must be taken into account. AREAS COVERED In this perspective, we summarize the concept of application of multi-target drugs for the treatment of complex diseases such as neurodegenerative diseases, schizophrenia, diabetes, and cancer. We discuss the aspects of target selection for multifunctional ligands and the application of in silico methods in their design and optimization. Furthermore, we highlight other challenges such as balancing affinities to different targets and drug-likeness of obtained compounds. Finally, we present success stories in the design of multi-target ligands for the treatment of common complex diseases. EXPERT OPINION Despite numerous challenges resulting from the design of multi-target ligands, these efforts are worth making. Appropriate target selection, activity balancing, and ligand drug-likeness belong to key aspects in the design of ligands acting on multiple targets. It should be emphasized that in silico methods, in particular inverse docking, pharmacophore modeling, machine learning methods and approaches derived from network pharmacology are valuable tools for the design of multi-target drugs.
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Affiliation(s)
- Agata Zięba
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, Lublin, Poland
| | - Piotr Stępnicki
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, Lublin, Poland
| | - Dariusz Matosiuk
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, Lublin, Poland
| | - Agnieszka A Kaczor
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, Lublin, Poland.,School of Pharmacy, University of Eastern Finland, Kuopio, Finland
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Douglass EF, Allaway RJ, Szalai B, Wang W, Tian T, Fernández-Torras A, Realubit R, Karan C, Zheng S, Pessia A, Tanoli Z, Jafari M, Wan F, Li S, Xiong Y, Duran-Frigola M, Bertoni M, Badia-i-Mompel P, Mateo L, Guitart-Pla O, Chung V, Tang J, Zeng J, Aloy P, Saez-Rodriguez J, Guinney J, Gerhard DS, Califano A. A community challenge for a pancancer drug mechanism of action inference from perturbational profile data. Cell Rep Med 2022; 3:100492. [PMID: 35106508 PMCID: PMC8784774 DOI: 10.1016/j.xcrm.2021.100492] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 08/08/2021] [Accepted: 12/15/2021] [Indexed: 12/14/2022]
Abstract
The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action. Drug-perturbed RNA sequencing data can be used to identify drug targets Technology-based drug-target definitions often subsume literature definitions Literature and screening datasets provide complementary information on drug mechanisms
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Affiliation(s)
- Eugene F. Douglass
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave., New York, NY 10032, USA
- Pharmaceutical and Biomedical Sciences, University of Georgia, 250 W. Green Street, Athens, GA 30602, USA
| | - Robert J. Allaway
- Computational Oncology Group, Sage Bionetworks, 2901 Third Ave., Ste 330, Seattle, WA 98121, USA
| | - Bence Szalai
- Semmelweis University, Faculty of Medicine, Department of Physiology, Budapest, Hungary
| | - Wenyu Wang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Tingzhong Tian
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Adrià Fernández-Torras
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Ron Realubit
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave., New York, NY 10032, USA
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave., New York, NY 10032, USA
| | - Shuyu Zheng
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Alberto Pessia
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ziaurrehman Tanoli
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Mohieddin Jafari
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Fangping Wan
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Shuya Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Yuanpeng Xiong
- Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
| | - Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Martino Bertoni
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Pau Badia-i-Mompel
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Lídia Mateo
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Oriol Guitart-Pla
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Verena Chung
- Computational Oncology Group, Sage Bionetworks, 2901 Third Ave., Ste 330, Seattle, WA 98121, USA
| | | | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Justin Guinney
- Computational Oncology Group, Sage Bionetworks, 2901 Third Ave., Ste 330, Seattle, WA 98121, USA
| | - Daniela S. Gerhard
- Office of Cancer Genomics, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave., New York, NY 10032, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 1130 Saint Nicholas Ave., New York, NY 10032, USA
- Department of Medicine, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY 10032, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, 701 W 168th Street, New York, NY 10032, USA
- Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 W 168th Street, New York, NY 10032, USA
- Corresponding author
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