Identification of disease treatment mechanisms through the multiscale interactome
Open accessPublished version of a preprint
Abstract
Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug’s therapeutic effects are not limited to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network. We then develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and physical protein-protein interactions. On three key pharmacological tasks, the multiscale interactome predicts drug-disease treatment, identifies proteins and biological functions related to treatment, and predicts genes that alter a treatment’s efficacy and adverse reactions. Our results indicate that physical interactions between proteins alone cannot explain treatment since many drugs treat diseases by affecting the biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for explaining treatment, even when drugs seem unrelated to the diseases they are recommended for.
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These strategies identify therapeutic candidates based on their impact on cell signaling, gene expression and disease phenotypes.
“These strategies identify therapeutic candidates based on their impact on cell signaling, gene expression and disease phenotypes5,10–12.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [12]
Machine learning has been used to analyze high-throughput molecular interactomes to unravel genetic architecture perturbed in disease and help design therapies to target them.
“Machine learning has been used to analyze high-throughput molecular interactomes to unravel genetic architecture perturbed in disease12,13 and help design therapies to target them14.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [12]
The standard benchmarking strategy used to evaluate drug-repurposing AI models aligns with the approach predominantly used in the literature.
“This evaluation method aligns with the approach predominantly used in the literature12,14–16,18–20.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [12]
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References 117 · Cited by 172
Research works printed but not linked: 2
- [110]Welter, D., Jupp, S. & Osumi-Sutherland, D. Human Cell Atlas Ontology. In Proc. 9th International Conference on Biological Ontology (ICBO) (eds Jaiswal, P., Cooper, L., Haendel, M. A. & Mungall, C. J.) Vol. 2285 (CEUR-WS.org, 2018).
- [111]Ruiz, C., Zitnik, M. & Leskovec, J. Identification of Disease Treatment Mechanisms Through the Multiscale Interactome, GitHub10.5281/zenodo.4435258 (2021). PMC7979814 33741907
- Authors
- Ruiz, C., Zitnik, M., & Leskovec, J.
- Affiliations
- Stanford University (US)
- Chan Zuckerberg Biohub San Francisco (US)
- Corresponding author
- Leskovec, Jure
- Venue
- Nature Communications
- Published
- 2021-03-19
- Volume
- 12
- Issue
- 1
- Pages
- 1796-1796
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7979814
- OpenAlex
- W3137864305
- Cited by
- 172
- References
- 117
- Citations by year
- 2026: 32 · 2025: 35 · 2024: 35 · 2023: 35 · 2022: 19 · 2021: 16
- FWCI
- 8.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (1934578, DGE-165651 8, 1835598, 2030459, 1656518, 1918940, 2030477, 2033384)
- Nvidia
- UnitedHealth Group
- Defense Advanced Research Projects Agency (N660011924033)
- Multidisciplinary University Research Initiative
- Wu Tsai Neurosciences Institute, Stanford University
- Harvard Data Science Initiative, Harvard University
- Division of Graduate Education (1656518, DGE1656518)
- Army Research Office (W911NF-16-1-0342, W911NF-16-1-, W911NF)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- drug repurposing, protein-protein interaction, random walks, therapeutic target, adverse drug reactions, treatment efficacy prediction, mechanism of action
- MeSH terms
- Algorithms, Animals, Drug Therapy, Pharmaceutical Preparations, Humans, Models, Theoretical, Protein Binding, Proteins, Computational Biology, Protein Interaction Mapping, Multiprotein Complexes, Protein Interaction Maps
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