Interactome INSIDER: a structural interactome browser for genomic studies

Meyer, Michael J.; Beltrán, Juan Felipe; Liang, Siqi; Fragoza, Robert; Rumack, Aaron; Liang, Jin; Wei, Xiaomu; Yu, Haiyuan

doi:10.1038/nmeth.4540

Cited by 144 publications

(179 citation statements)

References 80 publications

(103 reference statements)

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“…The size of the interaction interface between heteromeric paralogs was obtained from Interactome INSIDER (Meyer et al, 2018). The structures of the interacting paralogs (Appendix Fig S15) were obtained from Interactome3D (Mosca et al, 2013).…”

Section: Protein Interaction Interfacesmentioning

confidence: 99%

Paralog dependency indirectly affects the robustness of human cells

Dandage

Landry

2019

Molecular Systems Biology

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The protective redundancy of paralogous genes partly relies on the fact that they carry their functions independently. However, a significant fraction of paralogous proteins may form functionally dependent pairs, for instance, through heteromerization. As a consequence, one could expect these heteromeric paralogs to be less protective against deleterious mutations. To test this hypothesis, we examined the robustness landscape of gene loss‐of‐function by CRISPR‐Cas9 in more than 450 human cell lines. This landscape shows regions of greater deleteriousness to gene inactivation as a function of key paralog properties. Heteromeric paralogs are more likely to occupy such regions owing to their high expression and large number of protein–protein interaction partners. Further investigation revealed that heteromers may also be under stricter dosage balance, which may also contribute to the higher deleteriousness upon gene inactivation. Finally, we suggest that physical dependency may contribute to the deleteriousness upon loss‐of‐function as revealed by the correlation between the strength of interactions between paralogs and their higher deleteriousness upon loss of function.

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Section: Protein Interaction Interfacesmentioning

confidence: 99%

Paralog dependency indirectly affects the robustness of human cells

Dandage

Landry

2019

Molecular Systems Biology

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“…Since the structure-supported network localizes the PPI to domains within the interacting proteins, it can be used to guide functional studies of disease-associated mutations that map to interfaces and disrupt PPI more frequently than mutations away from interfaces. 13,77 Importantly, we provide links to clusters of homologous domain-domain interfaces observed not just within the protein biological assemblies but also within their other crystal forms and/or secondary interfaces to enable studies of mutation effects on other potential biologically-relevant interfaces. Even if the mutations do not locate to any of the interfaces, use of domain-based interfaces as the foundational blocks for PPI allows prioritization of experimental studies to domains containing the relevant mutations.…”

Section: Discussionmentioning

confidence: 99%

“…They mapped nearly 22,000 disease-associated mutations to the network proteins, and later expanded the mutation mapping to more binary PPI. 13 Aloy and co-workers developed Interactome3D 14 based on the 3did 15 database, a resource containing structural details for over 12,000 binary PPI from interactomes in eight model organisms, and tools to build 3D models for new PPI based on structural templates. They also later mapped disease-related missense mutations to the human interactome.…”

Section: Introductionmentioning

confidence: 99%

Protein domain-based structural interfaces help interpret biologically-relevant interactions in the human interaction network

Kilambi

Gururaj

et al. 2020

Preprint

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A high-quality map of the human protein-protein interaction (PPI) network can help us better understand complex genotype-phenotype relationships. Each edge between two interacting proteins supported through an interface in a three-dimensional (3D) structure of a protein complex adds credibility to the biological relevance of the interaction. Such structure-supported interactions would augment an interaction map primarily built using high-throughput cell-based biophysical methods. Here, we integrate structural information with the human PPI network to build the structure-supported human interactome, a subnetwork of PPI between proteins that contain domains or regions known to form interfaces in the 3D structures of protein complexes. We expand the coverage of our structure-supported human interactome by using Pfam-based domain definitions, whereby we include homologous interactions if a human complex structure is unavailable. The structure-supported interactome predicts one-eighth of the total network PPI to interact through domain-domain interfaces. It identifies with higher resolution the interacting subunits in multi-protein complexes and enables us to characterize functional and diseaserelevant neighborhoods in the network map with higher accuracy, allowing for structural insights into disease-associated genes and pathways. We expand the structural coverage beyond domain-domain interfaces by identifying the most common non-enzymatic peptide-binding domains with structural support. Adding these interactions between protein domains on one side and peptide regions on the other approximately doubles the number of structure-supported PPI. The human structure-supported interactome is a resource to prioritize investigations of smaller-scale context-specific experimental PPI neighborhoods of biological or clinical significance. Short abstractA high-quality map of the human protein-protein interaction (PPI) network can help us better understand genotype-phenotype relationships. Each edge between two interacting proteins supported through an interface in a three-dimensional structure of a protein complex adds credibility to the biological relevance of the interaction aiding experimental prioritization. Here, we integrate structural information with the human interactome to build the structure-supported human interactome, a subnetwork of PPI between proteins that contain domains or regions known to form interfaces in the structures of protein complexes. The structure-supported interactome predicts one-eighth of the total PPI to interact through domaindomain interfaces. It identifies with higher resolution the interacting subunits in multi-protein complexes and enables us to structurally characterize functional, disease-relevant network neighborhoods. We also expand the structural coverage by identifying PPI between non-enzymatic peptide-binding domains on one side and peptide regions on the other, thereby doubling the number of structure-supported PPI.

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“…However, these algorithms tend to exploit a single information type Currently, many prioritization methods and online interpretation tools exist with different approaches to data interpretation and analysis. These approaches include algorithms that use proteinprotein interactions (PPI) networks, 231,232 functional similarity networks built utilizing pathway involvement, 233 structural variation data, 234 exploit thorough functional annotation, 235 or a combination of PPI based approach and phenotype similarity. 236 In Efforts to facilitate data and material exchange promise to bridge gaps between hospitals, specialized centers, and laboratories.…”

Section: We S Panel S and Wg Smentioning

confidence: 99%

Early‐onset inflammatory bowel disease as a model disease to identify key regulators of immune homeostasis mechanisms

Pázmándi

Kalinichenko

Ardy

et al. 2018

Immunological Reviews

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Summary Rare, monogenetic diseases present unique models to dissect gene functions and biological pathways, concomitantly enhancing our understanding of the etiology of complex (and often more common) traits. Although inflammatory bowel disease (IBD) is a generally prototypic complex disease, it can also manifest in an early‐onset, monogenic fashion, often following Mendelian modes of inheritance. Recent advances in genomic technologies have spurred the identification of genetic defects underlying rare, very early‐onset IBD (VEO‐IBD) as a disease subgroup driven by strong genetic influence, pinpointing key players in the delicate homeostasis of the immune system in the gut and illustrating the intimate relationships between bowel inflammation, systemic immune dysregulation, and primary immunodeficiency with increased susceptibility to infections. As for other human diseases, it is likely that adult‐onset diseases may represent complex diseases integrating the effects of host genetic susceptibility and environmental triggers. Comparison of adult‐onset IBD and VEO‐IBD thus provides beautiful models to investigate the relationship between monogenic and multifactorial/polygenic diseases. This review discusses the present and novel findings regarding monogenic IBD as well as key questions and future directions of IBD research.

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Interactome INSIDER: a structural interactome browser for genomic studies

Cited by 144 publications

References 80 publications

Paralog dependency indirectly affects the robustness of human cells

Paralog dependency indirectly affects the robustness of human cells

Protein domain-based structural interfaces help interpret biologically-relevant interactions in the human interaction network

Early‐onset inflammatory bowel disease as a model disease to identify key regulators of immune homeostasis mechanisms

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