An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders

Chen, Siwei; Fragoza, Robert; Klei, Lambertus; Liu, Yuan; Wang, Jiebiao; Roeder, Kathryn; Devlin, Bernie; Yu, Haiyuan

doi:10.1038/s41588-018-0130-z

Cited by 68 publications

(77 citation statements)

References 71 publications

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“…Akin to the biologically impactful phosphorylation events in our dataset, impactful mutations exist amidst many less meaningful polymorphisms [45][46][47] . Recent efforts to identify the key mutations that underpin human disease phenotypes have utilized the expanding collection of protein structural information [48][49][50][51] , with the logic being that mutations that occur at or near the interfaces where proteins interact will have a higher likelihood of impacting protein function. Building on this logic, here we streamlined the identification of the functional phosphosites by identifying those located at or near proteininteraction interfaces (Fig.…”

Section: D Phosphoproteome Analysis Provides Insights Into Function mentioning

confidence: 99%

In-depth and 3-Dimensional Exploration of the Budding Yeast Phosphoproteome

Lanz

Yugandhar

Gupta

et al. 2019

Preprint

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Phosphorylation is one of the most dynamic and widespread post-translational modifications regulating virtually every aspect of eukaryotic cell biology. Here we present a comprehensive phosphoproteomic dataset for budding yeast, comprised of over 30,000 high confidence phosphorylation sites identified by mass spectrometry. This single dataset nearly doubles the size of the known phosphoproteome in budding yeast and defines a set of cell cycle-regulated phosphorylation events. With the goal of enhancing the identification of functional phosphorylation events, we performed computational positioning of phosphorylation sites on available 3D protein structures and systematically identified events predicted to regulate protein complex architecture. Results reveal a large number of phosphorylation sites mapping to or near protein interaction interfaces, many of which result in steric or electrostatic "clashes" predicted to disrupt the interaction. Phosphorylation site mutants experimentally validate our predictions and support a role for phosphorylation in negatively regulating protein-protein interactions. With the advancement of Cryo-EM and the increasing number of available structures, our approach should help drive the functional and spatial exploration of the phosphoproteome.

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Section: D Phosphoproteome Analysis Provides Insights Into Function mentioning

confidence: 99%

In-depth and 3-Dimensional Exploration of the Budding Yeast Phosphoproteome

Lanz

Yugandhar

Gupta

et al. 2019

Preprint

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“…Yu and coworkers show that whether a mutation falls in known or predicted protein-protein interaction interfaces is related to the likelihood to disrupt these interactions, and mostly these mutations are found on the interfaces of hub proteins [42]. Overall, they suggest that network topology should be considered when interpreting the impact of mutations.…”

Section: Resultsmentioning

confidence: 99%

3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients

Dinçer¹,

Kaya²,

Keskin

et al. 2019

Preprint

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Mutation profiles of Glioblastoma (GBM) tumors are very heterogeneous which is the main challenge in the interpretation of the effects of mutations in disease. Additionally, the impact of the mutations is not uniform across the proteins and protein-protein interactions. The pathway level representation of the mutations is very limited. In this work, we approach these challenges through a systems level perspective in which we analyze how the mutations in GBM tumors are distributed in protein structures/interfaces and how they are organized at the network level. Our results show that out of 14644 mutations, 4392 have structural information and ~13% of them form spatial patches. Despite a small portion of all mutations, 3D patches partially decrease the heterogeneity across the patients. Hub proteins adapt multiple patches of mutations usually with a very large one and connects mutations in multiple binding sites through the core of the protein. We reconstructed patient specific networks for 290 GBM tumors. Network-guided analysis of mutations completes the interaction components that mutated proteins potentially affect, and groups the patients according to the reconstructed networks. As a result, we found 4 tumor clusters that overcome the heterogeneity in mutation profiles, and reveal predominant pathways in each group. Additionally, the network-based similarity analysis shows that each group of patients carries a set of signature 3D mutation patches. We believe that this study provides another perspective to the analysis of mutation effects and a good training towards the network-guided precision medicine.

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“…One promising route to decipher this complexity is from the view that the cell is a network of interacting biomolecules where proteins carry out diverse functions by interacting with other proteins. We have previously demonstrated that one key feature in understanding the functional impact of mutations is whether they fall in the binding interfaces that mediate interactions with other proteins and critically, which specific interactions they mediate 21,22 . While studies of known disease mutations have already reported a strong association with protein interaction interfaces 23,24 , application of this feature has been largely limited by low coverage of structural information on interacting proteins; co-crystal structures and homology models together cover merely ~6% of all known human interactions 25 .…”

Section: Introductionmentioning

confidence: 99%

A full-proteome, interaction-specific characterization of mutational hotspots across human cancers

Zhang

et al. 2019

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Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically-informed computational framework that characterizes the functional relevance of all 1,107 published mutational hotspots identified in ~25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole human interactome, we uncover genomewide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we demonstrate that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

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An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders

Cited by 68 publications

References 71 publications

In-depth and 3-Dimensional Exploration of the Budding Yeast Phosphoproteome

In-depth and 3-Dimensional Exploration of the Budding Yeast Phosphoproteome

3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients

A full-proteome, interaction-specific characterization of mutational hotspots across human cancers

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