Knowledge mining from large scale protein-protein interaction datasets at the era of big data

AbstractTechnological and computational advances in genomics and interactomics have made it possible to identify rapidly how disease mutations perturb interaction networks within human cells. In this study, we investigate at large-scale the effects of network perturbations caused by disease mutations within the human three-dimensional (3D), structurally-resolved macromolecular interactome. We show that disease-associated germline mutations are significantly enriched in sequences encoding protein-protein interfaces compared to mutations identified in healthy subjects from the 1000 Genomes and ExAC projects; these interface mutations correspond to protein-protein interaction (PPI)-perturbing alleles including p.Ser127Arg in PCSK9 at the PCSK9-LDLR interface. In addition, somatic missense mutations are significantly enriched in PPI interfaces compared to non-interfaces in 10,861 human exomes across 33 cancer subtypes/types from The Cancer Genome Atlas. Using a binomial statistical model, we computationally identified 470 PPIs harboring a statistically significant excess number of missense mutations at protein-protein interfaces (termed putative oncoPPIs) in pan-cancer analysis. We demonstrate that the oncoPPIs, including histone H4 complex in individual cancer types, are highly correlated with patient survival and drug resistance/sensitivity in human cancer cell lines and patient-derived xenografts. We experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay. We further showed that ALOX5 p.Met146Lys at the ALOX5-MAD1L1 interface and RXRA p.Ser427Phe at the RXRA-PPARG interface promote significant tumor cell growth using cell line-based functional assays, providing a functional proof-of-concept. In summary, if broadly applied, this human 3D interactome network analysis offers a powerful tool for prioritizing alleles with mutations altering PPIs that may contribute to the pathobiology of human diseases, and may offer disease-specific targets for genotype-informed therapeutic discovery.

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Filtering and Interpreting Large-Scale Experimental Protein–Protein Interaction Data

Methods in Molecular Biology - Network Biology ◽

10.1007/978-1-61779-276-2_14 ◽

2011 ◽

pp. 295-309

Author(s):

Gabriel Musso ◽

Andrew Emili ◽

Zhaolei Zhang

Keyword(s):

Protein Interaction ◽

Large Scale ◽

Protein Interaction Data ◽

Interaction Data ◽

Protein Protein Interaction

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Protein-protein-interaction Network Organization of the Hypusine Modification System

Molecular & Cellular Proteomics ◽

10.1074/mcp.m112.019059 ◽

2012 ◽

Vol 11 (11) ◽

pp. 1289-1305 ◽

Cited By ~ 12

Author(s):

Henning Sievert ◽

Simone Venz ◽

Oscar Platas-Barradas ◽

Vishnu M. Dhople ◽

Martin Schaletzky ◽

...

Keyword(s):

Protein Interaction ◽

Large Scale ◽

Affinity Purification ◽

Interaction Network ◽

Initiation Factor ◽

Post Translational Modification ◽

Comprehensive Overview ◽

Data Set ◽

Modification System ◽

Protein Protein Interaction

Hypusine modification of eukaryotic initiation factor 5A (eIF-5A) represents a unique and highly specific post-translational modification with regulatory functions in cancer, diabetes, and infectious diseases. However, the specific cellular pathways that are influenced by the hypusine modification remain largely unknown. To globally characterize eIF-5A and hypusine-dependent pathways, we used an approach that combines large-scale bioreactor cell culture with tandem affinity purification and mass spectrometry: “bioreactor-TAP-MS/MS.” By applying this approach systematically to all four components of the hypusine modification system (eIF-5A1, eIF-5A2, DHS, and DOHH), we identified 248 interacting proteins as components of the cellular hypusine network, with diverse functions including regulation of translation, mRNA processing, DNA replication, and cell cycle regulation. Network analysis of this data set enabled us to provide a comprehensive overview of the protein-protein interaction landscape of the hypusine modification system. In addition, we validated the interaction of eIF-5A with some of the newly identified associated proteins in more detail. Our analysis has revealed numerous novel interactions, and thus provides a valuable resource for understanding how this crucial homeostatic signaling pathway affects different cellular functions.

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