The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.

Mapping protein-protein interactions is an invaluable tool for understanding protein function. Here, we report the first large-scale study of protein-protein interactions in human cells using a mass spectrometry-based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large-scale immunoprecipitation of Flag-tagged versions of these proteins followed by LC-ESI-MS/MS analysis resulted in the identification of 24 540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross-validated using previously published and predicted human protein interactions. In-depth mining of the data set shows that it represents a valuable source of novel protein-protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations.

Prevailing drug discovery approaches focus on compounds with molecular selectivity, inhibiting disease-relevant targets over others in vitro. However in vivo, many such agents are not therapeutically selective, either because of undesirable activity at effective doses or because the biological system responds to compensate. In theory, drug combinations should permit increased control of such complex biology, but there is a common concern that therapeutic synergy will generally be mirrored by synergistic side-effects. Here we provide evidence, from 94,110 multi-dose combination experiments representing diverse disease areas and large scale flux balance simulations of inhibited bacterial metabolism, that multi-target synergies are more specific than single agent activities to particular cellular contexts. Using an anti-inflammatory combination, we show how multi-target synergy can achieve therapeutic selectivity in animals through differential target expression. Synergistic combinations can increase the number of selective therapies using the current pharmacopeia, and offer opportunities for more precise control of biological systems.

SUMMARY Balanced chromosomal abnormalities (BCAs) represent a reservoir of single gene disruptions in neurodevelopmental disorders (NDD). We sequenced BCAs in autism and related NDDs, revealing disruption of 33 loci in four general categories: 1) genes associated with abnormal neurodevelopment (e.g., AUTS2, FOXP1, CDKL5), 2) single gene contributors to microdeletion syndromes (MBD5, SATB2, EHMT1, SNURF-SNRPN), 3) novel risk loci (e.g., CHD8, KIRREL3, ZNF507), and 4) genes associated with later onset psychiatric disorders (e.g., TCF4, ZNF804A, PDE10A, GRIN2B, ANK3). We also discovered profoundly increased burden of copy number variants among 19,556 neurodevelopmental cases compared to 13,991 controls (p = 2.07×10−47) and enrichment of polygenic risk alleles from autism and schizophrenia genome-wide association studies (p = 0.0018 and 0.0009, respectively). Our findings suggest a polygenic risk model of autism incorporating loci of strong effect and indicate that some neurodevelopmental genes are sensitive to perturbation by multiple mutational mechanisms, leading to variable phenotypic outcomes that manifest at different life stages.