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Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes.

ABSTRACT: Macromolecular protein complexes carry out many of the essential functions of cells, and many genetic diseases arise from disrupting the functions of such complexes. Currently, there is great interest in defining the complete set of human protein complexes, but recent published maps lack comprehensive coverage. Here, through the synthesis of over 9,000 published mass spectrometry experiments, we present hu.MAP, the most comprehensive and accurate human protein complex map to date, containing > 4,600 total complexes, > 7,700 proteins, and > 56,000 unique interactions, including thousands of confident protein interactions not identified by the original publications. hu.MAP accurately recapitulates known complexes withheld from the learning procedure, which was optimized with the aid of a new quantitative metric (k-cliques) for comparing sets of sets. The vast majority of complexes in our map are significantly enriched with literature annotations, and the map overall shows improved coverage of many disease-associated proteins, as we describe in detail for ciliopathies. Using hu.MAP, we predicted and experimentally validated candidate ciliopathy disease genes in vivo in a model vertebrate, discovering CCDC138, WDR90, and KIAA1328 to be new cilia basal body/centriolar satellite proteins, and identifying ANKRD55 as a novel member of the intraflagellar transport machinery. By offering significant improvements to the accuracy and coverage of human protein complexes, hu.MAP (http://proteincomplexes.org) serves as a valuable resource for better understanding the core cellular functions of human proteins and helping to determine mechanistic foundations of human disease.

SUBMITTER: Drew K

PROVIDER: S-EPMC5488662 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes.

Drew Kevin K Lee Chanjae C Huizar Ryan L RL Tu Fan F Borgeson Blake B McWhite Claire D CD Ma Yun Y Wallingford John B JB Marcotte Edward M EM

Molecular systems biology 20170608 6

Macromolecular protein complexes carry out many of the essential functions of cells, and many genetic diseases arise from disrupting the functions of such complexes. Currently, there is great interest in defining the complete set of human protein complexes, but recent published maps lack comprehensive coverage. Here, through the synthesis of over 9,000 published mass spectrometry experiments, we present hu.MAP, the most comprehensive and accurate human protein complex map to date, containing > 4 ...[more]

PMID: 28596423

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Project description:Advances in sensitivity, resolution, mass accuracy, and throughput have considerably increased the number of protein identifications made via mass spectrometry. Despite these advances, state-of-the-art experimental methods for the study of protein-protein interactions yield more candidate interactions than may be expected biologically owing to biases and limitations in the experimental methodology. In silico methods, which distinguish between true and false interactions, have been developed and applied successfully to reduce the number of false positive results yielded by physical interaction assays. Such methods may be grouped according to: (1) the type of data used: methods based on experiment-specific measurements (e.g., spectral counts or identification scores) versus methods that extract knowledge encoded in external annotations (e.g., public interaction and functional categorisation databases); (2) the type of algorithm applied: the statistical description and estimation of physical protein properties versus predictive supervised machine learning or text-mining algorithms; (3) the type of protein relation evaluated: direct (binary) interaction of two proteins in a cocomplex versus probability of any functional relationship between two proteins (e.g., co-occurrence in a pathway, sub cellular compartment); and (4) initial motivation: elucidation of experimental data by evaluation versus prediction of novel protein-protein interaction, to be experimentally validated a posteriori. This work reviews several popular computational scoring methods and software platforms for protein-protein interactions evaluation according to their methodology, comparative strengths and weaknesses, data representation, accessibility, and availability. The scoring methods and platforms described include: CompPASS, SAINT, Decontaminator, MINT, IntAct, STRING, and FunCoup. References to related work are provided throughout in order to provide a concise but thorough introduction to a rapidly growing interdisciplinary field of investigation.

Project description:In a bottom-up shotgun approach, the proteins of a mixture are enzymatically digested, separated, and analyzed via tandem mass spectrometry. The mass spectra relating fragment ion intensities (abundance) to the mass-to-charge are used to deduce the amino acid sequence and identify the peptides and proteins. The variables that influence intensity were characterized using a multi-factorial mixed-effects model, a ten-fold cross-validation, and stepwise feature selection on 6,352,528 fragment ions from 61,543 peptide ions. Intensity was higher in fragment ions that did not have neutral mass loss relative to any mass loss or that had a +1 charge state. Peptide ions classified for proton mobility as non-mobile had lowest intensity of all mobility levels. Higher basic residue (arginine, lysine or histidine) counts in the peptide ion and low counts in the fragment ion were associated with lower fragment ion intensities. Higher counts of proline in peptide and fragment ions were associated with lower intensities. These results are consistent with the mobile proton theory. Opposite trends between peptide and fragment ion counts and intensity may be due to the different impact of factor under consideration at different stages of the MS/MS experiment or to the different distribution of observations across peptide and fragment ion levels. Presence of basic residues at all three positions next to the fragmentation site was associated with lower fragment ion intensity. The presence of proline proximal to the fragmentation site enhanced fragmentation and had the opposite trend when located distant from the site. A positive association between fragment ion intensity and presence of sulfur residues (cysteine and methionine) on the vicinity of the fragmentation site was identified. These results highlight the multi-factorial nature of fragment ion intensity and could improve the algorithms for peptide identification and the simulation in tandem mass spectrometry experiments.

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Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes.

Publications

Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes.

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