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Hu.MAP 2.0: integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies.

ABSTRACT: A general principle of biology is the self-assembly of proteins into functional complexes. Characterizing their composition is, therefore, required for our understanding of cellular functions. Unfortunately, we lack knowledge of the comprehensive set of identities of protein complexes in human cells. To address this gap, we developed a machine learning framework to identify protein complexes in over 15,000 mass spectrometry experiments which resulted in the identification of nearly 7,000 physical assemblies. We show our resource, hu.MAP 2.0, is more accurate and comprehensive than previous state of the art high-throughput protein complex resources and gives rise to many new hypotheses, including for 274 completely uncharacterized proteins. Further, we identify 253 promiscuous proteins that participate in multiple complexes pointing to possible moonlighting roles. We have made hu.MAP 2.0 easily searchable in a web interface (http://humap2.proteincomplexes.org/), which will be a valuable resource for researchers across a broad range of interests including systems biology, structural biology, and molecular explanations of disease.

SUBMITTER: Drew K

PROVIDER: S-EPMC8111494 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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hu.MAP 2.0: integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies.

Drew Kevin K Wallingford John B JB Marcotte Edward M EM

Molecular systems biology 20210501 5

A general principle of biology is the self-assembly of proteins into functional complexes. Characterizing their composition is, therefore, required for our understanding of cellular functions. Unfortunately, we lack knowledge of the comprehensive set of identities of protein complexes in human cells. To address this gap, we developed a machine learning framework to identify protein complexes in over 15,000 mass spectrometry experiments which resulted in the identification of nearly 7,000 physica ...[more]

PMID: 33973408

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Project description:In contrast to the dynamic intracellular environment, structural extracellular matrix (ECM) proteins with half-lives measured in decades, are susceptible to accumulating damage. Whilst conventional approaches such as histology, immunohistochemistry and mass spectrometry are able to identify age- and disease-related changes in protein abundance or distribution, these techniques are poorly suited to characterising molecular damage. We have previously shown that mass spectrometry can detect tissue-specific differences in the proteolytic susceptibility of protein regions within fibrillin-1 and collagen VI alpha-3. Here, we present a novel proteomic approach to detect damage-induced "peptide fingerprints" within complex multi-component ECM assemblies (fibrillin and collagen VI microfibrils) following their exposure to ultraviolet radiation (UVR) by broadband UVB or solar simulated radiation (SSR). These assemblies were chosen because, in chronically photoaged skin, fibrillin and collagen VI microfibril architectures are differentially susceptible to UVR. In this study, atomic force microscopy revealed that fibrillin microfibril ultrastructure was significantly altered by UVR exposure whereas the ultrastructure of collagen VI microfibrils was resistant. UVR-induced molecular damage was further characterised by proteolytic peptide generation with elastase followed by liquid chromatography tandem mass spectrometry (LC-MS/MS). Peptide mapping revealed that UVR exposure increased regional proteolytic susceptibility within the protein structures of fibrillin-1 and collagen VI alpha-3. This allowed the identification of UVR-induced molecular changes within these two key ECM assemblies. Additionally, similar changes were observed within protein regions of co-purifying, microfibril-associated receptors integrins αv and β1. This study demonstrates that LC-MS/MS mapping of peptides enables the characterisation of molecular post-translational damage (via direct irradiation and radiation-induced oxidative mechanisms) within a complex in vitro model system. This peptide fingerprinting approach reliably allows both the identification of UVR-induced molecular damage in and between proteins and the identification of specific protein domains with increased proteolytic susceptibility as a result of photo-denaturation. This has the potential to serve as a sensitive method of identifying accumulated molecular damage in vivo using conventional mass spectrometry data-sets.

Dataset Information

Hu.MAP 2.0: integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies.

Publications

hu.MAP 2.0: integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies.

Similar Datasets

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