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

Drew, Kevin; Wallingford, John B.; Marcotte, Edward M.

doi:10.15252/msb.202010016

Cited by 96 publications

(90 citation statements)

References 66 publications

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“…To provide the information about known physical protein-protein interactions of each hit with the query, we used the confidence values for interactions from BioGRID ( 23 ) and Hu.Map 2.0 ( 24 ), as well as predictions of protein complexes from Hu.Map 2.0.…”

Section: Methodsmentioning

confidence: 99%

“…This new web server reveals functional relationships between individual domains beyond the detection based on whole-protein sequences. In addition, DEPCOD introduces a combination of methodological and functional features, including: (i) expanded scope of species in evolutionary profiles at two scales: 244 eukaryotic genomes or 506 genomes from all three domains of life: Eukaryota , Bacteria , and Archaea ; (ii) incorporation of phylogeny of the searched genomes into the correlation of conservation patterns; (iii) visualization of known BioGRID ( 23 ) and hu.MAP ( 24 ) interactions and shared protein complexes between the query and the detected hits; (iv) analysis of GO ( 25 , 26 ), KEGG ( 27 , 28 ) and Reactome ( 29 ) pathway enrichment among the detected hits and (v) visualization of details and sources of detected patterns similarities (conservation values for individual species, taxonomic trees, links to the information about detected domains and domain families, etc).…”

Section: Introductionmentioning

confidence: 99%

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DEPCOD: a tool to detect and visualize co-evolution of protein domains

Sadreyev

2022

Nucleic Acids Research

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Proteins with similar phylogenetic patterns of conservation or loss across evolutionary taxa are strong candidates to work in the same cellular pathways or engage in physical or functional interactions. Our previously published tools implemented our method of normalized phylogenetic sequence profiling to detect functional associations between non-homologous proteins. However, many proteins consist of multiple protein domains subjected to different selective pressures, so using protein domain as the unit of analysis improves the detection of similar phylogenetic patterns. Here we analyze sequence conservation patterns across the whole tree of life for every protein domain from a set of widely studied organisms. The resulting new interactive webserver, DEPCOD (DEtection of Phylogenetically COrrelated Domains), performs searches with either a selected pre-defined protein domain or a user-supplied sequence as a query to detect other domains from the same organism that have similar conservation patterns. Top similarities on two evolutionary scales (the whole tree of life or eukaryotic genomes) are displayed along with known protein interactions and shared complexes, pathway enrichment among the hits, and detailed visualization of sources of detected similarities. DEPCOD reveals functional relationships between often non-homologous domains that could not be detected using whole-protein sequences. The web server is accessible at http://genetics.mgh.harvard.edu/DEPCOD.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DEPCOD: a tool to detect and visualize co-evolution of protein domains

Sadreyev

2022

Nucleic Acids Research

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“…( 31 ) have used the Complex Portal as a verification dataset in their analysis of potential transcription cofactors. By combining inferred complexes from hu.MAP 2.0 ( 32 ), curated complexes from CORUM ( 33 ) and curated physical interaction data from IntAct [this volume NAR paper] and BioGrid ( 34 ) with a selected set of transcription-related Gene Ontology terms we have identified more than 1500 putative transcription cofactors. 415 of these are already participants of complexes in Complex Portal, and the remaining proteins will be curated into Complex Portal if they are identified as components of complexes.…”

Section: Collaboration and Community Involvementmentioning

confidence: 99%

Complex Portal 2022: new curation frontiers

Meldal

Perfetto

Combe

et al. 2021

Nucleic Acids Research

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The Complex Portal (www.ebi.ac.uk/complexportal) is a manually curated, encyclopaedic database of macromolecular complexes with known function from a range of model organisms. It summarizes complex composition, topology and function along with links to a large range of domain-specific resources (i.e. wwPDB, EMDB and Reactome). Since the last update in 2019, we have produced a first draft complexome for Escherichia coli, maintained and updated that of Saccharomyces cerevisiae, added over 40 coronavirus complexes and increased the human complexome to over 1100 complexes that include approximately 200 complexes that act as targets for viral proteins or are part of the immune system. The display of protein features in ComplexViewer has been improved and the participant table is now colour-coordinated with the nodes in ComplexViewer. Community collaboration has expanded, for example by contributing to an analysis of putative transcription cofactors and providing data accessible to semantic web tools through Wikidata which is now populated with manually curated Complex Portal content through a new bot. Our data license is now CC0 to encourage data reuse. Users are encouraged to get in touch, provide us with feedback and send curation requests through the ‘Support’ link.

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“…Hence, integrating PPIs across multiple experiments, as for e.g. the networks hu.MAP 1.0 [6] and hu.MAP 2.0 [7] that integrate over 9,000 and 15,000 mass spectrometry experiments respectively from AP/MS [8]- [11] and CF/MS data [12]- [15], can help to mitigate the effects of experimental errors. Combining such approaches with algorithms to cluster proteins and identify complexes from the PPI network should result in more accurate determination of protein complexes.…”

Section: Introductionmentioning

confidence: 99%

“…All three methods, SCI-SVM, SCI-BN, and ClusterSS use a greedy heuristic algorithm for selecting the neighbor to add to the subgraph in the growth process, with ClusterSS considering only the top neighbors by degree for speed improvements. However, since the methods use serial candidate community sampling, this negatively impacts their scalability to large networks like hu.MAP 1.0 [6] with ~8k proteins and ~60k interactions, and hu.MAP 2.0 [7] with ~10k proteins and over 40k interactions. To combat this, Super.Complex (supervised complex detection algorithm) was developed for high scalability and accuracy [24].…”

Section: Introductionmentioning

confidence: 99%

Molecular complex detection in protein interaction networks through reinforcement learning

Palukuri

Patil

Marcotte

2022

Preprint

Self Cite

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Many, if not most, proteins assemble into higher-order complexes to perform their biological functions. Such protein-protein interactions (PPI) are often experimentally measured for pairs of proteins and summarized in a weighted PPI network, to which community detection algorithms can be applied to define the various higher-order protein complexes. Current methods, which include both unsupervised and supervised approaches, often assume that protein complexes manifest only as dense subgraphs, and in the case of supervised approaches, focus only on learning which subgraphs correspond to complexes, not how to find them in a network, a task that is currently solved using heuristics. However, learning to walk trajectories on a network with the goal of finding protein complexes lends itself naturally to a reinforcement learning (RL) approach, a strategy that has not been extensively explored for community detection. Here, we evaluated the use of a reinforcement learning pipeline for community detection in weighted protein-protein interaction networks to detect new protein complexes. Using known complexes, the algorithm is trained to calculate the value of different possible subgraph densities in the process of walking on the network to find a protein complex. Then, a distributed prediction algorithm scales the RL pipeline to search for protein complexes on large PPI networks. The reinforcement learning pipeline applied to a human PPI network consisting of 8k proteins and 60k PPI results in 1,157 protein complexes and shows competitive accuracy with improved speed when compared to previous algorithms. We highlight protein complexes harboring minimally characterized proteins including C4orf19, C18orf21, and KIAA1522, suggest TMC04 to be a putative additional subunit of the KICSTOR complex, and confirm the participation of C15orf41 in a higher-order complex with CDAN1, ASF1A, and HIRA by 3D structural modeling. Reinforcement learning offers several distinct advantages for community detection, including scalability and knowledge of the walk trajectories defining those communities.

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

Cited by 96 publications

References 66 publications

DEPCOD: a tool to detect and visualize co-evolution of protein domains

DEPCOD: a tool to detect and visualize co-evolution of protein domains

Complex Portal 2022: new curation frontiers

Molecular complex detection in protein interaction networks through reinforcement learning

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