PDB-wide identification of physiological hetero-oligomeric assemblies based on conserved quaternary structure geometry

Dey, Sucharita; Levy, Emmanuel D.

doi:10.1016/j.str.2021.07.012

Cited by 12 publications

(7 citation statements)

References 53 publications

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“…Yet, when stoichiometries are unknown, the results are difficult to interpret (Evans et al , 2022; Burke et al , 2021). Systematic searching of stoichiometries in protein structure prediction is an active area of research (Bryant et al , 2022b), and experimental efforts to determine stoichiometries are collected systematically (Hu et al , 2019; Dey & Levy, 2021).…”

Section: Discussionmentioning

confidence: 99%

Protein complexes in cells by AI-assisted structural proteomics

O’Reilly

Graziadei

Forbrig

et al. 2022

Preprint

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Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enable a candidate-based approach to systematically model novel protein assemblies. Here, we use a combination of in-cell crosslinking mass spectrometry, co-fractionation mass spectrometry and the SubtiWiki database to identify protein-protein interactions in the model Gram-positive bacterium Bacillus subtilis. Pairing this with structure prediction by AIphaFold-Multimer, we identify novel interactors of central machineries that include the ribosome, RNA polymerase and pyruvate dehydrogenase, as well as interactions involving uncharacterized proteins, which we functionally validate. After controlling for the false-positive rate of the AlphaFold approach, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. We show that crosslinking MS data can independently validate AlphaFold predictions in situ. Our approach uncovers protein-protein interactions inside cells, provides structural insight into their interaction interface, and is applicable to genetically intractable organisms, including pathogenic bacteria.

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Section: Discussionmentioning

confidence: 99%

Protein complexes in cells by AI-assisted structural proteomics

O’Reilly

Graziadei

Forbrig

et al. 2022

Preprint

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“…The use of evolution and coevolution information together with deep learning has also seen recent developments for scoring and predicting protein-protein interactions ( Andreani et al, 2020 ; Quadir et al, 2021 ; Quignot et al, 2021 ; Yan and Huang, 2021 ). Complementary to such residue-level information, the use of subunit interaction geometry conservation as evidence of a QS being physiological is a powerful approach, which yields accurate predictions ( Dey et al, 2018 ; Dey and Levy, 2021 ). In that respect, we hope that making this prediction pipeline available as a web server will help biologists identify relevant QS of proteins and will help them investigate the QS of specific proteins.…”

Section: Discussionmentioning

confidence: 99%

“…Like PISA, EPPIC ( Baskaran et al, 2014 ) predicts full QSs while placing emphasis on the conservation of amino acids at interfaces to infer their physiological relevance. QSalign ( Dey et al, 2018 ; Dey and Levy, 2021 ) also employs evolutionary conservation, but it does not rely on sequence conservation. Instead, it searches for structural conservation of the structure of a QS across homologs.…”

Section: Introductionmentioning

confidence: 99%

QSalignWeb: A Server to Predict and Analyze Protein Quaternary Structure

Dey

Prilusky

Levy

2022

Front. Mol. Biosci.

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The identification of physiologically relevant quaternary structures (QSs) in crystal lattices is challenging. To predict the physiological relevance of a particular QS, QSalign searches for homologous structures in which subunits interact in the same geometry. This approach proved accurate but was limited to structures already present in the Protein Data Bank (PDB). Here, we introduce a webserver (www.QSalign.org) allowing users to submit homo-oligomeric structures of their choice to the QSalign pipeline. Given a user-uploaded structure, the sequence is extracted and used to search homologs based on sequence similarity and PFAM domain architecture. If structural conservation is detected between a homolog and the user-uploaded QS, physiological relevance is inferred. The web server also generates alternative QSs with PISA and processes them the same way as the query submitted to widen the predictions. The result page also shows representative QSs in the protein family of the query, which is informative if no QS conservation was detected or if the protein appears monomeric. These representative QSs can also serve as a starting point for homology modeling.

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“…It is estimated that up to 17% of the X-ray structures in the PDB have one or more incorrect annotations of the most likely biological assembly ( 10 ). The error rates for PISA and EPPIC, two major biological assembly sources, are even higher: 13% and 18% for homodimers and 16% and 32% for larger homooligomeric complexes ( 9 ), 25% and 33% for heterodimers, and 39% and 45% for larger hetero complexes respectively ( 43 ). The inconsistency rates for PDB, PISA and EPPIC from our ProtCAD common clusters with at least five CFs are 15%, 23% and 24%, respectively.…”

Section: Discussionmentioning

confidence: 99%

The protein common assembly database (ProtCAD)—a comprehensive structural resource of protein complexes

Dunbrack

2022

Nucleic Acids Research

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Proteins often act through oligomeric interactions with other proteins. X-ray crystallography and cryo-electron microscopy provide detailed information on the structures of biological assemblies, defined as the most likely biologically relevant structures derived from experimental data. In crystal structures, the most relevant assembly may be ambiguously determined, since multiple assemblies observed in the crystal lattice may be plausible. It is estimated that 10–15% of PDB entries may have incorrect or ambiguous assembly annotations. Accurate assemblies are required for understanding functional data and training of deep learning methods for predicting assembly structures. As with any other kind of biological data, replication via multiple independent experiments provides important validation for the determination of biological assembly structures. Here we present the Protein Common Assembly Database (ProtCAD), which presents clusters of protein assembly structures observed in independent structure determinations of homologous proteins in the Protein Data Bank (PDB). ProtCAD is searchable by PDB entry, UniProt identifiers, or Pfam domain designations and provides downloads of coordinate files, PyMol scripts, and publicly available assembly annotations for each cluster of assemblies. About 60% of PDB entries contain assemblies in clusters of at least 2 independent experiments. All clusters and coordinates are available on ProtCAD web site (http://dunbrack2.fccc.edu/protcad).

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PDB-wide identification of physiological hetero-oligomeric assemblies based on conserved quaternary structure geometry

Cited by 12 publications

References 53 publications

Protein complexes in cells by AI-assisted structural proteomics

Protein complexes in cells by AI-assisted structural proteomics

QSalignWeb: A Server to Predict and Analyze Protein Quaternary Structure

The protein common assembly database (ProtCAD)—a comprehensive structural resource of protein complexes

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