Rapid determination of quaternary protein structures in complex biological samples.


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
14 01 2019
Historique:
received: 27 04 2018
accepted: 07 12 2018
entrez: 16 1 2019
pubmed: 16 1 2019
medline: 20 2 2019
Statut: epublish

Résumé

The understanding of complex biological systems is still hampered by limited knowledge of biologically relevant quaternary protein structures. Here, we demonstrate quaternary structure determination in biological samples using a combination of chemical cross-linking, high-resolution mass spectrometry and high-accuracy protein structure modeling. This approach, termed targeted cross-linking mass spectrometry (TX-MS), relies on computational structural models to score sets of targeted cross-linked peptide signals acquired using a combination of mass spectrometry acquisition techniques. We demonstrate the utility of TX-MS by creating a high-resolution quaternary model of a 1.8 MDa protein complex composed of a pathogen surface protein and ten human plasma proteins. The model is based on a dense network of cross-link distance constraints obtained directly in a mixture of human plasma and live bacteria. These results demonstrate that TX-MS can increase the applicability of flexible backbone docking algorithms to large protein complexes by providing rich cross-link distance information from complex biological samples.

Identifiants

pubmed: 30643114
doi: 10.1038/s41467-018-07986-1
pii: 10.1038/s41467-018-07986-1
pmc: PMC6331586
doi:

Substances chimiques

Blood Proteins 0
Cross-Linking Reagents 0
Multiprotein Complexes 0
Recombinant Proteins 0

Types de publication

Evaluation Study Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

192

Subventions

Organisme : European Research Council
ID : 309831
Pays : International

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Auteurs

Simon Hauri (S)

Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Klinikgatan 32, SE-22184, Lund, Sweden.

Hamed Khakzad (H)

S3IT, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
Institute for Computational Science, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.

Lotta Happonen (L)

Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Klinikgatan 32, SE-22184, Lund, Sweden.

Johan Teleman (J)

Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Klinikgatan 32, SE-22184, Lund, Sweden.

Johan Malmström (J)

Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Klinikgatan 32, SE-22184, Lund, Sweden. johan.malmstrom@med.lu.se.

Lars Malmström (L)

Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Klinikgatan 32, SE-22184, Lund, Sweden. lars.malmstroem@uzh.ch.
S3IT, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland. lars.malmstroem@uzh.ch.
Institute for Computational Science, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland. lars.malmstroem@uzh.ch.

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Classifications MeSH