Format

Send to

Choose Destination
Nat Protoc. 2018 Mar;13(3):478-494. doi: 10.1038/nprot.2017.146. Epub 2018 Feb 8.

Structural prediction of protein models using distance restraints derived from cross-linking mass spectrometry data.

Author information

1
Mass Spectrometry and Protein Chemistry, Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC),Vienna, Austria.
2
Mass Spectrometry and Protein Chemistry, Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), Vienna, Austria.
3
Department of Biochemistry and Cell Biology, Max F. Perutz Laboratories, University of Vienna, Vienna, Austria.
4
Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria.
5
Department of Chromosome Biology, Max F. Perutz Laboratories, University of Vienna, Vienna, Austria.

Abstract

This protocol describes a workflow for creating structural models of proteins or protein complexes using distance restraints derived from cross-linking mass spectrometry experiments. The distance restraints are used (i) to adjust preliminary models that are calculated on the basis of a homologous template and primary sequence, and (ii) to select the model that is in best agreement with the experimental data. In the case of protein complexes, the cross-linking data are further used to dock the subunits to one another to generate models of the interacting proteins. Predicting models in such a manner has the potential to indicate multiple conformations and dynamic changes that occur in solution. This modeling protocol is compatible with many cross-linking workflows and uses open-source programs or programs that are free for academic users and do not require expertise in computational modeling. This protocol is an excellent additional application with which to use cross-linking results for building structural models of proteins. The established protocol is expected to take 6-12 d to complete, depending on the size of the proteins and the complexity of the cross-linking data.

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center