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

Nature Protocols volume 13, pages 478–494 (2018)Cite this article

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An Author Correction to this article was published on 25 June 2018

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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.

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Figure 1: Overview of the structural modeling workflow using XL-MS data.
Figure 2: Cross-link data–driven modeling workflow for predicting structures of proteins and protein complexes.
Figure 3: Chemical cross-links mapped onto the resulting comparative open protein–protein model of the HOP2–MND1 heterodimeric complex.
Figure 4: Predicted protein structure of calmodulin.
Figure 5: Predicted structure of the plectin ABD–calmodulin complex.
Figure 6: Comparison of the predicted structure and the crystal structure of the PPP2R1A–PPP2CA complex.
Figure 7: Predicted protein structure of bovine cytochrome C.
Figure 8: Simulated distribution of alpha-carbon distances between cross-linked residues in two resulting comparative models.

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Change history

  • 25 June 2018

    In the version of this article initially published online, the authors used incorrectly defined restraints for specifying the distance between residues when using the HADDOCK portal. Following the publication of a Correspondence by the developers of the HADDOCK portal (Nat. Protoc. https://dx.doi.org/10.1038/s41596-018-0017-6, 2018) and a Reply by the authors of the Protocol (Nat. Protoc. https://dx.doi.org/10.1038/s41596-018-0018-5, 2018), the syntax in Step 21 has been corrected. In addition, the input files (available as Supplementary Data 5–7) have been replaced. This error has been corrected in the HTML and PDF versions of the article.

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Acknowledgements

This research was funded by the Austrian Science Fund (SFB F3402, P24685-B24 and TRP 308-N15) and the EU FP7 project MEIOsys (222883-2). We thank the Research Institute of Molecular Pathology (IMP) and the Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA) for general funding. We acknowledge M. Hartl and T. Gossenreiter for useful and inspiring scientific discussions. Molecular graphics and analyses were performed with the UCSF Chimera package. Chimera was developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco (supported by NIGMS P41-GM103311). Images in Chimera were created using Persistence of Vision Raytracer (v3.6), Persistence of Vision Pty. Ltd. (2004), and were retrieved from http://www.povray.org/download/.

Author information

Authors and Affiliations

  1. Mass Spectrometry and Protein Chemistry, Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC),, Vienna, Austria

    Zsuzsanna Orbán-Németh, Rebecca Beveridge, Evelyn Rampler, Thomas Stranzl, Otto Hudecz, Johannes Doblmann & Karl Mechtler

  2. Mass Spectrometry and Protein Chemistry, Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), Vienna, Austria

    Zsuzsanna Orbán-Németh, Rebecca Beveridge, Evelyn Rampler, Thomas Stranzl, Otto Hudecz, Johannes Doblmann & Karl Mechtler

  3. Department of Biochemistry and Cell Biology, Max F. Perutz Laboratories, University of Vienna, Vienna, Austria

    David M Hollenstein

  4. Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria

    Evelyn Rampler

  5. Department of Chromosome Biology, Max F. Perutz Laboratories, University of Vienna, Vienna, Austria

    Peter Schlögelhofer

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  1. Zsuzsanna Orbán-Németh
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Contributions

Z.O.-N., E.R., D.M.H., T.S. and K.M. designed and developed the modeling workflow. J.D. and O.H. performed data analysis and tested the workflow. Z.O.-N., R.B. and D.M.H. wrote and edited the manuscript. P.S. provided substantial input into the method development.

Corresponding author

Correspondence to Karl Mechtler.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Data 1

I-TASSER prediction of bovine cytochrome c. Example input and output files, along with instructions for comparative modeling of bovine cytochrome C using cross-linking data (utilizes XL data originally published by Kao et al.25). (ZIP 250 kb)

Supplementary Data 2

I-TASSER prediction of HOP2. Example input and output files, along with instructions for comparative modeling of HOP2 from A. thaliana using cross-linking data (utilizes XL data originally published by Rampler et al.8). (ZIP 767 kb)

Supplementary Data 3

I-TASSER prediction of full length calmodulin. Example input and output files, along with instructions for comparative modeling of human calmodulin using cross-linking data (utilizes XL data originally published by Yilmaz et al.31). (ZIP 506 kb)

Supplementary Data 4

I-TASSER prediction of MND1. Example input and output files, along with instructions for comparative modeling of MND1 from A. thaliana using cross-linking data (utilizes XL data originally published by Rampler et al.8). (ZIP 639 kb)

Supplementary Data 5

HADDOCK docking of calmodulin to plectin. Example input and output files, along with instructions for protein–protein docking of human N-lobe of calmodulin and the actin-binding domain of mouse plectin using cross-linking data (utilizes XL data originally published by Yilmaz et al.31 and Song et al.51). (ZIP 6127 kb)

Supplementary Data 6

HADDOCK docking of PPP2R1A to PPP2CA. Example input and output files, along with instructions for protein–protein docking of mouse Ppp2r1a and human PPP2CA using cross-linking data (utilizes XL data originally published by Herzog et al. 4). (ZIP 6841 kb)

Supplementary Data 7

HADDOCK docking of HOP2 to MND1. Example input and output files, along with instructions for protein–protein docking of HOP2 and MND1 from A. thaliana using cross-linking data (utilizes XL data originally published by Rampler et al. 8). (ZIP 7835 kb)

Supplementary Data 8

Windows batch script that can be used to automatically run Xwalk on a series of PDB files. (TXT 1 kb)

Supplementary Data 9

Description of structure and content of supplementary data sets. It is recommended to read this file before using the supplementary data sets. (PDF 192 kb)

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Orbán-Németh, Z., Beveridge, R., Hollenstein, D. et al. Structural prediction of protein models using distance restraints derived from cross-linking mass spectrometry data. Nat Protoc 13, 478–494 (2018). https://doi.org/10.1038/nprot.2017.146

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