This work was supported by the Foundation of Knut and Alice Wallenberg (grant no. 2016.0023) and the Swiss National Science Foundation (grant no. P2ZHP3_191289). In addition, we thank S3IT, University of Zurich, for its computational infrastructure and technical support.

1. Submit workflow at&#160;https://txms.org.
<ol>
	<li>Go to https://txms.org and click &#34;Use Cheetah-MS.&#34;</li>
	<li>To submit workflow, you need to provide two PDB files and one MS/MS mzML or MGF file. You can also click on the &#34;load sample data&#34; to see the demo version of the workflow. 
	NOTE: Please look at the manual page of the webserver for detailed information on how to submit a job. The web server supports different non-cleavable cross-linker agents, up to 12 post-translational modifications (PTMs), options related to computational modeling and MS data analysis. Small help buttons are also designed on the submit page to show more information regarding each option.</li>
</ol>
2. Run Cheetah-MS.
NOTE: Convert the vendor-specific formats to mzML or MGF using the ProteoWizard MSConvert software19.
<ol>
	<li>Upload the MS data to https://txms.org. Then, click on &#34;Choose file&#34; and select the MS data, which must be in the mzML/MGF data formats18. 
	NOTE: Example data are available on https://txms.org. These data are also directly accessible through zenodo.org, DOI&#160;10.5281/zenodo.3361621.</li>
	<li>Upload two PDB files to https://txms.org. Click on &#34;Choose file&#34; and select the PDB files to upload. 
	NOTE: If no experimental structures exist, create models using, for example, SWISS-MODEL20&#160;if homologue structures are available, or trRosetta21,22&#160;or Robetta23,24&#160;web servers for de novo structure predictions.</li>
	<li>Submit a new workflow. Click on &#34;Submit&#34; to receive a job identifier tag. Then, follow the form to the results section using this tag. 
	NOTE: Computing the result takes time, so please wait until the workflow finishes, and store the job identifier tag to return to the results page. The computation is carried out on remote computational infrastructure. If you want to run TX-MS locally, please refer to Hauri et al.10.</li>
	<li>Inspect the Jupyter Notebook report using the online viewer. Then, scroll down to &#34;Report&#34; in the results section using the job identifier tag.</li>
</ol>
3. Install JupyterHub.
<ol>
	<li>Install docker as instructed at&#160;https://docs.docker.com/install/.</li>
	<li>Download the JupyterHub docker container with the Jupyter openBIS25&#160;extension. The general command is &#34;docker pull malmstroem/jove:latest,&#34; but might differ on other platforms. 
	NOTE: For a general discussion on how to download containers, please refer to https://www.docker.com/get-started. It is also possible to download the container from zenodo.org, DOI&#160;10.5281/zenodo.3361621. 
	NOTE: The Jupyter openBIS extension source code is available here:&#160;https://pypi.org/project/jupyter-openbis-extension/.</li>
	<li>Start the docker container: docker run -p 8178:8000 malmstroem/jove:latest. 
	NOTE: The port that JupyterHub uses by default is 8000. This port is configurable, and the commands above need to be adjusted accordingly if changed. Port 8178 is an arbitrary choice and can be changed. The example URLs provided below need to be adjusted accordingly.</li>
	<li>Go to the following address: http://127.0.0.1:8178. Log in using the username &#34;user&#34; and the password &#34;user.&#34; 
	NOTE: The address http://127.0.0.1&#160;implies that the docker container is running on the local computer. If the docker container is run on a server, use the server&#39;s IP address or URL (e.g., https://example.com). The docker container is based on Ubuntu Bionic 18.04, JupyterHub 0.9.6, and Jupyter openBIS extension 0.2. It is possible to install this in other operating systems, but this was not tested.</li>
</ol>
4. Download the report.
<ol>
	<li>Create a new notebook by clicking New| Python 3&#160;using the menu located near the top right part of the page. This will open a new tab with a notebook called Untitled (or something similar).</li>
	<li>Click &#34;Configure openBIS Connections&#34; in the Jupyter tool menu.</li>
	<li>Fill in the name: txms; URL:&#160;https://txms.org; user: guest; password: guestpasswd.</li>
	<li>Click &#34;Connect.&#34;</li>
	<li>Choose the new connection and click &#34;Choose Connection.&#34;</li>
	<li>Search for the report template (e.g., /CHEETAH/WF70) and click Download. 
	NOTE: You need to adjust the report template based on the results and report you obtained from running your job on the Cheetah-MS web server.</li>
	<li>Rerun the report by clicking Cell | Run All.</li>
</ol>
5. Extend the report.
<ol>
	<li>Add a new cell at the bottom: Cell | Insert Below.</li>
	<li>Type in the wanted code. For an example, please see the Representative Results section below.</li>
	<li>Execute the cell by pressing &#34;Shift-Enter.&#34;</li>
</ol>

<ol>
	<li>Berman, H. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Protein+Data+Bank.">The Protein Data Bank.</a> Acta Crystallographica Section D: Biological Crystallography. 58 (6), 899-907 (2002).</li><li>Herzog, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structural+Probing+of+a+Protein+Phosphatase+2A+Network+by+Chemical+Cross-Linking+and+Mass+Spectrometry.">Structural Probing of a Protein Phosphatase 2A Network by Chemical Cross-Linking and Mass Spectrometry.</a> Science. 337 (6100), 1348-1352 (2012).</li><li>Hoopmann, M. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kojak:+efficient+analysis+of+chemically+cross-linked+protein+complexes.">Kojak: efficient analysis of chemically cross-linked protein complexes.</a> Journal of Proteome Research. 14 (5), 2190-2198 (2015).</li><li>Seebacher, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+cross-linking+analysis+using+mass+spectrometry,+isotope-coded+cross-linkers,+and+integrated+computational+data+processing.">Protein cross-linking analysis using mass spectrometry, isotope-coded cross-linkers, and integrated computational data processing.</a> Journal of Proteome Research. 5 (9), 2270-2282 (2006).</li><li>Rinner, O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+cross-linked+peptides+from+large+sequence+databases.">Identification of cross-linked peptides from large sequence databases.</a> Nature Methods. 5 (4), 315-318 (2008).</li><li>Walzthoeni, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=False+discovery+rate+estimation+for+cross-linked+peptides+identified+by+mass+spectrometry.">False discovery rate estimation for cross-linked peptides identified by mass spectrometry.</a> Nature Methods. 9 (9), 901-903 (2012).</li><li>Yang, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+cross-linked+peptides+from+complex+samples.">Identification of cross-linked peptides from complex samples.</a> Nature Methods. 9 (9), 904-906 (2012).</li><li>Chu, F., Baker, P. R., Burlingame, A. L., Chalkley, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Finding+Chimeras:+a+Bioinformatics+Strategy+for+Identification+of+Cross-linked+Peptides.">Finding Chimeras: a Bioinformatics Strategy for Identification of Cross-linked Peptides.</a> Molecular &amp; Cellular Proteomics. 9 (1), 25-31 (2010).</li><li>Holding, A. N., Lamers, M. H., Stephens, E., Skehel, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hekate:+Software+Suite+for+the+Mass+Spectrometric+Analysis+and+Three-Dimensional+Visualization+of+Cross-Linked+Protein+Samples.">Hekate: Software Suite for the Mass Spectrometric Analysis and Three-Dimensional Visualization of Cross-Linked Protein Samples.</a> Journal of Proteome Research. 12 (12), 5923-5933 (2013).</li><li>Hauri, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+determination+of+quaternary+protein+structures+in+complex+biological+samples.">Rapid determination of quaternary protein structures in complex biological samples.</a> Nature Communications. 10 (1), 192 (2019).</li><li>R&#246;st, H. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=OpenSWATH+enables+automated,+targeted+analysis+of+data-independent+acquisition+MS+data.">OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.</a> Nature Biotechnology. 32 (3), 219-223 (2014).</li><li>R&#246;st, H. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=OpenMS:+a+flexible+open-source+software+platform+for+mass+spectrometry+data+analysis.">OpenMS: a flexible open-source software platform for mass spectrometry data analysis.</a> Nature Methods. 13 (9), 741-748 (2016).</li><li>Quandt, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+synthetic+peptides+to+benchmark+peptide+identification+software+and+search+parameters+for+MS/MS+data+analysis.">Using synthetic peptides to benchmark peptide identification software and search parameters for MS/MS data analysis.</a> EuPA Open Proteomics. 5, 21-31 (2014).</li><li>Bradley, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Free+modeling+with+Rosetta+in+CASP6.">Free modeling with Rosetta in CASP6.</a> Proteins: Structure, Function, and Bioinformatics. 61 (S7), 128-134 (2005).</li><li>Gray, J. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+protein-protein+docking.">High-resolution protein-protein docking.</a> Current Opinion in Structural Biology. 16 (2), 183-193 (2006).</li><li>Khakzad, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cheetah-MS:+a+web+server+to+model+protein+complexes+using+tandem+cross-linking+mass+spectrometry+data.">Cheetah-MS: a web server to model protein complexes using tandem cross-linking mass spectrometry data.</a> Bioinformatics. , (2021).</li><li>Malmstr&#246;m, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chapter+15:+Computational+Proteomics+with+Jupyter+and+Python.">Chapter 15: Computational Proteomics with Jupyter and Python.</a> Methods in Molecular Biology. 15, 237-248 (1977).</li><li>Martens, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=mzML--a+community+standard+for+mass+spectrometry+data.">mzML--a community standard for mass spectrometry data.</a> Molecular &amp; Cellular Proteomics. 10 (1), (2011).</li><li>Chambers, M. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+cross-platform+toolkit+for+mass+spectrometry+and+proteomics.">A cross-platform toolkit for mass spectrometry and proteomics.</a> Nature Biotechnology. 30 (10), 918-920 (2012).</li><li>Waterhouse, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SWISS-MODEL:+homology+modelling+of+protein+structures+and+complexes.">SWISS-MODEL: homology modelling of protein structures and complexes.</a> Nucleic Acids Research. 46 (W1), W296-W303 (2018).</li><li>Yang, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+protein+structure+prediction+using+predicted+interresidue+orientations.">Improved protein structure prediction using predicted interresidue orientations.</a> Proceedings of the National Academy of Sciences. 117 (3), 1496-1503 (2020).</li><li>Koehler Leman, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Macromolecular+modeling+and+design+in+Rosetta:+recent+methods+and+frameworks.">Macromolecular modeling and design in Rosetta: recent methods and frameworks.</a> Nature Methods. 17 (7), 665-680 (2020).</li><li>Chivian, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+CASP6+structures+using+automated+Robetta+protocols.">Prediction of CASP6 structures using automated Robetta protocols.</a> Proteins: Structure, Function, and Bioinformatics. 61 (S7), 157-166 (2005).</li><li>Chivian, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+prediction+of+CASP-5+structures+using+the+Robetta+server.">Automated prediction of CASP-5 structures using the Robetta server.</a> Proteins: Structure, Function, and Bioinformatics. 53 (S6), 524-533 (2003).</li><li>Bauch, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=openBIS:+a+flexible+framework+for+managing+and+analyzing+complex+data+in+biology+research.">openBIS: a flexible framework for managing and analyzing complex data in biology research.</a> BMC Bioinformatics. 12, 468 (2011).</li><li>Lima, D. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SIM-XL:+A+powerful+and+user-friendly+tool+for+peptide+cross-linking+analysis.">SIM-XL: A powerful and user-friendly tool for peptide cross-linking analysis.</a> Journal of Proteomics. 129, 51-55 (2015).</li><li>Happonen, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+quantitative+Streptococcus+pyogenes-human+protein-protein+interaction+map+reveals+localization+of+opsonizing+antibodies.">A quantitative Streptococcus pyogenes-human protein-protein interaction map reveals localization of opsonizing antibodies.</a> Nature Communications. 10, 2727 (2019).</li><li>Khakzad, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structural+determination+of+Streptococcus+pyogenes+M1+protein+interactions+with+human+immunoglobulin+G+using+integrative+structural+biology.">Structural determination of Streptococcus pyogenes M1 protein interactions with human immunoglobulin G using integrative structural biology.</a> PLOS Computational Biology. 17 (1), E1008169 (2021).</li><li>Khakzad, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+vivo+cross-linking+MS+of+the+complement+system+MAC+assembled+on+live+Gram-positive+bacteria.">In vivo cross-linking MS of the complement system MAC assembled on live Gram-positive bacteria.</a> Frontiers in Genetics. 11, (2020).</li></ol>

The authors have nothing to disclose.

Modern computational workflows are often complex, with multiple tools from many different vendors, complex interdependencies, high data volumes, and multifaceted results. Consequently, it is increasingly difficult to accurately document all the steps required to obtain a result, making it difficult to reproduce the given result. Here, we demonstrate a general strategy that combines the automation and ease of an automated workflow that produces a generic report, with the flexibility to customize the report in a reproducible fashion.
Three requirements need to be fulfilled for the protocol to work: First, the proteins selected for analysis need to interact in such a manner that the chemical cross-linking experiment can produce cross-linked species at a sufficiently high concentration to be detected by the mass spectrometer; different mass spectrometers have different levels of detection and are also dependent on the acquisition protocol as well as the choice of cross-linking reagent. The current version of TX-MS protocol only allows for DSS, a lysine-lysine homobifunctional cross-linking reagent. Still, this limitation is primarily due to the possibility that the machine learning step would need to be adjusted for other reagents. This limitation has been improved in the Cheetah-MS web server as two more cross-linking reagents can be considered, but all three are non-cleavable reagents. Second, the two proteins need either to have an experimentally determined structure or be modeled using comparative modeling techniques or de novo techniques. Not all proteins can be modeled, but a combination of improved software and a constant deposition of experimental structures in the PDB expands the number of proteins that can be modeled. Third, the interacting proteins should remain sufficiently similar in their bound and unbound states so that the docking algorithms in use by TX-MS and Cheetah-MS can create quaternary structures of adequate quality to enable scoring. This requirement is relatively vague, as acceptable quality is highly system-dependent, where smaller proteins of known structure are generally easier to compare than larger proteins of unknown structure.
In case of a negative result, first check that TX-MS found intra-links, cross-links between residues that are part of the same polypeptide chain. If none are discovered, the most likely explanation is that something went wrong with the sample preparation or the data acquisition. If multiple distance constraints do not support the models, visually inspect the models to ensure that the conformation is supported by cross-linked residues. There is no obvious way to pivot one of the interactors without disrupting at least one cross-link. If there are cross-links longer than the permitted distance for the given cross-linking reagent, try to improve the modeling of the interactors by incorporating cross-linking data.
It is possible to use alternative software applications to accomplish equivalent results provided that the sensitivity of the chosen software is comparable to the sensitivity of TX-MS. For example, there are online versions of RosettaDock, HADDOCK, and others. It is also possible to analyze chemical cross-linking data through xQuest/xProphet5,6, plink7, and SIM-XL26. 
We are continuously applying TX-MS and Cheetah-MS to new projects27,28,29, thereby improving the reports produced by these approaches to allow for a more detailed analysis of results without making the reports larger.

Protein-protein interactions underpin the structure and function of biological systems. Having access to quaternary structures of proteins can provide insights into how two or more proteins interact to form high-order structures. Unfortunately, obtaining quaternary structures remains challenging; this is reflected in the comparatively small number of Protein DataBank (PDB) entries1&#160;containing more than one polypeptide. Protein-protein interactions can be studied with technologies such as X-ray crystallography, NMR, and cryo-EM, but obtaining a sufficient amount of purified protein under conditions where the methods can be applied can be time-consuming.
Chemical cross-linking mass spectrometry was developed to obtain experimental data on protein-protein interactions with fewer restrictions on sample preparation as mass spectrometry can be used to acquire data on arbitrarily complex samples2,3,4,5,6,7,8,9. However, the combinatorial nature of the data analysis and the relatively small number of cross-linked peptides require that the samples be fractionated before analysis. To address this shortcoming, we developed TX-MS, a method that combines computational modeling with chemical cross-linking mass spectrometry10. TX-MS can be used on arbitrarily complex samples and is significantly more sensitive compared to previous methods10. It accomplishes this by scoring all data associated with a given protein-protein interaction as a set instead of interpreting each MS spectrum independently. TX-MS also uses up to three different MS acquisition protocols: high-resolution MS1 (hrMS1), data-dependent acquisition (DDA), and data-independent acquisition (DIA), further providing opportunities to identify a cross-linked peptide by combining multiple observations. The TX-MS computational workflow is complex for several reasons. First, it relies on multiple MS analysis software programs11,12,13&#160;to create protein structure models14,15. Second, the amount of data can be considerable. Third, the modeling step can consume significant amounts of computer processing power.
Consequently, TX-MS is best used as an automated, simplified computational workflow through Cheetah-MS web server16&#160;that runs on large computational infrastructures such as computer clouds or clusters. To facilitate the interpretation of the results, we produced an interactive Jupyter Notebook17. Here, we demonstrate how the Jupyter Notebook report can be extended to yield a more in-depth analysis of a given result.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Two Protein DataBank files of the proteins of interest.</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Example files available on txms.org and zenodo.org, DOI 10.5281/zenodo.3361621</td>
		</tr>
		<tr>
			<td>An mzML data file acquired on a sample where the proteins of interest were crosslinked.</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Example files available on txms.org or zenodo.org, DOI 10.5281/zenodo.3361621</td>
		</tr>
	</tbody>
</table>

quaternary structure modeling through chemical cross-linking mass spectrometry: extending tx-ms jupyter reports

Protein-protein interactions can be challenging to study yet provide insights into how biological systems function. Targeted cross-linking mass spectrometry (TX-MS), a method combining quaternary protein structure modeling and chemical cross-linking mass spectrometry, creates high-accuracy structure models using data obtained from complex, unfractionated samples. This removes one of the major obstacles to protein complex structure analysis because the proteins of interest no longer need to be purified in large quantities. Cheetah-MS web server was developed to make the simplified version of the protocol more accessible to the community. Considering the tandem MS/MS data, Cheetah-MS generates a Jupyter Notebook, a graphical report summarizing the most important analysis results. Extending the Jupyter Notebook can yield more in-depth insights and better understand the model and the mass spectrometry data supporting it. The technical protocol presented here demonstrates some of the most common extensions and explains what information can be obtained. It contains blocks to help analyze tandem MS/MS acquisition data and the overall impact of the detected XLs on the reported quaternary models. The result of such analyses can be applied to structural models that are embedded in the notebook using NGLView.

TX-MS provides structural outputs supported by MS-derived experimental constraints. It works by combining different MS data acquisition types with computational modeling. Therefore, it is helpful to parse each MS data separately and provide visualization of the output structure. Supplementary Data 1&#160;contains an example notebook that can parse DDA and DIA data produced as TX-MS output. Users can select the XL of interest. By running the notebook, the MS2 spectrum of that XL will be shown where different colors help to discriminate between fragments related to the first peptide, second peptide, and the combinatorial fragment ions. The XL can also be mapped to the structure using the NGLView widget embedded in a Jupyter Notebook.
Another cell in this notebook can help users to parse and visualize DIA data. However, visualizing DIA data is more difficult because the analyzed data need to be prepared in the correct format.
Figure 1 shows an example structure of M1 and albumin with top XLs mapped on the structure. TX-MS obtained all XLs after parsing hrMS1, DDA, and DIA data, and the RosettaDock protocol provided the computational models.
As this report is a Jupyter Notebook, any valid Python code can be added to new notebook cells. For example, the code below will create a histogram over the MS2 counts, indicating how well supported each cross-link is by the underlying data. 
import seaborn as sns 
sns.distplot(ms2[&#39;count&#39;]);
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60311/60311fig01.jpg" /> 
Figure 1: Structural model of Streptococcus pyogenes M1 protein and human albumin with XLs mapped on the structure. The M1 protein is shown in gray and constitutes a homodimer. The six albumin molecules are presented as pairs in various shades of blue. Cross-links and distances are given in red with black text. <a href="https://www.jove.com/files/ftp_upload/60311/60311fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary File. Jupyter notebook data. <a href="https://www.jove.com/files/ftp_upload/60311/Supplementary Data 1.ipynb" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Quaternary Structure Modeling Through Chemical Cross-Linking Mass Spectrometry: Extending TX-MS Jupyter Reports at JoVE.com

Quaternary Structure Modeling Through Chemical Cross-Linking Mass Spectrometry: Extending TX-MS Jupyter Reports

Targeted cross-linking mass spectrometry creates quaternary protein structure models using mass spectrometry data acquired using up to three different acquisition protocols. When executed as a simplified workflow on the Cheetah-MS web server, the results are reported in a Jupyter Notebook. Here, we demonstrate the technical aspects of how the Jupyter Notebook can be extended for a more in-depth analysis.

Protein-protein interactions can be challenging to study yet provide insights into how biological systems function. Targeted cross-linking mass ...

quaternary-structure-modeling-through-chemical-cross-linking-mass

Research

JoVE Journal

Biochemistry

2.3K Views.  Ecole Normale Supérieure Paris-Saclay. Targeted cross-linking mass spectrometry creates quaternary protein structure models using mass spectrometry data acquired using up to three different acquisition protocols. When executed as a simplified workflow on the Cheetah-MS web server, the results are reported in a Jupyter Notebook. Here, we demonstrate the technical aspects of how the Jupyter Notebook can be extended for a more in-depth analysis.

Video: Quaternary Structure Modeling Through Chemical Cross-Linking Mass Spectrometry: Extending TX-MS Jupyter Reports

Time-resolved ElectroSpray Ionization Hydrogen-deuterium Exchange Mass Spectrometry for Studying Protein Structure and Dynamics

Intrinsically disordered proteins (IDPs) have long been a challenge to structural biologists due to their lack of stable secondary structure elements. Hydrogen-Deuterium Exchange (HDX) measured at rapid time scales is uniquely suited to detect structures and hydrogen bonding networks that are briefly populated, allowing for the characterization of transient conformers in native ensembles. Coupling of HDX to mass spectrometry offers several key advantages, including high sensitivity, low sample consumption and no restriction on protein size. This technique has advanced greatly in the last several decades, including the ability to monitor HDX labeling times on the millisecond time scale. In addition, by incorporating the HDX workflow onto a microfluidic platform housing an acidic protease microreactor, we are able to localize dynamic properties at the peptide level. In this study, Time-Resolved ElectroSpray Ionization Mass Spectrometry (TRESI-MS) coupled to HDX was used to provide a detailed picture of residual structure in the tau protein, as well as the conformational shifts induced upon hyperphosphorylation.

Conformational flexibility plays a critical role in protein function. Herein, we describe the use of time-resolved electrospray ionization mass spectrometry coupled to hydrogen-deuterium exchange for probing the rapid structural changes that drive function in ordered and disordered proteins.

Intrinsically disordered proteins (IDPs) have long been a challenge to structural biologists due to their lack of stable secondary structure elements. ...

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological processes. Affinity purification coupled to mass spectrometry (AP-MS) has become the method of choice for identifying protein-protein interactions. However, conventional AP-MS studies provide information on protein interactions, but the organizational information is lost. To address this issue, we developed a strategy to unravel the distinct functional assemblies a protein might be involved in, by resolving affinity-purified protein complexes prior to their characterization by mass spectrometry. Protein complexes isolated through affinity purification of a bait protein using an epitope tag and competitive elution are separated through blue native electrophoresis. Comparison of protein migration profiles through correlation profiling using quantitative mass spectrometry allows assignment of interacting proteins to distinct molecular entities. This method is able to resolve protein complexes of close molecular weights that might not be resolved by traditional chromatographic techniques such as gel filtration. With little more work than conventional AP-geLC-MS/MS, we demonstrate this strategy may in many cases be adequate for obtaining protein complex topological information concomitantly to identifying protein interactions.

Here we present protocols for affinity purification of protein complexes and their separation by blue native PAGE, followed by protein correlation profiling using label free quantitative mass spectrometry. This method is useful to resolve interactomes into distinct protein complexes.

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological ...

Lipid Droplet Isolation for Quantitative Mass Spectrometry Analysis

Lipid droplets are vital to the replication of a variety of different pathogens, most prominently the Hepatitis C Virus (HCV), as the putative site of virion morphogenesis. Quantitative lipid droplet proteome analysis can be used to identify proteins that localize to or are displaced from lipid droplets under conditions such as virus infections. Here, we describe a protocol that has been successfully used to characterize the changes in the lipid droplet proteome following infection with HCV. We use Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) and thus label the complete proteome of one population of cells with &#34;heavy&#34; amino acids to quantitate the proteins by mass spectrometry. For lipid droplet isolation, the two cell populations (i.e.&#160;HCV-infected/&#34;light&#34; amino acids and uninfected control/&#34;heavy&#34; amino acids) are mixed 1:1 and lysed mechanically in hypotonic buffer. After removing the nuclei and cell debris by low speed centrifugation, lipid droplet-associated proteins are enriched by two subsequent ultracentrifugation steps followed by three washing steps in isotonic buffer. The purity of the lipid droplet fractions is analyzed by western blotting with antibodies recognizing different subcellular compartments. Lipid droplet-associated proteins are then separated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE) followed by Coomassie staining. After tryptic digest, the peptides are quantified by liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS). Using this method, we identified proteins recruited to lipid droplets upon HCV infection that might represent pro- or antiviral host factors. Our method can be applied to a variety of different cells and culture conditions, such as infection with pathogens, environmental stress, or drug treatment.

Lipid droplets are important organelles for the replication of several pathogens, including the Hepatitis C Virus (HCV). We describe a method to isolate lipid droplets for quantitative mass spectrometry of associated proteins; it can be used under a variety of conditions, such as virus infection, environmental stress, or drug treatment.

Lipid droplets are vital to the replication of a variety of different pathogens, most prominently the Hepatitis C Virus (HCV), as the putative site of ...

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Noncoding RNAs play important roles in several nuclear processes, including regulating gene expression, chromatin structure, and DNA repair. In most cases, the action of noncoding RNAs is mediated by proteins whose functions are in turn regulated by these interactions with noncoding RNAs. Consistent with this, a growing number of proteins involved in nuclear functions have been reported to bind RNA and in a few cases the RNA-binding regions of these proteins have been mapped, often through laborious, candidate-based methods.
Here, we report a detailed protocol to perform a high-throughput, proteome-wide unbiased identification of RNA-binding proteins and their RNA-binding regions. The methodology relies on the incorporation of a photoreactive uridine analog in the cellular RNA, followed by UV-mediated protein-RNA crosslinking, and mass spectrometry analyses to reveal RNA-crosslinked peptides within the proteome. Although we describe the procedure for mouse embryonic stem cells, the protocol should be easily adapted to a variety of cultured cells.

We describe a protocol to identify RNA-binding proteins and map their RNA-binding regions in live cells using UV-mediated photocrosslinking and mass spectrometry.

Noncoding RNAs play important roles in several nuclear processes, including regulating gene expression, chromatin structure, and DNA repair. In most ...

Optimal Preparation of Formalin Fixed Samples for Peptide Based Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Workflows

The use of matrix-assisted laser desorption/ionization, mass spectrometry imaging (MALDI MSI) has rapidly expanded, since this technique analyzes a host of biomolecules from drugs and lipids to N-glycans. Although various sample preparation techniques exist, detecting peptides from formaldehyde preserved tissues remains one of the most difficult challenges for this type of mass spectrometric analysis. For this reason, we have created and optimized a robust methodology that preserves the spatial information contained within the sample, while eliciting the greatest number of ionizable peptides. We have also aimed to achieve this in a cost effective and simple way, thereby eliminating potential bias or preparation error, which can occur when using automated instrumentation. The end result is a reproducible and inexpensive protocol.

This protocol describes a reproducible and reliable method for the sublimation-based preparation of formalin fixed tissue destined for imaging mass spectrometry.

The use of matrix-assisted laser desorption/ionization, mass spectrometry imaging (MALDI MSI) has rapidly expanded, since this technique analyzes a host ...

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

The posttranslational modification of proteins by the small protein ubiquitin is involved in many cellular events. After tryptic digestion of ubiquitinated proteins, peptides with a diglycine remnant conjugated to the epsilon amino group of lysine (&#39;K-&#949;-diglycine&#39; or simply &#39;diGly&#39;) can be used to track back the original modification site. Efficient immunopurification of diGly peptides combined with sensitive detection by mass spectrometry has resulted in a huge increase in the number of ubiquitination sites identified up to date. We have made several improvements to this workflow, including offline high pH reverse-phase fractionation of peptides prior to the enrichment procedure, and the inclusion of more advanced peptide fragmentation settings in the ion routing multipole. Also, more efficient cleanup of the sample using a filter-based plug in order to retain the antibody beads results in a greater specificity for diGly peptides. These improvements result in the routine detection of more than 23,000 diGly peptides from human cervical cancer cells (HeLa) cell lysates upon proteasome inhibition in the cell. We show the efficacy of this strategy for in-depth analysis of the ubiquitinome profiles of several different cell types and of in vivo samples, such as brain tissue. This study presents an original addition to the toolbox for protein ubiquitination analysis to uncover the deep cellular ubiquitinome.

We present a method for the purification, detection, and identification of diGly peptides that originate from ubiquitinated proteins from complex biological samples. The presented method is reproducible, robust, and outperforms published methods with respect to the level of depth of the ubiquitinome analysis.

The posttranslational modification of proteins by the small protein ubiquitin is involved in many cellular events. After tryptic digestion of ...

Nitropeptide Profiling and Identification Illustrated by Angiotensin II

Protein nitration is one of the most important post-translational modifications (PTM) on tyrosine residues and it can be induced by chemical actions of reactive oxygen species (ROS) and reactive nitrogen species (RNS) in eukaryotic cells. Precise identification of nitration sites on proteins is crucial for understanding the physiological and pathological processes related to protein nitration, such as inflammation, aging, and cancer. Since the nitrated proteins are of low abundance in cells even under induced conditions, no universal and efficient methods have been developed for the profiling and identification of protein nitration sites. Here we describe a protocol for nitropeptide enrichment by using a chemical reduction reaction and biotin labeling, followed by high resolution mass spectrometry. In our method, nitropeptide derivatives can be identified with high accuracy. Our method exhibits two advantages compared to the previously reported methods. First, dimethyl labeling is used to block the primary amine on nitropeptides, which can be used to generate quantitative results. Second, a disulfide bond containing NHS-biotin reagent is used for the enrichment, which can be further reduced and alkylated to enhance the detection signal on a mass spectrometer. This protocol has been successfully applied to the model peptide Angiotensin II in the current paper.

Proteomic profiling of tyrosine-nitrated proteins has been a challenging technique due to the low abundance of the 3-nitrotyrosine modification. Here we describe a novel approach for nitropeptide enrichment and profiling by using Angiotensin II as the model. This method can be extended for other in vitro or in vivo systems.

Protein nitration is one of the most important post-translational modifications (PTM) on tyrosine residues and it can be induced by chemical actions of ...

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation

Sample preparation for mass spectrometry analysis in proteomics requires enzymatic cleavage of proteins into a peptide mixture. This process involves numerous incubation and liquid transfer steps in order to achieve denaturation, reduction, alkylation, and cleavage. Adapting this workflow onto an automated workstation can increase efficiency and reduce coefficients of variance, thereby providing more reliable data for statistical comparisons between sample types. We previously described an automated proteomic sample preparation workflow1. Here, we report the development of a more efficient and better controlled workflow with the following advantages: 1) The number of liquid transfer steps is reduced from nine to six by combining reagents; 2) Pipetting time is reduced by selective tip pipetting using a 96-position pipetting head with multiple channels; 3) Potential throughput is increased by the availability of up to 45 deck positions; 4) Complete enclosure of the system provides improved temperature and environmental control and reduces the potential for contamination of samples or reagents; and 5) The addition of stable isotope labeled peptides, as well as &#946;-galactosidase protein, to each sample makes monitoring and quality control possible throughout the entire process. These hardware and process improvements provide good reproducibility and improve intra-assay and inter-assay precision (CV of less than 20%) for LC-MS based protein and peptide quantification. The entire workflow for digesting 96 samples in a 96-well plate can be completed in approximately 5 hours.

Here, we present an automated plasma protein digestion method for mass spectrometry-based quantitative proteomic analysis. In this protocol, the liquid transferring and incubation steps for protein denaturation, reduction, alkylation, and trypsin digestion reactions are streamlined and automated. It takes approximately five hours to prepare a 96-well plate with desired precision.

Sample preparation for mass spectrometry analysis in proteomics requires enzymatic cleavage of proteins into a peptide mixture. This process involves ...

Sample Preparation for Probe Electrospray Ionization Mass Spectrometry

Mass spectrometry (MS) is a powerful tool in analytical chemistry because it provides very accurate information about molecules, such as mass-to-charge ratios (m/z), which are useful to deduce molecular weights and structures. While it is essentially a destructive analytical method, recent advancements in the ambient ionization technique have enabled us to acquire data while leaving tissue in a relatively intact state in terms of integrity. Probe electrospray ionization (PESI) is a so-called direct method because it does not require complex and time-consuming pretreatment of samples. A fine needle serves as a sample picker, as well as an ionization emitter. Based on the very sharp and fine property of the probe tip, destruction of the samples is minimal, allowing us to acquire the real-time molecular information from living things in situ. Herein, we introduce three applications of PESI-MS technique that will be useful for biomedical research and development. One involves the application to solid tissue, which is the basic application of this technique for the medical diagnosis. As this technique requires only 10 mg of the sample, it may be very useful in the routine clinical settings. The second application is for in vitro medical diagnostics where human blood serum is measured. The ability to measure fluid samples is also valuable in various biological experiments where a sufficient volume of sample for conventional analytical techniques cannot be provided. The third application leans toward the direct application of probe needles in living animals, where we can obtain real-time dynamics of metabolites or drugs in specific organs. In each application, we can infer the molecules that have been detected by MS or use artificial intelligence to obtain a medical diagnosis.

This article introduces sample preparation methods for a unique real-time analytical method based on the ambient mass spectrometry. This method lets us perform real-time analysis of the biological molecules in vivo without any special pretreatments.

Mass spectrometry (MS) is a powerful tool in analytical chemistry because it provides very accurate information about molecules, such as mass-to-charge ...

Absolute Quantification of Cell-Free Protein Synthesis Metabolism by Reversed-Phase Liquid Chromatography-Mass Spectrometry

Cell-free protein synthesis (CFPS) is an emerging technology in systems and synthetic biology for the in vitro production of proteins. However, if CFPS is going to move beyond the laboratory and become a widespread and standard just in time manufacturing technology, we must understand the performance limits of these systems. Toward this question, we developed a robust protocol to quantify 40 compounds involved in glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, energy metabolism and cofactor regeneration in CFPS reactions. The method uses internal standards tagged with 13C-aniline, while compounds in the sample are derivatized with 12C-aniline. The internal standards and sample were mixed and analyzed by reversed-phase liquid chromatography-mass spectrometry (LC/MS). The co-elution of compounds eliminated ion suppression, allowing the accurate quantification of metabolite concentrations over 2-3 orders of magnitude where the average correlation coefficient was 0.988. Five of the forty compounds were untagged with aniline, however, they were still detected in the CFPS sample and quantified with a standard curve method. The chromatographic run takes approximately 10 min to complete. Taken together, we developed a fast, robust method to separate and accurately quantify 40 compounds involved in CFPS in a single LC/MS run. The method is a comprehensive and accurate approach to characterize cell-free metabolism, so that ultimately, we can understand and improve the yield, productivity and energy efficiency of cell-free systems.

Here, we present a robust protocol to quantify 40 compounds involved in central carbon and energy metabolism in cell-free protein synthesis reactions. The cell-free synthesis mixture is derivatized with aniline for effective separation using reversed-phase liquid chromatography and then quantified by mass spectrometry using isotopically labelled internal standards.

Cell-free protein synthesis (CFPS) is an emerging technology in systems and synthetic biology for the in vitro production of proteins. However, if CFPS is ...