Biochemistry
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Quaternary Structure Modeling Through Chemical Cross-Linking Mass Spectrometry: Extending TX-MS Jupyter Reports
Chapters
Summary October 20th, 2021
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.
Transcript
This method use the power of Jupyter Notebook to deliver informative and visually appealing reports while allowing the users to extend and customize the report in a reproducible and traceable manner. This flexible technique allows all user modifications to be captured in a sufficient detail to be reproduced in a later timeframe or by different user. Although this method is generally applicable in any research field, it's primarily designed for Mucor life science, an area of research that can produce complex data.
Before beginning the analysis, open the targeted cross-linking mass spectrometry website. Upon navigation to the website, a welcome screen providing a high level description of the TX MS.web service will appear. Under the tutorial tab a detailed description of the services provided including information about how to use the service and how to analyze the results can be observed.
Under the download tab, software and resources can be downloaded. All of the software is available under an open source license. Under the License tab, the BSD 3-clause license explicitly makes the software as broadly usable as possible.
The Contact tab provides contact information for submitting questions or comments. To submit an email requesting user credentials, return to the Cheetah tab and click the email address hyperlink at the bottom of the page. Enter the relevant information into the subject line in the body of the email.
A reply will be sent as quickly as possible. After receiving a confirmation email with the user credentials, log in and upload the mass spectrometry data to the website. Click Submit workflow and enter a title and a description.
Then click view workflow and select the Cheetah workflow. After following the wizard, use the online viewer to inspect the Jupyter Notebook. To install the JupyterHub install the Docker as instructed and download the JupyterHub Docker container with the Jupyter openBIS extension.
After starting the Docker run P8171:8000 malmstroem/jove:latest container. Navigate to the indicated web address and log in with the username and password User. To download the report click new in Python 3 to open a new tab with an untitled notebook.
Click configure openBIS connections in the Jupyter tool menu and enter TX MS for the name, the TX MS web address for the URL, Guest for the user and G-U-E-S-T-P-A-S-S-W-D for the password. Select the new connection and click choose connection to search for the report. Then click, download in cell and run all to return the report.
To extend the report, click cell and insert below to add a new cell at the bottom. Then click code and press shift and enter to execute the cell. To upload the report, click the upload button to create a new data set.
A representative structure of M1 and albumin with top crosslinks mapped on the structure are shown here. All of the crosslinks were obtained by targeted crosslinking mass spectrometry after parsing high resolution MS1 data dependent and independent acquisition data and the computational models were provided by the RosettaDock protocol. It's important to remember that protein protein interactions are diverse in terms of their stability.
So therefore, your results may vary. The current methodology is limited to binary interactions and therefore cannot be applied to more complex protein quaternary structures. However, modeling individual pairs can provide a good base to build more complex structures.
Details about the binding interface can be useful in many ways, for example, to suggest mutations that can either stabilize or destabilize the protein interactions. So, this can be helpful to understand the role of protein-protein interactions better.
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