Abstract

Hydrogen-deuterium exchange (HDX) is a comprehensive yet detailed probe of protein structure and dynamics and, coupled to mass spectrometry, has become a powerful tool for investigating an increasingly large array of systems. Computer simulations are often used to help rationalize experimental observations of exchange, but interpretations have frequently been limited to simple, subjective correlations between microscopic dynamical fluctuations and the observed macroscopic exchange behavior. With this in mind, we previously developed the HDX ensemble reweighting approach and associated software, HDXer, to aid the objective interpretation of HDX data using molecular simulations. HDXer has two main functions; first, to compute H-D exchange rates that describe each structure in a candidate ensemble of protein structures, for example from molecular simulations, and second, to objectively reweight the conformational populations present in a candidate ensemble to conform to experimental exchange data. In this article, we first describe the HDXer approach, theory, and implementation. We then guide users through a suite of tutorials that demonstrate the practical aspects of preparing experimental data, computing HDX levels from molecular simulations, and performing ensemble reweighting analyses. Finally we provide a practical discussion of the capabilities and limitations of the HDXer methods including recommendations for a user's own analyses. Overall, this article is intended to provide an up-to-date, pedagogical counterpart to the software, which is freely available at https://github.com/Lucy-Forrest-Lab/HDXer.

Highlights

  • A pioneering descriptive framework developed in the 1950s by Linderstrøm-Lang and co-workers first linked the hydrogen exchange rates of backbone amide functional groups directly to protein structure [1, 2]

  • 5.2.4 Conclusion After completing the first notebook, users should have a clearer understanding of the type of data needed by HDX ensemble reweighting (HDXer), as well as the specifics of processing raw data files into the required formats

  • By allowing for reweighting of the structural ensemble, HDXer can rigorously account for all these sources of error

Read more

Summary

A LiveCoMS Tutorial

Bradshaw1*†, Fabrizio Marinelli, Kyle Kihn, Ally Smith, Patrick L. This LiveCoMS document is maintained online on GitHub at https:// github. Com/ Lucy-Forrest-Lab/ hdxer_tutorials_livecoms ; to provide feedback, suggestions, or help improve it, please visit the GitHub repository and participate via the issue tracker This LiveCoMS document is maintained online on GitHub at https:// github. com/ Lucy-Forrest-Lab/ hdxer_tutorials_livecoms ; to provide feedback, suggestions, or help improve it, please visit the GitHub repository and participate via the issue tracker

Introduction
Software The HDXer tutorials are written in
Hardware
Interpreting experimental data with ensemble reweighting techniques
The initial structural ensemble
Predictive models for HDX
Ensemble reweighting using the maximum entropy principle
Workflow of HDXer
Method
Collating inputs for HDXer analyses
Curating target HDX data
Curating a structural ensemble
Selecting parameters for HDXer analyses
Parameters for computing HDX-MS data from structures
Parameters for ensemble reweighting
Tutorials
Installing the HDXer software
Prerequisites
Conclusion
Conclusion During
Tutorial 3
Tutorial steps and results
Conclusion In
Minimizing uncertainty and improving robustness
Experimental
Uncertainty arising from target data
Uncertainty arising from the predictive model
Uncertainty resulting from the candidate ensemble
Considerations in experimental design
Author Contributions
Funding Information

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.