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Interpreting hydrogen-deuterium exchange experiments with molecular simulations: Tutorials and applications of the HDXer ensemble reweighting software [Article v1.0]
Author(s) -
Paul Suhwan Lee,
Richard T. Bradshaw,
Fabrizio Marinelli,
Kyle C. Kihn,
Aleah Smith,
Patrick L. Wintrode,
Daniel Deredge,
José D. Faráldo-Gómez,
Lucy R. Forrest
Publication year - 2022
Publication title -
living journal of computational molecular science
Language(s) - English
Resource type - Journals
ISSN - 2575-6524
DOI - 10.33011/livecoms.3.1.1521
Subject(s) - computer science , hydrogen–deuterium exchange , software , suite , molecular dynamics , statistical physics , data mining , machine learning , deuterium , physics , chemistry , computational chemistry , programming language , archaeology , quantum mechanics , history
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.

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