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Interpretation of HDX Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles by Bradshaw, Richard T.; Marinelli, Fabrizio; Faraldo-Gómez, José D.; Forrest, Lucy R. is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
What is Interpretation of HDX Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles about?
Hydrogen-deuterium exchange combined with mass spectrometry (HDX-MS) is a widely applied biophysical technique that probes the structure and dynamics of biomolecules without the need for site-directed modifications or bio-orthogonal labels. The mechanistic interpretation of HDX data, however, is often qualitative and subjective, owing to a lack of quantitative methods to rigorously translate observed deuteration levels into atomistic structural information. To help address this problem, we have developed a methodology to generate structural ensembles that faithfully reproduce HDX-MS measurements. In this approach, an ensemble of protein conformations is first generated, typically using molecular dynamics simulations. A maximum-entropy bias is then applied post hoc to the resulting ensemble such that averaged peptide-deuteration levels, as predicted by an empirical model, agree with target values within a given level of uncertainty. We evaluate this approach, referred to as HDX ensemble reweighting (HDXer), for artificial target data reflecting the two major conformational states of a binding protein. We demonstrate that the information provided by HDX-MS experiments and by the mode
Who reads Interpretation of HDX Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles?
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Bradshaw, Richard T.; Marinelli, Fabrizio; Faraldo-Gómez, José D.; Forrest, Lucy R.
- Publisher
- Biophysical Society; Elsevier ; Elsevier BV (ISSN 0006-3495)
- Published
- 2020
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)