Abstract
AbstractHydroxyl radical protein footprinting (HRPF) in combination with mass spectrometry reveals the relative solvent exposure of labeled residues within a protein, thereby providing insight into protein tertiary structure. HRPF labels nineteen residues with varying degrees of reliability and reactivity. Here, we are presenting a dynamics-driven HRPF-guided algorithm for protein structure prediction. In a benchmark test of our algorithm, usage of the dynamics data in a score term resulted in notable improvement of the root-mean-square deviations of the lowest-scoring ab initio models and improved the funnel-like metric Pnear for all benchmark proteins. We identified models with accurate atomic detail for three of the four benchmark proteins. This work suggests that HRPF data along with side chain dynamics sampled by a Rosetta mover ensemble can be used to accurately predict protein structure.
Funder
National Science Foundation
U.S. Department of Health & Human Services | National Institutes of Health
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry
Cited by
32 articles.
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