Utility of Covalent Labeling Mass Spectrometry Data in Protein Structure Prediction with Rosetta.

ABSTRACT: Covalent labeling mass spectrometry experiments are growing in popularity and provide important information regarding protein structure. Information obtained from these experiments correlates with residue solvent exposure within the protein in solution. However, it is impossible to determine protein structure from covalent labeling data alone. Incorporation of sparse covalent labeling data into the protein structure prediction software Rosetta has been shown to improve protein tertiary structure prediction. Here, covalent labeling techniques were analyzed computationally to provide insight into what labeling data is needed to optimize tertiary protein structure prediction in Rosetta. We have successfully implemented a new scoring functionality that provides improved predictions. We develop

SUBMITTER: Aprahamian ML 

PROVIDER: S-EPMC6520167 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature