| Autor: |
Biehn SE; Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States., Picarello DM; Department of Bioengineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States.; Rosetta Commons Research Experience for Undergraduates, Rosetta Commons, https://www.rosettacommons.org/about/intern., Pan X; Department of Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts 01003, United States., Vachet RW; Department of Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts 01003, United States., Lindert S; Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States. |
| Abstrakt: |
Covalent labeling mass spectrometry allows for protein structure elucidation via covalent modification and identification of exposed residues. Diethylpyrocarbonate (DEPC) is a commonly used covalent labeling reagent that provides insight into structure through the labeling of lysine, histidine, serine, threonine, and tyrosine residues. We recently implemented a Rosetta algorithm that used binary DEPC labeling data to improve protein structure prediction efforts. In this work, we improved on our modeling efforts by accounting for the level of hydrophobicity of neighboring residues in the microenvironment of serine, threonine, and tyrosine residues to obtain a more accurate estimate of the hydrophobic neighbor count. This was incorporated into Rosetta functionality, along with considerations for solvent-exposed histidine and lysine residues. Overall, our new Rosetta score term successfully identified best scoring models with less than 2 Å root-mean-squared deviations (RMSDs) for five of the seven benchmark proteins tested. We additionally developed a confidence metric to measure prediction success for situations in which a native structure is unavailable. |
