Rapid Simulation of Unprocessed DEER Decay Data for Protein Fold Prediction. Biophys J 2020 Jan 21;118(2):366-375
Date
01/02/2020Pubmed ID
31892409Pubmed Central ID
PMC6976798DOI
10.1016/j.bpj.2019.12.011Scopus ID
2-s2.0-85077146138 (requires institutional sign-in at Scopus site) 20 CitationsAbstract
Despite advances in sampling and scoring strategies, Monte Carlo modeling methods still struggle to accurately predict de novo the structures of large proteins, membrane proteins, or proteins of complex topologies. Previous approaches have addressed these shortcomings by leveraging sparse distance data gathered using site-directed spin labeling and electron paramagnetic resonance spectroscopy to improve protein structure prediction and refinement outcomes. However, existing computational implementations entail compromises between coarse-grained models of the spin label that lower the resolution and explicit models that lead to resource-intense simulations. These methods are further limited by their reliance on distance distributions, which are calculated from a primary refocused echo decay signal and contain uncertainties that may require manual refinement. Here, we addressed these challenges by developing RosettaDEER, a scoring method within the Rosetta software suite capable of simulating double electron-electron resonance spectroscopy decay traces and distance distributions between spin labels fast enough to fold proteins de novo. We demonstrate that the accuracy of resulting distance distributions match or exceed those generated by more computationally intensive methods. Moreover, decay traces generated from these distributions recapitulate intermolecular background coupling parameters even when the time window of data collection is truncated. As a result, RosettaDEER can discriminate between poorly folded and native-like models by using decay traces that cannot be accurately converted into distance distributions using regularized fitting approaches. Finally, using two challenging test cases, we demonstrate that RosettaDEER leverages these experimental data for protein fold prediction more effectively than previous methods. These benchmarking results confirm that RosettaDEER can effectively leverage sparse experimental data for a wide array of modeling applications built into the Rosetta software suite.
Author List
Del Alamo D, Tessmer MH, Stein RA, Feix JB, Mchaourab HS, Meiler JAuthor
Jimmy B. Feix PhD Emeritus Professor in the Biophysics department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
Electron Spin Resonance SpectroscopyMonte Carlo Method
Nitrogen Oxides
Protein Folding
Spin Labels
Time Factors
