Rapid Simulation of Unprocessed DEER Decay Data for Protein Fold Prediction.

Rapid Simulation of Unprocessed DEER Decay Data for Protein Fold Prediction.
Biophys J. 2019 Dec 18;:
Authors: Del Alamo D, Tessmer MH, Stein RA, Feix JB, Mchaourab HS, Meiler J
Abstract
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 computati...

Source: Biophysical Journal - December 17, 2019 Category: Physics Authors: Del Alamo D, Tessmer MH, Stein RA, Feix JB, Mchaourab HS, Meiler J Tags: Biophys J Source Type: research