Rapid Simulation of Unprocessed DEER Decay Data for Protein Fold Prediction

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 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.

DEER-PREdict: Software for efficient calculation of spin-labeling EPR and NMR data from conformational ensembles

“…The default values r max = 12 nm, t min = 0.01 μ s, t max = 5.5 μ s and d t = 0.01 μ s can be overridden by the user. Following the correction of the experimental DEER time trace for the intermolecular background [ 36 , 42 ], the resulting form factor can directly be compared with where 0.02 ≤ λ ≤ 0.5 is the modulation depth of the experimental signal [ 43 ], quantifying the efficiency of the DEER pump pulse [ 8 ].…”

Characterization of the ExoU activation mechanism using EPR and integrative modeling

“…In addition to serving as a template, the conserved nature of this loop suggests an important function, consistent with our experimental observations. To further enhance our modeling we utilized the RosettaDEER module, allowing full use of the DEER distance distributions 40 .…”

“…Our approach using SDSL-EPR and integrative modeling allowed us to obtain experimentally consistent models of the 20-residue ExoU loop 427–446 , which is a larger region than can currently be achieved with confidence by de novo modeling alone 39 . RosettaDEER allowed for the use of full DEER distance distributions to construct unresolved residues of the ExoU crystal structure, a relatively new capability that overcomes several obstacles when modeling proteins with DEER 40 . Ensemble modeling provided insights into the dynamic nature of ExoU loop 427–446 .…”

“…S8. Full length ExoU was modelled using Rosetta 3.10 54 with the RosettaDEER 40 module. The Rosetta hybridize mover 41 was used to model approximately 10,000 ExoU structures using the ExoU and pf ExoU crystal structures (PDB: 3TU3, 4QMK) as templates supplemented with DEER distance distributions as restraints (see Supplementary procedure 1 ) and fragment libraries generated by the Robetta web server 55 .…”

DEER-PREdict: Software for Efficient Calculation of Spin-Labeling EPR and NMR Data from Conformational Ensembles

“…Following the correction of the experimental DEER time trace for the intermolecular background [36, 42], the resulting form factor can directly be compared with where 0.02 ≤ λ ≤ 0.5 is the modulation depth of the experimental signal [43], quantifying the efficiency of the DEER pump pulse [8].…”