Synthetic test data set for DEER spectroscopy based on T4 lysozyme

Abstract

Tikhonov regularization is the most commonly used method for extracting distance distributions from experimental double electron-electron resonance (DEER) spectroscopy data. This method requires the selection of a regularization parameter, α, and a regularization operator, L. We analyze the performance of a large set of α selection methods and several regularization operators, using a test set of over half a million synthetic noisy DEER traces. These are generated from distance distributions obtained from in silico double labeling of a protein crystal structure of T4 lysozyme with the spin label MTSSL. We compare the methods and operators based on their ability to recover the model distance distributions from the noisy time traces. The results indicate that several α selection methods perform quite well, among them the Akaike information criterion and the generalized cross validation method with either the first- or second-derivative operator. They perform significantly better than currently utilized L-curve methods.

This test set was developed as part of the 2018 publication in J. Magn. Reson., "Optimal Tikhonov Regularization for DEER Spectroscopy." Using scripts adapted from the Matlab toolbox MMM, the PDB ID structure 2LZM (T4 lysozyme) was in silico labeled at every accessible amino acid using the rotamer library R1A_298K_UFF_216_r1_CASD for the MTSSL spin label. The resulting pairwise distance distributions between each pair of labels was calculated, resulting in 5622 distance distributions. From these, 621030 synthetic DEER data traces were simulated. The test set includes DEER data spanning the range of experimentally reasonable noise levels, truncation lengths, time-step sizes, and underlying distribution characteristics.

Reference: T.H. Edwards, S. Stoll, Optimal Tikhonov regularization for DEER spectroscopy, J. Magn. Reson. 288 (2018) 58-68. https://doi.org/10.1016/j.jmr.2018.01.021