Chemistry Faculty Researchhttp://hdl.handle.net/1773/409832021-01-24T22:09:54Z2021-01-24T22:09:54ZSource code for producing photothermal point spread functions sourced by DDA optical fieldsMasiello, Davidhttp://hdl.handle.net/1773/416382018-03-19T18:33:17Z2018-03-19T00:00:00ZSource code for producing photothermal point spread functions sourced by DDA optical fields
Masiello, David
This is the python source code for computing photothermal point spread functions sourced by optical fields computed using Draine's discrete-dipole approximation.
2018-03-19T00:00:00ZThermal Discrete-Dipole Approximation Source CodeMasiello, Davidhttp://hdl.handle.net/1773/416372018-03-19T05:22:29Z2014-03-26T00:00:00ZThermal Discrete-Dipole Approximation Source Code
Masiello, David
Source code for T-DDA. This code solves the steady-state heat diffusion equation for arbitrarily-shaped targets, powered by an incident electromagnetic field. The latter should be computed from Draine's DDSCAT code, which can be downloaded directly from his website.
2014-03-26T00:00:00ZSynthetic test data set for DEER spectroscopy based on T4 lysozymeEdwards, Thomas H.Stoll, Stefanhttp://hdl.handle.net/1773/409842018-03-12T16:51:35Z2018-02-01T00:00:00ZSynthetic test data set for DEER spectroscopy based on T4 lysozyme
Edwards, Thomas H.; Stoll, Stefan
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
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Contents of the test set
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(Edwards, Stoll, Optimal Tikhonov Regularization for DEER Spectroscopy)
The test set is contained in three files:
- distributions_2LZM: model distributions
- timetraces_2LZM: noise-free time-domain traces
- Sdata_2LMZ: noisy time-domain traces
distributons_2LZM.mat
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This file contains the model distributions obtained from the 2LZM PBD crystal
structure of T4 lysozyme, and associated statistics.
sitePair array of residue indices for all double labeled mutants
P0 array of all 5622 model distributions, 1341 points each
r0 associated high-resolution distance vector
r_xy xy-th percentile distance for each model distribution
r_iqr inter-quartile range for each distribution
r_mean mean distance for each distribution
r_median median distance for each distribution
r_mode modal distance for each distribution
r_std standard deviation for each distribution
npeaks number of significant local maxima for each distribution
skew skewness for each distribution
timetraces_2LZM.mat
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This file contains all unique noise-free time-domain DEER traces generated
from the model distributions.
data.S0 noise-free time trace
data.Pidx index of model distribution (P0 in distributions_2LZM.mat)
data.tmin minimum t (microseconds)
data.tmax maximum t (microseconds)
data.dt time increment (microseconds)
data.nt number of points
data.sigma noise standard deviation
data.seeds seeds for random-number generator to generate noise
(one seed for each of 10 noise realizations)
Sdata_2LZM.mat
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This file contains all noisy time-traces generated from the noise-free traces.
Sdata.S all noisy time traces
Sdata.tmin minimum t (microseconds)
Sdata.tmax maximum t (microseconds)
Sdata.dt time increment (microseconds)
Sdata.nt number of points
Sdata.sigma noise standard deviation
Sdata.seed seed for random-number generator
Sdata.sites residue numbers of associated doubly-labeled mutant
Sdata.idxP index of associated model distribution (for distributions_2LZM.mat)
Sdata.idxS0 index of associated noise-free trace (for timetraces_2LZM.mat)
sdata.idxseed index into the seed vector (in timetraces_2LZM.mat)
2018-02-01T00:00:00Z