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Deep neural network processing of DEER data

Deep neural network processing of DEER data
Deep neural network processing of DEER data

The established model-free methods for the processing of two-electron dipolar spectroscopy data [DEER (double electron-electron resonance), PELDOR (pulsed electron double resonance), DQ-EPR (double-quantum electron paramagnetic resonance), RIDME (relaxation-induced dipolar modulation enhancement), etc.] use regularized fitting. In this communication, we describe an attempt to process DEER data using artificial neural networks trained on large databases of simulated data. Accuracy and reliability of neural network outputs from real experimental data were found to be unexpectedly high. The networks are also able to reject exchange interactions and to return a measure of uncertainty in the resulting distance distributions. This paper describes the design of the training databases, discusses the training process, and rationalizes the observed performance. Neural networks produced in this work are incorporated as options into Spinach and DeerAnalysis packages.

2375-2548
Worswick, Steven G.
8cbd4343-9bbc-4b6f-8365-241e6c8f1322
Spencer, James A.
87f66ad2-74cf-44f4-91e9-dbc0e673241b
Jeschke, Gunnar
cee7d355-19d4-4984-9278-72997819fc0d
Kuprov, Ilya
bb07f28a-5038-4524-8146-e3fc8344c065
Worswick, Steven G.
8cbd4343-9bbc-4b6f-8365-241e6c8f1322
Spencer, James A.
87f66ad2-74cf-44f4-91e9-dbc0e673241b
Jeschke, Gunnar
cee7d355-19d4-4984-9278-72997819fc0d
Kuprov, Ilya
bb07f28a-5038-4524-8146-e3fc8344c065

Worswick, Steven G., Spencer, James A., Jeschke, Gunnar and Kuprov, Ilya (2018) Deep neural network processing of DEER data. Science Advances, 4 (8), [eaat5218]. (doi:10.1126/sciadv.aat5218).

Record type: Article

Abstract

The established model-free methods for the processing of two-electron dipolar spectroscopy data [DEER (double electron-electron resonance), PELDOR (pulsed electron double resonance), DQ-EPR (double-quantum electron paramagnetic resonance), RIDME (relaxation-induced dipolar modulation enhancement), etc.] use regularized fitting. In this communication, we describe an attempt to process DEER data using artificial neural networks trained on large databases of simulated data. Accuracy and reliability of neural network outputs from real experimental data were found to be unexpectedly high. The networks are also able to reject exchange interactions and to return a measure of uncertainty in the resulting distance distributions. This paper describes the design of the training databases, discusses the training process, and rationalizes the observed performance. Neural networks produced in this work are incorporated as options into Spinach and DeerAnalysis packages.

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More information

Accepted/In Press date: 20 July 2018
e-pub ahead of print date: 24 August 2018
Published date: August 2018

Identifiers

Local EPrints ID: 424394
URI: http://eprints.soton.ac.uk/id/eprint/424394
ISSN: 2375-2548
PURE UUID: dc987330-32f3-4680-99b3-624d3940c1ed
ORCID for Ilya Kuprov: ORCID iD orcid.org/0000-0003-0430-2682

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Date deposited: 05 Oct 2018 11:36
Last modified: 26 Nov 2021 02:58

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Contributors

Author: Steven G. Worswick
Author: James A. Spencer
Author: Gunnar Jeschke
Author: Ilya Kuprov ORCID iD

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