Neural networks in pulsed dipolar spectroscopy: a practical guide

Jake Keeley, Tajwar Choudhury, Laura Galazzo, Enrica Bordignon, Akiva Feintuch, Daniella Goldfarb, Hannah Russell, Michael J. Taylor, Janet E. Lovett, Andrea Eggeling, Luis Fabregas Ibanez, Katharina Keller, Maxim Yulikov, Gunnar Jeschke, Ilya Kuprov*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
5 Downloads (Pure)

Abstract

This is a methodological guide to the use of deep neural networks in the processing of pulsed dipolar spectroscopy (PDS) data encountered in structural biology, organic photovoltaics, photosynthesis research, and other domains featuring long-lived radical pairs and paramagnetic metal ions. PDS uses distance dependence of magnetic dipolar interactions; measuring a single well-defined distance is straightforward, but extracting distance distributions is a hard and mathematically ill-posed problem requiring careful regularisation and background fitting. Neural networks do this exceptionally well, but their “robust black box” reputation hides the complexity of their design and training – particularly when the training dataset is effectively infinite. The objective of this paper is to give insight into training against simulated databases, to discuss network architecture choices, to describe options for handling DEER (double electron-electron resonance) and RIDME (relaxation-induced dipolar modulation enhancement) experiments, and to provide a practical data processing flowchart.
Original languageEnglish
Article number107186
Number of pages14
JournalJournal of Magnetic Resonance
Volume338
Early online date25 Mar 2022
DOIs
Publication statusPublished - May 2022

Keywords

  • DEER
  • PELDOR
  • RIDME
  • DEERnet
  • Neural netowrk

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