G protein-coupled receptors (GPCR) activate numerous intracellular signaling pathways. In X-band, it is additionally possible that the obtained distance distribution is plagued by long distance artifacts. It is shown that ignoring the ZFS in the data analysis of LaserIMD traces can lead to errors in the obtained modulation depths and background decays. Experimentally recorded LiDEER and LaserIMD data confirm these findings. This decay is not that pronounced in Q-band but can be quite noticeable for lower magnetic field strengths in X-band. However, the ZFS leads to an additional decay in the dipolar trace in LaserIMD. Simulations based on this model show that the effect of the ZFS is not that pronounced in LiDEER for experimentally relevant conditions. For a detailed understanding of the effect of the ZFS, a theoretical description for LaserIMD and LiDEER is derived, taking into account the non-secular terms of the ZFS. Here, we explore the limits of this assumption and show that the ZFS can have a significant effect on the shape of the dipolar trace. To date, LaserIMD and LiDEER have been analyzed with software tools that were developed for a pair of two S=1/2 spins and that neglected the zero-field splitting (ZFS) of the excited triplet. These techniques use the photoexcitation of a chromophore to the triplet state and measure its dipolar coupling to a neighboring electron spin, which allows the determination of distance restraints. Laser-induced magnetic dipole (LaserIMD) spectroscopy and light-induced double electron–electron resonance (LiDEER) spectroscopy are important techniques in the emerging field of light-induced pulsed dipolar electron paramagnetic resonance (EPR) spectroscopy (light-induced PDS). (Bottom row) Mean and maximum error (over the test database) as functions of the number of - triads in the architecture shown in Figure 2. (Top right) Effect of the distribution normalisation layer on the mean and maximum (over the test database) regression error by the networks. softplus ) in the presence or absence of batch normalisation (BN) layers, and distribution normalisation (DN) layers. (Top left) Validation error and training time for different choices of activation function (logsigmoidal vs. The error is defined as root mean square deviation between the network output and the ground truth for the 512-element non-negative output vector, normalised (for numerical accuracy reasons in single-precision arithmetic) to have the mean value of 1. Scatter plots and statistics refer to sets of independently trained (different random initial guess, different databases) DEERNets on a batch of 64,000 datasets generated as described in and Section 3 below. Performance effect of neural network architecture decisions. 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. 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. 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. 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.
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