GloPel is a novel and sophisticated PELDOR/DEER data analysis tool to reliably unravel distance distributions of spin probes. It allows for an optimised data acquisition procedure that saves measurement time and consequently experiment costs (cooling gases, spectrometer use). It is a user-friendly, open-source computer program for a number of operating systems (Linux, MacOS, Windows).
Pulsed electron–electron double resonance (PELDOR, alternatively called DEER for double electron–electron resonance) is nowadays a widely used electron paramagnetic resonance (EPR) method to investigate structures and structural rearrangements of biological systems based on distance determinations between pairs of intrinsic or extrinsic paramagnetic species. The use of spin probes, the latter introduced, e.g., by site-directed spin labeling, opens up the means to study a wealth of diamagnetic macromolecular systems. PELDOR pulse sequences allow for the detection of echo decay curves that are modulated by dipole–dipole-coupling frequencies of interacting electron spins. With increasing distance between them, the echo decay needs to be monitored over a progressively extended time period. However, since the echo intensity typically falls off exponentially with increasing time, this might be problematic with respect to the minimum signal-to-noise ratio required for a sound data analysis. In biochemical applications of PELDOR spectroscopy one commonly struggles with rather limited signal-to-noise ratios in particular in cases of membrane-embedded proteins with very short phase-memory times. Hence, the analysis of PELDOR signals with a low signal-to-noise ratio may become unreliable, especially when several spin-label distances result in a complex distance distribution. In such systems often only the dominant distance can be resolved.
GloPel (Global analysis of PELDOR data), an open-source Python-based application, is a new PELDOR analysis tool that allows to extract improved-quality distance distributions from PELDOR data for which no ideal signal-to-noise ratio can be achieved for a very long observation window. GloPel allows for the simultaneous analysis of two time traces acquired for a sample in two different observation time windows, thus taking advantage of both, the typically high signal-to-noise ratio of the time trace acquired at early times of the echo decay, and the best possible background function fitted for the decay at later times, which is in most cases superimposed with considerable noise. In this way, short distances are not overseen in the higher noise of the longer time traces while long distances are not artificially shortened by limiting the observation time window of the experiment. Following our suggested data acquisition procedure, a significant reduction of the measurement time may also be achieved.