Förster resonance energy transfer (FRET) and electron paramagnetic resonance (EPR) spectroscopy are complementary techniques for quantifying distances in the nanometer range. Both approaches are commonly employed for probing the conformations and conformational changes of biological macromolecules based on site-directed fluorescent or paramagnetic labeling. FRET can be applied in solution at ambient temperature and thus provides direct access to dynamics, especially if used at the single-molecule level, whereas EPR requires immobilization or work at cryogenic temperatures but provides data that can be more reliably used to extract distance distributions. However, a combined analysis of the complementary data from the two techniques has been complicated by the lack of a common modeling framework. Here, we demonstrate a systematic analysis approach based on rotamer libraries for both FRET and EPR labels to predict distance distributions between two labels from a structural model. Dynamics of the fluorophores within these distance distributions are taken into account by diffusional averaging, which improves the agreement with experiment. Benchmarking this methodology with a series of surface-exposed pairs of sites in a structured protein domain reveals that the lowest resolved distance differences can be as small as ∼0.25 nm for both techniques, with quantitative agreement between experimental and simulated transfer efficiencies within a range of ±0.045. Rotamer library analysis thus establishes a coherent way of treating experimental data from EPR and FRET and provides a basis for integrative structural modeling, including studies of conformational distributions and dynamics of biological macromolecules using both techniques.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8595751PMC
http://dx.doi.org/10.1016/j.bpj.2021.09.021DOI Listing

Publication Analysis

Top Keywords

distance distributions
12
fret epr
8
epr spectroscopy
8
rotamer libraries
8
biological macromolecules
8
fret
5
epr
5
resolving distance
4
distance variations
4
variations single-molecule
4

Similar Publications

To improve the scientific accuracy and precision of children's physical fitness evaluations, this study proposes a model that combines self-organizing maps (SOM) neural networks with cluster analysis. Existing evaluation methods often rely on traditional, single statistical analyses, which struggle to handle the complexity of high-dimensional, nonlinear data, resulting in a lack of precision and personalization. This study uses the SOM neural network to reduce the dimensionality of high-dimensional health data.

View Article and Find Full Text PDF

Intra-specific interactions among top carnivores are among the most intriguing behavioural aspects and essential components of population dynamics. Static interactions pertain to space use, while dynamic interactions involve spatio-temporal patterns influenced by social structure, distribution, mate selection, and density. Previous studies have focused on static interactions, successfully estimating spatial overlap but leading to a knowledge gap of dynamic interaction to be able to compute attraction and avoidance on similar spatio-temporal scales.

View Article and Find Full Text PDF

Sponges are key ecosystem engineers that shape, structure and enhance the biodiversity of marine benthic communities globally. Sponge aggregations and reefs are recognized as vulnerable marine ecosystems (or VMEs) due to their susceptibility to damage from bottom-contact fishing gears. Ensuring their long-term sustainability, preservation, and ecosystem functions requires the implementation of sound scientific conservation tools.

View Article and Find Full Text PDF

Spatiotemporal distribution of global peatland area during the Holocene.

Sci Data

January 2025

State Key Laboratory of Lithospheric and Environmental Coevolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, 100029, China.

Peatlands are a key component of terrestrial ecosystems, and their development has an important impact on global carbon cycle and climate change. However, the long-term evolution of global peatlands remains uncertain, particularly their spatial distribution. We compiled 4700 basal peatland data during Holocene, and 669 pollen data of Sphagnum with basal and end ages, to allow a more robust reconstruction of the spatial distribution of peatlands.

View Article and Find Full Text PDF

Coverage bias in small molecule machine learning.

Nat Commun

January 2025

Chair for Bioinformatics, Institute for Computer Science, Friedrich Schiller University Jena, Jena, Germany.

Small molecule machine learning aims to predict chemical, biochemical, or biological properties from molecular structures, with applications such as toxicity prediction, ligand binding, and pharmacokinetics. A recent trend is developing end-to-end models that avoid explicit domain knowledge. These models assume no coverage bias in training and evaluation data, meaning the data are representative of the true distribution.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!