FRETpredict: a Python package for FRET efficiency predictions using rotamer libraries

4Citations
Citations of this article
18Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Förster resonance energy transfer (FRET) is a widely-used and versatile technique for the structural characterization of biomolecules. Here, we introduce FRETpredict, an easy-to-use Python software to predict FRET efficiencies from ensembles of protein conformations. FRETpredict uses a rotamer library approach to describe the FRET probes covalently bound to the protein. The software efficiently and flexibly operates on large conformational ensembles such as those generated by molecular dynamics simulations to facilitate the validation or refinement of molecular models and the interpretation of experimental data. We provide access to rotamer libraries for many commonly used dyes and linkers and describe a general methodology to generate new rotamer libraries for FRET probes. We demonstrate the performance and accuracy of the software for different types of systems: a rigid peptide (polyproline 11), an intrinsically disordered protein (ACTR), and three folded proteins (HiSiaP, SBD2, and MalE). FRETpredict is open source (GPLv3) and is available at github.com/KULL-Centre/FRETpredict and as a Python PyPI package at pypi.org/project/FRETpredict.

Cite

CITATION STYLE

APA

Montepietra, D., Tesei, G., Martins, J. M., Kunze, M. B. A., Best, R. B., & Lindorff-Larsen, K. (2024). FRETpredict: a Python package for FRET efficiency predictions using rotamer libraries. Communications Biology, 7(1). https://doi.org/10.1038/s42003-024-05910-6

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free