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

• Published in: Communications biology Vol. 7; no. 1; pp. 298 - 10
• Main Authors: Montepietra, Daniele, Tesei, Giulio, Martins, João M., Kunze, Micha B. A., Best, Robert B., Lindorff-Larsen, Kresten
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 09.03.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/1647/794; 631/45/56; 631/45/612; 631/57/2265; 631/57/2266; Biomedical and Life Sciences; Fluorescence resonance energy transfer; Fluorescence Resonance Energy Transfer - methods; Intrinsically Disordered Proteins; Libraries; Life Sciences; Molecular Dynamics Simulation; Molecular modelling; Polyproline; Probes; Protein Conformation; Proteins; Software
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-024-05910-6

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 . The authors present and showcase applications for an easy-to-use Python package to predict FRET efficiencies from ensembles of protein conformations via a rotamer library approach.