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FRETpredict: a Python package for FRET efficiency predictions using rotamer libraries
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DOI：10.1038/s42003-024-05910-6.png)
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Abstract
En 中文
Forster 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.
Keywords:
SINGLE-MOLECULE FRET
SUBSTRATE-BINDING DOMAINS
CONFORMATIONAL DYNAMICS
DISORDERED PROTEINS
ENERGY-TRANSFER
HIGH-RESOLUTION
FORCE-FIELD
SPECTROSCOPY
TRANSPORTERS
SIMULATIONS
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