FRETpredict: a Python package for FRET efficiency predictions using rotamer libraries
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FRETpredict : a Python package for FRET efficiency predictions using rotamer libraries. / Montepietra, Daniele; Tesei, Giulio; Martins, João M.; Kunze, Micha B.A.; Best, Robert B.; Lindorff-Larsen, Kresten.
In: Communications Biology , Vol. 7, No. 1, 298, 2024.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - FRETpredict
T2 - a Python package for FRET efficiency predictions using rotamer libraries
AU - Montepietra, Daniele
AU - Tesei, Giulio
AU - Martins, João M.
AU - Kunze, Micha B.A.
AU - Best, Robert B.
AU - Lindorff-Larsen, Kresten
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
U2 - 10.1038/s42003-024-05910-6
DO - 10.1038/s42003-024-05910-6
M3 - Journal article
C2 - 38461354
AN - SCOPUS:85187136946
VL - 7
JO - Communications Biology
JF - Communications Biology
SN - 2399-3642
IS - 1
M1 - 298
ER -
ID: 385581370