ArticleCommunications biology2024
FRETpredict: a Python package for FRET efficiency predictions using rotamer libraries.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 15 citations in OpenAlex.
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- Identifying Sequence Effects on Chain Dimensions of Disordered Proteins by Integrating Experiments and Simulations.JACS Au · 2024Article
- Accounting for Fast vs Slow Exchange in Single Molecule FRET Experiments Reveals Hidden Conformational States.Journal of chemical theory and computation · 2024Article
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- Computational Methods to Investigate Intrinsically Disordered Proteins and their Complexes.ArXiv · 2024Article
- Accounting for fast vs slow exchange in single molecule FRET experiments reveals hidden conformational states.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
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 .
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