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

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

SUBMITTER: Montepietra D 

PROVIDER: S-EPMC10925062 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature