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

Abstract

Here, we introduce FRETpredict, a Python software program to predict FRET efficiencies from ensembles of protein conformations. FRETpredict uses an established Rotamer Library Approach to describe the FRET probes covalently bound to the protein. The software efficiently 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 demonstrate the performance and accuracy of the software for different types of systems: a relatively structured peptide (polyproline 11), an intrinsically disordered protein (ACTR), and three folded proteins (HiSiaP, SBD2, and MalE). We also describe a general approach to generate new rotamer libraries for FRET probes of interest. FRETpredict is open source (GPLv3) and is available at https://github.com/KULL-Centre/FRETpredict and as a Python PyPI package at https://pypi.org/project/FRETpredict.

Hierarchical Assembly of Single-Stranded RNA

“…However, judging from the L-curve analysis and the CDF of rank-ordered weights (Figure S17, orange, dark green, and dark red), the set of weights we chose seemed to impose a rather gentle bias with S KL < 0.2 for all three models. An important point to consider is how to properly perform the ensemble reweighting for polymers with attached labels. , In approaches such as FRETpredict, dyes are placed onto proteins using a rotamer library approach (RLA) to predict FRET efficiencies with individual statistical weights. In the present study, we reweighted the whole molecule, i.e., polymer chain plus the attached fluorophore molecules.…”

Hierarchical Assembly of Single-Stranded RNA

“…An important point to consider is how to properly perform the ensemble reweighting for polymers with attached labels. 39,72 In approaches such as FRETpredict 73 dyes are placed onto proteins using a rotamer library approach (RLA) in order to predict FRET efficiencies. Here, the dyes are given individual statistical weights.…”

Single-molecule imaging and molecular dynamics simulations reveal early activation of the MET receptorin situ

“…For this purpose, we used FRETpredict, a novel approach that overcomes limitations in AV calculations. FRETpredict systematically takes into account the protein conformational ensemble and accurately models the conformational ensemble of the fluorophore labels ( 39 ). The predictions for T-T from the replicas that were locally reorganized (R2 and R3) are incompatible with that from the replica that remained the closest to the initial model.…”

“…Reconciling this discrepancy required sampling the structural dynamics of the complex with MD simulations and using an accurate model of the smFRET experiment ( 39 ). Three independent MD replicas showed local rearrangements of the dimer ( Figure 5B ).…”