FRETpredict

FRETpredict predicts Förster resonance energy transfer (FRET) efficiencies by modeling rotamer conformations of covalently attached fluorescent probes on proteins to support structural characterization of biomolecules and analysis of molecular dynamics ensembles.

Key Features:

• Rotamer Library Approach: Uses rotamer libraries to describe spatial arrangements of FRET probes and derive distance and orientation distributions for efficiency calculations.
• Compatibility with Molecular Dynamics Ensembles: Applies probe modeling and FRET calculations across large structural ensembles generated by molecular dynamics simulations.
• Support for Common Dyes and Linkers: Provides pre-existing rotamer libraries for many commonly used fluorescent dyes and linkers.
• Methodology for New Rotamer Libraries: Includes a general procedure to generate new rotamer libraries tailored to specific FRET probes.
• Python Implementation: Implemented as a Python package for integration with computational workflows.

Scientific Applications:

• Rigid Peptides (polyproline 11): Prediction of FRET efficiencies in structurally constrained peptides such as polyproline 11.
• Intrinsically Disordered Proteins (ACTR): Analysis of FRET in flexible, dynamic systems exemplified by ACTR.
• Folded Proteins (HiSiaP, SBD2, MalE): Application to folded protein systems including HiSiaP, SBD2, and MalE to interpret experimental FRET data.

Methodology:

Models probe conformations using rotamer libraries on protein structures, computes FRET efficiencies across molecular dynamics-derived ensembles, and provides a general procedure to generate new rotamer libraries.