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

Area of Science:

• Biophysics
• Computational Biology
• Structural Biology

Background:

• Förster Resonance Energy Transfer (FRET) is a powerful technique for measuring distances within and between molecules.
• Interpreting FRET efficiency from experimental data often requires computational modeling of protein dynamics.
• Existing methods may not efficiently handle large conformational ensembles from simulations.

Approach:

• Introduced FRETpredict, a Python software for predicting FRET efficiencies from protein conformational ensembles.
• Employs the Rotamer Library Approach to model FRET probes attached to proteins.
• Optimized for processing large datasets, such as those from molecular dynamics simulations.

Key Points:

• Demonstrated FRETpredict's accuracy and performance on a range of systems: a peptide, an intrinsically disordered protein, and three folded proteins.
• Developed a general method for creating custom rotamer libraries for various FRET probes.
• The software is open-source and readily available for the scientific community.

Conclusions:

• FRETpredict facilitates the validation and refinement of molecular models using FRET data.
• Enables more accurate interpretation of experimental FRET measurements.
• Provides a valuable computational tool for structural biology and biophysics research.