FRETpredict API Reference

FRETpredict has four main classes. Two of them are dedicated to FRET efficiency calculations and the others to Rotamer Library generation.

FRET efficiency calculation

FRETpredict

Performs predictions of FRET efficiencies. It can be found in the FRET.py file.

Methods:

• compute_chromophore_vectors: calculates chromophores transition dipole moment vector and center-to-center distance distribution for k2 calculations,

• calculateR0: calculates FRET R0 between the donor and acceptor chromophores,

• trajectoryAnalysis: calculates distribution (i.e. one for each protein trajectory frame) in static, dynamic1, and dynamic2 regimes,

• save: calculates k2 distribution and k2, Static, Dynamic1, Dynamic2 averaging, with or without reweighting, and save data to file,

• reweight: Alias for reweigthing calculations, calls save() function with weights for reweighting,

• run: runs FRET efficiency calculations by calling trajectoryAnalysis() and saving data to file.

Operations

It is the base class containing attributes and methods inherited and used by the calculation classes. It can be found in the utils.py file.

Methods:

• precalculate_rotamer: selects placement residue atoms, compute Lennard-Jones and Electrostatic (Debye-Huckel) parameters for chromophore rotamers and protein.

The Lennard-Jones parameters of the CHARMM36 force field in the lennardjones.py file are used to calculate the external energy contribution to the Boltzmann weight of each conformer.

FRETpredict.lennardjones.lj_parameters = {
    'C': {
        'vdw': 1.70,
        'p_q': 0,
        'p_Rmin2': 2.275,
        'eps': -0.020
    }, 
    ...
}

• rotamer_placement: places chromophore rotamers on a selected protein residue by rotation and translation, return chromophore Universe object with new coordinates,

• lj_calculation: calculates Boltzmann weights for each rotamer pose by summing Electrostatic and potential energy contributions,

• rotamerWeights: calculates Boltzmann distribution for each rotamer library,

• weightedAvgSDSE: calculates the weighted average and standard deviation,

• calculate_ws: calculates per-frame weights for reweighting,

• fraction_frames: computes effective fraction of frames contributing to the averages.

Tutorial_FRETpredict_pp11 is a Jupyter Notebook with simple tutorials on how to compute the FRET efficiency and use the FRETpredict functions on the trajectory of a Poliproline 11 (pp11) system.

Rotamer Library generation

RotamerClusters

Creates a rotamer library starting from a dye + linker (FRET probe) trajectory. It can be found in the rotamer_libraries.py file.

Methods:

• calcDihe: calculates dihedral angles on the dye+linker trajectory,

• genPeaks: computes linker dihedral peaks,

• genClusters:
  

  1. generate combinations of dihedral angles from the peaks (cluster centers C1).

  2. K-means Clustering

     • Assign each trajectory frame to the cluster center C1 of least square deviation.

     • Calculate the average over the dihedral angles that were assigned to the same cluster center. This results in a set of new centers (C2).

     • Assign each trajectory frame to the cluster center C2 of least square deviation

  3. Find the trajectory frame that best represents the cluster center.

• filterCluster:
  

  1. Filter the cluster centers C2 based on a cutoff on N (cluster population). This results in a different number of centers (C3).

  2. Reassign the discarded frames to the remaining C3 cluster center of least square deviation.

• genRotLib: translates + rotates C3 cluster centers conformations, and write data to file,

• plotClustHist: plots dihedral distribution and peaks with cluster centers C3 dihedrals,

• plotClustPolar: plots dihedral distribution and peaks with cluster centers C3 dihedrals on a polar plot,

• run: runs all the calculations to generate a rotamer library from a dye+linker trajectory.

RotamerLibrary

Makes available the rotamer library attributes. Rotamers libraries consist of three different files:

• a structure file with a protein conformation (PDB format),

• a trajectory file with all the rotamer conformations (DCD format),

• a text file for the populations (i.e., weight) of each chromophore conformation. These files are included in the FRETpredict/lib folder.

Specific fluorophore parameters are specified in FRETpredict/lib/libraries.yml.

 'AlexaFluor 488 C1R': {'author': 'D Montepietra, G Tesei, JM Martins, MBA Kunze, RB Best, K Lindorff-Larsen',
                        'citation': 'https://doi.org/10.1101/2023.01.27.525885',
                        'filename': 'A48_C1R_cutoff30',
                        'licence': 'GPLv3',
                        'mu': ['C2', 'C13 and resname A48'],
                        'negative': ['S1 and resname A48',
                        'S2 and resname A48',
                        'C20 and resname A48'],
                        'positive': ['N1 and resname A48', 'N2 and resname A48'],
                        'r': ['C7 and resname A48']},

Tutorial_generate_new_rotamer_libraries.ipynb is Jupyter Notebook tutorial on how to create and add new rotamer libraries.