QUEST (QUenching ESTimation) simulates the dynamic quenching of xanthene dyes tethers to proteins by flexible linkers by simulating PET and the diffusion of dyes.

The dynamic quenching of a fluorescent dye coupled to a protein is simulated in three steps:

1. The dye's accessible volume (AV) is calculated, the positions of the quenching amino acids are determined, to every quenching amino acid a quenching rate constant is assigned.
2. The diffusion of the dye within it's accessible volume is simulated using Brownian dynamics (BD) simulations. In the BD simulations a dye that is close to the vicinity of the protein diffuses slower due to unspecific interactions.
3. The distance between the dye and the quenching amino acids is used to calculate the dye's fluorescence decay.

In QUEST the dyes are approximated by a sphere diffusing within their accessible volume (AV) (see labellib).

PET-quenching of the dye by MET, HIS, TYR and TRP residues is approximated by a step function where the dye is quenched with a provided rate contestant if it is closer than a given threshold distance.

The relevant simulation parameters can be adjusted either in a graphical user interface quest_gui or QuEst can be controlled using a command line interface (see documentation below).

Alternatively, QuEst can be used a library for potential integration into other simulations and/or data analysis pipelines (see Jupyter Notebook)

• Design of labeling positions for FRET experiments
• Calibration of accessible contact volume (ACVs) using the fluorescence lifetime of the donor

There are two QuEST versions:

1. GUI-QuEST a end-user software with graphical user interface for Windows (setup.exe, conda), Linux (conda), and macOS (conda). The conda installation is described below.
2. Command-QuEST a command line version for Windows, Linux and MacOS

Both versions are documented in the Wiki of this repository Wiki.

The windows GUI version can be installed using either a setup file (setup.exe) or conda. To install QuEst using conda use the conda repository tpeulen

conda install -c tpeulen quest

Following the installation via conda, quest can be started from a command line interface

quest

1. Go to the folder of the program in the command line (by clicking on shell.bat)
2. run: "python estimate_qy.py xxxxxx" xxxx are the parameter
3. mandatory parameters: the pdb-file, the chain id, the amino acid numbers

The command line tools are located in the folder tools.

python estimate_qy.py -f 3q5d_fixed.pdb -c " " -p 11 401

The argument -f corresponds to the PDB file, -c to the chain ID, -p tp the labeled residue number

To get a list of the parameters run:

python estimate_qy.py -h

Additionally, there is a helper script which replaces the resname of a given residue with "ALA". This might be usefull if you want to exclude one of the quenchers.

python hide_quencher.py     123        3q5d_fixed.pdb   out.pdb

where the first argument is the resid to exclude, the second is the PDB file, and the third is the ouput PDB filename.

1. QuEST determines precise values that are not necessary accurate.
2. QuEST was the first software to implement the ACVs. ACVs were later described in more detail (see: COSB2016. Differencies in the ACV implementation, may produce slightly different results.
3. QuEST operates on single static structures.
4. A crude approximation of the dye is used by a sinlge sphere is used.
5. Specific interactions e.g. binding pockets are not considered.

If you have used QuEST in a scientific publication, we would appreciate citations to the following paper:

Peulen, T.O., Opanasyuk, O., and Seidel, C.A., 2017. Combining Graphical and Analytical Methods with Molecular Simulations To Analyze Time-Resolved FRET Measurements of Labeled Macromolecules Accurately. The Journal of Physical Chemistry B 2017, 121, 35, 8211-8241 (Feature Article)

For more informations on accessible contact volumes (ACVs) see:

Dimura, M., Peulen, T.O., Hanke, C.A., Prakash, A., Gohlke, H. and Seidel, C.A., 2016. Quantitative FRET studies and integrative modeling unravel the structure and dynamics of biomolecular systems. Current opinion in structural biology, 40, pp.163-185.

To improve our dye models we need a larger set of experimental data. If you are interested in using, and improving experimental coarse- grained dye models for integrative modelling. Independently if you are a developer of not, you can contribute by

• assembling more experimental data
• improve the documentation

If you are interested, sign up on GitHub, contact the developers, and put a star on this project.

Author(s): Thomas-Otavio Peulen

Maintainer: tpeulen

License: MIT This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.