92
4. Protein solutions: proteins should be concentrated as much as
practical (ideally at least 500 μM) and exchanged or dialyzed
into the same buffer solution as used to prepare the reagent
stock solutions (see Note 4).
3 Methods
All experimental (noncomputational) work should be performed
at 4 °C unless otherwise specified.
First, prepare models of the target fusion protein in both its bound
and unbound conformations. This involves modeling both the
solute-binding and fluorescent domains with the appropriate
linker. Start with crystal structures or high-quality homology models of the desired binding core in both conformations and the fluorescent protein. Ensure no nonstandard residues exist in the
models, as these are not parameterized in the MARTINI forcefield
(see Note 5). Reconstruct any residues missing from the crystal
structures at the termini by which they are fused. Then, construct
the linker sequence as expressed experimentally (see Note 6).
Complete the model by fusing the two domains (see Note 7). Save
both SBP-linker-FP models as .PDB files.
Create a directory for each conformation, copy the appropriate
.PDB file into each, and also copy simulations.sh into each (see
Note 8). The script simulations.sh automatically prepares and runs
a number of MARTINI simulations. It depends on all of the software in Subheading 2.1 except MARTINI, which is downloaded
automatically by the script, provided all software dependencies are
available in your $PATH (see Note 9). The script can be configured by making a copy of it in each conformation directory and
editing the leading “CONFIG” portion. Most defaults should be
appropriate; however, the input_file, system_name and linker_residues variables should be set for your system (see Note 10).
Configure and run the script, read and follow the directions given,
then repeat for the other conformation.
Run the second script “process-data.py,” giving as the first two
arguments the locations of each set of output files. The residue
number of the central residue of the FP fluorophore and the range
of residues that should be checked for sensor generation should
also be given as arguments, respectively, with the –f and –r switches
(see Note 11). process-data.py reads the given .PDB files, and predicts dynamic ranges for sensors formed by labeling each residue in
the given range with a dye with configurable Forster distance. This
output is stored by default as comma-separated values with appropriate headers in sensor_predict.csv and can be visualized graphically using a program such as Excel.
3.1 Computational
Screening and Residue
Selection
Joshua A. Mitchell et al.
4. Protein solutions: proteins should be concentrated as much as
practical (ideally at least 500 μM) and exchanged or dialyzed
into the same buffer solution as used to prepare the reagent
stock solutions (see Note 4).
3 Methods
All experimental (noncomputational) work should be performed
at 4 °C unless otherwise specified.
First, prepare models of the target fusion protein in both its bound
and unbound conformations. This involves modeling both the
solute-binding and fluorescent domains with the appropriate
linker. Start with crystal structures or high-quality homology models of the desired binding core in both conformations and the fluorescent protein. Ensure no nonstandard residues exist in the
models, as these are not parameterized in the MARTINI forcefield
(see Note 5). Reconstruct any residues missing from the crystal
structures at the termini by which they are fused. Then, construct
the linker sequence as expressed experimentally (see Note 6).
Complete the model by fusing the two domains (see Note 7). Save
both SBP-linker-FP models as .PDB files.
Create a directory for each conformation, copy the appropriate
.PDB file into each, and also copy simulations.sh into each (see
Note 8). The script simulations.sh automatically prepares and runs
a number of MARTINI simulations. It depends on all of the software in Subheading 2.1 except MARTINI, which is downloaded
automatically by the script, provided all software dependencies are
available in your $PATH (see Note 9). The script can be configured by making a copy of it in each conformation directory and
editing the leading “CONFIG” portion. Most defaults should be
appropriate; however, the input_file, system_name and linker_residues variables should be set for your system (see Note 10).
Configure and run the script, read and follow the directions given,
then repeat for the other conformation.
Run the second script “process-data.py,” giving as the first two
arguments the locations of each set of output files. The residue
number of the central residue of the FP fluorophore and the range
of residues that should be checked for sensor generation should
also be given as arguments, respectively, with the –f and –r switches
(see Note 11). process-data.py reads the given .PDB files, and predicts dynamic ranges for sensors formed by labeling each residue in
the given range with a dye with configurable Forster distance. This
output is stored by default as comma-separated values with appropriate headers in sensor_predict.csv and can be visualized graphically using a program such as Excel.
3.1 Computational
Screening and Residue
Selection
Joshua A. Mitchell et al.