CFTR True Open Mutants

1. L218C and L100C 

2. R334C and A107C 

3. Y109C and T1121C

4. I121C and T1122C

5. E116C and R104C 

We tested R104C/E116C double mutant of CFTR in a two electrode-voltage clamp experiment. The data (shown below) suggest that once activated by PKA the mutant CFTR is locked into the open state. The conductance due to wild-type CFTR comes back to the baseline in FR (in < 10 min). After the FR washout of isoproterenol, 1 mM DTT was applied in the extracellular solution (Frog Ringer's). The conductance declined toward the baseline in the presence of DTT, suggesting the breakage of the disulfide bond by DTT allows closing of CFTR channels.

We have created (or are creating) 500 simulated annealing runs for the TM7-8 model for the low and high temperatures. Our TM7-8 model is alomost identical to 6MSM but the extracellular loop that connects TM7 and TM8 was created by CHARMM-GUI. A portion of this loop (ECL4) is missing in the cryo-EM structure (6MSM). We also have 3 replicates of 100 ns MD simulation for the TM7-8 model and also 3 replicates for the R104C-E116C model. There are other MD and SA simulations but we currently focus on these models.

The folloing mutants have been ordered with Azenta: E217C, S909C, T910C ($2,237), R334C, R334C-D110C, R334C-K114C, R334C-E116C ($2,974), S909C-E1126C, T910C-D110C, E217C-R104C ($881), R1128C, R1128C-D891C, R1128C-S912C ($881). These will be linearized with NheI and mRNA will be made with T7 Ultra mMessage mMachine. Other plasmids that need to be linearized include KCNJ3-pGHE (990 ng/ul, NheI), KCNJ5-pcDNA3.1 (1790 ng/ul, SmaI), KCNJ6-pcDNA3.1 (1530 ng/ul, SmaI), Galphas (3600 ng/ul, ApaI), PtxA (1800 ng/ul, ApaI or SacII).

Simulated annealing runs were conducted for wild-type CFTR (6msm). For each simulated anneling run, the diameter of the bottleneck observed during the simulation was analyzed with the program called "Hole". In order to run Hole, first extract the program file into your home holder (/home/'username', pwd command shows your current directory). This holder is called "hole2". You can test your Hole program with our sample data. The sample data contain PDB files of CFTR snapshots from the first high temperature simulated annealing run (SA1) for our TM7-8 model (The extracellular loop connecting TM7 and TM8 was constrcted by CHARMM-GUI). Run hole.py (command: python3 hole.py) and then radius.py to obtain the estimated radius at the bottleneck for each snapshot. You can see how the shape and radius of the bottleneck changes during the simulated annealing run. Each simulated annealing run displays unique dynamics of the bottleneck and a wide range of numbers are observed between different simulated annealing runs. The Hole program produces sph files for visualization of the pore. You should open the sph file with the corresponding protein structure to see the path for ions. The sph file shows the largest sphere that can pass through the pore at each level. The visual representation can be achieved with 'alter hole_out.sph, vdw=b'. Disply the sph file in mesh or surface representation.

We can investigate whether there is any correlation between the bottleneck radius and interaction energies of some residue pairs in CFTR. The pairwise interaction energies were calculated with NAMD with CFTREnergy.py. NAMD can be installed in Home folder (or Documents folder). In the Linux enironment (e.g. Ubuntu), you can copy this into a local bin and give execution permission (sudo cp CFTREnergy.py /usr/local/bin/CFTREnergy/ && chmod +x /usr/local/bin/CFTREnergy). Then just type 'CFTREnergy' and choose option #2 and hit enter. The required input files are pdb, psf and dcd which can be generated with VMD using the trajectory files for CFTR protein (gro and xtc). The program will start running calculating the interaction energy of every possible residue pair in CFTR for all snapshots. This calculation takes roughly a month on a modern desktop.

CFTREnergy.py produces a file called Residue Interaction Energy.csv. Not all 500 SA runs have been analyzed with CFTREnergy.py. The results so far are found in the Interaction Energy fodler. The Residue Interaction Energy.csv was analyzed with PCA.py. The principal component analysis identifies residue pairs that exhibit highly variable interaction energies between the snapshots. Some of these residue pairs would be involved in stabilization of the CFTR pore. PCA.py produces temporary files (e.g. SA1-energies.txt, modified-interaction-energies.txt) because the Residue Interaction Energy.csv is not in the right format for PCA by Scikit-Learn. pca_output.txt contains information about the principal components (e.g. PC1, PC2) such as the percentage or fraction of the total variance expalined by each principal component and the actual values of PC1, PC2, PC3 and PC4. The weights (loading scores) of residue pairs in PC1 are in eigenvector.txt. Nonzero weights of PC1 are summarized in nonzero_weights.txt.