Computational design of a PDZ domain peptide inhibitor that rescues CFTR activity

Abstract

The cystic fibrosis transmembrane conductance regulator (CFTR) is an epithelial chloride channel mutated in patients with cystic fibrosis (CF). The most prevalent CFTR mutation, ΔF508, blocks folding in the endoplasmic reticulum. Recent work has shown that some ΔF508-CFTR channel activity can be recovered by pharmaceutical modulators ("potentiators" and "correctors"), but ΔF508-CFTR can still be rapidly degraded via a lysosomal pathway involving the CFTR-associated ligand (CAL), which binds CFTR via a PDZ interaction domain. We present a study that goes from theory, to new structure-based computational design algorithms, to computational predictions, to biochemical testing and ultimately to epithelial-cell validation of novel, effective CAL PDZ inhibitors (called "stabilizers") that rescue ΔF508-CFTR activity. To design the "stabilizers", we extended our structural ensemble-based computational protein redesign algorithm K* to encompass protein-protein and protein-peptide interactions. The computational predictions achieved high accuracy: all of the top-predicted peptide inhibitors bound well to CAL. Furthermore, when compared to state-of-the-art CAL inhibitors, our design methodology achieved higher affinity and increased binding efficiency. The designed inhibitor with the highest affinity for CAL (kCAL01) binds six-fold more tightly than the previous best hexamer (iCAL35), and 170-fold more tightly than the CFTR C-terminus. We show that kCAL01 has physiological activity and can rescue chloride efflux in CF patient-derived airway epithelial cells. Since stabilizers address a different cellular CF defect from potentiators and correctors, our inhibitors provide an additional therapeutic pathway that can be used in conjunction with current methods.

Figure 1. (A) Structural model of the CAL PDZ domain (green and blue) bound to a CFTR C-terminus mimic (gray) used as input for computational designs (PDB id: 2LOB).

Residues shown in blue were modeled as flexible during the design search. (B) Model of the CFTR trafficking pathway with PDZ domain containing proteins NHERF1 and CAL. CAL is associated with lysosomal degradation of CFTR, while NHERF1 is associated with insertion of CFTR into the cell membrane.

Figure 2. Overview of Algorithm.

he algorithm searches over protein sequences and conformations to find the protein complexes with the best binding constant. takes an input model composed of an initial protein structure, a rotamer library to search over side-chain conformations, and an energy function to evaluate conformations. Minimization-aware DEE (minDEE) prunes rotamers that are not part of the lowest energy conformations for a given sequence. The remaining conformations from minDEE are enumerated in order of increasing energy lower bounds using A*. Finally, the conformations are Boltzmann-weighted and used to compute partition functions and ultimately a score for each sequence.

Figure 3. Summary of CAL peptide array.

(A) Summary statistics for peptide array. Higher BLU (biochemical light unit) values indicate stronger protein binding to a peptide. (B) Distribution of the peptide BLU values from the peptide array in units of standard deviation above the mean (). (C) Normalized amino acid frequencies for the top sequences that have a BLU value greater than 3 standard deviations from the average, which were considered as the peptides that bound CAL for the validation of predictions. The frequency of each amino acid type for each residue position was normalized by the total number of occurrences of that amino acid in the array at the given residue position.

Figure 4. enriched for peptide sequences that bind the CAL PDZ domain.

ROCs were calculated comparing predictions to (A) the entire HumLib peptide array data set (AUC = 0.84) and (B) only sequences in the HumLib array that matched the CAL binding motif (AUC = 0.71).

Figure 5. (A) G values for top- and poorly-ranked predictions that were experimentally tested using fluorescence polarization.

Predictions plotted in green denote that the binding affinity was higher than the best previously known hexamer (). Horizontal line represents average G for plotted sequences. Sequence information and binding data can be found in Tables 1 and 2. (B) Ensemble of top 100 conformations for the peptide (kCAL01: WQVTRV, orange sticks) with tightest binding to CAL (gray ribbon).

Figure 6. was used to predict binding between the CAL PDZ domain and the peptide array, ProLib (Figure S3), which contained peptide sequences that match the CAL binding motif.

The ROC curve shown compares the predictions to the observed peptide array binding data. AUC = 0.88.

Figure 7. Top binding peptide is biologically active.

The ΔF508-CFTR specific chloride flux is shown for a control peptide (kCAL31; WQDSGI; no CAL binding detected), the reference peptide (iCAL35; WQTSII), and the tightest binding design peptide (kCAL01; WQVTRV). kCAL01 shows a 12% increase in chloride efflux over the control peptide. values shown are for pairwise comparisons (). Values shown are mean standard error of the mean (SEM). N.S.: not significant, .