Structural Comparative Modeling of Multi-Domain F508del CFTR

Proteostasis Landscapes of Selective versus Poorly Responsive CFTR Variants Reveals Structural Vulnerabilities to Correction

Cystic Fibrosis (CF) is a lethal genetic disorder caused by variants in CF transmembrane conductance regulator (CFTR). Many disease variants are treatable with corrector compounds, which enhance the folding and trafficking of CFTR. However, correctors fail to elicit a response for every CFTR variant. Approximately 3% of persons with CF harbor poorly responsive CFTR variants. Here, we reveal that a group of poorly responsive variants overlap with selectively responsive variants in a critical domain interface (nucleotide-binding domain 1/intracellular loop 4 - NBD1/ICL4). Affinity purification mass spectrometry proteomics was used to profile the protein homeostasis (proteostasis) changes of CFTR variants during corrector treatment to assess modulated interactions with protein folding and maturation pathways. Responsive variant interactions converged on similar proteostasis pathways during correction. In contrast, poorly responsive variants subtly diverged, revealing a partial restoration of protein quality control surveillance and a capacity to correct some mutations. Computational structural modeling showed that corrector VX-445 failed to confer enough NBD1 stability to poorly responsive variants. NBD1 secondary stabilizing mutations rescued poorly responsive variants, revealing structural vulnerabilities in NBD1 required for treating poor responders. Our study provides a framework for discerning the underlying protein quality control and structural defects of CFTR variants not reached with existing drugs. These insights can help expand therapeutics to all susceptible CFTR variants to enhance personalized medicine efforts.

Benchmarking AlphaMissense pathogenicity predictions against cystic fibrosis variants

Variants in the cystic fibrosis transmembrane conductance regulator gene (CFTR) result in cystic fibrosis-a lethal autosomal recessive disorder. Missense variants that alter a single amino acid in the CFTR protein are among the most common cystic fibrosis variants, yet tools for accurately predicting molecular consequences of missense variants have been limited to date. AlphaMissense (AM) is a new technology that predicts the pathogenicity of missense variants based on dual learned protein structure and evolutionary features. Here, we evaluated the ability of AM to predict the pathogenicity of CFTR missense variants. AM predicted a high pathogenicity for CFTR residues overall, resulting in a high false positive rate and fair classification performance on CF variants from the CFTR2.org database. AM pathogenicity score correlated modestly with pathogenicity metrics from persons with CF including sweat chloride level, pancreatic insufficiency rate, and Pseudomonas aeruginosa infection rate. Correlation was also modest with CFTR trafficking and folding competency in vitro. By contrast, the AM score correlated well with CFTR channel function in vitro-demonstrating the dual structure and evolutionary training approach learns important functional information despite lacking such data during training. Different performance across metrics indicated AM may determine if polymorphisms in CFTR are recessive CF variants yet cannot differentiate mechanistic effects or the nature of pathophysiology. Finally, AM predictions offered limited utility to inform on the pharmacological response of CF variants i.e., theratype. Development of new approaches to differentiate the biochemical and pharmacological properties of CFTR variants is therefore still needed to refine the targeting of emerging precision CF therapeutics.

Benchmarking AlphaMissense Pathogenicity Predictions Against Cystic Fibrosis Variants

Variants in the cystic fibrosis transmembrane conductance regulator gene (CFTR) result in cystic fibrosis - a lethal autosomal recessive disorder. Missense variants that alter a single amino acid in the CFTR protein are among the most common cystic fibrosis variants, yet tools for accurately predicting molecular consequences of missense variants have been limited to date. AlphaMissense (AM) is a new technology that predicts the pathogenicity of missense variants based on dual learned protein structure and evolutionary features. Here, we evaluated the ability of AM to predict the pathogenicity of CFTR missense variants. AM predicted a high pathogenicity for CFTR residues overall, resulting in a high false positive rate and fair classification performance on CF variants from the CFTR2.org database. AM pathogenicity score correlated modestly with pathogenicity metrics from persons with CF including sweat chloride level, pancreatic insufficiency rate, and Pseudomonas aeruginosa infection rate. Correlation was also modest with CFTR trafficking and folding competency in vitro. By contrast, the AM score correlated well with CFTR channel function in vitro - demonstrating the dual structure and evolutionary training approach learns important functional information despite lacking such data during training. Different performance across metrics indicated AM may determine if polymorphisms in CFTR are recessive CF variants yet cannot differentiate mechanistic effects or the nature of pathophysiology. Finally, AM predictions offered limited utility to inform on the pharmacological response of CF variants i.e., theratype. Development of new approaches to differentiate the biochemical and pharmacological properties of CFTR variants is therefore still needed to refine the targeting of emerging precision CF therapeutics.

Dark nanodiscs for evaluating membrane protein thermostability by differential scanning fluorimetry

Measuring protein thermostability provides valuable information on the biophysical rules that govern the structure-energy relationships of proteins. However, such measurements remain a challenge for membrane proteins. Here, we introduce a new experimental system to evaluate membrane protein thermostability. This system leverages a recently developed nonfluorescent membrane scaffold protein to reconstitute proteins into nanodiscs and is coupled with a nano-format of differential scanning fluorimetry (nanoDSF). This approach offers a label-free and direct measurement of the intrinsic tryptophan fluorescence of the membrane protein as it unfolds in solution without signal interference from the "dark" nanodisc. In this work, we demonstrate the application of this method using the disulfide bond formation protein B (DsbB) as a test membrane protein. NanoDSF measurements of DsbB reconstituted in dark nanodiscs loaded with 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) and 1,2-dimyristoyl-sn-glycero-3-phosphorylglycerol (DMPG) lipids show a complex biphasic thermal unfolding pattern with a minor unfolding transition followed by a major transition. The inflection points of the thermal denaturation curve reveal two distinct unfolding midpoint melting temperatures (Tm) of 70.5°C and 77.5°C, consistent with a three-state unfolding model. Further, we show that the catalytically conserved disulfide bond between residues C41 and C130 drives the intermediate state of the unfolding pathway for DsbB in a DMPC and DMPG nanodisc. To extend the utility of this method, we evaluate and compare the thermostability of DsbB in different lipid environments. We introduce this method as a new tool that can be used to understand how compositionally and biophysically complex lipid environments drive membrane protein stability.

CFTR Folding: From Structure and Proteostasis to Cystic Fibrosis Personalized Medicine

Cystic fibrosis (CF) is a lethal genetic disease caused by mutations in the chloride ion channel cystic fibrosis transmembrane conductance regulator (CFTR). Class-II mutants of CFTR lack intermolecular interactions important for CFTR structural stability and lead to misfolding. Misfolded CFTR is detected by a diverse suite of proteostasis factors that preferentially bind and route mutant CFTR toward premature degradation, resulting in reduced plasma membrane CFTR levels and impaired chloride ion conductance associated with CF. CF treatment has been vastly improved over the past decade by the availability of small molecules called correctors. Correctors directly bind CFTR, stabilize its structure by conferring thermodynamically favorable interactions that compensate for mutations, and thereby lead to downstream folding fidelity. However, each of over 100 Class-II CF causing mutations causes unique structural defects and shows a unique response to drug treatment, described as theratype. Understanding CFTR structural defects, the proteostasis factors evaluating those defects, and the stabilizing effects of CFTR correctors will illuminate a path toward personalized medicine for CF. Here, we review recent advances in our understanding of CFTR folding, focusing on structure, corrector binding sites, the mechanisms of proteostasis factors that evaluate CFTR, and the implications for CF personalized medicine.

Computational Exploration of Potential CFTR Binding Sites for Type I Corrector Drugs

Cystic fibrosis (CF) is a recessive genetic disease that is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) protein. The recent development of a class of drugs called "correctors", which repair the structure and function of mutant CFTR, has greatly enhanced the life expectancy of CF patients. These correctors target the most common disease causing CFTR mutant F508del and are exemplified by the FDA-approved VX-809. While one binding site of VX-809 to CFTR was recently elucidated by cryo-electron microscopy, four additional binding sites have been proposed in the literature and it has been theorized that VX-809 and structurally similar correctors may engage multiple CFTR binding sites. To explore these five binding sites, ensemble docking was performed on wild-type CFTR and the F508del mutant using a large library of structurally similar corrector drugs, including VX-809 (lumacaftor), VX-661 (tezacaftor), ABBV-2222 (galicaftor), and a host of other structurally related molecules. For wild-type CFTR, we find that only one site, located in membrane spanning domain 1 (MSD1), binds favorably to our ligand library. While this MSD1 site also binds our ligand library for F508del-CFTR, the F508del mutation also opens a binding site in nucleotide binding domain 1 (NBD1), which enables strong binding of our ligand library to this site. This NBD1 site in F508del-CFTR exhibits the strongest overall binding affinity for our library of corrector drugs. This data may serve to better understand the structural changes induced by mutation of CFTR and how correctors bind to the protein. Additionally, it may aid in the design of new, more effective CFTR corrector drugs.

General trends in the effects of VX-661 and VX-445 on the plasma membrane expression of clinical CFTR variants

Cystic fibrosis (CF) is caused by mutations that compromise the expression and/or function of the cystic fibrosis transmembrane conductance regulator (CFTR) chloride channel. Most people with CF harbor a common misfolded variant (ΔF508) that can be partially rescued by therapeutic "correctors" that restore its expression. Nevertheless, many other CF variants are insensitive to correctors. Using deep mutational scanning, we quantitatively compare the effects of two correctors on the plasma membrane expression of 129 CF variants. Though structural calculations suggest corrector binding provides similar stabilization to most variants, it's those with intermediate expression and mutations near corrector binding pockets that exhibit the greatest response. Deviations in sensitivity appear to depend on the degree of variant destabilization and the timing of misassembly. Combining correctors appears to rescue more variants by doubling the binding energy and stabilizing distinct cotranslational folding transitions. These results provide an overview of rare CF variant expression and establish new tools for precision pharmacology.

Computational modeling and prediction of deletion mutants

In-frame deletion mutations can result in disease. The impact of these mutations on protein structure and subsequent functional changes remain understudied, partially due to the lack of comprehensive datasets including a structural readout. In addition, the recent breakthrough in structure prediction through deep learning demands an update of computational deletion mutation prediction. In this study, we deleted individually every residue of a small α-helical sterile alpha motif domain and investigated the structural and thermodynamic changes using 2D NMR spectroscopy and differential scanning fluorimetry. Then, we tested computational protocols to model and classify observed deletion mutants. We show a method using AlphaFold2 followed by RosettaRelax performs the best overall. In addition, a metric containing pLDDT values and Rosetta ΔΔG is most reliable in classifying tolerated deletion mutations. We further test this method on other datasets and show they hold for proteins known to harbor disease-causing deletion mutations.

Dark nanodiscs as a model membrane for evaluating membrane protein thermostability by differential scanning fluorimetry

Measuring protein thermostability provides valuable information on the biophysical rules that govern structure-energy relationships of proteins. However, such measurements remain a challenge for membrane proteins. Here, we introduce a new experimental system to evaluate membrane protein thermostability. This system leverages a recently-developed non-fluorescent membrane scaffold protein (MSP) to reconstitute proteins into nanodiscs and is coupled with a nano-format of differential scanning fluorimetry (nanoDSF). This approach offers a label-free and direct measurement of the intrinsic tryptophan fluorescence of the membrane protein as it unfolds in solution without signal interference from the "dark" nanodisc. In this work, we demonstrate the application of this method using the disulfide bond formation protein B (DsbB) as a test membrane protein. NanoDSF measurements of DsbB reconstituted in dark nanodiscs show a complex biphasic thermal unfolding pattern in the presence of lipids with a minor unfolding transition followed by a major transition. The inflection points of the thermal denaturation curve reveal two distinct unfolding midpoint melting temperatures (Tm) of 70.5 °C and 77.5 °C, consistent with a three-state unfolding model. Further, we show that the catalytically conserved disulfide bond between residues C41 and C130 drives the intermediate state of the unfolding pathway for DsbB in a nanodisc. We introduce this method as a new tool that can be used to understand how compositionally, and biophysically complex lipid environments drive membrane protein stability.

Benchmarking AlphaFold2 on peptide structure prediction

Recent advancements in computational tools have allowed protein structure prediction with high accuracy. Computational prediction methods have been used for modeling many soluble and membrane proteins, but the performance of these methods in modeling peptide structures has not yet been systematically investigated. We benchmarked the accuracy of AlphaFold2 in predicting 588 peptide structures between 10 and 40 amino acids using experimentally determined NMR structures as reference. Our results showed AlphaFold2 predicts α-helical, β-hairpin, and disulfide-rich peptides with high accuracy. AlphaFold2 performed at least as well if not better than alternative methods developed specifically for peptide structure prediction. AlphaFold2 showed several shortcomings in predicting Φ/Ψ angles, disulfide bond patterns, and the lowest RMSD structures failed to correlate with lowest pLDDT ranked structures. In summary, computation can be a powerful tool to predict peptide structures, but additional steps may be necessary to analyze and validate the results.