Therapeutic alphavirus cross-reactive E1 human antibodies inhibit viral egress.

Alphaviruses cause severe arthritogenic or encephalitic disease. The E1 structural glycoprotein is highly conserved in these viruses and mediates viral fusion with host cells. However, the role of antibody responses to the E1 protein in immunity is poorly understood. We isolated E1-specific human monoclonal antibodies (mAbs) with diverse patterns of recognition for alphaviruses (ranging from Eastern equine encephalitis virus [EEEV]-specific to alphavirus cross-reactive) from survivors of natural EEEV infection. Antibody binding patterns and epitope mapping experiments identified differences in E1 reactivity based on exposure of epitopes on the glycoprotein through pH-dependent mechanisms or presentation on the cell surface prior to virus egress. Therapeutic efficacy in vivo of these mAbs corresponded with potency of virus egress inhibition in vitro and did not require Fc-mediated effector functions for treatment against subcutaneous EEEV challenge. These studies reveal the molecular basis for broad and protective antibody responses to alphavirus E1 proteins.

Tissue factor binds to and inhibits interferon-α receptor 1 signaling.

Tissue factor (TF), which is a member of the cytokine receptor family, promotes coagulation and coagulation-dependent inflammation. TF also exerts protective effects through unknown mechanisms. Here, we showed that TF bound to interferon-α receptor 1 (IFNAR1) and antagonized its signaling, preventing spontaneous sterile inflammation and maintaining immune homeostasis. Structural modeling and direct binding studies revealed binding of the TF C-terminal fibronectin III domain to IFNAR1, which restricted the expression of interferon-stimulated genes (ISGs). Podocyte-specific loss of TF in mice (PodΔF3) resulted in sterile renal inflammation, characterized by JAK/STAT signaling, proinflammatory cytokine expression, disrupted immune homeostasis, and glomerulopathy. Inhibiting IFNAR1 signaling or loss of Ifnar1 expression in podocytes attenuated these effects in PodΔF3 mice. As a heteromer, TF and IFNAR1 were both inactive, while dissociation of the TF-IFNAR1 heteromer promoted TF activity and IFNAR1 signaling. These data suggest that the TF-IFNAR1 heteromer is a molecular switch that controls thrombo-inflammation.

Potently neutralizing and protective human antibodies against SARS-CoV-2.

The ongoing pandemic of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a major threat to global health1 and the medical countermeasures available so far are limited2,3. Moreover, we currently lack a thorough understanding of the mechanisms of humoral immunity to SARS-CoV-24. Here we analyse a large panel of human monoclonal antibodies that target the spike (S) glycoprotein5, and identify several that exhibit potent neutralizing activity and fully block the receptor-binding domain of the S protein (SRBD) from interacting with human angiotensin-converting enzyme 2 (ACE2). Using competition-binding, structural and functional studies, we show that the monoclonal antibodies can be clustered into classes that recognize distinct epitopes on the SRBD, as well as distinct conformational states of the S trimer. Two potently neutralizing monoclonal antibodies, COV2-2196 and COV2-2130, which recognize non-overlapping sites, bound simultaneously to the S protein and neutralized wild-type SARS-CoV-2 virus in a synergistic manner. In two mouse models of SARS-CoV-2 infection, passive transfer of COV2-2196, COV2-2130 or a combination of both of these antibodies protected mice from weight loss and reduced the viral burden and levels of inflammation in the lungs. In addition, passive transfer of either of two of the most potent ACE2-blocking monoclonal antibodies (COV2-2196 or COV2-2381) as monotherapy protected rhesus macaques from SARS-CoV-2 infection. These results identify protective epitopes on the SRBD and provide a structure-based framework for rational vaccine design and the selection of robust immunotherapeutic agents.

MutationExplorer: a webserver for mutation of proteins and 3D visualization of energetic impacts.

The possible effects of mutations on stability and function of a protein can only be understood in the context of protein 3D structure. The MutationExplorer webserver maps sequence changes onto protein structures and allows users to study variation by inputting sequence changes. As the user enters variants, the 3D model evolves, and estimated changes in energy are highlighted. In addition to a basic per-residue input format, MutationExplorer can also upload an entire replacement sequence. Previously the purview of desktop applications, such an upload can back-mutate PDB structures to wildtype sequence in a single step. Another supported variation source is human single nucelotide polymorphisms (SNPs), genomic coordinates input in VCF format. Structures are flexibly colorable, not only by energetic differences, but also by hydrophobicity, sequence conservation, or other biochemical profiling. Coloring by interface score reveals mutation impacts on binding surfaces. MutationExplorer strives for efficiency in user experience. For example, we have prepared 45 000 PDB depositions for instant retrieval and initial display. All modeling steps are performed by Rosetta. Visualizations leverage MDsrv/Mol*. MutationExplorer is available at: http://proteinformatics.org/mutation_explorer/.

Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins.

Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta's protein engineering toolbox that allow for the rational design of PTMs.

Integrated AlphaFold2 and DEER investigation of the conformational dynamics of a pH-dependent APC antiporter.

The Amino Acid-Polyamine-Organocation (APC) transporter GadC contributes to the survival of pathogenic bacteria under extreme acid stress by exchanging extracellular glutamate for intracellular γ-aminobutyric acid (GABA). Its structure, determined in an inward-facing conformation at alkaline pH, consists of the canonical LeuT-fold with a conserved five-helix inverted repeat, thereby resembling functionally divergent transporters such as the serotonin transporter SERT and the glucose-sodium symporter SGLT1. However, despite this structural similarity, it is unclear if the conformational dynamics of antiporters such as GadC follow the blueprint of these or other LeuT-fold transporters. Here, we used double electron-electron resonance (DEER) spectroscopy to monitor the conformational dynamics of GadC in lipid bilayers in response to acidification and substrate binding. To guide experimental design and facilitate the interpretation of the DEER data, we generated an ensemble of structural models in multiple conformations using a recently introduced modification of AlphaFold2 . Our experimental results reveal acid-induced conformational changes that dislodge the Cterminus from the permeation pathway coupled with rearrangement of helices that enables isomerization between inward- and outward-facing states. The substrate glutamate, but not GABA, modulates the dynamics of an extracellular thin gate without shifting the equilibrium between inward- and outward-facing conformations. In addition to introducing an integrated methodology for probing transporter conformational dynamics, the congruence of the DEER data with patterns of structural rearrangements deduced from ensembles of AlphaFold2 models illuminates the conformational cycle of GadC underpinning transport and exposes yet another example of the divergence between the dynamics of different families in the LeuT-fold.

Allele-specific activation, enzyme kinetics, and inhibitor sensitivities of EGFR exon 19 deletion mutations in lung cancer.

Oncogenic mutations within the epidermal growth factor receptor (EGFR) are found in 15 to 30% of all non-small-cell lung carcinomas. The term exon 19 deletion (ex19del) is collectively used to refer to more than 20 distinct genomic alterations within exon 19 that comprise the most common EGFR mutation subtype in lung cancer. Despite this heterogeneity, clinical treatment decisions are made irrespective of which EGFR ex19del variant is present within the tumor, and there is a paucity of information regarding how individual ex19del variants influence protein structure and function. Herein, we identified allele-specific functional differences among ex19del variants attributable to recurring sequence and structure motifs. We built all-atom structural models of 60 ex19del variants identified in patients and combined molecular dynamics simulations with biochemical and biophysical experiments to analyze three ex19del mutations (E746_A750, E746_S752 > V, and L747_A750 > P). We demonstrate that sequence variation in ex19del alters oncogenic cell growth, dimerization propensity, enzyme kinetics, and tyrosine kinase inhibitor (TKI) sensitivity. We show that in contrast to E746_A750 and E746_S752 > V, the L747_A750 > P variant forms highly active ligand-independent dimers. Enzyme kinetic analysis and TKI inhibition experiments suggest that E746_S752 > V and L747_A750 > P display reduced TKI sensitivity due to decreased adenosine 5'-triphosphate Km. Through these analyses, we propose an expanded framework for interpreting ex19del variants and considerations for therapeutic intervention.

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.

Structural analysis of the P132L disease mutation in caveolin-1 reveals its role in the assembly of oligomeric complexes.

Caveolin-1 (CAV1) is a membrane-sculpting protein that oligomerizes to generate flask-shaped invaginations of the plasma membrane known as caveolae. Mutations in CAV1 have been linked to multiple diseases in humans. Such mutations often interfere with oligomerization and the intracellular trafficking processes required for successful caveolae assembly, but the molecular mechanisms underlying these defects have not been structurally explained. Here, we investigate how a disease-associated mutation in one of the most highly conserved residues in CAV1, P132L, affects CAV1 structure and oligomerization. We show that P132 is positioned at a major site of protomer-protomer interactions within the CAV1 complex, providing a structural explanation for why the mutant protein fails to homo-oligomerize correctly. Using a combination of computational, structural, biochemical, and cell biological approaches, we find that despite its homo-oligomerization defects P132L is capable of forming mixed hetero-oligomeric complexes with WT CAV1 and that these complexes can be incorporated into caveolae. These findings provide insights into the fundamental mechanisms that control the formation of homo- and hetero-oligomers of caveolins that are essential for caveolae biogenesis, as well as how these processes are disrupted in human disease.

Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network.

Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

Epitope-focused immunogen design based on the ebolavirus glycoprotein HR2-MPER region.

The three human pathogenic ebolaviruses: Zaire (EBOV), Bundibugyo (BDBV), and Sudan (SUDV) virus, cause severe disease with high fatality rates. Epitopes of ebolavirus glycoprotein (GP) recognized by antibodies with binding breadth for all three ebolaviruses are of major interest for rational vaccine design. In particular, the heptad repeat 2 -membrane-proximal external region (HR2-MPER) epitope is relatively conserved between EBOV, BDBV, and SUDV GP and targeted by human broadly-neutralizing antibodies. To study whether this epitope can serve as an immunogen for the elicitation of broadly-reactive antibody responses, protein design in Rosetta was employed to transplant the HR2-MPER epitope identified from a co-crystal structure with the known broadly-reactive monoclonal antibody (mAb) BDBV223 onto smaller scaffold proteins. From computational analysis, selected immunogen designs were produced as recombinant proteins and functionally validated, leading to the identification of a sterile alpha motif (SAM) domain displaying the BDBV-HR2-MPER epitope near its C terminus as a promising candidate. The immunogen was fused to one component of a self-assembling, two-component nanoparticle and tested for immunogenicity in rabbits. Robust titers of cross-reactive serum antibodies to BDBV and EBOV GPs and moderate titers to SUDV GP were induced following immunization. To confirm the structural composition of the immunogens, solution NMR studies were conducted and revealed structural flexibility in the C-terminal residues of the epitope. Overall, our study represents the first report on an epitope-focused immunogen design based on the structurally challenging BDBV-HR2-MPER epitope.

Docking cholesterol to integral membrane proteins with Rosetta.

Lipid molecules such as cholesterol interact with the surface of integral membrane proteins (IMP) in a mode different from drug-like molecules in a protein binding pocket. These differences are due to the lipid molecule's shape, the membrane's hydrophobic environment, and the lipid's orientation in the membrane. We can use the recent increase in experimental structures in complex with cholesterol to understand protein-cholesterol interactions. We developed the RosettaCholesterol protocol consisting of (1) a prediction phase using an energy grid to sample and score native-like binding poses and (2) a specificity filter to calculate the likelihood that a cholesterol interaction site may be specific. We used a multi-pronged benchmark (self-dock, flip-dock, cross-dock, and global-dock) of protein-cholesterol complexes to validate our method. RosettaCholesterol improved sampling and scoring of native poses over the standard RosettaLigand baseline method in 91% of cases and performs better regardless of benchmark complexity. On the ß2AR, our method found one likely-specific site, which is described in the literature. The RosettaCholesterol protocol quantifies cholesterol binding site specificity. Our approach provides a starting point for high-throughput modeling and prediction of cholesterol binding sites for further experimental validation.

Recent Advances in Automated Structure-Based De Novo Drug Design.

As the number of determined and predicted protein structures and the size of druglike 'make-on-demand' libraries soar, the time-consuming nature of structure-based computer-aided drug design calls for innovative computational algorithms. De novo drug design introduces in silico heuristics to accelerate searching in the vast chemical space. This review focuses on recent advances in structure-based de novo drug design, ranging from conventional fragment-based methods, evolutionary algorithms, and Metropolis Monte Carlo methods to deep generative models. Due to the historical limitation of de novo drug design generating readily available drug-like molecules, we highlight the synthetic accessibility efforts in each category and the benchmarking strategies taken to validate the proposed framework.

Groebke Blackburn Bienaymé-mediated multi-component synthesis of selective HDAC6 inhibitors with anti-inflammatory properties.

Histone deacetylases (HDACs) are a class of enzymes that cleave acyl groups from lysine residues of histone and non-histone proteins. There are 18 human HDAC isoforms with different cellular targets and functions. Among them, HDAC6 was found to be overexpressed in different types of cancer. However, when used in monotherapy, HDAC6 inhibition by selective inhibitors fails to show pronounced anti-cancer effects. The HDAC6 enzyme also addresses non-histone proteins like α-tubulin and cortactin, making it important for cell migration and angiogenesis. Recently, the NLRP3 inflammasome was identified as an important regulator of inflammation and immune responses and, importantly, HDAC6 is critically involved the activation of the inflammasome. We herein report the design, synthesis and biological evaluation of a library of selective HDAC6 inhibitors. Starting from the previously published crystal structure of MAIP-032 in complex with CD2 of zHDAC6, we performed docking studies to evaluate additional possible interactions of the cap group with the L1-loop pocket. Based on the results we synthesized 13 novel HDAC6 inhibitors via the Groebke-Blackburn-Bienaymé three component reaction as the key step. Compounds 8k (HDAC1 IC50: 5.87 µM; HDAC6 IC50: 0.024 µM; selectivity factor (SF1/6): 245) and 8m (HDAC1 IC50: 3.07 µM; HDAC6 IC50: 0.026 µM; SF1/6: 118) emerged as the most potent and selective inhibitors of HDAC6 and outperformed the lead structure MAIP-032 (HDAC1 IC50: 2.20 µM; HDAC6 IC50: 0.058 µM; SF1/6: 38) both in terms of inhibitory potency and selectivity. Subsequent immunoblot analysis confirmed the high selectivity of 8k and 8m for HDAC6 in a cellular environment. While neither 8k and 8m nor the selectivity HDAC6 inhibitor tubastatin A showed antiproliferative effects in the U-87 MG glioblastoma cell line, compound 8m attenuated cell migration significantly in wound healing assays in U-87 MG cells. Moreover, in macrophages compounds 8k and 8m demonstrated significant inhibition of LPS-induced IL1B mRNA expression and TNF release. These findings suggest that our imidazo[1,2-a]pyridine-capped HDAC6 inhibitors may serve as promising candidates for the development of drugs to effectively treat NLRP3 inflammasome-driven inflammatory diseases.

Structural basis of ligand binding modes at the neuropeptide Y Y1 receptor.

Neuropeptide Y (NPY) receptors belong to the G-protein-coupled receptor superfamily and have important roles in food intake, anxiety and cancer biology 1,2 . The NPY-Y receptor system has emerged as one of the most complex networks with three peptide ligands (NPY, peptide YY and pancreatic polypeptide) binding to four receptors in most mammals, namely the Y1, Y2, Y4 and Y5 receptors, with different affinity and selectivity 3 . NPY is the most powerful stimulant of food intake and this effect is primarily mediated by the Y1 receptor (Y1R) 4 . A number of peptides and small-molecule compounds have been characterized as Y1R antagonists and have shown clinical potential in the treatment of obesity 4 , tumour 1 and bone loss 5 . However, their clinical usage has been hampered by low potency and selectivity, poor brain penetration ability or lack of oral bioavailability 6 . Here we report crystal structures of the human Y1R bound to the two selective antagonists UR-MK299 and BMS-193885 at 2.7 and 3.0 Å resolution, respectively. The structures combined with mutagenesis studies reveal the binding modes of Y1R to several structurally diverse antagonists and the determinants of ligand selectivity. The Y1R structure and molecular docking of the endogenous agonist NPY, together with nuclear magnetic resonance, photo-crosslinking and functional studies, provide insights into the binding behaviour of the agonist and for the first time, to our knowledge, determine the interaction of its N terminus with the receptor. These insights into Y1R can enable structure-based drug discovery that targets NPY receptors.

Template-free prediction of a new monotopic membrane protein fold and assembly by AlphaFold2.

AlphaFold2 (AF2) has revolutionized the field of protein structural prediction. Here, we test its ability to predict the tertiary and quaternary structure of a previously undescribed scaffold with new folds and unusual architecture, the monotopic membrane protein caveolin-1 (CAV1). CAV1 assembles into a disc-shaped oligomer composed of 11 symmetrically arranged protomers, each assuming an identical new fold, and contains the largest parallel ß-barrel known to exist in nature. Remarkably, AF2 predicts both the fold of the protomers and the interfaces between them. It also assembles between seven and 15 copies of CAV1 into disc-shaped complexes. However, the predicted multimers are energetically strained, especially the parallel ß-barrel. These findings highlight the ability of AF2 to correctly predict new protein folds and oligomeric assemblies at a granular level while missing some elements of higher-order complexes, thus positing a new direction for the continued development of deep-learning protein structure prediction approaches.

Rosetta's Predictive Ability for Low-Affinity Ligand Binding in Fragment-Based Drug Discovery.

Fragment-based drug discovery begins with the identification of small molecules with a molecular weight of usually less than 250 Da which weakly bind to the protein of interest. This technique is challenging for computational docking methods as binding is determined by only a few specific interactions. Inaccuracies in the energy function or slight deviations in the docking pose can lead to the prediction of incorrect binding or difficulties in ranking fragments in in silico screening. Here, we test RosettaLigand by docking a series of fragments to a cysteine-depleted variant of the TIM-barrel protein, HisF (UniProtKB Q9X0C6). We compare the computational results with experimental NMR spectroscopy screens. NMR spectroscopy gives details on binding affinities of individual ligands, which allows assessment of the ligand-ranking ability using RosettaLigand and also provides feedback on the location of the binding pocket, which serves as a reliable test of RosettaLigand's ability to identify plausible binding poses. From a library screen of 3456 fragments, we identified a set of 31 ligands with intrinsic affinities to HisF with dissociation constants as low as 400 µM. The same library of fragments was blindly screened in silico. RosettaLigand was able to rank binders before non-binders with an area under the curve of the receiver operating characteristics of 0.74. The docking poses observed for binders agreed with the binding pocket identified by NMR chemical shift perturbations for all fragments. Taken together, these results provide a baseline performance of RosettaLigand in a fragment-based drug discovery setting.

Mapping of the fibrinogen-binding site on the staphylocoagulase C-terminal repeat region.

Fibrin (Fbn) deposits are a hallmark of staphylocoagulase (SC)-positive endocarditis. Binding of the N terminus of Staphylococcus aureus SC to host prothrombin triggers formation of an active SC·prothrombin∗ complex that cleaves host fibrinogen to Fbn. In addition, the C-terminal domain of the prototypical SC contains one pseudorepeat (PR) and seven repeats (R1 → R7) that bind fibrinogen/Fbn fragment D (frag D) by a mechanism that is unclear. Here, we define affinities and stoichiometries of frag D binding to C-terminal SC constructs, using fluorescence equilibrium binding, NMR titration, alanine scanning, and native PAGE. We found that constructs containing the PR and single repeats bound frag D with KD ∼50 to 130 nM and a 1:1 stoichiometry, indicating a conserved binding site bridging the PR and each repeat. NMR titration of PR-R7 with frag D revealed that residues 22 to 49, bridging PR and R7, constituted the minimal peptide (MP) for binding, corroborated by alanine scanning, and binding of labeled MP to frag D. MP alignment with the PR-R and inter-repeat junctions identified critical conserved residues. Full-length PR-(R1 → R7) bound frag D with KD ∼20 nM and a stoichiometry of 1:5, whereas constructs containing the PR and various three repeats competed with PR-(R1 → R7) for frag D binding, with a 1:3 stoichiometry. These findings are consistent with binding at PR-R and R-R junctions with modest inter-repeat sequence variability. CD of PR-R7 and PR-(R1 → R7) suggested a disordered flexible structure, allowing binding of multiple fibrin(ogen) molecules. Taken together, these results provide insights into pathogen localization on host fibrin networks.

Molecular basis for variations in the sensitivity of pathogenic rhodopsin variants to 9-cis-retinal.

Over 100 mutations in the rhodopsin gene have been linked to a spectrum of retinopathies that include retinitis pigmentosa and congenital stationary night blindness. Though most of these variants exhibit a loss of function, the molecular defects caused by these underlying mutations vary considerably. In this work, we utilize deep mutational scanning to quantitatively compare the plasma membrane expression of 123 known pathogenic rhodopsin variants in the presence and absence of the stabilizing cofactor 9-cis-retinal. We identify 69 retinopathy variants, including 20 previously uncharacterized variants, that exhibit diminished plasma membrane expression in HEK293T cells. Of these apparent class II variants, 67 exhibit a measurable increase in expression in the presence of 9-cis-retinal. However, the magnitude of the response to this molecule varies considerably across this spectrum of mutations. Evaluation of the observed shifts relative to thermodynamic estimates for the coupling between binding and folding suggests underlying differences in stability constrains the magnitude of their response to retinal. Nevertheless, estimates from computational modeling suggest that many of the least sensitive variants also directly compromise binding. Finally, we evaluate the functional properties of three previous uncharacterized, retinal-sensitive variants (ΔN73, S131P, and R135G) and show that two of these retain residual function in vitro. Together, our results provide a comprehensive experimental characterization of the proteostatic properties of retinopathy variants and their response to retinal.

Sparse pseudocontact shift NMR data obtained from a non-canonical amino acid-linked lanthanide tag improves integral membrane protein structure prediction.

A single experimental method alone often fails to provide the resolution, accuracy, and coverage needed to model integral membrane proteins (IMPs). Integrating computation with experimental data is a powerful approach to supplement missing structural information with atomic detail. We combine RosettaNMR with experimentally-derived paramagnetic NMR restraints to guide membrane protein structure prediction. We demonstrate this approach using the disulfide bond formation protein B (DsbB), an α-helical IMP. Here, we attached a cyclen-based paramagnetic lanthanide tag to an engineered non-canonical amino acid (ncAA) using a copper-catalyzed azide-alkyne cycloaddition (CuAAC) click chemistry reaction. Using this tagging strategy, we collected 203 backbone HN pseudocontact shifts (PCSs) for three different labeling sites and used these as input to guide de novo membrane protein structure prediction protocols in Rosetta. We find that this sparse PCS dataset combined with 44 long-range NOEs as restraints in our calculations improves structure prediction of DsbB by enhancements in model accuracy, sampling, and scoring. The inclusion of this PCS dataset improved the Cα-RMSD transmembrane segment values of the best-scoring and best-RMSD models from 9.57 Å and 3.06 Å (no NMR data) to 5.73 Å and 2.18 Å, respectively.