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.

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.

Predicting the functional impact of KCNQ1 variants with artificial neural networks.

Recent advances in experimental and computational protein structure determination have provided access to high-quality structures for most human proteins and mutants thereof. However, linking changes in structure in protein mutants to functional impact remains an active area of method development. If successful, such methods can ultimately assist physicians in taking appropriate treatment decisions. This work presents three artificial neural network (ANN)-based predictive models that classify four key functional parameters of KCNQ1 variants as normal or dysfunctional using PSSM-based evolutionary and/or biophysical descriptors. Recent advances in predicting protein structure and variant properties with artificial intelligence (AI) rely heavily on the availability of evolutionary features and thus fail to directly assess the biophysical underpinnings of a change in structure and/or function. The central goal of this work was to develop an ANN model based on structure and physiochemical properties of KCNQ1 potassium channels that performs comparably or better than algorithms using only on PSSM-based evolutionary features. These biophysical features highlight the structure-function relationships that govern protein stability, function, and regulation. The input sensitivity algorithm incorporates the roles of hydrophobicity, polarizability, and functional densities on key functional parameters of the KCNQ1 channel. Inclusion of the biophysical features outperforms exclusive use of PSSM-based evolutionary features in predicting activation voltage dependence and deactivation time. As AI is increasingly applied to problems in biology, biophysical understanding will be critical with respect to 'explainable AI', i.e., understanding the relation of sequence, structure, and function of proteins. Our model is available at www.kcnq1predict.org.

Disease-linked supertrafficking of a potassium channel.

Gain-of-function (GOF) mutations in the voltage-gated potassium channel subfamily Q member 1 (KCNQ1) can induce cardiac arrhythmia. In this study, it was tested whether any of the known human GOF disease mutations in KCNQ1 act by increasing the amount of KCNQ1 that reaches the cell surface-"supertrafficking." Seven of the 15 GOF mutants tested were seen to surface traffic more efficiently than the WT channel. Among these, we found that the levels of R231C KCNQ1 in the plasma membrane were fivefold higher than the WT channel. This was shown to arise from the combined effects of enhanced efficiency of translocon-mediated membrane integration of the S4 voltage-sensor helix and from enhanced post-translational folding/trafficking related to the energetic linkage of C231 with the V129 and F166 side chains. Whole-cell electrophysiology recordings confirmed that R231C KCNQ1 in complex with the voltage-gated potassium channel-regulatory subfamily E member 1 not only exhibited constitutive conductance but also revealed that the single-channel activity of this mutant is only 20% that of WT. The GOF phenotype associated with R231C therefore reflects the effects of supertrafficking and constitutive channel activation, which together offset reduced channel activity. These investigations show that membrane protein supertrafficking can contribute to human disease.

Structural determinants of cholesterol recognition in helical integral membrane proteins.

Cholesterol is an integral component of mammalian membranes. It has been shown to modulate membrane fluidity and dynamics and alter integral membrane protein function. However, understanding the molecular mechanisms of how cholesterol impacts protein function is complicated by limited and conflicting structural data. Because of the nature of the crystallization and cryo-EM structure determination, it is difficult to distinguish between specific and biologically relevant interactions and a nonspecific association. The only widely recognized search algorithm for cholesterol-integral-membrane-protein interaction sites is sequence based, i.e., searching for the so-called "Cholesterol Recognition/interaction Amino acid Consensus" motif. Although these motifs are present in numerous integral membrane proteins, there is inconclusive evidence to support their necessity or sufficiency for cholesterol binding. Here, we leverage the increasing number of experimental cholesterol-integral-membrane-protein structures to systematically analyze putative interaction sites based on their spatial arrangement and evolutionary conservation. This analysis creates three-dimensional representations of general cholesterol interaction sites that form clusters across multiple integral membrane protein classes. We also classify cholesterol-integral-membrane-protein interaction sites as either likely-specific or nonspecific. Information gleaned from our characterization will eventually enable a structure-based approach to predict and design cholesterol-integral-membrane-protein interaction sites.

Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design.

Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.

The molecular basis of subtype selectivity of human kinin G-protein-coupled receptors.

G-protein-coupled receptors (GPCRs) are the most important signal transducers in higher eukaryotes. Despite considerable progress, the molecular basis of subtype-specific ligand selectivity, especially for peptide receptors, remains unknown. Here, by integrating DNP-enhanced solid-state NMR spectroscopy with advanced molecular modeling and docking, the mechanism of the subtype selectivity of human bradykinin receptors for their peptide agonists has been resolved. The conserved middle segments of the bound peptides show distinct conformations that result in different presentations of their N and C termini toward their receptors. Analysis of the peptide-receptor interfaces reveals that the charged N-terminal residues of the peptides are mainly selected through electrostatic interactions, whereas the C-terminal segments are recognized via both conformations and interactions. The detailed molecular picture obtained by this approach opens a new gateway for exploring the complex conformational and chemical space of peptides and peptide analogs for designing GPCR subtype-selective biochemical tools and drugs.

Site-specific dynamic nuclear polarization in a Gd(III)-labeled protein.

Dynamic nuclear polarization (DNP) of a biomolecule tagged with a polarizing agent has the potential to not only increase NMR sensitivity but also to provide specificity towards the tagging site. Although the general concept has been often discussed, the observation of true site-specific DNP and its dependence on the electron-nuclear distance has been elusive. Here, we demonstrate site-specific DNP in a uniformly isotope-labeled ubiquitin. By recombinant expression of three different ubiquitin point mutants (F4C, A28C, and G75C) post-translationally modified with a Gd3+-chelator tag, localized metal-ion DNP of 13C and 15N is investigated. Effects counteracting the site-specificity of DNP such as nuclear spin-lattice relaxation and proton-driven spin diffusion have been attenuated by perdeuteration of the protein. Particularly for 15N, large DNP enhancement factors on the order of 100 and above as well as localized effects within side-chain resonances differently distributed over the protein are observed. By analyzing the experimental DNP built-up dynamics combined with structural modeling of Gd3+-tags in ubiquitin supported by paramagnetic relaxation enhancement (PRE) in solution, we provide, for the first time, quantitative information on the distance dependence of the initial DNP transfer. We show that the direct 15N DNP transfer rate indeed linearly depends on the square of the hyperfine interaction between the electron and the nucleus following Fermi's golden rule, however, below a certain distance cutoff paramagnetic signal bleaching may dramatically skew the correlation.

Protein structure prediction using sparse NOE and RDC restraints with Rosetta in CASP13.

Computational methods that produce accurate protein structure models from limited experimental data, for example, from nuclear magnetic resonance (NMR) spectroscopy, hold great potential for biomedical research. The NMR-assisted modeling challenge in CASP13 provided a blind test to explore the capabilities and limitations of current modeling techniques in leveraging NMR data which had high sparsity, ambiguity, and error rate for protein structure prediction. We describe our approach to predict the structure of these proteins leveraging the Rosetta software suite. Protein structure models were predicted de novo using a two-stage protocol. First, low-resolution models were generated with the Rosetta de novo method guided by nonambiguous nuclear Overhauser effect (NOE) contacts and residual dipolar coupling (RDC) restraints. Second, iterative model hybridization and fragment insertion with the Rosetta comparative modeling method was used to refine and regularize models guided by all ambiguous and nonambiguous NOE contacts and RDCs. Nine out of 16 of the Rosetta de novo models had the correct fold (global distance test total score > 45) and in three cases high-resolution models were achieved (root-mean-square deviation < 3.5 å). We also show that a meta-approach applying iterative Rosetta + NMR refinement on server-predicted models which employed non-NMR-contacts and structural templates leads to substantial improvement in model quality. Integrating these data-assisted refinement strategies with innovative non-data-assisted approaches which became possible in CASP13 such as high precision contact prediction will in the near future enable structure determination for large proteins that are outside of the realm of conventional NMR.

A unified structural model of the mammalian translocator protein (TSPO).

The translocator protein (TSPO), previously known as the peripheral benzodiazepine receptor (PBR), is a membrane protein located on the outer mitochondrial membrane. Experimentally-derived structures of mouse TSPO (mTSPO) and its homologs from bacterial species have been determined by NMR spectroscopy and X-ray crystallography, respectively. These structures and ligand interactions within the TSPO binding pocket display distinct differences. Here, we leverage experimental and computational studies to derive a unified structural model of mTSPO in the presence and absence of the TSPO ligand, PK11195, and study the effects of DPC detergent micelles on the TSPO structure and ligand binding. From this work, we conclude that that the lipid-mimetic system used to solubilize mTSPO for NMR studies thermodynamically destabilizes the protein, introduces structural perturbations, and alters the characteristics of ligand binding. Furthermore, we used Rosetta to construct a unified mTSPO model that reconciles deviating features of the mammalian and bacterial TSPO. These deviating features are likely a consequence of the detergent system used for structure determination of mTSPO by NMR. The unified mTSPO model agrees with available experimental NMR data, appears to be physically realistic (i.e. thermodynamically not frustrated as judged by the Rosetta energy function), and simultaneously shares the structural features observed in sequence-conserved regions of the bacterial proteins. Finally, we identified the binding site for an imaging ligand VUIIS8310 that is currently positioned for clinical translation using NMR spectroscopy and propose a computational model of the VUIIS8310-mTSPO complex.

Structure and Function of the Transmembrane Domain of NsaS, an Antibiotic Sensing Histidine Kinase in Staphylococcus aureus.

NsaS is one of four intramembrane histidine kinases (HKs) in Staphylococcus aureus that mediate the pathogen's response to membrane active antimicrobials and human innate immunity. We describe the first integrative structural study of NsaS using a combination of solution state NMR spectroscopy, chemical-cross-linking, molecular modeling and dynamics. Three key structural features emerge: First, NsaS has a short N-terminal amphiphilic helix that anchors its transmembrane (TM) bundle into the inner leaflet of the membrane such that it might sense neighboring proteins or membrane deformations. Second, the transmembrane domain of NsaS is a 4-helix bundle with significant dynamics and structural deformations at the membrane interface. Third, the intracellular linker connecting the TM domain to the cytoplasmic catalytic domains of NsaS is a marginally stable helical dimer, with one state likely to be a coiled-coil. Data from chemical shifts, heteronuclear NOE, H/D exchange measurements and molecular modeling suggest that this linker might adopt different conformations during antibiotic induced signaling.

Personalized structural biology reveals the molecular mechanisms underlying heterogeneous epileptic phenotypes caused by de novo KCNC2 variants.

Whole-exome sequencing (WES) in the clinic has identified several rare monogenic developmental and epileptic encephalopathies (DEE) caused by ion channel variants. However, WES often fails to provide actionable insight for rare diseases, such as DEEs, due to the challenges of interpreting variants of unknown significance (VUS). Here, we describe a "personalized structural biology" (PSB) approach that leverages recent innovations in the analysis of protein 3D structures to address this challenge. We illustrate this approach in an Undiagnosed Diseases Network (UDN) individual with DEE symptoms and a de novo VUS in KCNC2 (p.V469L), the Kv3.2 voltage-gated potassium channel. A nearby KCNC2 variant (p.V471L) was recently suggested to cause DEE-like phenotypes. Computational structural modeling suggests that both affect protein function. However, despite their proximity, the p.V469L variant is likely to sterically block the channel pore, while the p.V471L variant is likely to stabilize the open state. Biochemical and electrophysiological analyses demonstrate heterogeneous loss-of-function and gain-of-function effects, as well as differential response to 4-aminopyridine treatment. Molecular dynamics simulations illustrate that the pore of the p.V469L variant is more constricted, increasing the energetic barrier for K+ permeation, whereas the p.V471L variant stabilizes the open conformation. Our results implicate variants in KCNC2 as causative for DEE and guide the interpretation of a UDN individual. They further delineate the molecular basis for the heterogeneous clinical phenotypes resulting from two proximal pathogenic variants. This demonstrates how the PSB approach can provide an analytical framework for individualized hypothesis-driven interpretation of protein-coding VUS.

An arginine residue in the outer segment of hASIC1a TM1 affects both proton affinity and channel desensitization.

Acid-sensing ion channels (ASICs) respond to changes in pH in the central and peripheral nervous systems and participate in synaptic plasticity and pain perception. Understanding the proton-mediated gating mechanism remains elusive despite the of their structures in various conformational states. We report here that R64, an arginine located in the outer segment of the first transmembrane domain of all three isoforms of mammalian ASICs, markedly impacts the apparent proton affinity of activation and the degree of desensitization from the open and preopen states. Rosetta calculations of free energy changes predict that substitutions of R64 in hASIC1a by aromatic residues destabilize the closed conformation while stabilizing the open conformation. Accordingly, F64 enhances the efficacy of proton-mediated gating of hASIC1a, which increases the apparent pH50 and facilitates channel opening when only one or two subunits are activated. F64 also lengthens the duration of opening events, thus keeping channels open for extended periods of time and diminishing low pH-induced desensitization. Our results indicate that activation of a proton sensor(s) with pH50 equal to or greater than pH 7.2-7.1 opens F64hASIC1a, whereas it induces steady-state desensitization in wildtype channels due to the high energy of activation imposed by R64, which prevents opening of the pore. Together, these findings suggest that activation of a high-affinity proton-sensor(s) and a common gating mechanism may mediate the processes of activation and steady-state desensitization of hASIC1a.

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks.

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

Structures Illuminate Cardiac Ion Channel Functions in Health and in Long QT Syndrome.

The cardiac action potential is critical to the production of a synchronized heartbeat. This electrical impulse is governed by the intricate activity of cardiac ion channels, among them the cardiac voltage-gated potassium (Kv) channels KCNQ1 and hERG as well as the voltage-gated sodium (Nav) channel encoded by SCN5A. Each channel performs a highly distinct function, despite sharing a common topology and structural components. These three channels are also the primary proteins mutated in congenital long QT syndrome (LQTS), a genetic condition that predisposes to cardiac arrhythmia and sudden cardiac death due to impaired repolarization of the action potential and has a particular proclivity for reentrant ventricular arrhythmias. Recent cryo-electron microscopy structures of human KCNQ1 and hERG, along with the rat homolog of SCN5A and other mammalian sodium channels, provide atomic-level insight into the structure and function of these proteins that advance our understanding of their distinct functions in the cardiac action potential, as well as the molecular basis of LQTS. In this review, the gating, regulation, LQTS mechanisms, and pharmacological properties of KCNQ1, hERG, and SCN5A are discussed in light of these recent structural findings.

Integrated Structural Modeling of Full-Length LRH-1 Reveals Inter-domain Interactions Contribute to Receptor Structure and Function.

Liver receptor homolog-1 (LRH-1; NR5A2) is a nuclear receptor that regulates a diverse array of biological processes. In contrast to dimeric nuclear receptors, LRH-1 is an obligate monomer and contains a subtype-specific helix at the C terminus of the DNA-binding domain (DBD), termed FTZ-F1. Although detailed structural information is available for individual domains of LRH-1, it is unknown how these domains exist in the intact nuclear receptor. Here, we developed an integrated structural model of human full-length LRH-1 using a combination of HDX-MS, XL-MS, Rosetta computational docking, and SAXS. The model predicts the DBD FTZ-F1 helix directly interacts with ligand binding domain helix 2. We confirmed several other predicted inter-domain interactions via structural and functional analyses. Comparison between the LRH-1/Dax-1 co-crystal structure and the integrated model predicted and confirmed Dax-1 co-repressor to modulate LRH-1 inter-domain dynamics. Together, these data support individual LRH-1 domains interacting to influence receptor structure and function.

Allosteric mechanism for KCNE1 modulation of KCNQ1 potassium channel activation.

The function of the voltage-gated KCNQ1 potassium channel is regulated by co-assembly with KCNE auxiliary subunits. KCNQ1-KCNE1 channels generate the slow delayed rectifier current, IKs, which contributes to the repolarization phase of the cardiac action potential. A three amino acid motif (F57-T58-L59, FTL) in KCNE1 is essential for slow activation of KCNQ1-KCNE1 channels. However, how this motif interacts with KCNQ1 to control its function is unknown. Combining computational modeling with electrophysiological studies, we developed structural models of the KCNQ1-KCNE1 complex that suggest how KCNE1 controls KCNQ1 activation. The FTL motif binds at a cleft between the voltage-sensing and pore domains and appears to affect the channel gate by an allosteric mechanism. Comparison with the KCNQ1-KCNE3 channel structure suggests a common transmembrane-binding mode for different KCNEs and illuminates how specific differences in the interaction of their triplet motifs determine the profound differences in KCNQ1 functional modulation by KCNE1 versus KCNE3.

Structure and physiological function of the human KCNQ1 channel voltage sensor intermediate state.

Voltage-gated ion channels feature voltage sensor domains (VSDs) that exist in three distinct conformations during activation: resting, intermediate, and activated. Experimental determination of the structure of a potassium channel VSD in the intermediate state has previously proven elusive. Here, we report and validate the experimental three-dimensional structure of the human KCNQ1 voltage-gated potassium channel VSD in the intermediate state. We also used mutagenesis and electrophysiology in Xenopus laevisoocytes to functionally map the determinants of S4 helix motion during voltage-dependent transition from the intermediate to the activated state. Finally, the physiological relevance of the intermediate state KCNQ1 conductance is demonstrated using voltage-clamp fluorometry. This work illuminates the structure of the VSD intermediate state and demonstrates that intermediate state conductivity contributes to the unusual versatility of KCNQ1, which can function either as the slow delayed rectifier current (IKs) of the cardiac action potential or as a constitutively active epithelial leak current.

Integrative Protein Modeling in RosettaNMR from Sparse Paramagnetic Restraints.

Computational methods to predict protein structure from nuclear magnetic resonance (NMR) restraints that only require assignment of backbone signals, hold great potential to study larger proteins. Ideally, computational methods designed to work with sparse data need to add atomic detail that is missing in the experimental restraints. We introduce a comprehensive framework into the Rosetta suite that uses NMR restraints derived from paramagnetic labeling. Specifically, RosettaNMR incorporates pseudocontact shifts, residual dipolar couplings, and paramagnetic relaxation enhancements. It continues to use backbone chemical shifts and nuclear Overhauser effect distance restraints. We assess RosettaNMR for protein structure prediction by folding 28 monomeric proteins and 8 homo-oligomeric proteins. Furthermore, the general applicability of RosettaNMR is demonstrated on two protein-protein and three protein-ligand docking examples. Paramagnetic restraints generated more accurate models for 85% of the benchmark proteins and, when combined with chemical shifts, sampled high-accuracy models (≤2Å) in 50% of the cases.

Upgraded molecular models of the human KCNQ1 potassium channel.

The voltage-gated potassium channel KCNQ1 (KV7.1) assembles with the KCNE1 accessory protein to generate the slow delayed rectifier current, IKS, which is critical for membrane repolarization as part of the cardiac action potential. Loss-of-function (LOF) mutations in KCNQ1 are the most common cause of congenital long QT syndrome (LQTS), type 1 LQTS, an inherited genetic predisposition to cardiac arrhythmia and sudden cardiac death. A detailed structural understanding of KCNQ1 is needed to elucidate the molecular basis for KCNQ1 LOF in disease and to enable structure-guided design of new anti-arrhythmic drugs. In this work, advanced structural models of human KCNQ1 in the resting/closed and activated/open states were developed by Rosetta homology modeling guided by newly available experimentally-based templates: X. leavis KCNQ1 and various resting voltage sensor structures. Using molecular dynamics (MD) simulations, the capacity of the models to describe experimentally established channel properties including state-dependent voltage sensor gating charge interactions and pore conformations, PIP2 binding sites, and voltage sensor-pore domain interactions were validated. Rosetta energy calculations were applied to assess the utility of each model in interpreting mutation-evoked KCNQ1 dysfunction by predicting the change in protein thermodynamic stability for 50 experimentally characterized KCNQ1 variants with mutations located in the voltage-sensing domain. Energetic destabilization was successfully predicted for folding-defective KCNQ1 LOF mutants whereas wild type-like mutants exhibited no significant energetic frustrations, which supports growing evidence that mutation-induced protein destabilization is an especially common cause of KCNQ1 dysfunction. The new KCNQ1 Rosetta models provide helpful tools in the study of the structural basis for KCNQ1 function and can be used to generate hypotheses to explain KCNQ1 dysfunction.