Toward high-resolution prediction and design of transmembrane helical protein structures

Comprehensive physicochemical, biophysical, and in vitro characterization of lung surfactant SP-A peptidomimetics

Surfactant protein-A (SP-A) is an endogenous and essential lung surfactant-specific protein that is integral to pulmonary immunity, including inhibition of asthma exacerbations. This study aims to comprehensively characterize two peptides (10-AA and 20-AA) of SP-A which confer activity similar to the full-length oligomeric SP-A protein. Spectroscopic and chromatographic analyses revealed that the phosphate (PS) and acetate (AC) salts exhibited distinct solubility and log P partitioning behavior, impacting their physicochemical properties. MD simulations and circular dichroism showed that SP-A 10-AA initially adopts an α-helical structure but loses helicity over time, while SP-A 20-AA remains disordered. Differential scanning calorimetry confirmed variations in thermal stability between salt forms and zeta potential measurements showed that PS salts had a more negative surface charge, potentially influencing membrane interactions. In vitro studies showed high cell viability (>90%) and stable TEER values at the air-liquid interface, confirming biocompatibility and potential epithelial permeability. These findings provide crucial insights into the structural and functional properties of SP-A peptides, supporting their potential as therapeutic agents for pulmonary diseases.

A machine learning framework to identify complex physicochemical features of B cell epitopes

During infection with Plasmodium falciparum in pregnancy, parasites express a unique virulence factor, VAR2CSA, that mediates binding of infected red blood cells to the placenta. A major goal in designing vaccines to protect pregnant women from malaria is to elicit antibodies to VAR2CSA. The challenge is that VAR2CSA is highly polymorphic and identifying conserved epitopes is essential to elicit strain-transcending immunity. Unexpectedly, a mouse monoclonal antibody, 3D10, raised against the unrelated Duffy binding protein from P. vivax (DBPII) cross-reacts with diverse alleles of VAR2CSA in vitro. To identify these potentially conserved epitopes in VAR2CSA, we designed a machine learning framework to analyse 3D10 reactivity to peptides derived from two alleles of VAR2CSA, DBPII, and PvEBP2 (negative control). We used decision trees and a panel of 430 features to extract features correlated to 3D10 binding. We analysed patterns of these features in the dataset and designed mutant peptides to test complex sequence motifs. Features associated with 3D10 reactivity were mapped onto predicted 3D structures of Plasmodium proteins and validated based on 3D10 reactivity to the recombinant antigens. While the array data identified certain linear epitopes, the framework predicted other epitopes that are conformational. With this approach, peptide array data can be mined to extract physicochemical properties of epitopes recognized by polyreactive antibodies.

Proteostasis Landscapes of Cystic Fibrosis Variants Reveals Drug Response Vulnerability

Cystic Fibrosis (CF) is a lethal genetic disorder caused by variants in CF transmembrane conductance regulator (CFTR). Many variants are treatable with correctors, which enhance the folding and trafficking of CFTR. However, approximately 3% of persons with CF harbor poorly responsive variants. Here, we used affinity purification mass spectrometry proteomics to profile the protein homeostasis (proteostasis) changes of CFTR variants during correction 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 partial correction. Computational structural modeling showed that corrector VX-445 failed to confer enough NBD1 stability to poor responders. 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 to expand therapeutics to all susceptible CFTR variants.

SIGNIFICANCE STATEMENT

Cystic Fibrosis (CF) is a lethal genetic disease with variants leading to misfolding of an anion channel protein. Enhancing productive channel folding using a novel class of small molecules called correctors has emerged as the current CF treatment paradigm. However, correctors fail to reach all patient variants. Using high throughput interactomics, Rosetta simulations, and biochemical trafficking assays, this study demonstrates poorly responsive CF variants experience diverse misfolding pathways caused by structural defects in the core of a nucleotide-binding domain. Stabilizing secondary mutations in this domain rescues poorly responsive variants, paving the way for mechanistic-based therapeutic development for untreatable CF variants and future protein misfolding corrector drugs.

Structural and dynamics characterization of the Zika virus NS2B using nuclear magnetic resonance and RosettaMP: A challenge for transmembrane protein studies

Zika virus (ZIKV) is an emergent flavivirus that represents a global public health concern due to its association with severe neurological disorders. NS2B is a multifunctional viral membrane protein primarily used to regulate viral protease activity and is crucial for virus replication, making it an appealing target for antiviral drugs. This study presents the structural elucidation of full-length ZIKV NS2B in sodium dodecyl sulfate (SDS) micelles using solution nuclear magnetic resonance experimental data and RosettaMP. The protein structure has four transmembrane α-helices, two amphipathic α-helices, and a β-hairpin in the hydrophilic region. NS2B presented secondary and tertiary stability in different concentrations of SDS. Furthermore, we studied the dynamics of NS2B in SDS micelles through relaxation parameters and paramagnetic relaxation enhancement experiments. The findings were consistent with the structural calculations. Our work will be essential in understanding the role of NS2B in viral replication and screening for inhibitors against ZIKV.

Dual mechanisms contribute to enhanced voltage dependence of an electric fish potassium channel

The voltage dependence of different voltage-gated potassium channels, described by the voltage at which half of the channels are open (V1/2), varies over a range of 80 mV and is influenced by factors such as the number of positive gating charges and the identity of the hydrophobic amino acids in the channel's voltage sensor (S4). Here we explore by experimental manipulations and molecular dynamics simulation the contributions of two derived features of an electric fish potassium channel (Kv1.7a) that is among the most voltage-sensitive Shaker family potassium channels known. These are a patch of four contiguous negatively charged glutamates in the S3-S4 extracellular loop and a glutamate in the S3b helix. We find that these negative charges affect V1/2 by separate, complementary mechanisms. In the closed state, the S3-S4 linker negative patch reduces the membrane surface charge biasing the channel to enter the open state while, upon opening, the negative amino acid in the S3b helix faces the second (R2) gating charge of the voltage sensor electrostatically biasing the channel to remain in the open state. This work highlights two evolutionary novelties that illustrate the potential influence of negatively charged amino acids in extracellular loops and adjacent helices to voltage dependence.

Validation of de novo designed water-soluble and transmembrane β-barrels by in silico folding and melting

In silico validation of de novo designed proteins with deep learning (DL)-based structure prediction algorithms has become mainstream. However, formal evidence of the relationship between a high-quality predicted model and the chance of experimental success is lacking. We used experimentally characterized de novo water-soluble and transmembrane β-barrel designs to show that AlphaFold2 and ESMFold excel at different tasks. ESMFold can efficiently identify designs generated based on high-quality (designable) backbones. However, only AlphaFold2 can predict which sequences have the best chance of experimentally folding among similar designs. We show that ESMFold can generate high-quality structures from just a few predicted contacts and introduce a new approach based on incremental perturbation of the prediction ("in silico melting"), which can reveal differences in the presence of favorable contacts between designs. This study provides a new insight on DL-based structure prediction models explainability and on how they could be leveraged for the design of increasingly complex proteins; in particular membrane proteins which have historically lacked basic in silico validation tools.

One-shot design elevates functional expression levels of a voltage-gated potassium channel

Membrane proteins play critical physiological roles as receptors, channels, pumps, and transporters. Despite their importance, however, low expression levels often hamper the experimental characterization of membrane proteins. We present an automated and web-accessible design algorithm called mPROSS (https://mPROSS.weizmann.ac.il), which uses phylogenetic analysis and an atomistic potential, including an empirical lipophilicity scale, to improve native-state energy. As a stringent test, we apply mPROSS to the Kv1.2-Kv2.1 paddle chimera voltage-gated potassium channel. Four designs, encoding 9-26 mutations relative to the parental channel, were functional and maintained potassium-selective permeation and voltage dependence in Xenopus oocytes with up to 14-fold increase in whole-cell current densities. Additionally, single-channel recordings reveal no significant change in the channel-opening probability nor in unitary conductance, indicating that functional expression levels increase without impacting the activity profile of individual channels. Our results suggest that the expression levels of other dynamic channels and receptors may be enhanced through one-shot design calculations.

Multiscale modelling of transport in polymer-based reverse-osmosis/nanofiltration membranes: present and future

Nanofiltration (NF) and reverse osmosis (RO) processes are physical separation technologies used to remove contaminants from liquid streams by employing dense polymer-based membranes with nanometric voids that confine fluids at the nanoscale. At this level, physical properties such as solvent and solute permeabilities are intricately linked to molecular interactions. Initially, numerous studies focused on developing macroscopic transport models to gain insights into separation properties at the nanometer scale. However, continuum-based models have limitations in nanoconfined situations that can be overcome by force field molecular simulations. Continuum-based models heavily rely on bulk properties, often neglecting critical factors like liquid structuring, pore geometry, and molecular/chemical specifics. Molecular/mesoscale simulations, while encompassing these details, often face limitations in time and spatial scales. Therefore, achieving a comprehensive understanding of transport requires a synergistic integration of both approaches through a multiscale approach that effectively combines and merges both scales. This review aims to provide a comprehensive overview of the state-of-the-art in multiscale modeling of transport through NF/RO membranes, spanning from the nanoscale to continuum media.

Modeling membrane geometries implicitly in Rosetta

Interactions between membrane proteins (MPs) and lipid bilayers are critical for many cellular functions. In the Rosetta molecular modeling suite, the implicit membrane energy function is based on a "slab" model, which represent the membrane as a flat bilayer. However, in nature membranes often have a curvature that is important for function and/or stability. Even more prevalent, in structural biology research MPs are reconstituted in model membrane systems such as micelles, bicelles, nanodiscs, or liposomes. Thus, we have modified the existing membrane energy potentials within the RosettaMP framework to allow users to model MPs in different membrane geometries. We show that these modifications can be utilized in core applications within Rosetta such as structure refinement, protein-protein docking, and protein design. For MP structures found in curved membranes, refining these structures in curved, implicit membranes produces higher quality models with structures closer to experimentally determined structures. For MP systems embedded in multiple membranes, representing both membranes results in more favorable scores compared to only representing one of the membranes. Modeling MPs in geometries mimicking the membrane model system used in structure determination can improve model quality and model discrimination.

Implicit model to capture electrostatic features of membrane environment

Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.

Implicit model to capture electrostatic features of membrane environment

Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.

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.

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 β-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 β-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.

Computational design of dynamic receptor-peptide signaling complexes applied to chemotaxis

Engineering protein biosensors that sensitively respond to specific biomolecules by triggering precise cellular responses is a major goal of diagnostics and synthetic cell biology. Previous biosensor designs have largely relied on binding structurally well-defined molecules. In contrast, approaches that couple the sensing of flexible compounds to intended cellular responses would greatly expand potential biosensor applications. Here, to address these challenges, we develop a computational strategy for designing signaling complexes between conformationally dynamic proteins and peptides. To demonstrate the power of the approach, we create ultrasensitive chemotactic receptor-peptide pairs capable of eliciting potent signaling responses and strong chemotaxis in primary human T cells. Unlike traditional approaches that engineer static binding complexes, our dynamic structure design strategy optimizes contacts with multiple binding and allosteric sites accessible through dynamic conformational ensembles to achieve strongly enhanced signaling efficacy and potency. Our study suggests that a conformationally adaptable binding interface coupled to a robust allosteric transmission region is a key evolutionary determinant of peptidergic GPCR signaling systems. The approach lays a foundation for designing peptide-sensing receptors and signaling peptide ligands for basic and therapeutic applications.

Recent Advances in NMR Protein Structure Prediction with ROSETTA

Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.

Mechanistic Insight into the Suppression of Polyglutamine Aggregation by SRCP1

Protein aggregation is a hallmark of the polyglutamine diseases. One potential treatment for these diseases is suppression of polyglutamine aggregation. Previous work identified the cellular slime mold Dictyostelium discoideum as being naturally resistant to polyglutamine aggregation. Further work identified serine-rich chaperone protein 1 (SRCP1) as a protein that is both necessary in Dictyostelium and sufficient in human cells to suppress polyglutamine aggregation. Therefore, understanding how SRCP1 suppresses aggregation may be useful for developing therapeutics for the polyglutamine diseases. Here we utilized a de novo protein modeling approach to generate predictions of SRCP1's structure. Using our best-fit model, we generated mutants that were predicted to alter the stability of SRCP1 and tested these mutants' stability in cells. Using these data, we identified top models of SRCP1's structure that are consistent with the C-terminal region of SRCP1 forming a β-hairpin with a highly dynamic N-terminal region. We next generated a series of peptides that mimic the predicted β-hairpin and validated that they inhibit aggregation of a polyglutamine-expanded mutant huntingtin exon 1 fragment in vitro. To further assess mechanistic details of how SRCP1 inhibits polyglutamine aggregation, we utilized biochemical assays to determine that SRCP1 inhibits secondary nucleation in a manner dependent upon the regions flanking the polyglutamine tract. Finally, to determine if SRCP1 more could generally suppress protein aggregation, we confirmed that it was sufficient to inhibit aggregation of polyglutamine-expanded ataxin-3. Together these studies provide details into the structural and mechanistic basis of the inhibition of protein aggregation by SRCP1.

Computationally designed GPCR quaternary structures bias signaling pathway activation

Communication across membranes controls critical cellular processes and is achieved by receptors translating extracellular signals into selective cytoplasmic responses. While receptor tertiary structures can be readily characterized, receptor associations into quaternary structures are challenging to study and their implications in signal transduction remain poorly understood. Here, we report a computational approach for predicting receptor self-associations, and designing receptor oligomers with various quaternary structures and signaling properties. Using this approach, we designed chemokine receptor CXCR4 dimers with reprogrammed binding interactions, conformations, and abilities to activate distinct intracellular signaling proteins. In agreement with our predictions, the designed CXCR4s dimerized through distinct conformations and displayed different quaternary structural changes upon activation. Consistent with the active state models, all engineered CXCR4 oligomers activated the G protein Gi, but only specific dimer structures also recruited β-arrestins. Overall, we demonstrate that quaternary structures represent an important unforeseen mechanism of receptor biased signaling and reveal the existence of a bias switch at the dimer interface of several G protein-coupled receptors including CXCR4, mu-Opioid and type-2 Vasopressin receptors that selectively control the activation of G proteins vs β-arrestin-mediated pathways. The approach should prove useful for predicting and designing receptor associations to uncover and reprogram selective cellular signaling functions.

Protein Design: From the Aspect of Water Solubility and Stability

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

Chemokine CXCL12 drives pericyte accumulation and airway remodeling in allergic airway disease

Background

Airway remodeling is a significant contributor to impaired lung function in chronic allergic airway disease. Currently, no therapy exists that is capable of targeting these structural changes and the consequent loss of function. In the context of chronic allergic inflammation, pericytes have been shown to uncouple from the pulmonary microvasculature, migrate to areas of inflammation, and significantly contribute to airway wall remodeling and lung dysfunction. This study aimed to elucidate the mechanism by which pulmonary pericytes accumulate in the airway wall in a model of chronic allergic airway inflammation.

Methods

Mice were subjected to a protocol of chronic airway inflammation driven by the common environmental aeroallergen house dust mite. Phenotypic changes to lung pericytes were assessed by flow cytometry and immunostaining, and the functional capacity of these cells was evaluated using in vitro migration assays. The molecular mechanisms driving these processes were targeted pharmacologically in vivo and in vitro.

Results

Pericytes demonstrated increased CXCR4 expression in response to chronic allergic inflammation and migrated more readily to its cognate chemokine, CXCL12. This increase in migratory capacity was accompanied by pericyte accumulation in the airway wall, increased smooth muscle thickness, and symptoms of respiratory distress. Pericyte uncoupling from pulmonary vessels and subsequent migration to the airway wall were abrogated following topical treatment with the CXCL12 neutraligand LIT-927.

Conclusion

These results provide new insight into the role of the CXCL12/CXCR4 signaling axis in promoting pulmonary pericyte accumulation and airway remodeling and validate a novel target to address tissue remodeling associated with chronic inflammation.

Structural Comparative Modeling of Multi-Domain F508del CFTR

Cystic fibrosis (CF) is a rare genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial anion channel expressed in several vital organs. Absence of functional CFTR results in imbalanced osmotic equilibrium and subsequent mucus build up in the lungs-which increases the risk of infection and eventually causes death. CFTR is an ATP-binding cassette (ABC) transporter family protein composed of two transmembrane domains (TMDs), two nucleotide binding domains (NBDs), and an unstructured regulatory domain. The most prevalent patient mutation is the deletion of F508 (F508del), making F508del CFTR the primary target for current FDA approved CF therapies. However, no experimental multi-domain F508del CFTR structure has been determined and few studies have modeled F508del using multi-domain WT CFTR structures. Here, we used cryo-EM density data and Rosetta comparative modeling (RosettaCM) to compare a F508del model with published experimental data on CFTR NBD1 thermodynamics. We then apply this modeling method to generate multi-domain WT and F508del CFTR structural models. These models demonstrate the destabilizing effects of F508del on NBD1 and the NBD1/TMD interface in both the inactive and active conformation of CFTR. Furthermore, we modeled F508del/R1070W and F508del bound to the CFTR corrector VX-809. Our models reveal the stabilizing effects of VX-809 on multi-domain models of F508del CFTR and pave the way for rational design of additional drugs that target F508del CFTR for treatment of CF.

A flexible GAS belt responds to pore mutations changing the ion selectivity of proton-gated channels

Proton-gated ion channels conduct mainly Na+ to induce postsynaptic membrane depolarization. Finding the determinants of ion selectivity requires knowledge of the pore structure in the open conformation, but such information is not yet available. Here, the open conformation of the hASIC1a channel was computationally modeled, and functional effects of pore mutations were analyzed in light of the predicted structures. The open pore structure shows two constrictions of similar diameter formed by the backbone of the GAS belt and, right beneath it, by the side chains of H28 from the reentrant loop. Models of nonselective mutant channels, but not those that maintain ion selectivity, predict enlargement of the GAS belt, suggesting that this motif is quite flexible and that the loss of stabilizing interactions in the central pore leads to changes in size/shape of the belt. Our results are consistent with the "close-fit" mechanism governing selectivity of hASIC1a, wherein the backbone of the GAS substitutes at least part of the hydration shell of a permeant ion to enable crossing the pore constriction.

Mechanism of negative membrane curvature generation by I-BAR domains

The membrane sculpting ability of BAR domains has been attributed to the intrinsic curvature of their banana-shaped dimeric structure. However, there is often a mismatch between this intrinsic curvature and the diameter of the membrane tubules generated. I-BAR domains are especially mysterious since they are almost flat but generate high negative membrane curvature. Here, we use atomistic implicit-solvent computer modeling to show that the membrane bending of the IRSp53 I-BAR domain is dictated by its higher oligomeric structure, whose curvature is completely unrelated to the intrinsic curvature of the dimer. Two other I-BARs give similar results, whereas a flat F-BAR sheet develops a concave membrane-binding interface, consistent with its observed positive membrane curvature generation. Laterally interacting helical spirals of I-BAR dimers on tube interiors are stable and have an enhanced binding energy that is sufficient for membrane bending to experimentally observed tubule diameters at a reasonable surface density.

Endocannabinoid activation of the TRPV1 ion channel is distinct from activation by capsaicin

Transient receptor potential vanilloid 1 (TRPV1) ion channel serves as the detector for noxious temperature above 42 °C, pungent chemicals like capsaicin, and acidic extracellular pH. This channel has also been shown to function as an ionotropic cannabinoid receptor. Despite the solving of high-resolution three-dimensional structures of TRPV1, how endocannabinoids such as anandamide and N-arachidonoyl dopamine bind to and activate this channel remains largely unknown. Here we employed a combination of patch-clamp recording, site-directed mutagenesis, and molecular docking techniques to investigate how the endocannabinoids structurally bind to and open the TRPV1 ion channel. We found that these endocannabinoid ligands bind to the vanilloid-binding pocket of TRPV1 in the “tail-up, head-down” configuration, similar to capsaicin; however, there is a unique interaction with TRPV1 Y512 residue critical for endocannabinoid activation of TRPV1 channels. These data suggest that a differential structural mechanism is involved in TRPV1 activation by endocannabinoids compared with the classic agonist capsaicin.

Principles and Methods in Computational Membrane Protein Design

After decades of progress in computational protein design, the design of proteins folding and functioning in lipid membranes appears today as the next frontier. Some notable successes in the de novo design of simplified model membrane protein systems have helped articulate fundamental principles of protein folding, architecture and interaction in the hydrophobic lipid environment. These principles are reviewed here, together with the computational methods and approaches that were used to identify them. We provide an overview of the methodological innovations in the generation of new protein structures and functions and in the development of membrane-specific energy functions. We highlight the opportunities offered by new machine learning approaches applied to protein design, and by new experimental characterization techniques applied to membrane proteins. Although membrane protein design is in its infancy, it appears more reachable than previously thought.

Molecular determinants of pro-arrhythmia proclivity of d- and l-sotalol via a multi-scale modeling pipeline

Drug isomers may differ in their proarrhythmia risk. An interesting example is the drug sotalol, an antiarrhythmic drug comprising d- and l- enantiomers that both block the hERG cardiac potassium channel and confer differing degrees of proarrhythmic risk. We developed a multi-scale in silico pipeline focusing on hERG channel – drug interactions and used it to probe and predict the mechanisms of pro-arrhythmia risks of the two enantiomers of sotalol. Molecular dynamics (MD) simulations predicted comparable hERG channel binding affinities for d- and l-sotalol, which were validated with electrophysiology experiments. MD derived thermodynamic and kinetic parameters were used to build multi-scale functional computational models of cardiac electrophysiology at the cell and tissue scales. Functional models were used to predict inactivated state binding affinities to recapitulate electrocardiogram (ECG) QT interval prolongation observed in clinical data. Our study demonstrates how modeling and simulation can be applied to predict drug effects from the atom to the rhythm for dl-sotalol and also increased proarrhythmia proclivity of d- vs. l-sotalol when accounting for stereospecific beta-adrenergic receptor blocking.

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.

Prediction of amphipathic helix-membrane interactions with Rosetta

Amphipathic helices have hydrophobic and hydrophilic/charged residues situated on opposite faces of the helix. They can anchor peripheral membrane proteins to the membrane, be attached to integral membrane proteins, or exist as independent peptides. Despite the widespread presence of membrane-interacting amphipathic helices, there is no computational tool within Rosetta to model their interactions with membranes. In order to address this need, we developed the AmphiScan protocol with PyRosetta, which runs a grid search to find the most favorable position of an amphipathic helix with respect to the membrane. The performance of the algorithm was tested in benchmarks with the RosettaMembrane, ref2015_memb, and franklin2019 score functions on six engineered and 44 naturally-occurring amphipathic helices using membrane coordinates from the OPM and PDBTM databases, OREMPRO server, and MD simulations for comparison. The AmphiScan protocol predicted the coordinates of amphipathic helices within less than 3Å of the reference structures and identified membrane-embedded residues with a Matthews Correlation Constant (MCC) of up to 0.57. Overall, AmphiScan stands as fast, accurate, and highly-customizable protocol that can be pipelined with other Rosetta and Python applications.

Amphipathic helices are important targets as antibacterial peptides and as domains of membrane proteins that play a role in sensing the membrane environment. Understanding how amphipathic helices interact with membrane enables us to design better peptides and understand how membrane proteins use them to interact with their environment. However, there is a limited number of tools available for the modeling of amphipathic helices in membranes. Implicit membrane models can be used for this purpose as simplistic representations of the membrane environment. In this work, we developed the AmphiScan protocol that can be used to predict membrane coordinates of amphipathic helices starting with a helix structure in an implicit membrane environment. We benchmarked the performance of AmphiScan on engineered LK peptides, naturally-occurring amphipathic helices, and hydrophobic and hydrophilic peptides. Our approach provides a reliable and customizable tool to model amphipathic helix–membrane interactions, and pose a platform for the screening of amphipathic helix properties in silico.

The Role of ICL1 and H8 in Class B1 GPCRs; Implications for Receptor Activation

The first intracellular loop (ICL1) of G protein-coupled receptors (GPCRs) has received little attention, although there is evidence that, with the 8^th helix (H8), it is involved in early conformational changes following receptor activation as well as contacting the G protein β subunit. In class B1 GPCRs, the distal part of ICL1 contains a conserved R^12.48KLRCxR^2.46b motif that extends into the base of the second transmembrane helix; this is weakly conserved as a [R/H]^12.48KL[R/H] motif in class A GPCRs. In the current study, the role of ICL1 and H8 in signaling through cAMP, _iCa²⁺ and ERK1/2 has been examined in two class B1 GPCRs, using mutagenesis and molecular dynamics. Mutations throughout ICL1 can either enhance or disrupt cAMP production by CGRP at the CGRP receptor. Alanine mutagenesis identified subtle differences with regard elevation of _iCa²⁺, with the distal end of the loop being particularly sensitive. ERK1/2 activation displayed little sensitivity to ICL1 mutation. A broadly similar pattern was observed with the glucagon receptor, although there were differences in significance of individual residues. Extending the study revealed that at the CRF1 receptor, an insertion in ICL1 switched signaling bias between _iCa²⁺ and cAMP. Molecular dynamics suggested that changes in ICL1 altered the conformation of ICL2 and the H8/TM7 junction (ICL4). For H8, alanine mutagenesis showed the importance of E390^8.49b for all three signal transduction pathways, for the CGRP receptor, but mutations of other residues largely just altered ERK1/2 activation. Thus, ICL1 may modulate GPCR bias via interactions with ICL2, ICL4 and the Gβ subunit.

Membrane Protein Engineering with Rosetta

Protein engineering can yield new molecular tools for nanotechnology and therapeutic applications through modulating physiochemical and biological properties. Engineering membrane proteins is especially attractive because they perform key cellular processes including transport, nutrient uptake, removal of toxins, respiration, motility, and signaling. In this chapter, we describe two protocols for membrane protein engineering with the Rosetta software: (1) ΔΔG calculations for single point mutations and (2) sequence optimization in different membrane lipid compositions. These modular protocols are easily adaptable for more complex problems and serve as a foundation for efficient membrane protein engineering calculations.

ISSEC: inferring contacts among protein secondary structure elements using deep object detection

BACKGROUND

The formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts is crucial to protein structure prediction. One of the existing strategies infers inter-SSE contacts directly from the predicted possibilities of inter-residue contacts without any preprocessing, and thus suffers from the excessive noises existing in the predicted inter-residue contacts. Another strategy defines SSEs based on protein secondary structure prediction first, and then judges whether each candidate SSE pair could form contact or not. However, it is difficult to accurately determine boundary of SSEs due to the errors in secondary structure prediction. The incorrectly-deduced SSEs definitely hinder subsequent prediction of the contacts among them.

RESULTS

We here report an accurate approach to infer the inter-SSE contacts (thus called as ISSEC) using the deep object detection technique. The design of ISSEC is based on the observation that, in the inter-residue contact map, the contacting SSEs usually form rectangle regions with characteristic patterns. Therefore, ISSEC infers inter-SSE contacts through detecting such rectangle regions. Unlike the existing approach directly using the predicted probabilities of inter-residue contact, ISSEC applies the deep convolution technique to extract high-level features from the inter-residue contacts. More importantly, ISSEC does not rely on the pre-defined SSEs. Instead, ISSEC enumerates multiple candidate rectangle regions in the predicted inter-residue contact map, and for each region, ISSEC calculates a confidence score to measure whether it has characteristic patterns or not. ISSEC employs greedy strategy to select non-overlapping regions with high confidence score, and finally infers inter-SSE contacts according to these regions.

CONCLUSIONS

Comprehensive experimental results suggested that ISSEC outperformed the state-of-the-art approaches in predicting inter-SSE contacts. We further demonstrated the successful applications of ISSEC to improve prediction of both inter-residue contacts and tertiary structure as well.

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, I_Ks, 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.

Experimental determination and data-driven prediction of homotypic transmembrane domain interfaces

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Homotypic TMD interfaces identified by different techniques share strong similarities.

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The GxxxG motif is the feature most strongly associated with interfaces.

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Other features include conservation, polarity, coevolution, and depth in the membrane

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The role of each of each feature strongly depends on the individual protein.

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Machine-learning helps predict interfaces from evolutionary sequence data

Interactions between their transmembrane domains (TMDs) frequently support the assembly of single-pass membrane proteins to non-covalent complexes. Yet, the TMD-TMD interactome remains largely uncharted. With a view to predicting homotypic TMD-TMD interfaces from primary structure, we performed a systematic analysis of their physical and evolutionary properties. To this end, we generated a dataset of 50 self-interacting TMDs. This dataset contains interfaces of nine TMDs from bitopic human proteins (Ire1, Armcx6, Tie1, ATP1B1, PTPRO, PTPRU, PTPRG, DDR1, and Siglec7) that were experimentally identified here and combined with literature data. We show that interfacial residues of these homotypic TMD-TMD interfaces tend to be more conserved, coevolved and polar than non-interfacial residues. Further, we suggest for the first time that interface positions are deficient in β-branched residues, and likely to be located deep in the hydrophobic core of the membrane. Overrepresentation of the GxxxG motif at interfaces is strong, but that of (small)xxx(small) motifs is weak. The multiplicity of these features and the individual character of TMD-TMD interfaces, as uncovered here, prompted us to train a machine learning algorithm. The resulting prediction method, THOIPA (www.thoipa.org), excels in the prediction of key interface residues from evolutionary sequence data.

Determinants of Endoplasmic Reticulum-to-Lipid Droplet Protein Targeting

Lipid droplet (LD) formation from the endoplasmic reticulum (ER) is accompanied by the targeting and accumulation of specific hydrophobic, membrane-embedded proteins on LDs. The determinants of this process are unknown. Here, we study the hydrophobic membrane motifs of two Drosophila melanogaster proteins, GPAT4 and ALG14, that utilize this pathway, and we identify crucial sequence features that mediate LD accumulation. Molecular dynamics simulations and studies in cells reveal that LD targeting of these motifs requires deeply inserted tryptophans that have lower free energy in the LD oil phase and positively charged residues near predicted hairpin hinges that become less constrained in the LD environment. Analyzing hydrophobic motifs from similar LD-targeting proteins, it appears that the distribution of tryptophan and positively charged residues distinguishes them from non-LD-targeting membrane motifs. Our studies identify specific sequence features and principles of hydrophobic membrane motifs that mediate their accumulation on LDs.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Protein docking and steered molecular dynamics suggest alternative phospholamban-binding sites on the SERCA calcium transporter

The transport activity of the sarco(endo)plasmic reticulum calcium ATPase (SERCA) in cardiac myocytes is modulated by an inhibitory interaction with a transmembrane peptide, phospholamban (PLB). Previous biochemical studies have revealed that PLB interacts with a specific inhibitory site on SERCA, and low-resolution structural evidence suggests that PLB interacts with distinct alternative sites on SERCA. High-resolution details of the structural determinants of SERCA regulation have been elusive because of the dynamic nature of the regulatory complex. In this study, we used computational approaches to develop a structural model of SERCA-PLB interactions to gain a mechanistic understanding of PLB-mediated SERCA transport regulation. We combined steered molecular dynamics and membrane protein-protein docking experiments to achieve both a global search and all-atom force calculations to determine the relative affinities of PLB for candidate sites on SERCA. We modeled the binding of PLB to several SERCA conformations, representing different enzymatic states sampled during the calcium transport catalytic cycle. The results of the steered molecular dynamics and docking experiments indicated that the canonical PLB-binding site (comprising transmembrane helices M2, M4, and M9) is the preferred site. This preference was even more stringent for a superinhibitory PLB variant. Interestingly, PLB-binding specificity became more ambivalent for other SERCA conformers. These results provide evidence for polymorphic PLB interactions with novel sites on M3 and with the outside of the SERCA helix M9. Our findings are compatible with previous physical measurements that suggest that PLB interacts with multiple binding sites, conferring dynamic responsiveness to changing physiological conditions.

Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane

Protein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. Although soluble protein design has advanced, membrane protein design remains challenging because of difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational benchmarks against experimental targets, including prediction of protein orientations in the bilayer, ΔΔG calculations, native structure discrimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Furthermore, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.

A Computational Pipeline to Predict Cardiotoxicity: From the Atom to the Rhythm

RATIONALE

Drug-induced proarrhythmia is so tightly associated with prolongation of the QT interval that QT prolongation is an accepted surrogate marker for arrhythmia. But QT interval is too sensitive a marker and not selective, resulting in many useful drugs eliminated in drug discovery.

OBJECTIVE

To predict the impact of a drug from the drug chemistry on the cardiac rhythm.

METHODS AND RESULTS

In a new linkage, we connected atomistic scale information to protein, cell, and tissue scales by predicting drug-binding affinities and rates from simulation of ion channel and drug structure interactions and then used these values to model drug effects on the hERG channel. Model components were integrated into predictive models at the cell and tissue scales to expose fundamental arrhythmia vulnerability mechanisms and complex interactions underlying emergent behaviors. Human clinical data were used for model framework validation and showed excellent agreement, demonstrating feasibility of a new approach for cardiotoxicity prediction.

CONCLUSIONS

We present a multiscale model framework to predict electrotoxicity in the heart from the atom to the rhythm. Novel mechanistic insights emerged at all scales of the system, from the specific nature of proarrhythmic drug interaction with the hERG channel, to the fundamental cellular and tissue-level arrhythmia mechanisms. Applications of machine learning indicate necessary and sufficient parameters that predict arrhythmia vulnerability. We expect that the model framework may be expanded to make an impact in drug discovery, drug safety screening for a variety of compounds and targets, and in a variety of regulatory processes.

Computational design of G Protein-Coupled Receptor allosteric signal transductions

Membrane receptors sense and transduce extracellular stimuli into intracellular signaling responses but the molecular underpinnings remain poorly understood. We report a computational approach for designing protein allosteric signaling functions. By combining molecular dynamics simulations and design calculations, the method engineers amino-acid 'microswitches' at allosteric sites that modulate receptor stability or long-range coupling, to reprogram specific signaling properties. We designed 36 dopamine D2 receptor variants, whose constitutive and ligand-induced signaling agreed well with our predictions, repurposed the D2 receptor into a serotonin biosensor and predicted the signaling effects of more than 100 known G-protein-coupled receptor (GPCR) mutations. Our results reveal the existence of distinct classes of allosteric microswitches and pathways that define an unforeseen molecular mechanism of regulation and evolution of GPCR signaling. Our approach enables the rational design of allosteric receptors with enhanced stability and function to facilitate structural characterization, and reprogram cellular signaling in synthetic biology and cell engineering applications.

Competitive Interactions between PIRT, the Cold Sensing Ion Channel TRPM8, and PIP2 Suggest a Mechanism for Regulation

TRPM8 is a member of the transient receptor potential ion channel family where it functions as a cold and pain sensor in humans and other higher organisms. Previous studies show that TRPM8 requires the signaling phosphoinositide lipid PIP2 to function. TRPM8 function is further regulated by other diverse mechanisms, including the small modulatory membrane protein PIRT (phosphoinositide regulator of TRP). Like TRPM8, PIRT also binds PIP2 and behavioral studies have shown that PIRT is required for normal TRPM8-mediated cold-sensing. To better understand the molecular mechanism of PIRT regulation of TRPM8, solution nuclear magnetic resonance (NMR) spectroscopy was used to assign the backbone resonances of full-length human PIRT and investigate the direct binding of PIRT to PIP2 and the human TRPM8 S1-S4 transmembrane domain. Microscale thermophoresis (MST) binding studies validate the NMR results and identify a competitive PIRT interaction between PIP2 and the TRPM8 S1-S4 domain. Computational PIP2 docking to a human TRPM8 comparative model was performed to help localize where PIRT may bind TRPM8. Taken together, our data suggest a mechanism where TRPM8, PIRT, and PIP2 form a regulatory complex and PIRT modulation of TRPM8 arises, at least in part, by regulating local concentrations of PIP2 accessible to TRPM8.

Upgraded molecular models of the human KCNQ1 potassium channel

The voltage-gated potassium channel KCNQ1 (K_V7.1) assembles with the KCNE1 accessory protein to generate the slow delayed rectifier current, I_KS, 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.

Structural mechanisms underlying activation of TRPV1 channels by pungent compounds in gingers

BACKGROUND AND PURPOSE

Like chili peppers, gingers produce pungent stimuli by a group of vanilloid compounds that activate the nociceptive transient receptor potential vanilloid 1 (TRPV1) ion channel. How these compounds interact with TRPV1 remains unclear.

EXPERIMENTAL APPROACH

We used computational structural modelling, functional tests (electrophysiology and calcium imaging), and mutagenesis to investigate the structural mechanisms underlying ligand-channel interactions.

KEY RESULTS

The potency of three principal pungent compounds from ginger -shogaol, gingerol, and zingerone-depends on the same two residues in the TRPV1 channel that form a hydrogen bond with the chili pepper pungent compound, capsaicin. Computational modelling revealed binding poses of these ginger compounds similar to those of capsaicin, including a "head-down tail-up" orientation, two specific hydrogen bonds, and important contributions of van der Waals interactions by the aliphatic tail. Our study also identified a novel horizontal binding pose of zingerone that allows it to directly interact with the channel pore when bound inside the ligand-binding pocket. These observations offer a molecular level explanation for how unique structures in the ginger compounds affect their channel activation potency.

CONCLUSIONS AND IMPLICATIONS

Mechanistic insights into the interactions of ginger compounds and the TRPV1 cation channel should help guide drug discovery efforts to modulate nociception.

A lipophilicity-based energy function for membrane-protein modelling and design

Membrane-protein design is an exciting and increasingly successful research area which has led to landmarks including the design of stable and accurate membrane-integral proteins based on coiled-coil motifs. Design of topologically more complex proteins, such as most receptors, channels, and transporters, however, demands an energy function that balances contributions from intra-protein contacts and protein-membrane interactions. Recent advances in water-soluble all-atom energy functions have increased the accuracy in structure-prediction benchmarks. The plasma membrane, however, imposes different physical constraints on protein solvation. To understand these constraints, we recently developed a high-throughput experimental screen, called dsTβL, and inferred apparent insertion energies for each amino acid at dozens of positions across the bacterial plasma membrane. Here, we express these profiles as lipophilicity energy terms in Rosetta and demonstrate that the new energy function outperforms previous ones in modelling and design benchmarks. Rosetta ab initio simulations starting from an extended chain recapitulate two-thirds of the experimentally determined structures of membrane-spanning homo-oligomers with <2.5Å root-mean-square deviation within the top-predicted five models (available online: http://tmhop.weizmann.ac.il). Furthermore, in two sequence-design benchmarks, the energy function improves discrimination of stabilizing point mutations and recapitulates natural membrane-protein sequences of known structure, thereby recommending this new energy function for membrane-protein modelling and design.

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.

Specificity of the chromophore-binding site in human cone opsins

The variable composition of the chromophore-binding pocket in visual receptors is essential for vision. The visual phototransduction starts with the cis-trans isomerization of the retinal chromophore upon absorption of photons. Despite sharing the common 11-cis-retinal chromophore, rod and cone photoreceptors possess distinct photochemical properties. Thus, a detailed molecular characterization of the chromophore-binding pocket of these receptors is critical to understanding the differences in the photochemistry of vision between rods and cones. Unlike for rhodopsin (Rh), the crystal structures of cone opsins remain to be determined. To obtain insights into the specific chromophore-protein interactions that govern spectral tuning in human visual pigments, here we harnessed the unique binding properties of 11-cis-6-membered-ring-retinal (11-cis-6mr-retinal) with human blue, green, and red cone opsins. To unravel the specificity of the chromophore-binding pocket of cone opsins, we applied 11-cis-6mr-retinal analog-binding analyses to human blue, green, and red cone opsins. Our results revealed that among the three cone opsins, only blue cone opsin can accommodate the 11-cis-6mr-retinal in its chromophore-binding pocket, resulting in the formation of a synthetic blue pigment (B6mr) that absorbs visible light. A combination of primary sequence alignment, molecular modeling, and mutagenesis experiments revealed the specific amino acid residue 6.48 (Tyr-262 in blue cone opsins and Trp-281 in green and red cone opsins) as a selectivity filter in human cone opsins. Altogether, the results of our study uncover the molecular basis underlying the binding selectivity of 11-cis-6mr-retinal to the cone opsins.

ORAI1 channel gating and selectivity is differentially altered by natural mutations in the first or third transmembrane domain

KEY POINTS

Gain-of-function mutations in the highly selective Ca²⁺ channel ORAI1 cause tubular aggregate myopathy (TAM) characterized by muscular pain, weakness and cramping. TAM-associated mutations in ORAI1 first and third transmembrane domain facilitate channel opening by STIM1, causing constitutive Ca²⁺ influx and increasing the currents evoked by Ca²⁺ store depletion. Mutation V107M additionally decreases the channel selectivity for Ca²⁺ ions and its inhibition by acidic pH, while mutation T184M does not alter the channel sensitivity to pH or to reactive oxygen species. The ORAI blocker GSK-7975A prevents the constitutive activity of TAM-associated channels and might be used in therapy for patients suffering from TAM.

ABSTRACT

Skeletal muscle differentiation relies on store-operated Ca²⁺ entry (SOCE) mediated by STIM proteins linking the depletion of endoplasmic/sarcoplasmic reticulum Ca²⁺ stores to the activation of membrane Ca²⁺ -permeable ORAI channels. Gain-of-function mutations in STIM1 or ORAI1 isoforms cause tubular aggregate myopathy (TAM), a skeletal muscle disorder with muscular pain, weakness and cramping. Here, we characterize two overactive ORAI1 mutants from patients with TAM: V107M and T184M, located in the first and third transmembrane domain of the channel. When ectopically expressed in HEK-293T cells or human primary myoblasts, the mutated channels increased basal and store-operated Ca²⁺ entry. The constitutive activity of V107M, L138F, T184M and P245L mutants was prevented by low concentrations of GSK-7975A while the G98S mutant was resistant to inhibition. Electrophysiological recordings confirmed ORAI1-V107M constitutive activity and revealed larger STIM1-gated V107M- and T184M-mediated currents with conserved fast and slow Ca²⁺ -dependent inactivation. Mutation V107M altered the channel selectivity for Ca²⁺ ions and conferred resistance to acidic inhibition. Ca²⁺ imaging and molecular dynamics simulations showed a preserved sensitivity of T184M to the negative regulation by reactive oxygen species. Both mutants were able to mediate SOCE in Stim1^-/- /Stim2^-/- mouse embryonic fibroblasts expressing the binding-deficient STIM1-F394H mutant, indicating a higher sensitivity for STIM1-mediated gating, with ORAI1-T184M gain-of-function being strictly dependent on STIM1. These findings provide new insights into the permeation and regulatory properties of ORAI1 mutants that might translate into therapies against diseases with gain-of-function mutations in ORAI1.

A Membrane Burial Potential with H-Bonds and Applications to Curved Membranes and Fast Simulations

We use the statistics of a large and curated training set of transmembrane helical proteins to develop a knowledge-based potential that accounts for the dependence on both the depth of burial of the protein in the membrane and the degree of side-chain exposure. Additionally, the statistical potential includes depth-dependent energies for unsatisfied backbone hydrogen bond donors and acceptors, which are found to be relatively small, ∼2 RT. Our potential accurately places known proteins within the bilayer. The potential is applied to the mechanosensing MscL channel in membranes of varying thickness and curvature, as well as to the prediction of protein structure. The potential is incorporated into our new Upside molecular dynamics algorithm. Notably, we account for the exchange of protein-lipid interactions for protein-protein interactions as helices contact each other, thereby avoiding overestimating the energetics of helix association within the membrane. Simulations of most multimeric complexes find that isolated monomers and the oligomers retain the same orientation in the membrane, suggesting that the assembly of prepositioned monomers presents a viable mechanism of oligomerization.

hCALCRL mutation causes autosomal recessive nonimmune hydrops fetalis with lymphatic dysplasia

We report the first case of nonimmune hydrops fetalis (NIHF) associated with a recessive, in-frame deletion of V205 in the G protein-coupled receptor, Calcitonin Receptor-Like Receptor (hCALCRL). Homozygosity results in fetal demise from hydrops fetalis, while heterozygosity in females is associated with spontaneous miscarriage and subfertility. Using molecular dynamic modeling and in vitro biochemical assays, we show that the hCLR(V205del) mutant results in misfolding of the first extracellular loop, reducing association with its requisite receptor chaperone, receptor activity modifying protein (RAMP), translocation to the plasma membrane and signaling. Using three independent genetic mouse models we establish that the adrenomedullin-CLR-RAMP2 axis is both necessary and sufficient for driving lymphatic vascular proliferation. Genetic ablation of either lymphatic endothelial Calcrl or nonendothelial Ramp2 leads to severe NIHF with embryonic demise and placental pathologies, similar to that observed in humans. Our results highlight a novel candidate gene for human congenital NIHF and provide structure-function insights of this signaling axis for human physiology.

Photoaffinity Cross-Linking and Unnatural Amino Acid Mutagenesis Reveal Insights into Calcitonin Gene-Related Peptide Binding to the Calcitonin Receptor-like Receptor/Receptor Activity-Modifying Protein 1 (CLR/RAMP1) Complex

Calcitonin gene-related peptide (CGRP) binds to the complex of the calcitonin receptor-like receptor (CLR) with receptor activity-modifying protein 1 (RAMP1). How CGRP interacts with the transmembrane domain (including the extracellular loops) of this family B receptor remains unclear. In this study, a photoaffinity cross-linker, p-azido l-phenylalanine (azF), was incorporated into CLR, chiefly in the second extracellular loop (ECL2) using genetic code expansion and unnatural amino acid mutagenesis. The method was optimized to ensure efficient photolysis of azF residues near the transmembrane bundle of the receptor. A CGRP analogue modified with fluorescein at position 15 was used for detection of ultraviolet-induced cross-linking. The methodology was verified by confirming the known contacts of CGRP to the extracellular domain of CLR. Within ECL2, the chief contacts were I284 on the loop itself and L291, at the top of the fifth transmembrane helix (TM5). Minor contacts were noted along the lip of ECL2 between S286 and L290 and also with M223 in TM3 and F349 in TM6. Full length molecular models of the bound receptor complex suggest that CGRP sits at the top of the TM bundle, with Thr6 of the peptide making contacts with L291 and H295. I284 is likely to contact Leu12 and Ala13 of CGRP, and Leu16 of CGRP is at the ECL/extracellular domain boundary of CLR. The reduced potency, Emax, and affinity of [Leu16Ala]-human α CGRP are consistent with this model. Contacts between Thr6 of CGRP and H295 may be particularly important for receptor activation.

Discovery, Characterization, and Effects on Renal Fluid and Electrolyte Excretion of the Kir4.1 Potassium Channel Pore Blocker, VU0134992

The inward rectifier potassium (Kir) channel Kir4.1 (KCNJ10) carries out important physiologic roles in epithelial cells of the kidney, astrocytes in the central nervous system, and stria vascularis of the inner ear. Loss-of-function mutations in KCNJ10 lead to EAST/SeSAME syndrome, which is characterized by epilepsy, ataxia, renal salt wasting, and sensorineural deafness. Although genetic approaches have been indispensable for establishing the importance of Kir4.1 in the normal function of these tissues, the availability of pharmacological tools for acutely manipulating the activity of Kir4.1 in genetically normal animals has been lacking. We therefore carried out a high-throughput screen of 76,575 compounds from the Vanderbilt Institute of Chemical Biology library for small-molecule modulators of Kir4.1. The most potent inhibitor identified was 2-(2-bromo-4-isopropylphenoxy)-N-(2,2,6,6-tetramethylpiperidin-4-yl)acetamide (VU0134992). In whole-cell patch-clamp electrophysiology experiments, VU0134992 inhibits Kir4.1 with an IC50 value of 0.97 µM and is 9-fold selective for homomeric Kir4.1 over Kir4.1/5.1 concatemeric channels (IC50 = 9 µM) at -120 mV. In thallium (Tl+) flux assays, VU0134992 is greater than 30-fold selective for Kir4.1 over Kir1.1, Kir2.1, and Kir2.2; is weakly active toward Kir2.3, Kir6.2/SUR1, and Kir7.1; and is equally active toward Kir3.1/3.2, Kir3.1/3.4, and Kir4.2. This potency and selectivity profile is superior to Kir4.1 inhibitors amitriptyline, nortriptyline, and fluoxetine. Medicinal chemistry identified components of VU0134992 that are critical for inhibiting Kir4.1. Patch-clamp electrophysiology, molecular modeling, and site-directed mutagenesis identified pore-lining glutamate 158 and isoleucine 159 as critical residues for block of the channel. VU0134992 displayed a large free unbound fraction (fu) in rat plasma (fu = 0.213). Consistent with the known role of Kir4.1 in renal function, oral dosing of VU0134992 led to a dose-dependent diuresis, natriuresis, and kaliuresis in rats. Thus, VU0134992 represents the first in vivo active tool compound for probing the therapeutic potential of Kir4.1 as a novel diuretic target for the treatment of hypertension.