Multipass membrane protein structure prediction using Rosetta

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.

Atomistic mechanisms of the regulation of small-conductance Ca2+-activated K+ channel (SK2) by PIP2

Small-conductance Ca2+-activated K+ channels (SK, KCa2) are gated solely by intracellular microdomain Ca2+. The channel has emerged as a therapeutic target for cardiac arrhythmias. Calmodulin (CaM) interacts with the CaM binding domain (CaMBD) of the SK channels, serving as the obligatory Ca2+ sensor to gate the channels. In heterologous expression systems, phosphatidylinositol 4,5-bisphosphate (PIP2) coordinates with CaM in regulating SK channels. However, the roles and mechanisms of PIP2 in regulating SK channels in cardiomyocytes remain unknown. Here, optogenetics, magnetic nanoparticles, combined with Rosetta structural modeling, and molecular dynamics (MD) simulations revealed the atomistic mechanisms of how PIP2 works in concert with Ca2+-CaM in the SK channel activation. Our computational study affords evidence for the critical role of the amino acid residue R395 in the S6 transmembrane segment, which is localized in propinquity to the intracellular hydrophobic gate. This residue forms a salt bridge with residue E398 in the S6 transmembrane segment from the adjacent subunit. Both R395 and E398 are conserved in all known isoforms of SK channels. Our findings suggest that the binding of PIP2 to R395 residue disrupts the R395:E398 salt bridge, increasing the flexibility of the transmembrane segment S6 and the activation of the channel. Importantly, our findings serve as a platform for testing of structural-based drug designs for therapeutic inhibitors and activators of the SK channel family. The study is timely since inhibitors of SK channels are currently in clinical trials to treat atrial arrhythmias.

The chemistry of electrical signaling in sodium channels from bacteria and beyond

Electrical signaling is essential for all fast processes in biology, but its molecular mechanisms have been uncertain. This review article focuses on studies of bacterial sodium channels in order to home in on the essential molecular and chemical mechanisms underlying transmembrane ion conductance and voltage-dependent gating without the overlay of complex protein interactions and regulatory mechanisms in mammalian sodium channels. This minimalist approach has yielded a nearly complete picture of sodium channel function at the atomic level that are mostly conserved in mammalian sodium channels, including sodium selectivity and conductance, voltage sensing and activation, electromechanical coupling to pore opening and closing, slow inactivation, and pathogenic dysfunction in a debilitating channelopathy. Future studies of nature's simplest sodium channels may continue to yield key insights into the fundamental molecular and chemical principles of their function and further elucidate the chemical basis of electrical signaling.

Molecular mechanisms of two novel and selective TRPV1 channel activators

Venomous toxins hold immense value as tools in elucidating the intricate structure and underlying mechanisms of ion channels. In this article, we identified of two novel toxins, Hainantoxin-XXI (HNTX-XXI) and Hainantoxin-XXII (HNTX-XXII), derived from the venom of the Chinese spider Ornithoctonus hainana. HNTX-XXI, boasting a molecular weight of 6869.095 Da, comprises 64 amino acid residues and contains 8 cysteines. Meanwhile, HNTX-XXII, with a molecular weight of 8623.732 Da, comprises 77 amino acid residues and contains 12 cysteines. Remarkably, we discovered that both HNTX-XXI and HNTX-XXII possess the ability to activate TRPV1. They activated TRPV1 with EC50 values of 3.6 ± 0.19 μM and 862 ± 56 nM, respectively. Furthermore, the current generated by the activation of TRPV1 by these toxins can be rapidly blocked by ruthenium red. Intriguingly, our analysis revealed that the interaction between HNTX-XXI and TRPV1 is mediated by three key amino acid residues: L465, V469, and D471. Similarly, the interaction between HNTX-XXII and TRPV1 is facilitated by four key amino acid residues: A657, F659, E600, and R601. These findings provide profound insights into the molecular basis of toxin-TRPV1 interactions and pave the way for future research exploring the therapeutic potential of these toxic peptides.

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

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

Challenges of Protein-Protein Docking of the Membrane Proteins

Despite the recent advances in the determination of high-resolution membrane protein (MP) structures, the structural and functional characterization of MPs remains extremely challenging, mainly due to the hydrophobic nature, low abundance, poor expression, purification, and crystallization difficulties associated with MPs. Whereby the major challenges/hurdles for MP structure determination are associated with the expression, purification, and crystallization procedures. Although there have been significant advances in the experimental determination of MP structures, only a limited number of MP structures (approximately less than 1% of all) are available in the Protein Data Bank (PDB). Therefore, the structures of a large number of MPs still remain unresolved, which leads to the availability of widely unplumbed structural and functional information related to MPs. As a result, recent developments in the drug discovery realm and the significant biological contemplation have led to the development of several novel, low-cost, and time-efficient computational methods that overcome the limitations of experimental approaches, supplement experiments, and provide alternatives for the characterization of MPs. Whereby the fine tuning and optimizations of these computational approaches remains an ongoing endeavor.Computational methods offer a potential way for the elucidation of structural features and the augmentation of currently available MP information. However, the use of computational modeling can be extremely challenging for MPs mainly due to insufficient knowledge of (or gaps in) atomic structures of MPs. Despite the availability of numerous in silico methods for 3D structure determination the applicability of these methods to MPs remains relatively low since all methods are not well-suited or adequate for MPs. However, sophisticated methods for MP structure predictions are constantly being developed and updated to integrate the modifications required for MPs. Currently, different computational methods for (1) MP structure prediction, (2) stability analysis of MPs through molecular dynamics simulations, (3) modeling of MP complexes through docking, (4) prediction of interactions between MPs, and (5) MP interactions with its soluble partner are extensively used. Towards this end, MP docking is widely used. It is notable that the MP docking methods yet few in number might show greater potential in terms of filling the knowledge gap. In this chapter, MP docking methods and associated challenges have been reviewed to improve the applicability, accuracy, and the ability to model macromolecular complexes.

A multiscale predictive digital twin for neurocardiac modulation

Cardiac function is tightly regulated by the autonomic nervous system (ANS). Activation of the sympathetic nervous system increases cardiac output by increasing heart rate and stroke volume, while parasympathetic nerve stimulation instantly slows heart rate. Importantly, imbalance in autonomic control of the heart has been implicated in the development of arrhythmias and heart failure. Understanding of the mechanisms and effects of autonomic stimulation is a major challenge because synapses in different regions of the heart result in multiple changes to heart function. For example, nerve synapses on the sinoatrial node (SAN) impact pacemaking, while synapses on contractile cells alter contraction and arrhythmia vulnerability. Here, we present a multiscale neurocardiac modelling and simulator tool that predicts the effect of efferent stimulation of the sympathetic and parasympathetic branches of the ANS on the cardiac SAN and ventricular myocardium. The model includes a layered representation of the ANS and reproduces firing properties measured experimentally. Model parameters are derived from experiments and atomistic simulations. The model is a first prototype of a digital twin that is applied to make predictions across all system scales, from subcellular signalling to pacemaker frequency to tissue level responses. We predict conditions under which autonomic imbalance induces proarrhythmia and can be modified to prevent or inhibit arrhythmia. In summary, the multiscale model constitutes a predictive digital twin framework to test and guide high-throughput prediction of novel neuromodulatory therapy. KEY POINTS: A multi-layered model representation of the autonomic nervous system that includes sympathetic and parasympathetic branches, each with sparse random intralayer connectivity, synaptic dynamics and conductance based integrate-and-fire neurons generates firing patterns in close agreement with experiment. A key feature of the neurocardiac computational model is the connection between the autonomic nervous system and both pacemaker and contractile cells, where modification to pacemaker frequency drives initiation of electrical signals in the contractile cells. We utilized atomic-scale molecular dynamics simulations to predict the association and dissociation rates of noradrenaline with the β-adrenergic receptor. Multiscale predictions demonstrate how autonomic imbalance may increase proclivity to arrhythmias or be used to terminate arrhythmias. The model serves as a first step towards a digital twin for predicting neuromodulation to prevent or reduce disease.

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.

Phosphatidylserine-dependent structure of synaptogyrin remodels the synaptic vesicle membrane

Synaptic vesicles are small membrane-enclosed organelles that store neurotransmitters at presynaptic terminals. The uniform morphology of synaptic vesicles is important for brain function, because it enables the storage of well-defined amounts of neurotransmitters and thus reliable synaptic transmission. Here, we show that the synaptic vesicle membrane protein synaptogyrin cooperates with the lipid phosphatidylserine to remodel the synaptic vesicle membrane. Using NMR spectroscopy, we determine the high-resolution structure of synaptogyrin and identify specific binding sites for phosphatidylserine. We further show that phosphatidylserine binding changes the transmembrane structure of synaptogyrin and is critical for membrane bending and the formation of small vesicles. Cooperative binding of phosphatidylserine to both a cytoplasmic and intravesicular lysine-arginine cluster in synaptogyrin is required for the formation of small vesicles. Together with other synaptic vesicle proteins, synaptogyrin thus can sculpt the membrane of synaptic vesicles.

Elucidation of a dynamic interplay between a beta-2 adrenergic receptor, its agonist, and stimulatory G protein

G protein-coupled receptors (GPCRs) represent the largest group of membrane receptors for transmembrane signal transduction. Ligand-induced activation of GPCRs triggers G protein activation followed by various signaling cascades. Understanding the structural and energetic determinants of ligand binding to GPCRs and GPCRs to G proteins is crucial to the design of pharmacological treatments targeting specific conformations of these proteins to precisely control their signaling properties. In this study, we focused on interactions of a prototypical GPCR, beta-2 adrenergic receptor (β2AR), with its endogenous agonist, norepinephrine (NE), and the stimulatory G protein (Gs). Using molecular dynamics (MD) simulations, we demonstrated the stabilization of cationic NE, NE(+), binding to β2AR by Gs protein recruitment, in line with experimental observations. We also captured the partial dissociation of the ligand from β2AR and the conformational interconversions of Gs between closed and open conformations in the NE(+)-β2AR-Gs ternary complex while it is still bound to the receptor. The variation of NE(+) binding poses was found to alter Gs α subunit (Gsα) conformational transitions. Our simulations showed that the interdomain movement and the stacking of Gsα α1 and α5 helices are significant for increasing the distance between the Gsα and β2AR, which may indicate a partial dissociation of Gsα The distance increase commences when Gsα is predominantly in an open state and can be triggered by the intracellular loop 3 (ICL3) of β2AR interacting with Gsα, causing conformational changes of the α5 helix. Our results help explain molecular mechanisms of ligand and GPCR-mediated modulation of G protein activation.

The Role of Transmembrane Proteins in Plant Growth, Development, and Stress Responses

Transmembrane proteins participate in various physiological activities in plants, including signal transduction, substance transport, and energy conversion. Although more than 20% of gene products are predicted to be transmembrane proteins in the genome era, due to the complexity of transmembrane domains they are difficult to reliably identify in the predicted protein, and they may have different overall three-dimensional structures. Therefore, it is challenging to study their biological function. In this review, we describe the typical structures of transmembrane proteins and their roles in plant growth, development, and stress responses. We propose a model illustrating the roles of transmembrane proteins during plant growth and response to various stresses, which will provide important references for crop breeding.

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

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

Modeling of protein conformational changes with Rosetta guided by limited experimental data

Conformational changes are an essential component of functional cycles of many proteins, but their characterization often requires an integrative structural biology approach. Here, we introduce and benchmark ConfChangeMover (CCM), a new method built into the widely used macromolecular modeling suite Rosetta that is tailored to model conformational changes in proteins using sparse experimental data. CCM can rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest. We benchmarked CCM on soluble and membrane proteins with simulated Cα-Cα distance restraints and sparse experimental double electron-electron resonance (DEER) restraints, respectively. In both benchmarks, CCM outperformed state-of-the-art Rosetta methods, showing that it can model a diverse array of conformational changes. In addition, the Rosetta framework allows a wide variety of experimental data to be integrated with CCM, thus extending its capability beyond DEER restraints. This method will contribute to the biophysical characterization of protein dynamics.

Novel Dimeric hHv1 Model and Structural Bioinformatic Analysis Reveal an ATP-Binding Site Resulting in a Channel Activating Effect

The human voltage-gated proton channel (hHv1) is a highly selective ion channel codified by the HVCN1 gene. It plays a fundamental role in several physiological processes such as innate and adaptive immunity, insulin secretion, and sperm capacitation. Moreover, in humans, a higher hHv1 expression/function has been reported in several types of cancer cells. Here we report a multitemplate homology model of the hHv1 channel, built and refined as a dimer in Rosetta. The model was then subjected to extensive Gaussian accelerated molecular dynamics (GaMD) for enhanced conformational sampling, and representative snapshots were extracted by clustering analysis. Combining different structure- and sequence-based methodologies, we predicted a putative ATP-binding site located on the intracellular portion of the channel. Furthermore, GaMD simulations of the ATP-bound dimeric hHv1 model showed that ATP interacts with a cluster of positively charged residues from the cytoplasmic N and C terminal segments. According to the in silico predictions, we found that 3 mM intracellular ATP significantly increases the H⁺ current mediated by the hHv1 channel expressed in HEK293 cells and measured by the patch-clamp technique in an inside-out configuration (2.86 ± 0.63 fold over control at +40 mV). When ATP was added on the extracellular side, it was not able to activate the channel supporting the idea that the ATP-binding site resides in the intracellular face of the hHV1 channel. In a physiological and pathophysiological context, this ATP-mediated modulation could integrate the cell metabolic state with the H⁺ efflux, especially in cells where hHv1 channels are relevant for pH regulation, such as pancreatic β-cells, immune cells, and cancer cells.

Differential Activation of TRPM8 by the Stereoisomers of Menthol

The stereoisomers of menthol elicit cooling sensation to various levels. Though the high-resolution three-dimensional structures of the menthol receptor, the transient receptor potential melastatin 8 (TRPM8) ion channels, have been revolved in different states, the menthol-bound state structure is not determined and how the stereoisomers of menthol interact with TRPM8 remains largely elusive. Taking advantage of the identical atom composition but distinct spatial orientation of chemical groups in menthol stereoisomers, we performed thermodynamic mutant cycle analysis (TMCA) with patch-clamp recordings to probe the interaction between these ligands and TRPM8. By comparing (−)-menthol with (+)-neoisomenthol or (+)-neomenthol, we observed that the isopropyl or hydroxyl group in menthol interacts with the S4 or S3 helix in TRPM8, respectively. These interactions were also corroborated in our molecular docking of the stereoisomers, though the predicted structural details in the interactions of these ligands with TRPM8 residues are different. Therefore, we suggest similar molecular mechanisms of TRPM8 activation by the stereoisomers of menthol, while the binding configuration of an individual stereoisomer is varied.

Small integral membrane protein 10 like 1 downregulation enhances differentiation of adipose progenitor cells

Small integral membrane protein 10 like 1 (SMIM10L1) was identified by RNA sequencing as the most significantly downregulated gene in Phosphatase and Tensin Homologue (PTEN) knockdown adipose progenitor cells (APCs). PTEN is a tumor suppressor that antagonizes the growth promoting Phosphoinositide 3-kinase (PI3K)/AKT/mechanistic Target of Rapamycin (mTOR) cascade. Diseases caused by germline pathogenic variants in PTEN are summarized as PTEN Hamartoma Tumor Syndrome (PHTS). This overgrowth syndrome is associated with lipoma formation, especially in pediatric patients. The mechanisms underlying this adipose tissue dysfunction remain elusive. We observed that SMIM10L1 downregulation in APCs led to an enhanced adipocyte differentiation in two- and three-dimensional cell culture and increased expression of adipogenesis markers. Furthermore, SMIM10L1 knockdown cells showed a decreased expression of PTEN, pointing to a mutual crosstalk between PTEN and SMIM10L1. In line with these observations, SMIM10L1 knockdown cells showed increased activation of PI3K/AKT/mTOR signaling and concomitantly increased expression of the adipogenic transcription factor SREBP1. We computationally predicted an α-helical structure and membrane association of SMIM10L1. These results support a specific role for SMIM10L1 in regulating adipogenesis, potentially by increasing PI3K/AKT/mTOR signaling, which might be conducive to lipoma formation in pediatric patients with PHTS.

The seizure-inducing plastic explosive RDX inhibits the α1β2γ2 GABAA receptor

Objective

Royal demolition explosive (RDX) can induce seizures in wildlife and humans following release into the environment or after voluntary consumption. During the Vietnam War, RDX intoxication was the most common cause of generalized seizures in US service personnel, and in some sections of the armed forces, eating of RDX has continued as “a dare” to this day. After its mechanism of action was long unknown, RDX was recently shown to be a GABA_A receptor antagonist. We here determined the GABA_A receptor subtype‐selectivity of RDX and mapped its functional binding site.

Methods

We used whole‐cell patch‐clamp to determine the potency of RDX on 10 recombinantly expressed GABA_A receptors and mapped the RDX binding site using a combination of Rosetta molecular modeling and site‐directed mutagenesis.

Results

RDX was found to reversibly inhibit the α1β2γ2 GABA_A receptor with an IC₅₀ of 23 μmol/L (95% CI 15.1–33.3 μmol/L), whereas α4 and α6 containing GABA_A receptor combinations were 4–10‐fold less sensitive. RDX is binding to the noncompetitive antagonist (NCA) site in the pore. In a molecular model based on the cryo‐EM structure of the resting state of the α1β2γ2 receptor, RDX forms two hydrogen bonds with the threonines at the T6’ ring and makes hydrophobic interactions with the valine and alanine in 2′ position of the α1 or β2 subunits.

Interpretation

Our findings characterize the mechanism of action of RDX at the atomistic level and suggest that RDX‐induced seizures should be susceptible to treatment with GABA_A modulating drugs such as benzodiazepines, barbiturates, propofol, or neurosteroids.

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.

Ab-Initio Membrane Protein Amphipathic Helix Structure Prediction Using Deep Neural Networks

Amphipathic helix (AH)features the segregation of polar and nonpolar residues and plays important roles in many membrane-associated biological processes through interacting with both the lipid and the soluble phases. Although the AH structure has been discovered for a long time, few ab initio machine learning-based prediction models have been reported, due to the limited amount of training data. In this study, we report a new deep learning-based prediction model, which is composed of a residual neural network and the uneven-thresholds decision algorithm. It is constructed on 121 membrane proteins, in total 51640 residue samples, which are curated from an up-to-date membrane protein structure database. Through a rigid 10-fold nested cross-validation experiment, we demonstrate that our model can achieve promising predictions and exceed current state-of-the-art approaches in this field. This presents a new avenue for accurately predicting AHs. Analysis on the contribution of the input residues and some cases further reveals the high interpretability and the generalization of our model.

Mapping the binding sites of UDP and prostaglandin E2 glyceryl ester in the nucleotide receptor P2Y6

Cyclooxygenase‐2 catalyzes the biosynthesis of prostaglandins from arachidonic acid and the biosynthesis of prostaglandin glycerol esters (PG‐Gs) from 2‐arachidonoylglycerol. PG‐Gs are mediators of several biological actions such as macrophage activation, hyperalgesia, synaptic plasticity, and intraocular pressure. Recently, the human UDP receptor P2Y₆ was identified as a target for the prostaglandin E2 glycerol ester (PGE₂‐G). Here, we show that UDP and PGE₂‐G are evolutionary conserved endogenous agonists at vertebrate P2Y₆ orthologs. Using sequence comparison of P2Y₆ orthologs, homology modeling, and ligand docking studies, we proposed several receptor positions participating in agonist binding. Site‐directed mutagenesis and functional analysis of these P2Y₆ mutants revealed that both UDP and PGE₂‐G share in parts one ligand‐binding site. Thus, the convergent signaling of these two chemically very different agonists has already been manifested in the evolutionary design of the ligand‐binding pocket.

Different arrhythmia-associated calmodulin mutations have distinct effects on cardiac SK channel regulation

Calmodulin (CaM) plays a critical role in intracellular signaling and regulation of Ca²⁺-dependent proteins and ion channels. Mutations in CaM cause life-threatening cardiac arrhythmias. Among the known CaM targets, small-conductance Ca²⁺-activated K⁺ (SK) channels are unique, since they are gated solely by beat-to-beat changes in intracellular Ca²⁺. However, the molecular mechanisms of how CaM mutations may affect the function of SK channels remain incompletely understood. To address the structural and functional effects of these mutations, we introduced prototypical human CaM mutations in human induced pluripotent stem cell–derived cardiomyocyte-like cells (hiPSC-CMs). Using structural modeling and molecular dynamics simulation, we demonstrate that human calmodulinopathy-associated CaM mutations disrupt cardiac SK channel function via distinct mechanisms. CaM_D96V and CaM_D130G mutants reduce SK currents through a dominant-negative fashion. By contrast, specific mutations replacing phenylalanine with leucine result in conformational changes that affect helix packing in the C-lobe, which disengage the interactions between apo-CaM and the CaM-binding domain of SK channels. Distinct mutant CaMs may result in a significant reduction in the activation of the SK channels, leading to a decrease in the key Ca²⁺-dependent repolarization currents these channels mediate. The findings in this study may be generalizable to other interactions of mutant CaMs with Ca²⁺-dependent proteins within cardiac myocytes.

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.

Methodological approaches for the analysis of transmembrane domain interactions: A systematic review

The study of protein-protein interactions (PPI) has proven fundamental for the understanding of the most relevant cell processes. Any protein domain can participate in PPI, including transmembrane (TM) segments that can establish interactions with other TM domains (TMDs). However, the hydrophobic nature of TMDs and the environment they occupy complicates the study of intramembrane PPI, which demands the use of specific approaches and techniques. In this review, we will explore some of the strategies available to study intramembrane PPI in vitro, in vivo, and, in silico, focusing on those techniques that could be carried out in a standard molecular biology laboratory regarding its previous experience with membrane proteins.

Diverse Scientific Benchmarks for Implicit Membrane Energy Functions

Energy functions are fundamental to biomolecular modeling. Their success depends on robust physical formalisms, efficient optimization, and high-resolution data for training and validation. Over the past 20 years, progress in each area has advanced soluble protein energy functions. Yet, energy functions for membrane proteins lag behind due to sparse and low-quality data, leading to overfit tools. To overcome this challenge, we assembled a suite of 12 tests on independent data sets varying in size, diversity, and resolution. The tests probe an energy function's ability to capture membrane protein orientation, stability, sequence, and structure. Here, we present the tests and use the franklin2019 energy function to demonstrate them. We then identify areas for energy function improvement and discuss potential future integration with machine-learning-based optimization methods. The tests are available through the Rosetta Benchmark Server (https://benchmark.graylab.jhu.edu/) and GitHub (https://github.com/rfalford12/Implicit-Membrane-Energy-Function-Benchmark).

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.

Methodology for rigorous modeling of protein conformational changes by Rosetta using DEER distance restraints

We describe an approach for integrating distance restraints from Double Electron-Electron Resonance (DEER) spectroscopy into Rosetta with the purpose of modeling alternative protein conformations from an initial experimental structure. Fundamental to this approach is a multilateration algorithm that harnesses sets of interconnected spin label pairs to identify optimal rotamer ensembles at each residue that fit the DEER decay in the time domain. Benchmarked relative to data analysis packages, the algorithm yields comparable distance distributions with the advantage that fitting the DEER decay and rotamer ensemble optimization are coupled. We demonstrate this approach by modeling the protonation-dependent transition of the multidrug transporter PfMATE to an inward facing conformation with a deviation to the experimental structure of less than 2Å Cα RMSD. By decreasing spin label rotamer entropy, this approach engenders more accurate Rosetta models that are also more closely clustered, thus setting the stage for more robust modeling of protein conformational changes.

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.

Computational structure prediction provides a plausible mechanism for electron transfer by the outer membrane protein Cyc2 from Acidithiobacillus ferrooxidans

Cyc2 is the key protein in the outer membrane of Acidithiobacillus ferrooxidans that mediates electron transfer between extracellular inorganic iron and the intracellular central metabolism. This cytochrome c is specific for iron and interacts with periplasmic proteins to complete a reversible electron transport chain. A structure of Cyc2 has not yet been characterized experimentally. Here we describe a structural model of Cyc2, and associated proteins, to highlight a plausible mechanism for the ferrous iron electron transfer chain. A comparative modeling protocol specific for trans membrane beta barrel (TMBB) proteins in acidophilic conditions (pH ~ 2) was applied to the primary sequence of Cyc2. The proposed structure has three main regimes: Extracellular loops exposed to low-pH conditions, a TMBB, and an N-terminal cytochrome-like region within the periplasmic space. The Cyc2 model was further refined by identifying likely iron and heme docking sites. This represents the first computational model of Cyc2 that accounts for the membrane microenvironment and the acidity in the extracellular matrix. This approach can be used to model other TMBBs which can be critical for chemolithotrophic microbial growth.

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.

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.

Recent advances in de novo protein design: Principles, methods, and applications

The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.

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.

The Voltage-Dependent Deactivation of the KvAP Channel Involves the Breakage of Its S4 Helix

Voltage-gated potassium channels (Kv) allow ion permeation upon changes of the membrane electrostatic potential (Vm). Each subunit of these tetrameric channels is composed of six transmembrane helices, of which the anti-parallel helix bundle S1-S4 constitutes the voltage-sensor domain (VSD) and S5-S6 forms the pore domain. Here, using 82 molecular dynamics (MD) simulations involving 266 replicated VSDs, we report novel responses of the archaebacterial potassium channel KvAP to membrane polarization. We show that the S4 α-helix, which is straight in the experimental crystal structure solved under depolarized conditions (Vm ∼ 0), breaks into two segments when the cell membrane is hyperpolarized (Vm << 0), and reversibly forms a single straight helix following depolarization (Vm = 0). The outermost segment of S4 translates along the normal to the membrane, bringing new perspective to previously paradoxical accessibility experiments that were initially thought to imply the displacement of the whole VSD across the membrane. The novel model is applied through steered and unbiased MD simulations to the recently solved whole structure of KvAP. The simulations show that the resting state involves a re-orientation of the S5 α-helix by ∼ 5-6 degrees in respect to the normal of the bilayer, which could result in the constriction and closure of the selectivity filter. Our findings support the idea that the breakage of S4 under (hyper)polarization is a general feature of Kv channels with a non-swapped topology.

Distinguishing Potassium Channel Resting State Conformations in Live Cells with Environment-Sensitive Fluorescence

Ion channels are polymorphic membrane proteins whose high-resolution structures offer images of individual conformations, giving us starting points for identifying the complex and transient allosteric changes that give rise to channel physiology. Here, we report live-cell imaging of voltage-dependent structural changes of voltage-gated Kv2.1 channels using peptidyl tarantula toxins labeled with an environment-sensitive fluorophore, whose spectral shifts enable identification of voltage-dependent conformation changes in the resting voltage sensing domain (VSD) of the channel. We synthesize a new environment-sensitive, far-red fluorophore, julolidine phenoxazone (JP) azide, and conjugate it to tarantula toxin GxTX to characterize Kv2.1 VSD allostery during membrane depolarization. JP has an inherent response to the polarity of its immediate surroundings, offering site-specific structural insight into each channel conformation. Using voltage-clamp spectroscopy to collect emission spectra as a function of membrane potential, we find that they vary with toxin labeling site, the presence of Kv2 channels, and changes in membrane potential. With a high-affinity conjugate in which the fluorophore itself interacts closely with the channel, the emission shift midpoint is 50 mV more negative than the Kv2.1 gating current midpoint. This suggests that substantial conformational changes at the toxin-channel interface are associated with early gating charge transitions and these are not concerted with VSD motions at more depolarized potentials. These fluorescent probes enable study of conformational changes that can be correlated with electrophysiology, putting channel structures and models into a context of live-cell membranes and physiological states.

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.

Molecular mechanisms underlying menthol binding and activation of TRPM8 ion channel

Menthol in mints elicits coolness sensation by selectively activating TRPM8 channel. Although structures of TRPM8 were determined in the apo and liganded states, the menthol-bounded state is unresolved. To understand how menthol activates the channel, we docked menthol to the channel and systematically validated our menthol binding models with thermodynamic mutant cycle analysis. We observed that menthol uses its hydroxyl group as a hand to specifically grab with R842, and its isopropyl group as legs to stand on I846 and L843. By imaging with fluorescent unnatural amino acid, we found that menthol binding induces wide-spread conformational rearrangements within the transmembrane domains. By Φ analysis based on single-channel recordings, we observed a temporal sequence of conformational changes in the S6 bundle crossing and the selectivity filter leading to channel activation. Therefore, our study suggested a 'grab and stand' mechanism of menthol binding and how menthol activates TRPM8 at the atomic level.

A Cross-linking Mass Spectrometry Approach Defines Protein Interactions in Yeast Mitochondria

Protein cross-linking and the analysis of cross-linked peptides by mass spectrometry is currently receiving much attention. Not only is this approach applied to isolated complexes to provide information about spatial arrangements of proteins, but it is also increasingly applied to entire cells and their organelles. As in quantitative proteomics, the application of isotopic labeling further makes it possible to monitor quantitative changes in the protein-protein interactions between different states of a system. Here, we cross-linked mitochondria from Saccharomyces cerevisiae grown on either glycerol- or glucose-containing medium to monitor protein-protein interactions under non-fermentative and fermentative conditions. We investigated qualitatively the protein-protein interactions of the 400 most abundant proteins applying stringent data-filtering criteria, i.e. a minimum of two cross-linked peptide spectrum matches and a cut-off in the spectrum scoring of the used search engine. The cross-linker BS3 proved to be equally suited for connecting proteins in all compartments of mitochondria when compared with its water-insoluble but membrane-permeable derivative DSS. We also applied quantitative cross-linking to mitochondria of both the growth conditions using stable-isotope labeled BS3. Significant differences of cross-linked proteins under glycerol and glucose conditions were detected, however, mainly because of the different copy numbers of these proteins in mitochondria under both the conditions. Results obtained from the glycerol condition indicate that the internal NADH:ubiquinone oxidoreductase Ndi1 is part of an electron transport chain supercomplex. We have also detected several hitherto uncharacterized proteins and identified their interaction partners. Among those, Min8 was found to be associated with cytochrome c oxidase. BN-PAGE analyses of min8Δ mitochondria suggest that Min8 promotes the incorporation of Cox12 into cytochrome c oxidase.

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.

Cooperative transport mechanism of human monocarboxylate transporter 2

Proton-linked monocarboxylate transporters (MCTs) must transport monocarboxylate efficiently to facilitate monocarboxylate efflux in glycolytically active cells, and transport monocarboxylate slowly or even shut down to maintain a physiological monocarboxylate concentration in glycolytically inactive cells. To discover how MCTs solve this fundamental aspect of intracellular monocarboxylate homeostasis in the context of multicellular organisms, we analyzed pyruvate transport activity of human monocarboxylate transporter 2 (MCT2). Here we show that MCT2 transport activity exhibits steep dependence on substrate concentration. This property allows MCTs to turn on almost like a switch, which is physiologically crucial to the operation of MCTs in the cellular context. We further determined the cryo-electron microscopy structure of the human MCT2, demonstrating that the concentration sensitivity of MCT2 arises from the strong inter-subunit cooperativity of the MCT2 dimer during transport. These data establish definitively a clear example of evolutionary optimization of protein function.

Proton-linked monocarboxylate transporters (MCTs) facilitate monocarboxylate efflux in glycolytically active cells and regulate transport down in glycolytically inactive cells. Here authors show a steep dependence of human MCT2 activity on substrate concentration and show the structural basis of cooperative transport.

A centipede toxin causes rapid desensitization of nociceptor TRPV1 ion channel

The nociceptive transient receptor potential vanilloid 1 (TRPV1) ion channel is a polymodal receptor for multiple painful stimuli, hence actively pursued as a target for analgesic drugs. We identified a small peptide toxin RhTx2 from the Chinese red-headed centipede that strongly modulates TRPV1 activities. RhTx2, a 31-amino-acid peptide, is similar to a TRPV1-activating toxin RhTx we have previously discovered but with four extra amino acids at the N terminus. We observed that, like RhTx, RhTx2 activated TRPV1, but RhTx2 rapidly desensitized the channel upon prolonged exposure. Desensitization was achieved by reducing both the open probability and the single-channel conductance. RhTx2 is not only a tool to study the desensitization mechanism of TRPV1, but also a promising starting molecule for developing novel analgesics.

The Hydrophobic Ligands Entry and Exit from the GPCR Binding Site-SMD and SuMD Simulations

Most G protein-coupled receptors that bind the hydrophobic ligands (lipid receptors and steroid receptors) belong to the most populated class A (rhodopsin-like) of these receptors. Typical examples of lipid receptors are: rhodopsin, cannabinoid (CB), sphingosine-1-phosphate (S1P) and lysophosphatidic (LPA) receptors. The hydrophobic ligands access the receptor binding site from the lipid bilayer not only because of their low solubility in water but also because of a large N-terminal domain plug preventing access to the orthosteric binding site from the extracellular milieu. In order to identify the most probable ligand exit pathway from lipid receptors CB1, S1P1 and LPA1 orthosteric binding sites we performed at least three repeats of steered molecular dynamics simulations in which ligands were pulled in various directions. For specific ligands being agonists, the supervised molecular dynamics approach was used to simulate the ligand entry events to the inactive receptor structures. For all investigated receptors the ligand entry/exit pathway goes through the gate between transmembrane helices TM1 and TM7, however, in some cases it combined with a direction toward water milieu.

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.

NMR "Crystallography" for Uniformly (13 C, 15 N)-Labeled Oriented Membrane Proteins

In oriented-sample (OS) solid-state NMR of membrane proteins, the angular-dependent dipolar couplings and chemical shifts provide a direct input for structure calculations. However, so far only ¹ H-¹⁵ N dipolar couplings and ¹⁵ N chemical shifts have been routinely assessed in oriented ¹⁵ N-labeled samples. The main obstacle for extending this technique to membrane proteins of arbitrary topology has remained in the lack of additional experimental restraints. We have developed a new experimental triple-resonance NMR technique, which was applied to uniformly doubly (¹⁵ N, ¹³ C)-labeled Pf1 coat protein in magnetically aligned DMPC/DHPC bicelles. The previously inaccessible ¹ H_α -¹³ C_α dipolar couplings have been measured, which make it possible to determine the torsion angles between the peptide planes without assuming α-helical structure a priori. The fitting of three angular restraints per peptide plane and filtering by Rosetta scoring functions has yielded a consensus α-helical transmembrane structure for Pf1 protein.

Rapid Simulation of Unprocessed DEER Decay Data for Protein Fold Prediction

Despite advances in sampling and scoring strategies, Monte Carlo modeling methods still struggle to accurately predict de novo the structures of large proteins, membrane proteins, or proteins of complex topologies. Previous approaches have addressed these shortcomings by leveraging sparse distance data gathered using site-directed spin labeling and electron paramagnetic resonance spectroscopy to improve protein structure prediction and refinement outcomes. However, existing computational implementations entail compromises between coarse-grained models of the spin label that lower the resolution and explicit models that lead to resource-intense simulations. These methods are further limited by their reliance on distance distributions, which are calculated from a primary refocused echo decay signal and contain uncertainties that may require manual refinement. Here, we addressed these challenges by developing RosettaDEER, a scoring method within the Rosetta software suite capable of simulating double electron-electron resonance spectroscopy decay traces and distance distributions between spin labels fast enough to fold proteins de novo. We demonstrate that the accuracy of resulting distance distributions match or exceed those generated by more computationally intensive methods. Moreover, decay traces generated from these distributions recapitulate intermolecular background coupling parameters even when the time window of data collection is truncated. As a result, RosettaDEER can discriminate between poorly folded and native-like models by using decay traces that cannot be accurately converted into distance distributions using regularized fitting approaches. Finally, using two challenging test cases, we demonstrate that RosettaDEER leverages these experimental data for protein fold prediction more effectively than previous methods. These benchmarking results confirm that RosettaDEER can effectively leverage sparse experimental data for a wide array of modeling applications built into the Rosetta software suite.

Comparative modeling and docking of chemokine-receptor interactions with Rosetta

Chemokine receptors are a subset of G protein-coupled receptors defined by the distinct property of binding small protein ligands in the chemokine family. Chemokine receptors recognize their ligands by a mechanism that is distinct from other class A GPCRs that bind peptides or small molecules. For this reason, structural information on other ligand-GPCR interactions are only indirectly relevant to understanding the chemokine receptor interface. Additionally, the experimentally determined structures of chemokine-GPCR complexes represent less than 3% of the known interactions of this complex, multi-ligand/multi-receptor network. To enable predictive modeling of the remaining 97% of interactions, a general in silico protocol was designed to utilize existing chemokine receptor crystal structures, co-crystal structures, and NMR ensembles of chemokines bound to receptor fragments. This protocol was benchmarked on the ability to predict each of the three published co-crystal structures, while being blinded to the target structure. Averaging ensembles selected from the top-ranking models reproduced up to 84% of the intermolecular contacts found in the crystal structure, with the lowest Cα-RMSD of the complex at 3.3 Å. The chemokine receptor N-terminus, unresolved in crystal structures, was included in the modeling and recapitulates contacts with known sulfotyrosine binding pockets seen in structures derived from experimental NMR data. This benchmarking experiment suggests that realistic homology models of chemokine-GPCR complexes can be generated by leveraging current structural data.

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.