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Many membrane proteins are prone to misfolding, which compromises their functional expression at the plasma membrane. This is particularly true for the mammalian gonadotropin-releasing hormone receptor GPCRs (GnRHR). We recently demonstrated that evolutionary GnRHR modifications appear to have coincided with adaptive changes in cotranslational folding efficiency. Though protein stability is known to shape evolution, it is unclear how cotranslational folding constraints modulate the synergistic, epistatic interactions between mutations. We therefore compared the pairwise interactions formed by mutations that disrupt the membrane topology (V276T) or tertiary structure (W107A) of GnRHR. Using deep mutational scanning, we evaluated how the plasma membrane expression of these variants is modified by hundreds of secondary mutations. An analysis of 251 mutants in three genetic backgrounds reveals that V276T and W107A form distinct epistatic interactions that depend on both the severity and the mechanism of destabilization. V276T forms predominantly negative epistatic interactions with destabilizing mutations in soluble loops. In contrast, W107A forms positive interactions with mutations in both loops and transmembrane domains that reflect the diminishing impacts of the destabilizing mutations in variants that are already unstable. These findings reveal how epistasis is remodeled by conformational defects in membrane proteins and in unstable proteins more generally., Competing Interests: LC, CK, AM, WP, CH, DR, HW, D, JM, JS No competing interests declared, (© 2023, Chamness et al.)

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., (© 2024 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

Measuring protein thermostability provides valuable information on the biophysical rules that govern the structure-energy relationships of proteins. However, such measurements remain a challenge for membrane proteins. Here, we introduce a new experimental system to evaluate membrane protein thermostability. This system leverages a recently developed nonfluorescent membrane scaffold protein to reconstitute proteins into nanodiscs and is coupled with a nano-format of differential scanning fluorimetry (nanoDSF). This approach offers a label-free and direct measurement of the intrinsic tryptophan fluorescence of the membrane protein as it unfolds in solution without signal interference from the "dark" nanodisc. In this work, we demonstrate the application of this method using the disulfide bond formation protein B (DsbB) as a test membrane protein. NanoDSF measurements of DsbB reconstituted in dark nanodiscs loaded with 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) and 1,2-dimyristoyl-sn-glycero-3-phosphorylglycerol (DMPG) lipids show a complex biphasic thermal unfolding pattern with a minor unfolding transition followed by a major transition. The inflection points of the thermal denaturation curve reveal two distinct unfolding midpoint melting temperatures (T m ) of 70.5°C and 77.5°C, consistent with a three-state unfolding model. Further, we show that the catalytically conserved disulfide bond between residues C41 and C130 drives the intermediate state of the unfolding pathway for DsbB in a DMPC and DMPG nanodisc. To extend the utility of this method, we evaluate and compare the thermostability of DsbB in different lipid environments. We introduce this method as a new tool that can be used to understand how compositionally and biophysically complex lipid environments drive membrane protein stability., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Declaration of interests The authors declare that they have no competing interests., (Copyright © 2022 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

A single experimental method alone often fails to provide the resolution, accuracy, and coverage needed to model integral membrane proteins (IMPs). Integrating computation with experimental data is a powerful approach to supplement missing structural information with atomic detail. We combine RosettaNMR with experimentally-derived paramagnetic NMR restraints to guide membrane protein structure prediction. We demonstrate this approach using the disulfide bond formation protein B (DsbB), an α-helical IMP. Here, we attached a cyclen-based paramagnetic lanthanide tag to an engineered non-canonical amino acid (ncAA) using a copper-catalyzed azide-alkyne cycloaddition (CuAAC) click chemistry reaction. Using this tagging strategy, we collected 203 backbone H N pseudocontact shifts (PCSs) for three different labeling sites and used these as input to guide de novo membrane protein structure prediction protocols in Rosetta. We find that this sparse PCS dataset combined with 44 long-range NOEs as restraints in our calculations improves structure prediction of DsbB by enhancements in model accuracy, sampling, and scoring. The inclusion of this PCS dataset improved the Cα-RMSD transmembrane segment values of the best-scoring and best-RMSD models from 9.57 Å and 3.06 Å (no NMR data) to 5.73 Å and 2.18 Å, respectively., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

Lipid molecules such as cholesterol interact with the surface of integral membrane proteins (IMP) in a mode different from drug-like molecules in a protein binding pocket. These differences are due to the lipid molecule's shape, the membrane's hydrophobic environment, and the lipid's orientation in the membrane. We can use the recent increase in experimental structures in complex with cholesterol to understand protein-cholesterol interactions. We developed the RosettaCholesterol protocol consisting of (1) a prediction phase using an energy grid to sample and score native-like binding poses and (2) a specificity filter to calculate the likelihood that a cholesterol interaction site may be specific. We used a multi-pronged benchmark (self-dock, flip-dock, cross-dock, and global-dock) of protein-cholesterol complexes to validate our method. RosettaCholesterol improved sampling and scoring of native poses over the standard RosettaLigand baseline method in 91% of cases and performs better regardless of benchmark complexity. On the β2AR, our method found one likely-specific site, which is described in the literature. The RosettaCholesterol protocol quantifies cholesterol binding site specificity. Our approach provides a starting point for high-throughput modeling and prediction of cholesterol binding sites for further experimental validation., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Marlow et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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.

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., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Cholesterol is an integral component of mammalian membranes. It has been shown to modulate membrane fluidity and dynamics and alter integral membrane protein function. However, understanding the molecular mechanisms of how cholesterol impacts protein function is complicated by limited and conflicting structural data. Because of the nature of the crystallization and cryo-EM structure determination, it is difficult to distinguish between specific and biologically relevant interactions and a nonspecific association. The only widely recognized search algorithm for cholesterol-integral-membrane-protein interaction sites is sequence based, i.e., searching for the so-called "Cholesterol Recognition/interaction Amino acid Consensus" motif. Although these motifs are present in numerous integral membrane proteins, there is inconclusive evidence to support their necessity or sufficiency for cholesterol binding. Here, we leverage the increasing number of experimental cholesterol-integral-membrane-protein structures to systematically analyze putative interaction sites based on their spatial arrangement and evolutionary conservation. This analysis creates three-dimensional representations of general cholesterol interaction sites that form clusters across multiple integral membrane protein classes. We also classify cholesterol-integral-membrane-protein interaction sites as either likely-specific or nonspecific. Information gleaned from our characterization will eventually enable a structure-based approach to predict and design cholesterol-integral-membrane-protein interaction sites., (Copyright © 2021 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: The authors have declared that no competing interests exist.

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

While great progress has been made, only 10% of the nearly 1,000 integral, α-helical, multi-span membrane protein families are represented by at least one experimentally determined structure in the PDB. Previously, we developed the algorithm BCL::MP-Fold, which samples the large conformational space of membrane proteins de novo by assembling predicted secondary structure elements guided by knowledge-based potentials. Here, we present a case study of rhodopsin fold determination by integrating sparse and/or low-resolution restraints from multiple experimental techniques including electron microscopy, electron paramagnetic resonance spectroscopy, and nuclear magnetic resonance spectroscopy. Simultaneous incorporation of orthogonal experimental restraints not only significantly improved the sampling accuracy but also allowed identification of the correct fold, which is demonstrated by a protein size-normalized transmembrane root-mean-square deviation as low as 1.2 Å. The protocol developed in this case study can be used for the determination of unknown membrane protein folds when limited experimental restraints are available., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Computational membrane protein design is challenging due to the small number of high-resolution structures available to elucidate the physical basis of membrane protein structure, multiple functionally important conformational states, and a limited number of high-throughput biophysical assays to monitor function. However, structural determination of membrane proteins has made tremendous progress in the past years. Concurrently the field of soluble computational design has made impressive inroads. These developments allow us to tackle the formidable challenge of designing functional membrane proteins. Herein, Rosetta is benchmarked for membrane protein design. We evaluate strategies to cope with the often reduced quality of experimental membrane protein structures. Further, we test the usage of symmetry in design protocols, which is particularly important as many membrane proteins exist as homo-oligomers. We compare a soluble scoring function with a scoring function optimized for membrane proteins, RosettaMembrane. Both scoring functions recovered around half of the native sequence when completely redesigning membrane proteins. However, RosettaMembrane recovered the most native-like amino acid property composition. While leucine was overrepresented in the inner and outer-hydrophobic regions of RosettaMembrane designs, it resulted in a native-like surface hydrophobicity indicating that it is currently the best option for designing membrane proteins with Rosetta., (© 2017 The Protein Society.)

One of the challenging problems in tertiary structure prediction of helical membrane proteins (HMPs) is the determination of rotation of α-helices around the helix normal. Incorrect prediction of helix rotations substantially disrupts native residue-residue contacts while inducing only a relatively small effect on the overall fold. We previously developed a method for predicting residue contact numbers (CNs), which measure the local packing density of residues within the protein tertiary structure. In this study, we tested the idea of incorporating predicted CNs as restraints to guide the sampling of helix rotation. For a benchmark set of 15 HMPs with simple to rather complicated folds, the average contact recovery (CR) of best-sampled models was improved for all targets, the likelihood of sampling models with CR greater than 20% was increased for 13 targets, and the average RMSD100 of best-sampled models was improved for 12 targets. This study demonstrated that explicit incorporation of CNs as restraints improves the prediction of helix-helix packing. Proteins 2017; 85:1212-1221. © 2017 Wiley Periodicals, Inc., (© 2017 Wiley Periodicals, Inc.)

De novo membrane protein structure prediction is limited to small proteins due to the conformational search space quickly expanding with length. Long-range contacts (24+ amino acid separation)-residue positions distant in sequence, but in close proximity in the structure, are arguably the most effective way to restrict this conformational space. Inverse methods for co-evolutionary analysis predict a global set of position-pair couplings that best explain the observed amino acid co-occurrences, thus distinguishing between evolutionarily explained co-variances and these arising from spurious transitive effects. Here, we show that applying machine learning approaches and custom descriptors improves evolutionary contact prediction accuracy, resulting in improvement of average precision by 6 percentage points for the top 1L non-local contacts. Further, we demonstrate that predicted contacts improve protein folding with BCL::Fold. The mean RMSD100 metric for the top 10 models folded was reduced by an average of 2 Å for a benchmark of 25 membrane proteins.

There is a compelling and growing need to accurately predict the impact of amino acid mutations on protein stability for problems in personalized medicine and other applications. Here the ability of 10 computational tools to accurately predict mutation-induced perturbation of folding stability (ΔΔG) for membrane proteins of known structure was assessed. All methods for predicting ΔΔG values performed significantly worse when applied to membrane proteins than when applied to soluble proteins, yielding estimated concordance, Pearson, and Spearman correlation coefficients of <0.4 for membrane proteins. Rosetta and PROVEAN showed a modest ability to classify mutations as destabilizing (ΔΔG < -0.5 kcal/mol), with a 7 in 10 chance of correctly discriminating a randomly chosen destabilizing variant from a randomly chosen stabilizing variant. However, even this performance is significantly worse than for soluble proteins. This study highlights the need for further development of reliable and reproducible methods for predicting thermodynamic folding stability in membrane proteins.

The UvsY recombination mediator protein is critical for efficient homologous recombination in bacteriophage T4 and is the functional analog of the eukaryotic Rad52 protein. During T4 homologous recombination, the UvsX recombinase has to compete with the prebound gp32 single-stranded binding protein for DNA-binding sites and UvsY stimulates this filament nucleation event. We report here the crystal structure of UvsY in four similar open-barrel heptameric assemblies and provide structural and biophysical insights into its function. The UvsY heptamer was confirmed in solution by centrifugation and light scattering, and thermodynamic analyses revealed that the UvsY-ssDNA interaction occurs within the assembly via two distinct binding modes. Using surface plasmon resonance, we also examined the binding of UvsY to both ssDNA and the ssDNA-gp32 complex. These analyses confirmed that ssDNA can bind UvsY and gp32 independently and also as a ternary complex. They also showed that residues located on the rim of the heptamer are required for optimal binding to ssDNA, thus identifying the putative ssDNA-binding surface. We propose a model in which UvsY promotes a helical ssDNA conformation that disfavors the binding of gp32 and initiates the assembly of the ssDNA-UvsX filament.

Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein-membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein-membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein-protein docking. TMH-Expo can be accessed via a Web server at www.meilerlab.org .

For many membrane proteins, the determination of their topology remains a challenge for methods like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. Electron paramagnetic resonance (EPR) spectroscopy has evolved as an alternative technique to study structure and dynamics of membrane proteins. The present study demonstrates the feasibility of membrane protein topology determination using limited EPR distance and accessibility measurements. The BCL::MP-Fold (BioChemical Library membrane protein fold) algorithm assembles secondary structure elements (SSEs) in the membrane using a Monte Carlo Metropolis (MCM) approach. Sampled models are evaluated using knowledge-based potential functions and agreement with the EPR data and a knowledge-based energy function. Twenty-nine membrane proteins of up to 696 residues are used to test the algorithm. The RMSD100 value of the most accurate model is better than 8 Å for 27, better than 6 Å for 22, and better than 4 Å for 15 of the 29 proteins, demonstrating the algorithms' ability to sample the native topology. The average enrichment could be improved from 1.3 to 2.5, showing the improved discrimination power by using EPR data., (© 2015 Wiley Periodicals, Inc.)

Interactions between protein domains and linear peptides underlie many biological processes. Among these interactions, the recognition of C-terminal peptides by PDZ domains is one of the most ubiquitous. In this work, we present a mathematical model for PDZ domain-peptide interactions capable of predicting both affinity and specificity of binding based on X-ray crystal structures and comparative modeling with ROSETTA. We developed our mathematical model using a large phage display dataset describing binding specificity for a wild type PDZ domain and 91 single mutants, as well as binding affinity data for a wild type PDZ domain binding to 28 different peptides. Structural refinement was carried out through several ROSETTA protocols, the most accurate of which included flexible peptide docking and several iterations of side chain repacking and backbone minimization. Our findings emphasize the importance of backbone flexibility and the energetic contributions of side chain-side chain hydrogen bonds in accurately predicting interactions. We also determined that predicting PDZ domain-peptide interactions became increasingly challenging as the length of the peptide increased in the N-terminal direction. In the training dataset, predicted binding energies correlated with those derived through calorimetry and specificity switches introduced through single mutations at interface positions were recapitulated. In independent tests, our best performing protocol was capable of predicting dissociation constants well within one order of magnitude of the experimental values and specificity profiles at the level of accuracy of previous studies. To our knowledge, this approach represents the first integrated protocol for predicting both affinity and specificity for PDZ domain-peptide interactions.

Translocator Protein 18 kDa (TSPO), previously known as the peripheral-type benzodiazepine receptor (PBR), is a mitochondrial outer membrane protein that has been identified as a key player in cholesterol and porphyrin transport, apoptotic signaling, and cancer development, as well as neurological inflammation and disease. Despite a number of TSPO ligands whose effects have been studied with respect to these varied biological activities, the nature of their interactions with TSPO and the molecular mechanism of their effects remain controversial, in part because of the lack of an atomic-resolution structure. We expressed and purified the homologue of mammalian TSPO from Rhodobacter sphaeroides (RsTSPO), as well as a mutant form in a proposed drug binding loop, RsTSPOW38C. We characterized their binding behaviors with endogenous ligands and a series of compounds that affect apoptosis by using a sensitive tryptophan fluorescence quenching assay. Our results show that RsTSPO behaves as a dimer in the purified state and binds with low micromolar affinity to many of these ligands, including retinoic acid, curcumin, and a known Bcl-2 inhibitor, gossypol, suggesting a possible direct role for TSPO in their regulation of apoptosis. A computational model of the RsTSPO dimer is constructed using EM-Fold, Rosetta, and a cryo-electron microscopy density map. Binding behaviors of known ligands are discussed in the context of the model with respect to regions that may be involved in binding.

Membrane protein structure determination remains a challenging endeavor. Computational methods that predict membrane protein structure from sequence can potentially aid structure determination for such difficult target proteins. The de novo protein structure prediction method BCL::Fold rapidly assembles secondary structure elements into three-dimensional models. Here, we describe modifications to the algorithm, named BCL::MP-Fold, in order to simulate membrane protein folding. Models are built into a static membrane object and are evaluated using a knowledge-based energy potential, which has been modified to account for the membrane environment. Additionally, a symmetry folding mode allows for the prediction of obligate homomultimers, a common property among membrane proteins. In a benchmark test of 40 proteins of known structure, the method sampled the correct topology in 34 cases. This demonstrates that the algorithm can accurately predict protein topology without the need for large multiple sequence alignments, homologous template structures, or experimental restraints., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Prediction of transmembrane spans and secondary structure from the protein sequence is generally the first step in the structural characterization of (membrane) proteins. Preference of a stretch of amino acids in a protein to form secondary structure and being placed in the membrane are correlated. Nevertheless, current methods predict either secondary structure or individual transmembrane states. We introduce a method that simultaneously predicts the secondary structure and transmembrane spans from the protein sequence. This approach not only eliminates the necessity to create a consensus prediction from possibly contradicting outputs of several predictors but bears the potential to predict conformational switches, i.e., sequence regions that have a high probability to change for example from a coil conformation in solution to an α-helical transmembrane state. An artificial neural network was trained on databases of 177 membrane proteins and 6048 soluble proteins. The output is a 3 × 3 dimensional probability matrix for each residue in the sequence that combines three secondary structure types (helix, strand, coil) and three environment types (membrane core, interface, solution). The prediction accuracies are 70.3% for nine possible states, 73.2% for three-state secondary structure prediction, and 94.8% for three-state transmembrane span prediction. These accuracies are comparable to state-of-the-art predictors of secondary structure (e.g., Psipred) or transmembrane placement (e.g., OCTOPUS). The method is available as web server and for download at www.meilerlab.org., (Copyright © 2013 Wiley Periodicals, Inc.)

Electron density maps of membrane proteins or large macromolecular complexes are frequently only determined at medium resolution between 4 Å and 10 Å, either by cryo-electron microscopy or X-ray crystallography. In these density maps, the general arrangement of secondary structure elements (SSEs) is revealed, whereas their directionality and connectivity remain elusive. We demonstrate that the topology of proteins with up to 250 amino acids can be determined from such density maps when combined with a computational protein folding protocol. Furthermore, we accurately reconstruct atomic detail in loop regions and amino acid side chains not visible in the experimental data. The EM-Fold algorithm assembles the SSEs de novo before atomic detail is added using Rosetta. In a benchmark of 27 proteins, the protocol consistently and reproducibly achieves models with root mean square deviation values <3 Å., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The concept of hydrophobicity is critical to our understanding of the principles of membrane protein (MP) folding, structure, and function. In the last decades, several groups have derived hydrophobicity scales using both experimental and statistical methods that are optimized to mimic certain natural phenomena as closely as possible. The present work adds to this toolset the first knowledge-based scale that unifies the characteristics of both alpha-helical and beta-barrel multispan MPs. This unified hydrophobicity scale (UHS) distinguishes between amino acid preference for solution, transition, and trans-membrane states. The scale represents average hydrophobicity values of amino acids in folded proteins, irrespective of their secondary structure type. We furthermore present the first knowledge-based hydrophobicity scale for mammalian alpha-helical MPs (mammalian hydrophobicity scale--MHS). Both scales are particularly useful for computational protein structure elucidation, for example as input for machine learning techniques, such as secondary structure or trans-membrane span prediction, or as reference energies for protein structure prediction or protein design. The knowledge-based UHS shows a striking similarity to a recent experimental hydrophobicity scale introduced by Hessa and coworkers (Hessa T et al., Nature 2007;450:U1026-U1032). Convergence of two very different approaches onto similar hydrophobicity values consolidates the major differences between experimental and knowledge-based scales observed in earlier studies. Moreover, the UHS scale represents an accurate absolute free energy measure for folded, multispan MPs--a feature that is absent from many existing scales. The utility of the UHS was demonstrated by analyzing a series of diverse MPs. It is further shown that the UHS outperforms nine established hydrophobicity scales in predicting trans-membrane spans along the protein sequence. The accuracy of the present hydrophobicity scale profits from the doubling of the number of integral MPs in the PDB over the past four years. The UHS paves the way for an increased accuracy in the prediction of trans-membrane spans.

Delivery to the correct membrane domain in polarized epithelial cells is a critical regulatory mechanism for transmembrane proteins. The trafficking of these proteins is directed by short amino acid sequences known as sorting motifs. In six basolaterally-localized proteins lacking the canonical tyrosine- and dileucine-based basolateral sorting motifs, a monoleucine-based sorting motif has been identified. This review will discuss these proteins with an identified monoleucine-based sorting motif, their conserved structural features, as well as the future directions of study for this non-canonical basolateral sorting motif. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Computational membrane protein design is challenging due to the small number of high-resolution structures available to elucidate the physical basis of membrane protein structure, multiple functionally important conformational states, and a limited number of high-throughput biophysical assays to monitor function. However, structural determination of membrane proteins has made tremendous progress in the past years. Concurrently the field of soluble computational design has made impressive inroads. These developments allow us to tackle the formidable challenge of designing functional membrane proteins. Herein, Rosetta is benchmarked for membrane protein design. We evaluate strategies to cope with the often reduced quality of experimental membrane protein structures. Further, we test the usage of symmetry in design protocols, which is particularly important as many membrane proteins exist as homo-oligomers. We compare a soluble scoring function with a scoring function optimized for membrane proteins, RosettaMembrane. Both scoring functions recovered around half of the native sequence when completely redesigning membrane proteins. However, RosettaMembrane recovered the most native-like amino acid property composition. While leucine was overrepresented in the inner and outer-hydrophobic regions of RosettaMembrane designs, it resulted in a native-like surface hydrophobicity indicating that it is currently the best option for designing membrane proteins with Rosetta. [ABSTRACT FROM AUTHOR]

A cellulose synthesis complex with a "rosette" shape is responsible for synthesis of cellulose chains and their assembly into microfibrils within the cell walls of land plants and their charophyte algal progenitors. The number of cellulose synthase proteins in this large multisubunit transmembrane protein complex and the number of cellulose chains in a microfibril have been debated for many years. This work reports a low resolution structure of the catalytic domain of CESA1 from Arabidopsis (Arabidopsis thaliana; AtCESA1CatD) determined by small-angle scattering techniques and provides the first experimental evidence for the self-assembly of CESA into a stable trimer in solution. The catalytic domain was overexpressed in Escherichia coli, and using a two-step procedure, it was possible to isolate monomeric and trimeric forms of AtCESA1CatD. The conformation of monomeric and trimeric AtCESA1CatD proteins were studied using small-angle neutron scattering and small-angle x-ray scattering. A series of AtCESA1CatD trimer computational models were compared with the small-angle x-ray scattering trimer profile to explore the possible arrangement of the monomers in the trimers. Several candidate trimers were identified with monomers oriented such that the newly synthesized cellulose chains project toward the cell membrane. In these models, the class-specific region is found at the periphery of the complex, and the plant-conserved region forms the base of the trimer. This study strongly supports the "hexamer of trimers" model for the rosette cellulose synthesis complex that synthesizes an 18-chain cellulose microfibril as its fundamental product. [ABSTRACT FROM AUTHOR]

[Display omitted] • LeuT-fold transporters mediate substrate uptake and efflux using electrochemical ion gradients. • Critical functions include neurotransmitter clearance in synapses and glucose uptake in kidneys. • Recent structures highlight a conserved architecture and identify motifs of ligands binding. • Despite these similarities, their mechanisms of alternating access are not conserved. • Recent biophysical studies reveal the energy landscapes of these transporters. Found in all domains of life, transporters belonging to the LeuT-fold class mediate the import and exchange of hydrophilic and charged compounds such as amino acids, metals, and sugar molecules. Nearly two decades of investigations on the eponymous bacterial transporter LeuT have yielded a library of high-resolution snapshots of its conformational cycle linked by solution-state experimental data obtained from multiple techniques. In parallel, its topology has been observed in symporters and antiporters characterized by a spectrum of substrate specificities and coupled to gradients of distinct ions. Here we review and compare mechanistic models of transport for LeuT, its well-studied homologs, as well as functionally distant members of the fold, emphasizing the commonalities and divergences in alternating access and the corresponding energy landscapes. Our integrated summary illustrates how fold conservation, a hallmark of the LeuT fold, coincides with divergent choreographies of alternating access that nevertheless capitalize on recurrent structural motifs. In addition, it highlights the knowledge gap that hinders the leveraging of the current body of research into detailed mechanisms of transport for this important class of membrane proteins. [ABSTRACT FROM AUTHOR]

The rapidly increasing number of high-resolution X-ray structures of G-protein coupled receptors (GPCRs) creates a unique opportunity to employ comparative modeling and docking to provide valuable insight into the function and ligand binding determinants of novel receptors, to assist in virtual screening and to design and optimize drug candidates. However, low sequence identity between receptors, conformational flexibility, and chemical diversity of ligands present an enormous challenge to molecular modeling approaches. It is our hypothesis that rapid Monte-Carlo sampling of protein backbone and side-chain conformational space with Rosetta can be leveraged to meet this challenge. This study performs unbiased comparative modeling and docking methodologies using 14 distinct high-resolution GPCRs and proposes knowledge-based filtering methods for improvement of sampling performance and identification of correct ligand-receptor interactions. On average, top ranked receptor models built on template structures over 50% sequence identity are within 2.9 Å of the experimental structure, with an average root mean square deviation (RMSD) of 2.2 Å for the transmembrane region and 5 Å for the second extracellular loop. Furthermore, these models are consistently correlated with low Rosetta energy score. To predict their binding modes, ligand conformers of the 14 ligands co-crystalized with the GPCRs were docked against the top ranked comparative models. In contrast to the comparative models themselves, however, it remains difficult to unambiguously identify correct binding modes by score alone. On average, sampling performance was improved by 103 fold over random using knowledge-based and energy-based filters. In assessing the applicability of experimental constraints, we found that sampling performance is increased by one order of magnitude for every 10 residues known to contact the ligand. Additionally, in the case of DOR, knowledge of a single specific ligand-protein contact improved sampling efficiency 7 fold. These findings offer specific guidelines which may lead to increased success in determining receptor-ligand complexes. [ABSTRACT FROM AUTHOR]

G protein-coupled receptors (GPCRs) represent the largest membrane protein family and a significant target class for therapeutics. Receptors from GPCRs' largest class, class A, influence virtually every aspect of human physiology. About 45% of the members of this family endogenously bind flexible peptides or peptides segments within larger protein ligands. While many of these peptides have been structurally characterized in their solution state, the few studies of peptides in their receptor-bound state suggest that these peptides interact with a shared set of residues and undergo significant conformational changes. For the purpose of understanding binding dynamics and the development of peptidomimetic drug compounds, further studies should investigate the peptide ligands that are complexed to their cognate receptor. [ABSTRACT FROM AUTHOR]

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. • SMIM10L1 was the most significantly downregulated gene in PTEN knockdown APCs. • SMIM10L1 downregulation enhances PI3K/AKT/mTOR- signaling via PTEN downregulation. • SMIM10L1 downregulation enhances adipogenesis in two- and three-dimensional culture. [ABSTRACT FROM AUTHOR]

The article presents a study which explores the molecular architecture of the human caveolin-1 complex . It mentions that membrane-sculpting proteins shape the morphology of cell membranes and facilitate remodeling in response to physiological and environmental cues. It discusses that complexes of the monotopic membrane protein caveolin function as essential curvature-generating components of caveolae, flask-shaped invaginations that sense and respond to plasma membrane tension.

Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward- and outward-facing states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy. Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity. Here, we present an approach to drive AF2 to sample alternative conformations of topologically diverse transporters and G-protein-coupled receptors that are absent from the AF2 training set. Whereas models of most proteins generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another, reducing the depth of the input multiple sequence alignments by stochastic subsampling led to the generation of accurate models in multiple conformations. In our benchmark, these conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average template modeling score of 0.94). These results suggest a straightforward approach to identifying native-like alternative states, while also highlighting the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states. [ABSTRACT FROM AUTHOR]

Membrane protein variants with diminished conformational stability often exhibit enhanced cellular expression at reduced growth temperatures. The expression of "temperature-sensitive" variants is also typically sensitive to corrector molecules that bind and stabilize the native conformation. There are many examples of temperature-sensitive rhodopsin variants, the misfolding of which is associated with the molecular basis of retinitis pigmentosa. In this work, we employ deep muta-tional scanning to compare the effects of reduced growth temperature and 9-cis-retinal, an investigational corrector, on the plasma membrane expression of 700 rhodopsin variants in HEK293T cells. We find that the change in expression at reduced growth temperatures correlates with the response to 9-cis-retinal among variants bearing mutations within a hy-drophobic transmembrane domain (TM2). The most sensitive variants appear to disrupt a native helical kink within this transmembrane domain. By comparison, mutants that alter the structure of a polar transmembrane domain (TM7) exhibit weaker responses to temperature and retinal that are poorly correlated. Statistical analyses suggest that this observed insensitivity cannot be attributed to a single variable, but likely arises from the composite effects of mutations on the energetics of membrane integration, the stability of the native conformation, and the integrity of the retinal-binding pocket. Finally, we show that the characteristics of purified temperature- and retinal-sensitive variants suggest that the proteostatic effects of retinal may be manifested during translation and cotranslational folding. Together, our findings highlight several biophysical constraints that appear to influence the sensitivity of genetic variants to temperature and small-molecule correctors. [ABSTRACT FROM AUTHOR]

Peripheral myelin protein 22 (PMP22) is a tetraspan membrane protein strongly expressed in myelinating Schwann cells of the peripheral nervous system. Myriad missense mutations in PMP22 result in varying degrees of peripheral neuropathy. We used Rosetta 3.5 to generate a homology model of PMP22 based on the recently published crystal structure of claudin-15. The model suggests that several mutations known to result in neuropathy act by disrupting transmembrane helix packing interactions. Our model also supports suggestions from previous studies that the first transmembrane helix is not tightly associated with the rest of the helical bundle. [ABSTRACT FROM AUTHOR]

The combination of paramagnetic tagging strategies with NMR or EPR spectroscopic techniques can revolutionize de novo structure determination of helical membrane proteins. Leveraging the full potential of this approach requires optimal labeling strategies and prediction of membrane protein topology from sparse and low-resolution distance restraints, as addressed by . [ABSTRACT FROM AUTHOR]

The article reports on the mechanism of the human KCNQ1 potassium channel dysfunction in long QT syndrome (LQTS) with mutations within voltage sensor domain (VSD). Topics include the analysis of the efficiency of plasma membrane trafficking, protein stability, and proteasome inhibition, the classification of mutation in various mechanistic categories, and the destabilization of the structure of VSD accompanied by degradation by proteasome.

Recent advancements in computational tools have allowed protein structure prediction with high accuracy. Computational prediction methods have been used for modeling many soluble and membrane proteins, but the performance of these methods in modeling peptide structures has not yet been systematically investigated. We benchmarked the accuracy of AlphaFold2 in predicting 588 peptide structures between 10 and 40 amino acids using experimentally determined NMR structures as reference. Our results showed AlphaFold2 predicts α-helical, β-hairpin, and disulfide-rich peptides with high accuracy. AlphaFold2 performed at least as well if not better than alternative methods developed specifically for peptide structure prediction. AlphaFold2 showed several shortcomings in predicting Φ/Ψ angles, disulfide bond patterns, and the lowest RMSD structures failed to correlate with lowest pLDDT ranked structures. In summary, computation can be a powerful tool to predict peptide structures, but additional steps may be necessary to analyze and validate the results. [Display omitted] • Performance of AlphaFold2 was compared with other protein or peptide folding methods • Deep-learning-based methods outperformed dedicated peptide structure prediction tools • Prediction accuracy is affected by peptide secondary structure McDonald et al. investigate the performance of AlphaFold2 in comparison with other peptide and protein structure prediction methods. Root-mean-square deviation analyses show deep-learning methods like AlphaFold2 and Omega-Fold perform the best in most cases but have reduced accuracy with non-helical secondary structure motifs and solvent-exposed peptides. [ABSTRACT FROM AUTHOR]

KCNE1 is a single-span membrane protein that modulates the voltage-gated potassium channel KCNQ1 (Kv7. 1) by slowing activation and enhancing channel conductance to generate the slow delayed rectifier current (IKs) that is critical for the repolarization phase of the cardiac action potential. Perturbation of channel function by inherited mutations in KCNE1 or KCNQ1 results in increased susceptibility to cardiac arrhythmias and sudden death with or without accompanying deafness. Here, we present the three- dimensional structure of KCNE1. The transmembrane domain (TMD) of KCNE1 is a curved a-helix and is flanked by intra- and extracellular domains comprised of a-helices joined by flexible linkers. Experimentally restrained docking of the KCNE 1 TMD to a closed state model of KCNQ 1 suggests that KCNE1 slows channel activation by sitting on and restricting the movement of the S4-S5 linker that connects the voltage sensor to the pore domain. We postulate that this is an adhesive interaction that must be disrupted before the channel can be opened in response to membrane depolarization. Docking to open KCNQ 1 indicates that the extracellular end of the KCNE 1 TMD forms an interface with an intersubunit cleft in the channel that is associated with most known gain-of-function disease mutations. Binding of KCNE1 to this "gain-of-function cleft" may explain how it increases conductance and stabilizes the open state. These working models for the KCNE1-KCNQ1 complexes may be used to formulate testable hypotheses for the molecular bases of disease phenotypes associated with the dozens of known inherited mutations in KCNE1 and KCNQ1. [ABSTRACT FROM AUTHOR]