Ez, a Depth-dependent Potential for Assessing the Energies of Insertion of Amino Acid Side-chains into Membranes: Derivation and Applications to Determining the Orientation of Transmembrane and Interfacial Helices

Improved prediction of anti-angiogenic peptides based on machine learning models and comprehensive features from peptide sequences

Angiogenesis is a key process for the proliferation and metastatic spread of cancer cells. Anti-angiogenic peptides (AAPs), with the capability of inhibiting angiogenesis, are promising candidates in cancer treatment. We propose AAPL, a sequence-based predictor to identify AAPs with machine learning models of improved prediction accuracy. Each peptide sequence was transformed to a vector of 4335 numeric values according to 58 different feature types, followed by a heuristic algorithm for feature selection. Next, the hyperparameters of six machine learning models were optimized with respect to the feature subset. We considered two datasets, one with entire peptide sequences and the other with 15 amino acids from peptide N-termini. AAPL achieved Matthew's correlation coefficients of 0.671 and 0.756 for independent tests based on the two datasets, respectively, outperforming existing predictors by a range of 5.3% to 24.6%. Further analyses show that AAPL yields higher prediction accuracy for peptides with more hydrophobic residues, and fewer hydrophilic and charged residues. The source code of AAPL is available at https://github.com/yunzheng2002/Anti-angiogenic .

De novo-designed transmembrane proteins bind and regulate a cytokine receptor

Transmembrane (TM) domains as simple as a single span can perform complex biological functions using entirely lipid-embedded chemical features. Computational design has the potential to generate custom tool molecules directly targeting membrane proteins at their functional TM regions. Thus far, designed TM domain-targeting agents have been limited to mimicking the binding modes and motifs of natural TM interaction partners. Here, we demonstrate the design of de novo TM proteins targeting the erythropoietin receptor (EpoR) TM domain in a custom binding topology competitive with receptor homodimerization. The TM proteins expressed in mammalian cells complex with EpoR and inhibit erythropoietin-induced cell proliferation. In vitro, the synthetic TM domain complex outcompetes EpoR homodimerization. Structural characterization reveals that the complex involves the intended amino acids and agrees with our designed molecular model of antiparallel TM helices at 1:1 stoichiometry. Thus, membrane protein TM regions can now be targeted in custom-designed topologies.

Missense3D-TM: Predicting the Effect of Missense Variants in Helical Transmembrane Protein Regions Using 3D Protein Structures

Variant effect predictors assess if a substitution is pathogenic or benign. Most predictors, including those that are structure-based, are designed for globular proteins in aqueous environments and do not consider that the variant residue is located within the membrane. We report Missense3D-TM that provides a structure-based assessment of the impact of a missense variant located within a membrane. On a dataset of 2,078 pathogenic and 1,060 benign variants, spanning 711 proteins from 706 structures, Missense3D-TM achieved an accuracy of 66%, Mathews correlation coefficient of 0.37, sensitivity of 58% and specificity of 81%. Missense3D-TM performed similarly to mCSM-membrane: accuracy 66% vs 61% (p = 0.02) on an unbalanced test set and 70% vs 67% (p = 0.20) on a balanced test set. The Missense3D-TM website provides an analysis of the structural effects of the variant along with its predicted position within the membrane. The web server is available at http://missense3d.bc.ic.ac.uk/.

Implicit model to capture electrostatic features of membrane environment

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

Implicit model to capture electrostatic features of membrane environment

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

Designed Transmembrane Proteins Inhibit the Erythropoietin Receptor in a Custom Binding Topology

Transmembrane (TM) domains as simple as a single span can perform complex biological functions using entirely lipid-embedded chemical features. Computational design has potential to generate custom tool molecules directly targeting membrane proteins at their functional TM regions. Thus far, designed TM domain-targeting agents have been limited to mimicking binding modes and motifs of natural TM interaction partners. Here, we demonstrate the design of de novo TM proteins targeting the erythropoietin receptor (EpoR) TM domain in a custom binding topology competitive with receptor homodimerization. The TM proteins expressed in mammalian cells complex with EpoR and inhibit erythropoietin-induced cell proliferation. In vitro, the synthetic TM domain complex outcompetes EpoR homodimerization. Structural characterization reveals that the complex involves the intended amino acids and agrees with our designed molecular model of antiparallel TM helices at 1:1 stoichiometry. Thus, membrane protein TM regions can now be targeted in custom designed topologies.

Polarization-Dependent Sum-Frequency Generation Spectroscopy for Ångstrom-Scale Depth Profiling of Molecules at Interfaces

The three-dimensional spatial distribution of molecules at soft matter interfaces is crucial for processes ranging from membrane biophysics to atmospheric chemistry. While several techniques can access surface composition, obtaining information on the depth distribution is challenging. We develop a noninvasive, polarization-resolved, surface-specific sum-frequency generation spectroscopy providing quantitative depth information. We demonstrate the technique on formic acid molecules at the air-water interface. With increasing molar fraction from 2.5% to 10%, the formic acid molecules shift, on average, ∼0.9  Å into the bulk. The consistency with the simulation data manifests that the technique allows for probing the Ångstrom-scale depth profile.

Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity

Peptides have positively impacted the pharmaceutical industry as drugs, biomarkers, or diagnostic tools of high therapeutic value. However, only a handful have progressed to the market. Toxicity is one of the main obstacles to translating peptides into clinics. Hemolysis or hemotoxicity, the principal source of toxicity, is a natural or disease-induced event leading to the death of vital red blood cells. Initial screenings for toxicity have been widely evaluated using erythrocytes as the gold standard. More recently, many online databases filled with peptide sequences and their biological meta-data have paved the way toward hemolysis prediction using user-friendly, fast-access machine learning-driven programs. This review details the growing contributions of in silico approaches developed in the last decade for the large-scale prediction of erythrocyte lysis induced by peptides. After an overview of the pharmaceutical landscape of peptide therapeutics, we highlighted the relevance of early hemolysis studies in drug development. We emphasized the computational models and algorithms used to this end in light of historical and recent findings in this promising field. We benchmarked seven predictors using peptides from different data sets, having 7–35 amino acids in length. According to our predictions, the models have scored an accuracy over 50.42% and a minimal Matthew’s correlation coefficient over 0.11. The maximum values for these statistical parameters achieved 100.0% and 1.00, respectively. Finally, strategies for optimizing peptide selectivity were described, as well as prospects for future investigations. The development of in silico predictive approaches to peptide toxicity has just started, but their important contributions clearly demonstrate their potential for peptide science and computer-aided drug design. Methodology refinement and increasing use will motivate the timely and accurate in silico identification of selective, non-toxic peptide therapeutics.

A kink in DWORF helical structure controls the activation of the sarcoplasmic reticulum Ca2+-ATPase

SERCA is a P-type ATPase embedded in the sarcoplasmic reticulum and plays a central role in muscle relaxation. SERCA's function is regulated by single-pass membrane proteins called regulins. Unlike other regulins, dwarf open reading frame (DWORF) expressed in cardiac muscle has a unique activating effect. Here, we determine the structure and topology of DWORF in lipid bilayers using a combination of oriented sample solid-state NMR spectroscopy and replica-averaged orientationally restrained molecular dynamics. We found that DWORF's structural topology consists of a dynamic N-terminal domain, an amphipathic juxtamembrane helix that crosses the lipid groups at an angle of 64°, and a transmembrane C-terminal helix with an angle of 32°. A kink induced by Pro15, unique to DWORF, separates the two helical domains. A single Pro15Ala mutant significantly decreases the kink and eliminates DWORF's activating effect on SERCA. Overall, our findings directly link DWORF's structural topology to its activating effect on SERCA.

How physical forces drive the process of helical membrane protein folding

Protein folding is a fundamental process of life with important implications throughout biology. Indeed, tens of thousands of mutations have been associated with diseases, and most of these mutations are believed to affect protein folding rather than function. Correct folding is also a key element of design. These factors have motivated decades of research on protein folding. Unfortunately, knowledge of membrane protein folding lags that of soluble proteins. This gap is partly caused by the greater technical challenges associated with membrane protein studies, but also because of additional complexities. While soluble proteins fold in a homogenous water environment, membrane proteins fold in a setting that ranges from bulk water to highly charged to apolar. Thus, the forces that drive folding vary in different regions of the protein, and this complexity needs to be incorporated into our understanding of the folding process. Here, we review our understanding of membrane protein folding biophysics. Despite the greater challenge, better model systems and new experimental techniques are starting to unravel the forces and pathways in membrane protein folding.

Spatial arrangement of proteins in planar and curved membranes by PPM 3.0

Cellular protrusions, invaginations, and many intracellular organelles have strongly curved membrane regions. Transmembrane and peripheral membrane proteins that induce, sense, or stabilize such regions cannot be properly fitted into a single flat bilayer. To treat such proteins, we developed a new method and a web tool, PPM 3.0, for positioning proteins in curved or planar, single or multiple membranes. This method determines the energetically optimal spatial position, the hydrophobic thickness, and the radius of intrinsic curvature of a membrane-deforming protein structure by arranging it in a single or several sphere-shaped or planar membrane sections. In addition, it can define the lipid-embedded regions of a protein that simultaneously spans several membranes or determine the optimal position of a peptide in a spherical micelle. The PPM 3.0 web server operates with 17 types of biological membranes and 4 types of artificial bilayers. It is publicly available at https://opm.phar.umich.edu/ppm_server3. PPM 3.0 was applied to identify and characterize arrangements in membranes of 128 proteins with a significant intrinsic curvature, such as BAR domains, annexins, Piezo, and MscS mechanosensitive channels, cation-chloride cotransporters, as well as mitochondrial ATP synthases, calcium uniporters, and TOM complexes. These proteins form large complexes that are mainly localized in mitochondria, plasma membranes, and endosomes. Structures of bacterial drug efflux pumps, AcrAB-TolC, MexAB-OrpM, and MacAB-TolC, were positioned in both membranes of the bacterial cell envelop, while structures of multimeric gap-junction channels were arranged in two opposed cellular membranes.

Spiers Memorial Lecture: Analysis and de novo design of membrane-interactive peptides

Membrane-peptide interactions play critical roles in many cellular and organismic functions, including protection from infection, remodeling of membranes, signaling, and ion transport. Peptides interact with membranes in a variety of ways: some associate with membrane surfaces in either intrinsically disordered conformations or well-defined secondary structures. Peptides with sufficient hydrophobicity can also insert vertically as transmembrane monomers, and many associate further into membrane-spanning helical bundles. Indeed, some peptides progress through each of these stages in the process of forming oligomeric bundles. In each case, the structure of the peptide and the membrane represent a delicate balance between peptide-membrane and peptide-peptide interactions. We will review this literature from the perspective of several biologically important systems, including antimicrobial peptides and their mimics, α-synuclein, receptor tyrosine kinases, and ion channels. We also discuss the use of de novo design to construct models to test our understanding of the underlying principles and to provide useful leads for pharmaceutical intervention of diseases.

BRANEart: Identify Stability Strength and Weakness Regions in Membrane Proteins

Understanding the role of stability strengths and weaknesses in proteins is a key objective for rationalizing their dynamical and functional properties such as conformational changes, catalytic activity, and protein-protein and protein-ligand interactions. We present BRANEart, a new, fast and accurate method to evaluate the per-residue contributions to the overall stability of membrane proteins. It is based on an extended set of recently introduced statistical potentials derived from membrane protein structures, which better describe the stability properties of this class of proteins than standard potentials derived from globular proteins. We defined a per-residue membrane propensity index from combinations of these potentials, which can be used to identify residues which strongly contribute to the stability of the transmembrane region or which would, on the contrary, be more stable in extramembrane regions, or vice versa. Large-scale application to membrane and globular proteins sets and application to tests cases show excellent agreement with experimental data. BRANEart thus appears as a useful instrument to analyze in detail the overall stability properties of a target membrane protein, to position it relative to the lipid bilayer, and to rationally modify its biophysical characteristics and function. BRANEart can be freely accessed from http://babylone.3bio.ulb.ac.be/BRANEart.

ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides

Viruses represent one of the greatest threats to human health, necessitating the development of new antiviral drug candidates. Antiviral peptides often possess excellent biological activity and a favourable toxicity profile, and therefore represent a promising field of novel antiviral drugs. As the quantity of sequencing data grows annually, the development of an accurate in silico method for the prediction of peptide antiviral activities is important. This study leverages advances in deep learning and cheminformatics to produce a novel sequence-based deep neural network classifier for the prediction of antiviral peptide activity. The method outperforms the existent best-in-class, with an external test accuracy of 93.9%, Matthews correlation coefficient of 0.87 and an Area Under the Curve of 0.93 on the dataset of experimentally validated peptide activities. This cutting-edge classifier is available as an online web server at https://research.timmons.eu/ennavia, facilitating in silico screening and design of peptide antiviral drugs by the wider research community.

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.

Physicochemical Features and Peculiarities of Interaction of AMP with the Membrane

Antimicrobial peptides (AMPs) are anti-infectives that have the potential to be used as a novel and untapped class of biotherapeutics. Modes of action of antimicrobial peptides include interaction with the cell envelope (cell wall, outer- and inner-membrane). A comprehensive understanding of the peculiarities of interaction of antimicrobial peptides with the cell envelope is necessary to perform a rational design of new biotherapeutics, against which working out resistance is hard for microbes. In order to enable de novo design with low cost and high throughput, in silico predictive models have to be invoked. To develop an efficient predictive model, a comprehensive understanding of the sequence-to-function relationship is required. This knowledge will allow us to encode amino acid sequences expressively and to adequately choose the accurate AMP classifier. A shared protective layer of microbial cells is the inner, plasmatic membrane. The interaction of AMP with a biological membrane (native and/or artificial) has been comprehensively studied. We provide a review of mechanisms and results of interactions of AMP with the cell membrane, relying on the survey of physicochemical, aggregative, and structural features of AMPs. The potency and mechanism of AMP action are presented in terms of amino acid compositions and distributions of the polar and apolar residues along the chain, that is, in terms of the physicochemical features of peptides such as hydrophobicity, hydrophilicity, and amphiphilicity. The survey of current data highlights topics that should be taken into account to come up with a comprehensive explanation of the mechanisms of action of AMP and to uncover the physicochemical faces of peptides, essential to perform their function. Many different approaches have been used to classify AMPs, including machine learning. The survey of knowledge on sequences, structures, and modes of actions of AMP allows concluding that only possessing comprehensive information on physicochemical features of AMPs enables us to develop accurate classifiers and create effective methods of prediction. Consequently, this knowledge is necessary for the development of design tools for peptide-based antibiotics.

Role of key amino acids in the transmembrane domain of the Newcastle disease virus fusion protein

Newcastle disease (ND), caused by the Newcastle disease virus (NDV), is transmitted by poultry with severe infectivity and a high fatality rate. The fusion (F) protein on the NDV envelope facilitates the merger of the viral and host cell membranes with the help of the homologous hemagglutinin-neuraminidase protein (HN). The transmembrane (TM) domains of viral fusion proteins are typically required for fusion, but the key amino acids in NDV F TM domains have not been identified. Site-directed mutagenesis was utilized to change the conserved amino acids at 500, 501, 502, 505, 510, 513, 516, 519, and 520 to alanine. It was found that mutants L519 and V520 had an interrupted protein expression, decreased to below 10%, and mutants A500, I505, V513, and V516 had a hypoactive impact on fusion activity, decreased to 85.38%, 67.05%, 55.38% and 51.13% of wt F, respectively. The results indicated that the TM domain plays a vital part in the fusion activity of the NDV F protein.

Novel anoplin-based (lipo)-peptide models show potent antimicrobial activity

The subject of this study is the synthesis and biological evaluation of anoplin-based (Gly-Leu-Leu³ -Lys-Arg⁵ -Ile-Lys-Thr⁸ -Leu-Leu-NH₂ )-designed (lipo)-peptides, aiming at the development of new antibiotic substances. The design of synthetic compounds based on natural bioactive molecules is an optimistic strategy for the development of new pharmaceutics. Antimicrobial peptides (AMPs) and (lipo)-peptides are two classes of promising compounds, with characteristics that allow them to express their activity by differentiated mechanisms of action. On this basis, anoplin, a natural AMP, was used as a scaffold to design five peptides and seven lipopeptide analogs of them. Substitutions were made on residues Leu³ and Arg⁵ of the interphase and on Thr⁸ of the polar phase, as well as N-terminus conjunctions with octanoic and decanoic acid. The outcome of the biological evaluation revealed that some analogs might have substantial clinical potential. Specifically, Ano 1-F, Ano 3-F, Ano 4-C₁₀ , and Ano 5-F are strongly active against Gram-negative bacteria at minimum inhibitory concentration (MIC) values of 3 μg/ml, while Ano 4-F is active against Gram-positive bacteria at 1 μg/ml. Ano 2-C₁₀ , C₁₀ -Gly-Leu-Lys³ -Lys-Ile⁵ -Ile-Lys-Lys⁸ -Leu-Leu-NH₂ , is the most promising compound (MIC = 0.5 μg/ml) for the development of new pharmaceutics. The conformational features of the synthetic peptides were investigated by circular dichroism spectroscopy, and their physicochemical parameters were calculated. Our study shows that appropriate substitutions in the anoplin sequence in combination with N^α -acylation may lead to new effective AMPs.

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.

ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides

The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agents. While many options are currently available for treatment, these are often expensive and impact the human body unfavourably. Anticancer peptides represent a promising emerging field of anticancer therapeutics, which are characterized by favourable toxicity profile. The development of accurate in silico methods for anticancer peptide prediction is of paramount importance, as the amount of available sequence data is growing each year. This study leverages advances in machine learning research to produce a novel sequence-based deep neural network classifier for anticancer peptide activity. The classifier achieves performance comparable to the best-in-class, with a cross-validated accuracy of 98.3%, Matthews correlation coefficient of 0.91 and an Area Under the Curve of 0.95. This innovative classifier is available as a web server at https://research.timmons.eu/ennaact, facilitating in silico screening and design of new anticancer peptide chemotherapeutics by the research community.

How bilayer properties influence membrane protein folding

The question of how proteins manage to organize into a unique three-dimensional structure has been a major field of study since the first protein structures were determined. For membrane proteins, the question is made more complex because, unlike water-soluble proteins, the solvent is not homogenous or even unique. Each cell and organelle has a distinct lipid composition that can change in response to environmental stimuli. Thus, the study of membrane protein folding requires not only understanding how the unfolded chain navigates its way to the folded state, but also how changes in bilayer properties can affect that search. Here we review what we know so far about the impact of lipid composition on bilayer physical properties and how those properties can affect folding. A better understanding of the lipid bilayer and its effects on membrane protein folding is not only important for a theoretical understanding of the folding process, but can also have a practical impact on our ability to work with and design membrane proteins.

Morphing of Amphipathic Helices to Explore the Activity and Selectivity of Membranolytic Antimicrobial Peptides

Naturally occurring membranolytic antimicrobial peptides (AMPs) are rarely cell-type selective and highly potent at the same time. Template-based peptide design can be used to generate AMPs with improved properties de novo. Following this approach, 18 linear peptides were obtained by computationally morphing the natural AMP Aurein 2.2d2 GLFDIVKKVVGALG into the synthetic model AMP KLLKLLKKLLKLLK. Eleven of the 18 chimeric designs inhibited the growth of Staphylococcus aureus, and six peptides were tested and found to be active against one resistant pathogenic strain or more. One of the peptides was broadly active against bacterial and fungal pathogens without exhibiting toxicity to certain human cell lines. Solution nuclear magnetic resonance and molecular dynamics simulation suggested an oblique-oriented membrane insertion mechanism of this helical de novo peptide. Temperature-resolved circular dichroism spectroscopy pointed to conformational flexibility as an essential feature of cell-type selective AMPs.

HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks

The growing prevalence of resistance to antibiotics motivates the search for new antibacterial agents. Antimicrobial peptides are a diverse class of well-studied membrane-active peptides which function as part of the innate host defence system, and form a promising avenue in antibiotic drug research. Some antimicrobial peptides exhibit toxicity against eukaryotic membranes, typically characterised by hemolytic activity assays, but currently, the understanding of what differentiates hemolytic and non-hemolytic peptides is limited. This study leverages advances in machine learning research to produce a novel artificial neural network classifier for the prediction of hemolytic activity from a peptide's primary sequence. The classifier achieves best-in-class performance, with cross-validated accuracy of [Formula: see text] and Matthews correlation coefficient of 0.71. This innovative classifier is available as a web server at https://research.timmons.eu/happenn , allowing the research community to utilise it for in silico screening of peptide drug candidates for high therapeutic efficacies.

A paradigm of thermal adaptation in penguins and elephants by tuning cold activation in TRPM8

Sensing temperature is critical for the survival of all living beings. Here, we show that during cold-induced activation of the archetypical temperature-sensitive TRPM8 ion channel, there are hydrophobic residues in the pore domain stabilized in the exposed state. Tuning hydrophobicity of these residues specifically alters cold response in TRPM8. Furthermore, TRPM8 orthologs in vertebrates evolved to employ such a mechanism, which physiologically tunes cold tolerance for better thermal adaptation. Our findings not only advance the understanding of the cold-induced activation mechanism of TRPM8 but also bring insights to the molecular evolution strategy for ambient-temperature adaptation in vertebrates.

To adapt to habitat temperature, vertebrates have developed sophisticated physiological and ecological mechanisms through evolution. Transient receptor potential melastatin 8 (TRPM8) serves as the primary sensor for cold. However, how cold activates TRPM8 and how this sensor is tuned for thermal adaptation remain largely unknown. Here we established a molecular framework of how cold is sensed in TRPM8 with a combination of patch-clamp recording, unnatural amino acid imaging, and structural modeling. We first observed that the maximum cold activation of TRPM8 in eight different vertebrates (i.e., African elephant and emperor penguin) with distinct side-chain hydrophobicity (SCH) in the pore domain (PD) is tuned to match their habitat temperature. We further showed that altering SCH for residues in the PD with solvent-accessibility changes leads to specific tuning of the cold response in TRPM8. We also observed that knockin mice expressing the penguin’s TRPM8 exhibited remarkable tolerance to cold. Together, our findings suggest a paradigm of thermal adaptation in vertebrates, where the evolutionary tuning of the cold activation in the TRPM8 ion channel through altering SCH and solvent accessibility in its PD largely contributes to the setting of the cold-sensitive/tolerant phenotype.

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.

De novo protein design, a retrospective

Proteins are molecular machines whose function depends on their ability to achieve complex folds with precisely defined structural and dynamic properties. The rational design of proteins from first-principles, or de novo, was once considered to be impossible, but today proteins with a variety of folds and functions have been realized. We review the evolution of the field from its earliest days, placing particular emphasis on how this endeavor has illuminated our understanding of the principles underlying the folding and function of natural proteins, and is informing the design of macromolecules with unprecedented structures and properties. An initial set of milestones in de novo protein design focused on the construction of sequences that folded in water and membranes to adopt folded conformations. The first proteins were designed from first-principles using very simple physical models. As computers became more powerful, the use of the rotamer approximation allowed one to discover amino acid sequences that stabilize the desired fold. As the crystallographic database of protein structures expanded in subsequent years, it became possible to construct proteins by assembling short backbone fragments that frequently recur in Nature. The second set of milestones in de novo design involves the discovery of complex functions. Proteins have been designed to bind a variety of metals, porphyrins, and other cofactors. The design of proteins that catalyze hydrolysis and oxygen-dependent reactions has progressed significantly. However, de novo design of catalysts for energetically demanding reactions, or even proteins that bind with high affinity and specificity to highly functionalized complex polar molecules remains an importnant challenge that is now being achieved. Finally, the protein design contributed significantly to our understanding of membrane protein folding and transport of ions across membranes. The area of membrane protein design, or more generally of biomimetic polymers that function in mixed or non-aqueous environments, is now becoming increasingly possible.

Comparing Interfacial Trp, Interfacial His and pH Dependence for the Anchoring of Tilted Transmembrane Helical Peptides

Charged and aromatic amino acid residues, being enriched toward the terminals of membrane-spanning helices in membrane proteins, help to stabilize particular transmembrane orientations. Among them, histidine is aromatic and can be positively charge at low pH. To enable investigations of the underlying protein-lipid interactions, we have examined the effects of single or pairs of interfacial histidine residues using the constructive low-dynamic GWALP23 (acetyl-GG²ALW⁵LALALALALALALW¹⁹LAG²²A-amide) peptide framework by incorporating individual or paired histidines at locations 2, 5, 19 or 22. Analysis of helix orientation by means of solid-state ²H NMR spectra of labeled alanine residues reveals marked differences with H^2,22 compared to W^2,22. Nevertheless, the properties of membrane-spanning H^2,22WALP23 helices show little pH dependence and are similar to those having Gly, Arg or Lys at positions 2 and 22. The presence of H5 or H19 influences the helix rotational preference but not the tilt magnitude. H5 affects the helical integrity, as residue 7 unwinds from the core helix; yet once again the helix orientation and dynamic properties show little sensitivity to pH. The overall results reveal that the detailed properties of transmembrane helices depend upon the precise locations of interfacial histidine residues.

GeTFEP: A general transfer free energy profile of transmembrane proteins

Free energy of transferring amino acid side-chains from aqueous environment into lipid bilayers, known as transfer free energy (TFE), provides important information on the thermodynamic stability of membrane proteins. In this study, we derived a TFE profile named General Transfer Free Energy Profile (GeTFEP) based on computation of the TFEs of 58 β-barrel membrane proteins (βMPs). The GeTFEP agrees well with experimentally measured and computationally derived TFEs. Analysis based on the GeTFEP shows that residues in different regions of the transmembrane (TM) segments of βMPs have different roles during the membrane insertion process. Results further reveal the importance of the sequence pattern of TM strands in stabilizing βMPs in the membrane environment. In addition, we show that GeTFEP can be used to predict the positioning and the orientation of βMPs in the membrane. We also show that GeTFEP can be used to identify structurally or functionally important amino acid residue sites of βMPs. Furthermore, the TM segments of α-helical membrane proteins can be accurately predicted with GeTFEP, suggesting that the GeTFEP is of general applicability in studying membrane protein.

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

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

Hydrophobic Characteristic Is Energetically Preferred for Cysteine in a Model Membrane Protein

The naturally occurring amino acid cysteine has often been implicated with a crucial role in maintaining protein structure and stability. An intriguing duality in the intrinsic hydrophobicity of the cysteine side chain is that it exhibits both polar as well as hydrophobic characteristics. Here, we have utilized a cysteine-scanning mutational strategy on the transmembrane β-barrel PagP to examine the membrane depth-dependent energetic contribution of the free cysteine side chain (thiolate) versus the parent residue at an experimental pH of 9.5 in phosphatidylcholine vesicles. We find that introduction of cysteine causes destabilization at several of the 26 lipid-facing sites of PagP that we mutated in this study. The destabilization is minimal (0.5-1.5 kcal/mol) when the mutation is toward the bilayer midplane, whereas it is higher in magnitude (3.0-5.0 kcal/mol) near the bilayer interface. These observations suggest that cysteine forms more favorable interactions with the hydrophobic lipid core as compared to the amphiphilic water-lipid interface. The destabilizing effect is more pronounced when cysteine replaces the interfacial aromatics, which are known to participate in tertiary interaction networks in transmembrane β-barrels. Our observations from experiments involving the introduction of cysteine at the bilayer midplane further strengthen previous views that the free cysteine side chain does possess strongly apolar characteristics. Additionally, the free energy changes observed upon cysteine incorporation show a depth-dependent correlation with the estimated energetic cost of partitioning derived from reported hydrophobicity scales. Our results and observations from the thermodynamic analysis of the PagP barrel may explain why cysteine, despite possessing a polar sulfhydryl group, tends to behave as a hydrophobic (rather than polar) residue in folded protein structures.

De Novo Design and In Vitro Testing of Antimicrobial Peptides against Gram-Negative Bacteria

Antimicrobial peptides (AMPs) have been identified as a potentially new class of antibiotics to combat bacterial resistance to conventional drugs. The design of de novo AMPs with high therapeutic indexes, low cost of synthesis, high resistance to proteases and high bioavailability remains a challenge. Such design requires computational modeling of antimicrobial properties. Currently, most computational methods cannot accurately calculate antimicrobial potency against particular strains of bacterial pathogens. We developed a tool for AMP prediction (Special Prediction (SP) tool) and made it available on our Web site (https://dbaasp.org/prediction). Based on this tool, a simple algorithm for the design of de novo AMPs (DSP) was created. We used DSP to design short peptides with high therapeutic indexes against gram-negative bacteria. The predicted peptides have been synthesized and tested in vitro against a panel of gram-negative bacteria, including drug resistant ones. Predicted activity against Escherichia coli ATCC 25922 was experimentally confirmed for 14 out of 15 peptides. Further improvements for designed peptides included the synthesis of D-enantiomers, which are traditionally used to increase resistance against proteases. One synthetic D-peptide (SP15D) possesses one of the lowest values of minimum inhibitory concentration (MIC) among all DBAASP database short peptides at the time of the submission of this article, while being highly stable against proteases and having a high therapeutic index. The mode of anti-bacterial action, assessed by fluorescence microscopy, shows that SP15D acts similarly to cell penetrating peptides. SP15D can be considered a promising candidate for the development of peptide antibiotics. We plan further exploratory studies with the SP tool, aiming at finding peptides which are active against other pathogenic organisms.

Using the Predicted Structure of the Amot Coiled Coil Homology Domain to Understand Lipid Binding

Angiomotins (Amots) are a family of adapter proteins that modulate cellular polarity, differentiation, proliferation, and migration. Amot family members have a characteristic lipid-binding domain, the coiled coil homology (ACCH) domain that selectively targets the protein to membranes, which has been directly linked to its regulatory role in the cell. Several spot blot assays were used to validate the regions of the domain that participate in its membrane association, deformation, and vesicle fusion activity, which indicated the need for a structure to define the mechanism. Therefore, we sought to understand the structure-function relationship of this domain in order to find ways to modulate these signaling pathways. After many failed attempts to crystallize the ACCH domain of each Amot family member for structural analysis, we decided to pursue homologous models that could be refined using small angle x-ray scattering data. Theoretical models were produced using the homology software SWISS-MODEL and threading software I-TASSER and LOMETS, followed by comparison to SAXS data for model selection and refinement. We present a theoretical model of the domain that is driven by alpha helices and short random coil regions. These alpha helical regions form a classic dimer interface followed by two wide spread legs that we predict to be the lipid binding interface.

Decoupling the Functional Roles of Cationic and Hydrophobic Groups in the Antimicrobial and Hemolytic Activities of Methacrylate Random Copolymers

In this study, we report the antimicrobial and hemolytic activities of ternary statistical methacrylate copolymers consisting of cationic ammonium (amino-ethyl methacrylate: AEMA), hydrophobic alkyl (ethyl methacrylate: EMA), and neutral hydroxyl (hydroxyethyl methacrylate: HEMA) side chain monomers. The cationic and hydrophobic functionalities of copolymers mimic the cationic amphiphilicity of naturally occurring antimicrobial peptides (AMPs). The HEMA monomer units were used to separately modulate the compositions of cationic and hydrophobic monomers, and we investigated the effect of each component on the antimicrobial and hemolytic activities of copolymers. Our data indicated that increasing the number of cationic groups of the copolymers to be more than 30 mol % did not increase their antimicrobial activity against Escherichia coli. The number of cationic side chains in a polymer chain at this threshold is 5.5-7.7, which is comparable to those of natural antimicrobial peptides such as maginin (+6). The MIC values of copolymers with >30 mol % of AEMA depend on only the mol % of EMA, indicating that the hydrophobic interactions of the copolymers with E. coli cell membranes determine the antimicrobial activity of copolymers. These results suggest that the roles of cationic and hydrophobic groups can be controlled independently by design in the ternary copolymers studied here.

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

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

Antimicrobial activity of a porphyrin binding peptide

Amphiphilic alpha-helices are common motifs used in numerous biological systems including membrane channels/pores and antimicrobial peptides (AMPs), and binding proteins, and a variety of synthetic biomaterials. Previously, an amphiphilic peptide with lysine-containing motifs was shown to reversibly bind the anionic porphyrin meso-Tetra(4-sulfonatophenyl)porphyrin (TPPS₄ ^2-) and promote the formation of excitonically coupled conductive J-aggregates. The work presented here focuses on the use of this amphiphilic peptide and derivatives as a potential antimicrobial agent. AMPs are naturally occurring components of the innate immune system, which selectively target and kill bacteria. Sequence derivatives were synthesized in which the position of the Trp, used as a fluorescence reporter, was changed. Additional variants were synthesized where the hydrophobic amino acids were replaced with Ala to reduce net hydrophobicity or where the cationic Lys residues were replaced with diaminopropionic acid (Dap). All peptide sequences retained the ability to bind TPPS₄ ^2- and promote the formation of J-aggregates. The peptides all exhibited a preference for binding anionic lipid vesicles compared to zwitterionic bilayers. The Trp position did not impact antimicrobial activity, but the substituted peptides exhibited markedly lower efficacy. The Dap-containing peptide was only active against E. coli and P. aeruginosa, while the Ala-substituted peptide was inactive at the concentrations tested. This trend was also evident in bacterial membrane permeabilization. The results indicate that the amphiphilic porphyrin binding peptides can also be used as antimicrobial peptides. The cationic nature is a driver in binding to lipid bilayers, but the overall hydrophobicity is important for antimicrobial activity and membrane disruption.

De novo designed transmembrane peptides activating the α5β1 integrin

Computationally designed transmembrane α-helical peptides (CHAMP) have been used to compete for helix-helix interactions within the membrane, enabling the ability to probe the activation of the integrins αIIbβ3 and αvβ3. Here, this method is extended towards the design of CHAMP peptides that inhibit the association of the α5β1 transmembrane (TM) domains, targeting the Ala-X3-Gly motif within α5. Our previous design algorithm was performed alongside a new workflow implemented within the widely used Rosetta molecular modeling suite. Peptides from each computational approach activated integrin α5β1 but not αVβ3 in human endothelial cells. Two CHAMP peptides were shown to directly associate with an α5 TM domain peptide in detergent micelles to a similar degree as a β1 TM peptide does. By solution-state nuclear magnetic resonance, one of these CHAMP peptides was shown to bind primarily the integrin β1 TM domain, which itself has a Gly-X3-Gly motif. The second peptide associated modestly with both α5 and β1 constructs, with slight preference for α5. Although the design goal was not fully realized, this work characterizes novel CHAMP peptides activating α5β1 that can serve as useful reagents for probing integrin biology.

Design of self-assembling transmembrane helical bundles to elucidate principles required for membrane protein folding and ion transport

Ion transporters and channels are able to identify and act on specific substrates among myriads of ions and molecules critical to cellular processes, such as homeostasis, cell signalling, nutrient influx and drug efflux. Recently, we designed Rocker, a minimalist model for Zn²⁺/H⁺ co-transport. The success of this effort suggests that de novo membrane protein design has now come of age so as to serve a key approach towards probing the determinants of membrane protein folding, assembly and function. Here, we review general principles that can be used to design membrane proteins, with particular reference to helical assemblies with transport function. We also provide new functional and NMR data that probe the dynamic mechanism of conduction through Rocker.This article is part of the themed issue 'Membrane pores: from structure and assembly, to medicine and technology'.

Statistically derived asymmetric membrane potentials from α-helical and β-barrel membrane proteins

Modeling membrane protein (MP) folding, insertion, association and their interactions with other proteins, lipids, and drugs requires accurate transfer free energies (TFEs). Various TFE scales have been derived to quantify the energy required or released to insert an amino acid or protein into the membrane. Experimental measurement of TFEs is challenging, and only few scales were extended to depth-dependent energetic profiles. Statistical approaches can be used to derive such potentials; however, this requires a sufficient number of MP structures. Furthermore, MPs are tightly coupled to bilayers that are heterogeneous in terms of lipid composition, asymmetry, and protein content between organisms and organelles. Here we derived asymmetric implicit membrane potentials from β-barrel and α-helical MPs and use them to predict topology, depth and orientation of proteins in the membrane. Our data confirm the ‘charge-outside’ and ‘positive-inside’ rules for β-barrels and α-helical proteins, respectively. We find that the β-barrel profiles have greater asymmetry than the ones from α-helical proteins, as a result of the different membrane architecture of gram-negative bacterial outer membranes and the existence of lipopolysaccharide in the outer leaflet. Our data further suggest that pore-facing residues in β-barrels have a larger contribution to membrane insertion and stability than previously suggested.

Distinct functions for the membrane-proximal ectodomain region (MPER) of HIV-1 gp41 in cell-free and cell-cell viral transmission and cell-cell fusion

HIV-1 is spread by cell-free virions and by cell-cell viral transfer. We asked whether the structure and function of a broad neutralizing antibody (bNAb) epitope, the membrane-proximal ectodomain region (MPER) of the viral gp41 transmembrane glycoprotein, differ in cell-free and cell-cell-transmitted viruses and whether this difference could be related to Ab neutralization sensitivity. Whereas cell-free viruses bearing W666A and I675A substitutions in the MPER lacked infectivity, cell-associated mutant viruses were able to initiate robust spreading infection. Infectivity was restored to cell-free viruses by additional substitutions in the cytoplasmic tail (CT) of gp41 known to disrupt interactions with the viral matrix protein. We observed contrasting effects on cell-free virus infectivity when W666A was introduced to two transmitted/founder isolates, but both mutants could still mediate cell-cell spread. Domain swapping indicated that the disparate W666A phenotypes of the cell-free transmitted/founder viruses are controlled by sequences in variable regions 1, 2, and 4 of gp120. The sequential passaging of an MPER mutant (W672A) in peripheral blood mononuclear cells enabled selection of viral revertants with loss-of-glycan suppressor mutations in variable region 1, suggesting a functional interaction between variable region 1 and the MPER. An MPER-directed bNAb neutralized cell-free virus but not cell-cell viral spread. Our results suggest that the MPER of cell-cell-transmitted virions has a malleable structure that tolerates mutagenic disruption but is not accessible to bNAbs. In cell-free virions, interactions mediated by the CT impose an alternative MPER structure that is less tolerant of mutagenic alteration and is efficiently targeted by bNAbs.

Membrane insertion of-and membrane potential sensing by-semiconductor voltage nanosensors: Feasibility demonstration

We developed membrane voltage nanosensors that are based on inorganic semiconductor nanoparticles. We provide here a feasibility study for their utilization. We use a rationally designed peptide to functionalize the nanosensors, imparting them with the ability to self-insert into a lipid membrane with a desired orientation. Once inserted, these nanosensors could sense membrane potential via the quantum confined Stark effect, with a single-particle sensitivity. With further improvements, these nanosensors could potentially be used for simultaneous recording of action potentials from multiple neurons in a large field of view over a long duration and for recording electrical signals on the nanoscale, such as across one synapse.

Computational design of membrane proteins using RosettaMembrane

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.

Energetics of side-chain partitioning of β-signal residues in unassisted folding of a transmembrane β-barrel protein

The free energy of water-to-interface amino acid partitioning is a major contributing factor in membrane protein folding and stability. The interface residues at the C terminus of transmembrane β-barrels form the β-signal motif required for assisted β-barrel assembly in vivo but are believed to be less important for β-barrel assembly in vitro. Here, we experimentally measured the thermodynamic contribution of all 20 amino acids at the β-signal motif to the unassisted folding of the model β-barrel protein PagP. We obtained the partitioning free energy for all 20 amino acids at the lipid-facing interface (ΔΔG⁰_w,i(φ)) and the protein-facing interface (ΔΔG⁰_w,i(π)) residues and found that hydrophobic amino acids are most favorably transferred to the lipid-facing interface, whereas charged and polar groups display the highest partitioning energy. Furthermore, the change in non-polar surface area correlated directly with the partitioning free energy for the lipid-facing residue and inversely with the protein-facing residue. We also demonstrate that the interface residues of the β-signal motif are vital for in vitro barrel assembly, because they exhibit a side chain–specific energetic contribution determined by the change in nonpolar accessible surface. We further establish that folding cooperativity and hydrophobic collapse are balanced at the membrane interface for optimal stability of the PagP β-barrel scaffold. We conclude that the PagP C-terminal β-signal motif influences the folding cooperativity and stability of the folded β-barrel and that the thermodynamic contributions of the lipid- and protein-facing residues in the transmembrane protein β-signal motif depend on the nature of the amino acid side chain.

Membrane remodeling by the M2 amphipathic helix drives influenza virus membrane scission

Membrane scission is a crucial step in all budding processes, from endocytosis to viral budding. Many proteins have been associated with scission, though the underlying molecular details of how scission is accomplished often remain unknown. Here, we investigate the process of M2-mediated membrane scission during the budding of influenza viruses. Residues 50–61 of the viral M2 protein bind membrane and form an amphipathic α-helix (AH). Membrane binding requires hydrophobic interactions with the lipid tails but not charged interactions with the lipid headgroups. Upon binding, the M2AH induces membrane curvature and lipid ordering, constricting and destabilizing the membrane neck, causing scission. We further show that AHs in the cellular proteins Arf1 and Epsin1 behave in a similar manner. Together, they represent a class of membrane-induced AH domains that alter membrane curvature and fluidity, mediating the scission of constricted membrane necks in multiple biological pathways.

Structural modeling defines transmembrane residues in ADAM17 that are crucial for Rhbdf2-ADAM17-dependent proteolysis

A disintegrin and metalloproteinase 17 (ADAM17) controls the release of the pro-inflammatory cytokine tumor necrosis factor α (TNFα, also known as TNF) and is crucial for protecting the skin and intestinal barrier by proteolytic activation of epidermal growth factor receptor (EGFR) ligands. The seven-membrane-spanning protein called inactive rhomboid 2 (Rhbdf2; also known as iRhom2) is required for ADAM17-dependent TNFα shedding and crosstalk with the EGFR, and a point mutation (known as sinecure, sin) in the first transmembrane domain (TMD) of Rhbdf2 (Rhbdf2^sin) blocks TNFα shedding, yet little is known about the underlying mechanism. Here, we used a structure-function analysis informed by structural modeling to evaluate the interaction between the TMD of ADAM17 and the first TMD of Rhbdf2, and the role of this interaction in Rhbdf2-ADAM17-dependent shedding. Moreover, we show that double mutant mice that are homozygous for Rhbdf2^sin/sin and lack Rhbdf1 closely resemble Rhbdf1/2^-/- double knockout mice, highlighting the severe functional impact of the Rhbdf2^sin/sin mutation on ADAM17 during mouse development. Taken together, these findings provide new mechanistic and conceptual insights into the critical role of the TMDs of ADAM17 and Rhbdf2 in the regulation of the ADAM17 and EGFR, and ADAM17 and TNFα signaling pathways.

Aromatic Side Chain Water-to-Lipid Transfer Free Energies Show a Depth Dependence across the Membrane Normal

Quantitating and understanding the physical forces responsible for the interactions of biomolecules are fundamental to the biological sciences. This is especially challenging for membrane proteins because they are embedded within cellular bilayers that provide a unique medium in which hydrophobic sequences must fold. Knowledge of the energetics of protein-lipid interactions is thus vital to understand cellular processes involving membrane proteins. Here we used a host-guest mutational strategy to calculate the Gibbs free energy changes of water-to-lipid transfer for the aromatic side chains Trp, Tyr, and Phe as a function of depth in the membrane. This work reveals an energetic gradient in the transfer free energies for Trp and Tyr, where transfer was most favorable to the membrane interfacial region and comparatively less favorable into the bilayer center. The transfer energetics follows the concentration gradient of polar atoms across the bilayer normal that naturally occurs in biological membranes. Additional measurements revealed nearest-neighbor coupling in the data set are influenced by a network of aromatic side chains in the host protein. Taken together, these results show that aromatic side chains contribute significantly to membrane protein stability through either aromatic-aromatic interactions or placement at the membrane interface.

Identifying possible sites for antibody neutralization escape: Implications for unique functional properties of the C-terminal tail of Human Immunodeficiency Virus Type 1 gp41

A previous amino acid sequence analyses from our laboratory reported nine potential sites in gp41 glycoprotein of HIV-1 that may contribute to virus escape from antibody neutralization. Besides four sites found outside the membrane of HIV-1 virus, five located in the C-terminal tail of gp41 specifically in the lentivirus lytic peptides motifs (LLPs). To further study the bioinformatical results, the virus infectivity assay and the standard neutralization assay were conducted on conservatively mutated virus. Two sites in the LLP3 domain stood out with the ability to alter the resistance of HIV-1 virus to certain broadly neutralizing antibodies (bNAbs). While the glycoprotein incorporation on the viral membrane and the interaction of the LLP3 domain with the lipid membrane remained unaltered, the increase in neutralization resistance of the mutant virus was associated with the changes on Env conformation. Our findings demonstrate different sensibility of bNAbs to mutations in the C-terminal tail and indicate an unrecognized potential role for even minor sequence variation in the C-terminal tail in modulating the antigenicity of the ectodomain of HIV-1 envelope glycoprotein complex.

Drugging Membrane Protein Interactions

The majority of therapeutics target membrane proteins, accessible on the surface of cells, to alter cellular signaling. Cells use membrane proteins to transduce signals into cells, transport ions and molecules, bind cells to a surface or substrate, and catalyze reactions. Newly devised technologies allow us to drug conventionally "undruggable" regions of membrane proteins, enabling modulation of protein-protein, protein-lipid, and protein-nucleic acid interactions. In this review, we survey the state of the art of high-throughput screening and rational design in drug discovery, and we evaluate the advances in biological understanding and technological capacity that will drive pharmacotherapy forward against unorthodox membrane protein targets.