Effective energy function for proteins in lipid membranes

Characterization of Mice Carrying a Neurodevelopmental Disease-Associated GluN2B(L825V) Variant

N-Methyl-d-aspartate receptors (NMDARs), encoded by GRIN genes, are ionotropic glutamate receptors playing a critical role in synaptic transmission, plasticity, and synapse development. Genome sequence analyses have identified variants in GRIN genes in patients with neurodevelopmental disorders, but the underlying disease mechanisms are not well understood. Here, we have created and evaluated a transgenic mouse line carrying a missense variant Grin2bL825V , corresponding to a de novo GRIN2B variant encoding GluN2B(L825V) found in a patient with intellectual disability (ID) and autism spectrum disorder (ASD). We used HEK293T cells expressing recombinant receptors and primary hippocampal neurons prepared from heterozygous Grin2bL825V/+ (L825V/+) and wild-type (WT) Grin2b+/+ (+/+) male and female mice to assess the functional impact of the variant. Whole-cell NMDAR currents were reduced in neurons from L825V/+ compared with +/+ mice. The peak amplitude of NMDAR-mediated evoked excitatory postsynaptic currents (NMDAR-eEPSCs) was unchanged, but NMDAR-eEPSCs in L825V/+ neurons had faster deactivation compared with +/+ neurons and were less sensitive to a GluN2B-selective antagonist ifenprodil. Together, these results suggest a decreased functional contribution of GluN2B subunits to synaptic NMDAR currents in hippocampal neurons from L825V/+ mice. The analysis of the GluN2B(L825V) subunit surface expression and synaptic localization revealed no differences compared with WT GluN2B. Behavioral testing of mice of both sexes demonstrated hypoactivity, anxiety, and impaired sensorimotor gating in the L825V/+ strain, particularly affecting males, as well as cognitive symptoms. The heterozygous L825V/+ mouse offers a clinically relevant model of GRIN2B-related ID/ASD, and our results suggest synaptic-level functional changes that may contribute to neurodevelopmental pathology.

GENESIS 2.1: High-Performance Molecular Dynamics Software for Enhanced Sampling and Free-Energy Calculations for Atomistic, Coarse-Grained, and Quantum Mechanics/Molecular Mechanics Models

GENeralized-Ensemble SImulation System (GENESIS) is a molecular dynamics (MD) software developed to simulate the conformational dynamics of a single biomolecule, as well as molecular interactions in large biomolecular assemblies and between multiple biomolecules in cellular environments. To achieve the latter purpose, the earlier versions of GENESIS emphasized high performance in atomistic MD simulations on massively parallel supercomputers, with or without graphics processing units (GPUs). Here, we implemented multiscale MD simulations that include atomistic, coarse-grained, and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. They demonstrate high performance and are integrated with enhanced conformational sampling algorithms and free-energy calculations without using external programs except for the QM programs. In this article, we review new functions, molecular models, and other essential features in GENESIS version 2.1 and discuss ongoing developments for future releases.

Free-energy landscapes of transmembrane homodimers by bias-exchange adaptively biased molecular dynamics

Membrane proteins play essential roles in various biological functions within the cell. One of the most common functional regulations involves the dimerization of two single-pass transmembrane (TM) helices. Glycophorin A (GpA) and amyloid precursor protein (APP) form TM homodimers in the membrane, which have been investigated both experimentally and computationally. The homodimer structures are well characterized using only four collective variables (CVs) when each TM helix is stable. The CVs are the interhelical distance, the crossing angle, and the Crick angles for two TM helices. However, conformational sampling with multi-dimensional replica-exchange umbrella sampling (REUS) requires too many replicas to sample all the CVs for exploring the conformational landscapes. Here, we show that the bias-exchange adaptively biased molecular dynamics (BE-ABMD) with the four CVs effectively explores the free-energy landscapes of the TM helix dimers of GpA, wild-type APP and its mutants in the IMM1 implicit membrane. Compared to the original ABMD, the bias-exchange algorithm in BE-ABMD can provide a more rapidly converged conformational landscape. The BE-ABMD simulations could also reveal TM packing interfaces of the membrane proteins and the dependence of the free-energy landscapes on the membrane thickness. This approach is valuable for numerous other applications, including those involving explicit solvent and a lipid bilayer in all-atom force fields or Martini coarse-grained models, and enhances our understanding of protein-protein interactions in biological membranes.

Structural constraints of pyocin S2 import through the ferripyoverdine receptor FpvAI

TonB-dependent transporters (TBDTs) mediate energized transport of essential nutrients into gram-negative bacteria. TBDTs are increasingly being exploited for the delivery of antibiotics to drug-resistant bacteria. While much is known about ground state complexes of TBDTs, few details have emerged about the transport process itself. In this study, we exploit bacteriocin parasitization of a TBDT to probe the mechanics of transport. Previous work has shown that the N-terminal domain of Pseudomonas aeruginosa-specific bacteriocin pyocin S2 (PyoS2NTD) is imported through the pyoverdine receptor FpvAI. PyoS2NTD transport follows the opening of a proton-motive force-dependent pore through FpvAI and the delivery of its own TonB box that engages TonB. We use molecular models and simulations to formulate a complete translocation pathway for PyoS2NTD that we validate using protein engineering and cytotoxicity measurements. We show that following partial removal of the FpvAI plug domain which occludes the channel, the pyocin's N-terminus enters the channel by electrostatic steering and ratchets to the periplasm. Application of force, mimicking that exerted by TonB, leads to unraveling of PyoS2NTD as it squeezes through the channel. Remarkably, while some parts of PyoS2NTD must unfold, complete unfolding is not required for transport, a result we confirmed by disulfide bond engineering. Moreover, the section of the FpvAI plug that remains embedded in the channel appears to serve as a buttress against which PyoS2NTD is pushed to destabilize the domain. Our study reveals the limits of structural deformation that accompanies import through a TBDT and the role the TBDT itself plays in accommodating transport.

Modeling membrane geometries implicitly in Rosetta

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

Conformational Changes and Unfolding of β-Amyloid Substrates in the Active Site of γ-Secretase

Alzheimer's disease (AD) is the leading cause of dementia and is characterized by a presence of amyloid plaques, composed mostly of the amyloid-β (Aβ) peptides, in the brains of AD patients. The peptides are generated from the amyloid precursor protein (APP), which undergoes a sequence of cleavages, referred as trimming, performed by γ-secretase. Here, we investigated conformational changes in a series of β-amyloid substrates (from less and more amyloidogenic pathways) in the active site of presenilin-1, the catalytic subunit of γ-secretase. The substrates are trimmed every three residues, finally leading to Aβ40 and Aβ42, which are the major components of amyloid plaques. To study conformational changes, we employed all-atom molecular dynamics simulations, while for unfolding, we used steered molecular dynamics simulations in an implicit membrane-water environment to accelerate changes. We have found substantial differences in the flexibility of extended C-terminal parts between more and less amyloidogenic pathway substrates. We also propose that the positively charged residues of presenilin-1 may facilitate the stretching and unfolding of substrates. The calculated forces and work/energy of pulling were exceptionally high for Aβ40, indicating why trimming of this substrate is so infrequent.

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.

Transmembrane β-Barrel Models of α-Synuclein Oligomers

The aggregation of α-synuclein is implicated in a number of neurodegenerative diseases, such as Parkinson's and Multiple System Atrophy, but the role of these aggregates in disease development is not clear. One possible mechanism of cytotoxicity is the disturbance or permeabilization of cell membranes by certain types of oligomers. However, no high-resolution structure of such membrane-embedded complexes has ever been determined. Here we construct and evaluate putative transmembrane β-barrels formed by this protein. Examination of the α-synuclein sequence reveals two regions that could form membrane-embedded β-hairpins: 64-92 (the NAC), and 35-56, which harbors many familial Parkinson's mutations. The stability of β-barrels formed by these hairpins is examined first in implicit membrane pores and then by multimicrosecond all-atom simulations. We find that a NAC region barrel remains stably inserted and hydrated for at least 10 μs. A 35-56 barrel remains stably inserted in the membrane but dehydrates and collapses if all His50 are neutral or if His50 is replaced by Q. If half of the His50 are doubly protonated, the barrel takes an oval shape but remains hydrated for at least 10 μs. Possible implications of these findings for α-synuclein pathology are discussed.

Simulations suggest a scaffolding mechanism of membrane deformation by the caveolin 8S complex

Caveolins form complexes of various sizes that deform membranes into polyhedral shapes. However, the recent structure of the 8S complex was disk-like with a flat membrane-binding surface. How can a flat complex deform membranes into nonplanar structures? Molecular dynamics simulations revealed that the 8S complex rapidly takes the form of a suction cup. Simulations on implicit membrane vesicles determined that binding is stronger when E140 gets protonated. In that case, the complex binds much more strongly to 5- and 10-nm-radius vesicles. A concave membrane-binding surface readily explains the membrane-deforming ability of caveolins by direct scaffolding. We propose that the 8S complex sits at the vertices of the caveolar polyhedra, rather than at the center of the polyhedral faces.

Putative Pore Structures of Amyloid β 25-35 in Lipid Bilayers

The amyloid β peptide aggregates to form extracellular plaques in the brains of Alzheimer's disease patients. Certain of its fragments have been found to have similar properties to those of the full-length peptide. The best-studied of these is 25-35, which aggregates into fibrils, is toxic to neurons, and forms ion channels in synthetic lipid bilayers. Here, we investigate possible pore-forming structures of oligomers of this peptide in a POPC/POPG membrane. We consider octameric and decameric β-barrels of different topology, strand orientation, and shear, evaluate their stability in an implicit membrane model, and subject the best models to multimicrosecond all-atom molecular dynamics simulations. We find two decameric structures that are kinetically stable in membranes on this time scale: an imperfectly closed antiparallel β-barrel with K28 in the pore lumen and a short parallel β-barrel with K28 toward the membrane interface. Both structures exhibit dehydrated gaps in the pore lumen, which are larger for the antiparallel barrel. Based on these results, the experimental cation selectivity, the dependence of ion channel activity on voltage direction, and certain mutation data, the parallel model seems more compatible with experimental data.

Incorporating physics to overcome data scarcity in predictive modeling of protein function: A case study of BK channels

Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ∆V1/2, with a RMSE ~ 32 mV and correlation coefficient of R ~ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ∆V1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction.

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.

Incorporating physics to overcome data scarcity in predictive modeling of protein function: a case study of BK channels

Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ΔV 1/2 , with a RMSE ∼ 32 mV and correlation coefficient of R ∼ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V 1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ΔV 1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction.

Author Summary

Deep machine learning has brought many exciting breakthroughs in chemistry, physics and biology. These models require large amount of training data and struggle when the data is scarce. The latter is true for predictive modeling of the function of complex proteins such as ion channels, where only hundreds of mutational data may be available. Using the big potassium (BK) channel as a biologically important model system, we demonstrate that a reliable predictive model of its voltage gating property could be derived from only 473 mutational data by incorporating physics-derived features, which include dynamic properties from molecular dynamics simulations and energetic quantities from Rosetta mutation calculations. We show that the final random forest model captures key trends and hotspots in mutational effects of BK voltage gating, such as the important role of pore hydrophobicity. A particularly curious prediction is that mutations of two adjacent residues on the S5 helix would always have opposite effects on the gating voltage, which was confirmed by experimental characterization of four novel mutations. The current work demonstrates the importance and effectiveness of incorporating physics in predictive modeling of protein function with scarce data.

GS-SMD server for steered molecular dynamics of peptide substrates in the active site of the γ-secretase complex

Despite recent advances in research, the mechanism of Alzheimer's disease is not fully understood yet. Understanding the process of cleavage and then trimming of peptide substrates, can help selectively block γ-secretase (GS) to stop overproduction of the amyloidogenic products. Our GS-SMD server (https://gs-smd.biomodellab.eu/) allows cleaving and unfolding of all currently known GS substrates (more than 170 peptide substrates). The substrate structure is obtained by threading of the substrate sequence into the known structure of GS complex. The simulations are performed in an implicit water-membrane environment so they are performed rather quickly, 2-6 h per job, depending on the mode of calculations (part of GS complex or the whole structure). It is also possible to introduce mutations to the substrate and GS and pull any part of the substrate in any direction using the steered molecular dynamics (SMD) simulations with constant velocity. The obtained trajectories are visualized and analyzed in the interactive way. One can also compare multiple simulations using the interaction frequency analysis. GS-SMD server can be useful for revealing mechanisms of substrate unfolding and role of mutations in this process.

Towards de novo design of transmembrane α-helical assemblies using structural modelling and molecular dynamics simulation

Computational de novo protein design involves iterative processes consisting of amino acid sequence design, structural modelling and scoring, and design validation by synthesis and experimental characterisation. Recent advances in protein structure prediction and modelling methods have enabled the highly efficient and accurate design of water-soluble proteins. However, the design of membrane proteins remains a major challenge. To advance membrane protein design, considering the higher complexity of membrane protein folding, stability, and dynamic interactions between water, ions, lipids, and proteins is an important task. For introducing explicit solvents and membranes to these design methods, all-atom molecular dynamics (MD) simulations of designed proteins provide useful information that cannot be obtained experimentally. In this review, we first describe two major approaches to designing transmembrane α-helical assemblies, consensus and de novo design. We further illustrate recent MD studies of membrane protein folding related to protein design, as well as advanced treatments in molecular models and conformational sampling techniques in the simulations. Finally, we discuss the possibility to introduce MD simulations after the existing static modelling and screening of design decoys as an additional step for refinement of the design, which considers membrane protein folding dynamics and interactions with explicit membranes.

Putative Structures of Membrane-Embedded Amyloid β Oligomers

Perturbation of cell membranes by amyloid β (Ab) peptide oligomers is one possible mechanism of cytotoxicity in Alzheimer's disease, but the structure of such Ab-membrane complexes is unknown. Here we examine the stability of several putative structures by implicit membrane and all-atom molecular dynamics simulations. The structures include (a) a variety of models proposed by other researchers in the past, (b) a heptameric β barrel determined by grafting the Ab sequence onto α-hemolysin, (c) a similar structure with modified strand orientation and turn location based on an experimental β-hairpin structure, (d) oligomers inserting C-terminal β hairpins into one leaflet of the bilayer, (e) oligomers forming parallel C-terminal β barrels, and (f) a helical hexamer made of C-terminal fragments. The α-hemolysin-grafted structure and its alternately oriented variant are stable in the membrane and form an aqueous pore. In contrast, the C-terminal parallel barrels are not stable, presumably due to excessive hydrophobicity of their inner surface. The helical hexamer also failed to stabilize an aqueous pore for the same reason. The C-terminal hairpin-inserting structures remain stably inserted but, again, do not form an aqueous pore. Our results suggest that only β-barrels inserting a combination of C-terminal and other residues can form stable aqueous pores.

Thermodynamic analysis of the GASright transmembrane motif supports energetic model of dimerization

The GASright motif, best known as the fold of the glycophorin A transmembrane dimer, is one of the most common dimerization motifs in membrane proteins, characterized by its hallmark GxxxG-like sequence motifs (GxxxG, AxxxG, GxxxS, and similar). Structurally, GASright displays a right-handed crossing angle and short interhelical distance. Contact between the helical backbones favors the formation of networks of weak hydrogen bonds between Cα-H carbon donors and carbonyl acceptors on opposing helices (Cα-H···O=C). To understand the factors that modulate the stability of GASright, we previously presented a computational and experimental structure-based analysis of 26 predicted dimers. We found that the contributions of van der Waals packing and Cα-H hydrogen bonding to stability, as inferred from the structural models, correlated well with relative dimerization propensities estimated experimentally with the in vivo assay TOXCAT. Here we test this model with a quantitative thermodynamic analysis. We used Förster resonance energy transfer (FRET) to determine the free energy of dimerization of a representative subset of seven of the 26 original TOXCAT dimers using FRET. To overcome the technical issue arising from limited sampling of the dimerization isotherm, we introduced a globally fitting strategy across a set of constructs comprising a wide range of stabilities. This strategy yielded precise thermodynamic data that show strikingly good agreement between the original propensities and ΔG° of association in detergent, suggesting that TOXCAT is a thermodynamically driven process. From the correlation between TOXCAT and thermodynamic stability, the predicted free energy for all the 26 GASright dimers was calculated. These energies correlate with the in silico ΔE scores of dimerization that were computed on the basis of their predicted structure. These findings corroborate our original model with quantitative thermodynamic evidence, strengthening the hypothesis that van der Waals and Cα-H hydrogen bond interactions are the key modulators of GASright stability.

MAIA, Fc receptor-like 3, supersedes JUNO as IZUMO1 receptor during human fertilization

Gamete fusion is a critical event of mammalian fertilization. A random one-bead one-compound combinatorial peptide library represented synthetic human egg mimics and identified a previously unidentified ligand as Fc receptor-like 3, named MAIA after the mythological goddess intertwined with JUNO. This immunoglobulin super family receptor was expressed on human oolemma and played a major role during sperm-egg adhesion and fusion. MAIA forms a highly stable interaction with the known IZUMO1/JUNO sperm-egg complex, permitting specific gamete fusion. The complexity of the MAIA isotype may offer a cryptic sexual selection mechanism to avoid genetic incompatibility and achieve favorable fitness outcomes.

COGRIMEN: Coarse-Grained Method for Modeling of Membrane Proteins in Implicit Environments

The presented methodology is based on coarse-grained representation of biomolecules in implicit environments and is designed for the molecular dynamics simulations of membrane proteins and their complexes. The membrane proteins are not only found in the cell membrane but also in all membranous compartments of the cell: Golgi apparatus, mitochondria, endosomes and lysosomes, and they usually form large complexes. To investigate such systems the methodology is proposed based on two independent approaches combining the coarse-grained MARTINI model for proteins and the effective energy function to mimic the water/membrane environments. The latter is based on the implicit environment developed for all-atom simulations in the IMM1 method. The force field solvation parameters for COGRIMEN were initially calculated from IMM1 all-atom parameters and then optimized using Genetic Algorithms. The new methodology was tested on membrane proteins, their complexes and oligomers. COGRIMEN method is implemented as a patch for NAMD program and can be useful for fast and brief studies of large membrane protein complexes.

Crystal structure of the lipid flippase MurJ in a "squeezed" form distinct from its inward- and outward-facing forms

The bacterial peptidoglycan enclosing the cytoplasmic membrane is a fundamental cellular architecture. The integral membrane protein MurJ plays an essential role in flipping the cell wall building block Lipid II across the cytoplasmic membrane for peptidoglycan biosynthesis. Previously reported crystal structures of MurJ have elucidated its V-shaped inward- or outward-facing forms with an internal cavity for substrate binding. MurJ transports Lipid II using its cavity through conformational transitions between these two forms. Here, we report two crystal structures of inward-facing forms from Arsenophonus endosymbiont MurJ and an unprecedented crystal structure of Escherichia coli MurJ in a "squeezed" form, which lacks a cavity to accommodate the substrate, mainly because of the increased proximity of transmembrane helices 2 and 8. Subsequent molecular dynamics simulations supported the hypothesis that the squeezed form is an intermediate conformation. This study fills a gap in our understanding of the Lipid II flipping mechanism.

Pregnane-based steroids are novel positive NMDA receptor modulators that may compensate for the effect of loss-of-function disease-associated GRIN mutations

BACKGROUND AND PURPOSE

N-methyl-D-aspartate receptors (NMDARs) play a critical role in synaptic plasticity, and mutations in human genes encoding NMDAR subunits have been described in individuals with various neuropsychiatric disorders. Compounds with a positive allosteric effect are thought to compensate for reduced receptor function.

EXPERIMENTAL APPROACH

We have used whole-cell patch-clamp electrophysiology on recombinant rat NMDARs and human variants found in individuals with neuropsychiatric disorders, in combination with in silico modelling, to explore the site of action of novel epipregnanolone-based NMDAR modulators.

KEY RESULTS

Analysis of the action of 4-(20-oxo-5β-pregnan-3β-yl) butanoic acid (EPA-But) at the NMDAR indicates that the effect of this steroid with a "bent" structure is different from that of cholesterol and oxysterols and shares a disuse-dependent mechanism of NMDAR potentiation with the "planar" steroid 20-oxo-pregn-5-en-3β-yl sulfate (PE-S). The potentiating effects of EPA-But and PE-S are additive. Alanine scan mutagenesis identified residues that reduce the potentiating effect of EPA-But. No correlation was found between the effects of EPA-But and PE-S at mutated receptors that were less sensitive to either steroid. The relative degree of potentiation induced by the two steroids also differed in human NMDARs carrying rare variants of hGluN1 or hGluN2B subunits found in individuals with neuropsychiatric disorders, including intellectual disability, epilepsy, developmental delay, and autism spectrum disorder.

CONCLUSION AND IMPLICATIONS

Our results show novel sites of action for pregnanolones at the NMDAR and provide an opportunity for the development of new therapeutic neurosteroid-based ligands to treat diseases associated with glutamatergic system hypofunction.

Mechanism of negative membrane curvature generation by I-BAR domains

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

Distinct Modes of Action of IAPP Oligomers on Membranes

Islet amyloid polypeptide (IAPP, also known as amylin) is a peptide hormone that is co-secreted with insulin by pancreatic β-cells and forms amyloid aggregates in type II diabetes. Various lines of evidence indicate that oligomers of this peptide may induce toxicity by disrupting or forming pores in cell membranes, but the structure of these pores is unknown. Here, we create models of pores for both helical and β-structured peptides using implicit membrane modeling and test their stability using multimicrosecond all-atom simulations. We find that the helical peptides behave similarly to antimicrobial peptides; they remain stably inserted in a highly tilted or partially unfolded configuration creating a narrow water channel. Parallel helix orientation creates a somewhat larger pore. An octameric β barrel of parallel β-hairpins is highly stable in the membrane, whereas the corresponding barrel made of antiparallel hairpins is not. We propose that certain experiments probe the helical pore state while others probe the β-structured pore state; this provides a possible explanation for lack of correlation that is sometimes observed between in vivo toxicity and in vitro liposome permeabilization experiments.

Diverse Scientific Benchmarks for Implicit Membrane Energy Functions

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

GPCRsignal: webserver for analysis of the interface between G-protein-coupled receptors and their effector proteins by dynamics and mutations

GPCRsignal (https://gpcrsignal.biomodellab.eu/) is a webserver devoted to signaling complexes of G-protein–coupled receptors (GPCRs). The recent improvement in cryo-electron microscopy resulted in the determination of a large number of high-resolution structures of GPCRs bound to their effector proteins: G proteins or arrestins. Analyzing the interfaces between receptor and an effector protein is of high importance since a selection of proper G protein or specific conformation of arrestin leads to changes of signaling that can significantly affect action of drugs. GPCRsignal provides a possibility of running molecular dynamics simulations of all currently available GPCR-effector protein complexes for curated structures: wild-type, with crystal/cryo-EM mutations, or with mutations introduced by the user. The simulations are performed in an implicit water-membrane environment, so they are rather fast. User can run several simulations to obtain statistically valid results. The simulations can be analyzed separately using dynamic FlarePlots for particular types of interactions. One can also compare groups of simulations in Interaction frequency analysis as HeatMaps and also in interaction frequency difference analysis as sticks, linking the interacting residues, of different color and size proportional to differences in contact frequencies.

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.

Prediction of amphipathic helix-membrane interactions with Rosetta

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

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

Baseline Comparisons of Complementary Sampling Methods for Assembly Driven by Short-Ranged Pair Potentials toward Fast and Flexible Hybridization

This work measures baseline sampling characteristics that highlight fundamental differences between sampling methods for assembly driven by short-ranged pair potentials. Such granular comparison is essential for fast, flexible, and accurate hybridization of complementary methods. Besides sampling speed, efficiency, and accuracy of uniform grid coverage, other sampling characteristics measured are (i) accuracy of covering narrow low energy regions that have low effective dimension (ii) ability to localize sampling to specific basins, and (iii) flexibility in sampling distributions. As a proof of concept, we compare a recently developed geometric methodology EASAL (Efficient Atlasing and Search of Assembly Landscapes) and the traditional Monte Carlo (MC) method for sampling the energy landscape of two assembling trans-membrane helices, driven by short-range pair potentials. By measuring the above-mentioned sampling characteristics, we demonstrate that EASAL provides localized and accurate coverage of crucial regions of the energy landscape of low effective dimension, under flexible sampling distributions, with much fewer samples and computational resources than MC sampling. EASAL's empirically validated theoretical guarantees permit credible extrapolation of these measurements and comparisons to arbitrary number and size of assembling units. Promising avenues for hybridizing the complementary advantages of the two methods are discussed.

Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View

Membrane proteins (MPs) encompass a large family of proteins with distinct cellular functions, and although representing over 50% of existing pharmaceutical drug targets, their structural and functional information is still very scarce. Over the last years, in silico analysis and algorithm development were essential to characterize MPs and overcome some limitations of experimental approaches. The optimization and improvement of these methods remain an ongoing process, with key advances in MPs' structure, folding, and interface prediction being continuously tackled. Herein, we discuss the latest trends in computational methods toward a deeper understanding of the atomistic and mechanistic details of MPs.

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.

Atg9 is a lipid scramblase that mediates autophagosomal membrane expansion

The molecular function of Atg9, the sole transmembrane protein in the autophagosome-forming machinery, remains unknown. Atg9 colocalizes with Atg2 at the expanding edge of the isolation membrane (IM), where Atg2 receives phospholipids from the endoplasmic reticulum (ER). Here we report that yeast and human Atg9 are lipid scramblases that translocate phospholipids between outer and inner leaflets of liposomes in vitro. Cryo-EM of fission yeast Atg9 reveals a homotrimer, with two connected pores forming a path between the two membrane leaflets: one pore, located at a protomer, opens laterally to the cytoplasmic leaflet; the other, at the trimer center, traverses the membrane vertically. Mutation of residues lining the pores impaired IM expansion and autophagy activity in yeast and abolished Atg9's ability to transport phospholipids between liposome leaflets. These results suggest that phospholipids delivered by Atg2 are translocated from the cytoplasmic to the luminal leaflet by Atg9, thereby driving autophagosomal membrane expansion.

Atlasing of Assembly Landscapes using Distance Geometry and Graph Rigidity

This Article describes a novel geometric methodology for analyzing free energy and kinetics of assembly driven by short-range pair-potentials in an implicit solvent and provides a proof-of-concept illustration of its unique capabilities. An atlas is a labeled partition of the assembly landscape into a roadmap of maximal, contiguous, nearly-equipotential-energy conformational regions or macrostates, together with their neighborhood relationships. The new methodology decouples the roadmap generation from sampling and produces: (1) a queryable atlas of local potential energy minima, their basin structure, energy barriers, and neighboring basins; (2) paths between a specified pair of basins, each path being a sequence of conformational regions or macrostates below a desired energy threshold; and (3) approximations of relative path lengths, basin volumes (configurational entropy), and path probabilities. Results demonstrating the core algorithm's capabilities and high computational efficiency have been generated by a resource-light, curated open source software implementation EASAL (Efficient Atlasing and Search of Assembly Landscapes, ACM Trans. Math. Softw. 2018 44, 1-48. 10.1145/3204472; see software, Efficient Atlasing and Search of Assembly Landscapes, 2016. https://bitbucket.org/geoplexity/easal; video, Video Illustrating the opensource software EASAL, 2016. https://cise.ufl.edu/~sitharam/EASALvideo.mpeg; and user guide, EASAL software user guide, 2016. https://bitbucket.org/geoplexity/easal/src/master/CompleteUserGuide.pdf). Running on a laptop with Intel(R) Core(TM) i7-7700@3.60 GHz CPU with 16GB of RAM, EASAL atlases several hundred thousand conformational regions or macrostates in minutes using a single compute core. Subsequent path and basin computations each take seconds. A parallelized EASAL version running on the same laptop with 4 cores gives a 3× speedup for atlas generation. The core algorithm's correctness, time complexity, and efficiency-accuracy trade-offs are formally guaranteed using modern distance geometry, geometric constraint systems and combinatorial rigidity. The methodology further links the shape of the input assembling units to a type of intuitive and queryable bar-code of the output atlas, which in turn determine stable assembled structures and kinetics. This succinct input-output relationship facilitates reverse analysis and control toward design. A novel feature that is crucial to both the high sampling efficiency and decoupling of roadmap generation from sampling is a recently developed theory of convex Cayley (distance-based) custom parametrizations specific to assembly, as opposed to folding. Representing microstates with macrostate-specific Cayley parameters, to generate microstate samples, avoids gradient-descent search used by all prevailing methods. Further, these parametrizations convexify conformational regions or macrostates. This ratchets up sampling efficiency, significantly reducing number of repeated and discarded samples. These features of the new stand-alone methodology can also be used to complement the strengths of prevailing methodologies including Molecular Dynamics, Monte Carlo, and Fast Fourier Transform based methods.

Site of Action of Brain Neurosteroid Pregnenolone Sulfate at the N-Methyl-D-Aspartate Receptor

N-methyl-D-aspartate receptor (NMDAR) hypofunction has been implicated in several neurodevelopmental disorders. NMDAR function can be augmented by positive allosteric modulators, including endogenous compounds, such as cholesterol and neurosteroid pregnenolone sulfate (PES). Here we report that PES accesses the receptor via the membrane, and its binding site is different from that of cholesterol. Alanine mutagenesis has identified residues that disrupt the steroid potentiating effect at the rat GluN1 (G638; I642) and GluN2B (W559; M562; Y823; M824) subunit. Molecular dynamics simulation indicates that, in the absence of PES, the GluN2B M1 helix residue W559 interacts with the M4 helix residue M824. In the presence of PES, the M1 and M4 helices of agonist-activated receptor rearrange, forming a tighter interaction with the GluN1 M3 helix residues G638 and I642. This stabilizes the open-state position of the GluN1 M3 helices. Together, our data identify a likely binding site for the NMDAR-positive allosteric modulator PES and describe a novel molecular mechanism by which NMDAR activity can be augmented.SIGNIFICANCE STATEMENT There is considerable interest in drugs that enhance NMDAR function and could compensate for receptor hypofunction associated with certain neuropsychiatric disorders. Positive allosteric modulators of NMDARs include an endogenous neurosteroid pregnenolone sulfate (PES), but the binding site of PES on the NMDAR and the molecular mechanism of potentiation are unknown. We use patch-clamp electrophysiology in combination with mutagenesis and in silico modeling to describe the interaction of PES with the NMDAR. Our data indicate that PES binds to the transmembrane domain of the receptor at a discrete group of residues at the GluN2B membrane helices M1 and M4 and the GluN1 helix M3, and that PES potentiates NMDAR function by stabilizing the open-state position of the GluN1 M3 helices.

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

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

Mechanisms of negative membrane curvature sensing and generation by ESCRT III subunit Snf7

Certain proteins have the propensity to bind to negatively curved membranes and generate negative membrane curvature. The mechanism of action of these proteins is much less studied and understood than those that sense and generate positive curvature. In this work, we use implicit membrane modeling to explore the mechanism of an important negative curvature sensing and generating protein: the main ESCRT III subunit Snf7. We find that Snf7 monomers alone can sense negative curvature and that curvature sensitivity increases for dimers and trimers. We have observed spontaneous bending of Snf7 oligomers into circular structures with preferred radius of ~20 nm. The preferred curvature of Snf7 filaments is further confirmed by the simulations of preformed spirals on a cylindrical membrane surface. Snf7 filaments cannot bind with the same interface to flat and curved membranes. We find that even when a filament has the preferred radius, it is always less stable on the flat membrane surface than on the interior cylindrical membrane surface. This provides an additional energy for membrane bending which has not been considered in the spiral spring model. Furthermore, the rings on the cylindrical spirals are bridged together by helix 4 and hence are extra stabilized compared to the spirals on the flat membrane surface.

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.

NMDA Receptor Opening and Closing-Transitions of a Molecular Machine Revealed by Molecular Dynamics

We report the first complete description of the molecular mechanisms behind the transition of the N-methyl-d-aspartate (NMDA) receptor from the state where the transmembrane domain (TMD) and the ion channel are in the open configuration to the relaxed unliganded state where the channel is closed. Using an aggregate of nearly 1 µs of unbiased all-atom implicit membrane and solvent molecular dynamics (MD) simulations we identified distinct structural states of the NMDA receptor and revealed functionally important residues (GluN1/Glu522, GluN1/Arg695, and GluN2B/Asp786). The role of the "clamshell" motion of the ligand binding domain (LBD) lobes in the structural transition is supplemented by the observed structural similarity at the level of protein domains during the structural transition, combined with the overall large rearrangement necessary for the opening and closing of the receptor. The activated and open states of the receptor are structurally similar to the liganded crystal structure, while in the unliganded receptor the extracellular domains perform rearrangements leading to a clockwise rotation of up to 45 degrees around the longitudinal axis of the receptor, which closes the ion channel. The ligand-induced rotation of extracellular domains transferred by LBD-TMD linkers to the membrane-anchored ion channel is responsible for the opening and closing of the transmembrane ion channel, revealing the properties of NMDA receptor as a finely tuned molecular machine.

Effects of Peptide Charge, Orientation, and Concentration on Melittin Transmembrane Pores

Melittin is a short cationic peptide that exerts cytolytic effects on bacterial and eukaryotic cells. Experiments suggest that in zwitterionic membranes, melittin forms transmembrane toroidal pores supported by four to eight peptides. A recently constructed melittin variant with a reduced cationic charge, MelP5, is active at 10-fold lower concentrations. In previous work, we performed molecular dynamics simulations on the microsecond timescale to examine the supramolecular pore structure of a melittin tetramer in zwitterionic and partially anionic membranes. We now extend that study to include the effects of peptide charge, initial orientation, and number of monomers on the pore formation and stabilization processes. Our results show that parallel transmembrane orientations of melittin and MelP5 are more consistent with experimental data. Whereas a MelP5 parallel hexamer forms a large stable pore during the 5-μs simulation time, a melittin hexamer and an octamer are not fully stable, with several monomers dissociating during the simulation time. Interaction-energy analysis shows that this difference in behavior between melittin and MelP5 is not due to stronger electrostatic repulsion between neighboring melittin peptides but to peptide-lipid interactions that disfavor the isolated MelP5 transmembrane monomer. The ability of melittin monomers to diffuse freely in the 1,2-dimyristoyl-SN-glycero-3-phosphocholine membrane leads to dynamic pores with varying molecularity.

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

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

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

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

Membrane Curvature Sensing by Amphipathic Helices: Insights from Implicit Membrane Modeling

Sensing and generation of lipid membrane curvature, mediated by the binding of specific proteins onto the membrane surface, play crucial roles in cell biology. A number of mechanisms have been proposed, but the molecular understanding of these processes is incomplete. All-atom molecular dynamics simulations have offered valuable insights but are extremely demanding computationally. Implicit membrane simulations could provide a viable alternative, but current models apply only to planar membranes. In this work, the implicit membrane model 1 is extended to spherical and tubular membranes. The geometric change from planar to curved shapes is straightforward but insufficient for capturing the full curvature effect, which includes changes in lipid packing. Here, these packing effects are taken into account via the lateral pressure profile. The extended implicit membrane model 1 is tested on the wild-types and mutants of the antimicrobial peptide magainin, the ALPS motif of arfgap1, α-synuclein, and an ENTH domain. In these systems, the model is in qualitative agreement with experiments. We confirm that favorable electrostatic interactions tend to weaken curvature sensitivity in the presence of strong hydrophobic interactions but may actually have a positive effect when those are weak. We also find that binding to vesicles is more favorable than binding to tubes of the same diameter and that the long helix of α-synuclein tends to orient along the axis of tubes, whereas shorter helices tend to orient perpendicular to it. Adoption of a specific orientation could provide a mechanism for coupling protein oligomerization to tubule formation.

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.

Structure of the EmrE multidrug transporter and its use for inhibitor peptide design

Small multidrug resistance (SMR) pumps represent a minimal paradigm of proton-coupled membrane transport in bacteria, yet no high-resolution structure of an SMR protein is available. Here, atomic-resolution structures of the Escherichia coli efflux-multidrug resistance E (EmrE) multidrug transporter in ligand-bound form are refined using microsecond molecular dynamics simulations biased using low-resolution data from X-ray crystallography. The structures are compatible with existing mutagenesis data as well as NMR and biochemical experiments, including pKas of the catalytic glutamate residues and the dissociation constant ([Formula: see text]) of the tetraphenylphosphonium⁺ cation. The refined structures show the arrangement of residue side chains in the EmrE active site occupied by two different ligands and in the absence of a ligand, illustrating how EmrE can adopt structurally diverse active site configurations. The structures also show a stable, well-packed binding interface between the helices H4 of the two monomers, which is believed to be crucial for EmrE dimerization. Guided by the atomic details of this interface, we design proteolysis-resistant stapled peptides that bind to helix H4 of an EmrE monomer. The peptides are expected to interfere with the dimerization and thereby inhibit drug transport. Optimal positions of the peptide staple were determined using free-energy simulations of peptide binding to monomeric EmrE Three of the four top-scoring peptides selected for experimental testing resulted in significant inhibition of proton-driven ethidium efflux in live cells without nonspecific toxicity. The approach described here is expected to be of general use for the design of peptide therapeutics.

A model for hydrophobic protrusions on peripheral membrane proteins

With remarkable spatial and temporal specificities, peripheral membrane proteins bind to biological membranes. They do this without compromising solubility of the protein, and their binding sites are not easily distinguished. Prototypical peripheral membrane binding sites display a combination of patches of basic and hydrophobic amino acids that are also frequently present on other protein surfaces. The purpose of this contribution is to identify simple but essential components for membrane binding, through structural criteria that distinguish exposed hydrophobes at membrane binding sites from those that are frequently found on any protein surface. We formulate the concepts of protruding hydrophobes and co-insertability and have analysed more than 300 families of proteins that are classified as peripheral membrane binders. We find that this structural motif strongly discriminates the surfaces of membrane-binding and non-binding proteins. Our model constitutes a novel formulation of a structural pattern for membrane recognition and emphasizes the importance of subtle structural properties of hydrophobic membrane binding sites.

Peripheral membrane proteins bind cellular membranes transiently, and are otherwise soluble proteins. As the interaction between proteins and membranes happens at cellular interfaces they are naturally involved in important interfacial processes such as recognition, signaling and trafficking. Commonly their binding sites are also soluble, and their binding mechanisms poorly understood. This complicates the elaboration of conceptual and quantitative models for peripheral membrane binding and makes binding site prediction difficult. It is therefore of great interest to discover traits that are common between these binding sites and that distinguishes them from other protein surfaces. In this work we identify simple and general structural features that facilitate membrane recognition by soluble proteins. We show that these motifs are highly over-represented on peripheral membrane proteins.