Transmembrane Protein Structure Research Articles

Getting to know each other: PPIMem, a novel approach for predicting transmembrane protein-protein complexes

Because of their considerable number and diversity, membrane proteins and their macromolecular complexes represent the functional units of cells. Their quaternary structure may be stabilized by interactions between the α-helices of different proteins in the hydrophobic region of the cell membrane. Membrane proteins equally represent potential pharmacological targets par excellence for various diseases. Unfortunately, their experimental 3D structure and that of their complexes with other intramembrane protein partners are scarce due to technical difficulties. To overcome this key problem, we devised PPIMem, a computational approach for the specific prediction of higher-order structures of α-helical transmembrane proteins. The novel approach involves proper identification of the amino acid residues at the interface of molecular complexes with a 3D structure. The identified residues compose then nonlinear interaction motifs that are conveniently expressed as mathematical regular expressions. These are efficiently implemented for motif search in amino acid sequence databases, and for the accurate prediction of intramembrane protein-protein complexes. Our template interface-based approach predicted 21,544 binary complexes between 1,504 eukaryotic plasma membrane proteins across 39 species. We compare our predictions to experimental datasets of protein-protein interactions as a first validation method. The online database that results from the PPIMem algorithm with the annotated predicted interactions are implemented as a web server and can be accessed directly at https://transint.univ-evry.fr.

Tween 20 regulate the function and structure of transmembrane proteins of Bacillus cereus: Promoting transmembrane transport of fluoranthene

Polycyclic aromatic hydrocarbons (PAHs) are degraded by the highly efficient degrading bacterium Bacillus cereus. Transmembrane transport is highly important in PAH degradation by bacteria. Surfactants are the key substances that promote PAH adsorption, uptake and transmembrane transport by Bacillus cereus. In this study, the isobaric tags for relative and absolute quantitation (iTRAQ) approach was used for high-throughput screening of key functional proteins during transmembrane fluoranthene transport by Bacillus cereus treated with Tween 20. In addition, SWISS-MODEL was used to simulate the tertiary structures of key transmembrane proteins and analyze how Tween 20 promotes transmembrane transport. Transmembrane fluoranthene transport into Bacillus cereus requires transmembrane proteins and energy. Tween 20 was observed to improve bacterial motility and transmembrane protein expression. The interior of representative transmembrane proteins is mostly composed of hydrophobic β-sheets while amphipathic α-helices are primarily distributed at their periphery. The primary reason for this configuration may be α-helices promote the aggregation of surfactants and the phospholipid bilayer and the β-sheets promote surfactant insertion into the phospholipid bilayer to enhance PAH transport into Bacillus cereus. Investigating the effect of Tween 20 on Bacillus cereus transmembrane proteins during transmembrane fluoranthene transport is important for understanding the mechanism of PAH degradation by microorganisms.

RIEDL tag: A novel pentapeptide tagging system for transmembrane protein purification

Affinity tag systems are an essential tool in biochemistry, biophysics, and molecular biology. Although several different tag systems have been developed, the epitope tag system, composed of a polypeptide “tag” and an anti-tag antibody, is especially useful for protein purification. However, almost all tag sequences, such as the FLAG tag, are added to the N- or C-termini of target proteins, as tags inserted in loops tend to disrupt the functional structure of multi-pass transmembrane proteins. In this study, we developed a novel “RIEDL tag system,” which is composed of a peptide with only five amino acids (RIEDL) and an anti-RIEDL monoclonal antibody (mAb), LpMab-7. To investigate whether the RIEDL tag system is applicable for protein purification, we conducted the purification of two kinds of RIEDL-tagged proteins using affinity column chromatography: whale podoplanin (wPDPN) with an N-terminal RIEDL tag (RIEDL-wPDPN) and human CD20 with an internal RIEDL tag insertion (CD20-169RIEDL170). Using an LpMab-7-Sepharose column, RIEDL-wPDPN and CD20-169RIEDL170 were efficiently purified in one-step purification procedures, and were strongly detected by LpMab-7 using Western blot and flow cytometry. These results show that the RIEDL tag system can be useful for the detection and one-step purification of membrane proteins when inserted at either the N-terminus or inserted in an internal loop structure of multi-pass transmembrane proteins.

IMPContact: An Interhelical Residue Contact Prediction Method.

As an important category of proteins, alpha-helix transmembrane proteins (αTMPs) play an important role in various biological activities. Because the solved αTMP structures are inadequate, predicting the residue contacts among the transmembrane segments of an αTMP exhibits the basis of protein fold, which can be used to further discover more protein functions. A few efforts have been devoted to predict the interhelical residue contact using machine learning methods based on the prior knowledge of transmembrane protein structure. However, it is still a challenge to improve the prediction accuracy, while the deep learning method provides an opportunity to utilize the structural knowledge in a different insight. For this purpose, we proposed a novel αTMP residue-residue contact prediction method IMPContact, in which a convolutional neural network (CNN) was applied to recognize those interhelical contacts in a TMP using its specific structural features. There were four sequence-based TMP-specific features selected to descript a pair of residues, namely, evolutionary covariation, predicted topology structure, residue relative position, and evolutionary conservation. An up-to-date dataset was used to train and test the IMPContact; our method achieved better performance compared to peer methods. In the case studies, IHRCs in the regular transmembrane helixes were better predicted than in the irregular ones.

Plasma Membrane Packing Asymmetry Drives Transmembrane Protein Localization

Phospholipid asymmetry between the two plasma membrane leaflets has been discovered 40 years ago and since then, various physiological processes have been associated with asymmetric lipid distributions and changes to lipid asymmetry. Nevertheless, it remains unclear how lipid asymmetry affects the biophysical properties of individual leaflets and whether this putative biophysical asymmetry affects transmembrane proteins. To address these questions, we conducted a detailed analysis of asymmetric plasma membrane leaflet lipidomes and leaflet-specific biophysical properties. We further investigated whether distinct leaflets are maintained in intracellular organelles and how such intracellular membrane asymmetry may affect transmembrane protein localization and structure. Lipidomics revealed a striking disparity in lipid acyl chains, with the inner plasma membrane leaflet containing two-fold more acyl chain unsaturations than the outer leaflet. Consistent with the difference in unsaturation, we observed that the outer leaflet was highly packed, resembling a liquid ordered phase, whereas the inner leaflet was much more disordered. A bioinformatic analysis revealed that transmembrane domains (TMDs) of single-pass transmembrane proteins in the plasma membrane are broadly asymmetric in shape, with smaller accessible surface areas in the outer leaflet than the inner (i.e. thinner outside half of the TMD, fatter inside). We inferred that this shape asymmetry should facilitate insertion into the asymmetrically packed plasma membrane. We verified this hypothesis by creating TMD constructs with several shapes and discovered that proteins with a small accessible surface area in the outer leaflet preferred to localize at the plasma membrane regardless of their inner leaflet counterpart. This study delivers new insights into the structural organization of cell membranes and reveals that outer leaflet packing can drive protein localization to the plasma membrane.

Using MemBlob to Analyze Transmembrane Regions Based on Cryo-EM Maps.

Transmembrane proteins include membrane channels, pores, and receptors and, as such, comprise an important part of the proteome, yet our knowledge about them is much less complete than about soluble, globular proteins. An important aspect of transmembrane protein structure is their exact position within the lipid bilayer, a feature hard to investigate experimentally at the atomic level. Here we describe MemBlob, a novel approach utilizing difference electron density maps obtained by cryo-EM studies of transmembrane proteins. The idea behind is that the nonprotein part of such maps carries information on the exact localization of the membrane mimetics used in the experiment and can be used to extract the positional information of the protein within the membrane. MemBlob uses a structural model of the protein and an experimental electron density map to provide an estimation of the surface residues interacting with the membrane.

TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues

Transmembrane proteins (TMPs) play vital and diverse roles in many biological processes, such as molecular transportation and immune response. Like other proteins, many major interactions with other molecules happen in TMPs’ surface area, which is important for function annotation and drug discovery. Under the condition that the structure of TMP is hard to derive from experiment and prediction, it is a practical way to predict the TMP residues’ surface area, measured by the relative accessible surface area (rASA), based on computational methods. In this study, we presented a novel deep learning-based predictor TMP-SSurface for both alpha-helical and beta-barrel transmembrane proteins (α-TMP and β-TMP), where convolutional neural network (CNN), inception blocks, and CapsuleNet were combined to construct a network framework, simply accepting one-hot code and position-specific score matrix (PSSM) of protein fragment as inputs. TMP-SSurface was tested against an independent dataset achieving appreciable performance with 0.584 Pearson correlation coefficients (CC) value. As the first TMP’s rASA predictor utilizing the deep neural network, our method provided a referenceable sample for the community, as well as a practical step to discover the interaction sites of TMPs based on their sequence.

Molecular Switch between Structural Compaction and Thermodynamic Stability by the Xxx-Pro Interface in Transmembrane β-Barrels.

Transmembrane β-barrel scaffolds found in outer membrane proteins are formed and stabilized by a defined pattern of interstrand intraprotein H-bonds, in hydrophobic lipid bilayers. Introducing the conformationally constrained proline in β-barrels can cause significant destabilization of these structural regions that require H-bonding, with proline additionally acting as a secondary structure breaker. Membrane protein β-barrels are therefore expected to show poor tolerance to the presence of a transmembrane proline. Here, we assign a thermodynamic measure for the extent to which a single proline can be tolerated at the C-terminal interface of the model transmembrane β-barrel PagP. We find that proline drastically destabilizes PagP by 7.0 kcal mol-1 with respect to wild-type PagP (F161 → P161). Interestingly, strategic modulation of the preceding residue can modify the measured energetics. Placing a hydrophobic or bulky side chain as the preceding residue increases the thermodynamic stability by ≤8.0 kcal mol-1. While polar substituents at the preceding residue decrease the PagP stability, these residues demonstrate stronger tertiary packing interactions in the barrel and retain the catalytic activity of native PagP. This biophysical interplay between enhanced thermodynamic stability and attaining a structurally compact functional β-barrel scaffold highlights the detrimental effect caused by proline incorporation. Our findings also reveal alternative mechanisms that protein sequences can employ to salvage the structural integrity of transmembrane protein structures.

Membrane directed expression in Escherichia coli of BBA57 and other virulence factors from the Lyme disease agent Borrelia burgdorferi

Membrane-embedded proteins are critical to the establishment, survival and persistence in the host of the Lyme disease bacterium Borrelia burgdorferi (Bb), but to date, there are no solved structures of transmembrane proteins representing these attractive therapeutic targets. All available structures from the genus Borrelia represent proteins expressed without a membrane-targeting signal peptide, thus avoiding conserved pathways that modify, fold and assemble membrane protein complexes. Towards elucidating structure and function of these critical proteins, we directed translocation of eleven expression-optimized Bb virulence factors, including the signal sequence, to the Escherichia coli membrane, of which five, BBA57, HtrA, BB0238, BB0323, and DipA, were expressed with C-terminal His-tags. P66 was also expressed using the PelB signal sequence fused to maltose binding protein. Membrane-associated BBA57 lipoprotein was solubilized by non-ionic and zwitterionic detergents. We show BBA57 translocation to the outer membrane, purification at a level sufficient for structural studies, and evidence for an α-helical multimer. Previous studies showed multiple critical roles of BBA57 in transmission, joint arthritis, carditis, weakening immune responses, and regulating other Bb outer surface proteins. In describing the first purification of membrane-translocated BBA57, this work will support subsequent studies that reveal the precise mechanisms of this important Lyme disease virulence factor.

DR-SIP: protocols for higher order structure modeling with distance restraints- and cyclic symmetry-imposed packing

Quaternary structure determination for transmembrane/soluble proteins requires a reliable computational protocol that leverages observed distance restraints and/or cyclic symmetry (Cn symmetry) found in most homo-oligomeric transmembrane proteins. We survey 118 X-ray crystallographically solved structures of homo-oligomeric transmembrane proteins (HoTPs) and find that ∼97% are Cn symmetric. Given the prevalence of Cn symmetric HoTPs and the benefits of incorporating geometry restraints in aiding quaternary structure determination, we introduce two new filters, the distance-restraints (DR) and the Symmetry-Imposed Packing (SIP) filters. SIP relies on a new method that can rebuild the closest ideal Cn symmetric complex from docking poses containing a homo-dimer without prior knowledge of the number (n) of monomers. Using only the geometrical filter, SIP, near-native poses of 7 HoTPs in their monomeric states can be correctly identified in the top-10 for 71% of all cases, or 29% among 31 HoTP structures obtained through homology modeling, while ZDOCK alone returns 14 and 3%, respectively. When the n is given, the optional n-mer filter is applied with SIP and returns the near-native poses for 76% of the test set within the top-10, outperforming M-ZDOCK's 55% and Sam's 47%. While applying only SIP to three HoTPs that comes with distance restraints, we found the near-native poses were ranked 1st, 1st and 10th among 54 000 possible decoys. The results are further improved to 1st, 1st and 3rd when both DR and SIP filters are used. By applying only DR, a soluble system with distance restraints is recovered at the 1st-ranked pose. https://github.com/capslockwizard/drsip. Supplementary data are available at Bioinformatics online.

The TMCrys server for supporting crystallization of transmembrane proteins.

MotivationDue to their special properties, the structures of transmembrane proteins are extremely hard to determine. Several methods exist to predict the propensity of successful completion of the structure determination process. However, available predictors incorporate data of any kind of proteins, hence they can hardly differentiate between crystallizable and non-crystallizable membrane proteins.ResultsWe implemented a web server to simplify running TMCrys prediction method that was developed specifically to separate crystallizable and non-crystallizable membrane proteins.Availability and implementation http://tmcrys.enzim.ttk.mta.hu Supplementary information Supplementary data are available at Bioinformatics online.

MAINMAST-MELD-MDFF: Denovo Structure-Determination with Data-Guided Molecular Dynamics

The past three decades of hybrid modeling has seen molecular dynamics (MD) simulations leverage NMR, X-ray crystallography and cryo EM data for the determination of medium- to high-resolution structures atomic structures. However, all these approaches, including our popular Molecular Dynamics Flexible Fitting (MDFF), and its various extensions work under the conventional molecular replacement paradigm, whereby any initial search model is morphed to satiate the data-imposed constraints. As a natural consequence, quality of the determined model remains heavily biased by choices of the initial model. Here, we deliver a novel modeling pipeline that iteratively combines minimum spanning tree-based backbone tracing tool (MAINMAST), Bayesian-likelihood based protein-folding methodology (MELD), and a resolution exchange-based fitting protocol (ReMDFF). Starting from only sequence information, the algorithm places C-alpha atoms into the density, fits a random coil to this C-alpha trace, generates protein secondary structures on-the-fly, and exhaustively samples the backbone and side chain geometries to deliver a refined model. Overcoming limitations of traditional approaches, the need for an initial model or homology information is completely subsided, and de novo modeling is now made feasible even at low resolutions. Available on cloud computing resources, the method is equally applicable for determining globular and transmembrane protein structures, as demonstrated for ubiquitin, TRPV1 and Magnesium-channel CorA (all in the 3-5 Å resolution range), and the de novo atomic structure of a lipoprotein flp3.

Nitrated Fatty Acids Modulate the Physical Properties of Model Membranes and the Structure of Transmembrane Proteins.

Nitrated fatty acids (NO2 -FAs) act as anti-inflammatory signal mediators, albeit the molecular mechanisms behind NO2 -FAs' influence on diverse metabolic and signaling pathways in inflamed tissues are essentially elusive. Here, we combine fluorescence measurements with surface-specific sum frequency generation vibrational spectroscopy and coarse-grained computer simulations to demonstrate that NO2 -FAs alter lipid organization by accumulation at the membrane-water interface. As the function of membrane proteins strongly depends on both, protein structure as well as membrane properties, we consecutively follow the structural dynamics of an integral membrane protein in presence of NO2 -FAs. Based on our results, we suggest a molecular mechanism of the NO2 -FA in vivo activity: Driven by the NO2 -FA-induced lipid layer reorganization, the structure and function of membrane-associated (signaling) proteins is indirectly affected.

Computing the Diamagnetic Susceptibility and Diamagnetic Anisotropy of Membrane Proteins from Structural Subunits.

The behavior of large, complex molecules in the presence of magnetic fields is experimentally challenging to measure and computationally intensive to predict. This work proposes a novel, mixed-methods approach for efficiently computing the principal magnetic susceptibilities and diamagnetic anisotropy of membrane proteins. The hierarchical primary (amino acid), secondary (α helical and β sheet), and tertiary (α helix and β barrel) structure of transmembrane proteins enables analysis of a complex molecule using discrete subunits of varying size and resolution. The proposed method converts the magnetic susceptibility tensor for all protein subunits to a unit coordinate system and sums them to build the magnetic susceptibility tensor for the membrane protein. Using this approach, we calculate the diamagnetic anisotropy for all transmembrane proteins of known structure and investigate the effect of different subunit resolutions on the resulting predictions of diamagnetic anisotropy. We demonstrate that amino acid residues with aromatic side groups exhibit higher diamagnetic anisotropies. On average, high percentages of aromatic amino acid subunits, a β barrel tertiary structure, and a small volume are correlated with high volumetric diamagnetic anisotropy. Finally, we demonstrate that accounting for the spatial position of the residues with respect to one another is critical to accurately computing the magnetic properties of the complex protein molecule.

Extension of the Highly Mobile Membrane Mimetic to Transmembrane Systems through Customized in Silico Solvents.

The mechanics of the protein-lipid interactions of transmembrane proteins are difficult to capture with conventional atomic molecular dynamics, due to the slow lateral diffusion of lipids restricting sampling to states near the initial membrane configuration. The highly mobile membrane mimetic (HMMM) model accelerates lipid dynamics by modeling the acyl tails nearest to the membrane center as a fluid organic solvent while maintaining an atomic description of the lipid headgroups and short acyl tails. The HMMM has been applied to many peripheral protein systems; however, the organic solvent used to date caused deformations in transmembrane proteins by intercalating into the protein and disrupting interactions between individual side chains. We ameliorate the effect of the solvent on transmembrane protein structure through the development of two new in silico Lennard-Jones solvents. The parameters for the new solvents were determined through an extensive parameter search in order to match the bulk properties of alkanes in a highly simplified model. Using these new solvents, we substantially improve the insertion free energy profiles of 10 protein side chain analogues across the entire bilayer. In addition, we reduce the intercalation of solvent into transmembrane systems, resulting in native-like transmembrane protein structures from five different topological classes within a HMMM bilayer. The parametrization of the solvents, in addition to their computed physical properties, is discussed. By combining high lipid lateral diffusion with intact transmembrane proteins, we foresee the developed solvents being useful to efficiently identify membrane composition inhomogeneities and lipid binding caused by the presence of membrane proteins.

Membrane Phase-Dependent Occlusion of Intramolecular GLUT1 Cavities Demonstrated by Simulations

Experimental evidence has shown a close correlation between the composition and physical state of the membrane bilayer and glucose transport activity via the glucose transporter GLUT1. Cooling alters the membrane lipids from the fluid to gel phase, and also causes a large decrease in the net glucose transport rate. The goal of this study is to investigate how the physical phase of the membrane alters glucose transporter structural dynamics using molecular-dynamics simulations. Simulations from an initial fluid to gel phase reduce the size of the cavities and tunnels traversing the protein and connecting the external regions of the transporter and the central binding site. These effects can be ascribed solely to membrane structural changes since in silico cooling of the membrane alone, while maintaining the higher protein temperature, shows protein structural and dynamic changes very similar to those observed with uniform cooling. These results demonstrate that the protein structure is sensitive to the membrane phase, and have implications for how transmembrane protein structures respond to their physical environment.

TMPL: a database of experimental and theoretical transmembrane protein models positioned in the lipid bilayer.

Knowing the position of protein structures within the membrane is crucial for fundamental and applied research in the field of molecular biology. Only few web resources propose coordinate files of oriented transmembrane proteins, and these exclude predicted structures, although they represent the largest part of the available models. In this article, we present TMPL (http://www.dsimb.inserm.fr/TMPL/), a database of transmembrane protein structures (α-helical and β-sheet) positioned in the lipid bilayer. It is the first database to include theoretical models of transmembrane protein structures, making it a large repository with more than 11 000 entries. The TMPL database also contains experimentally solved protein structures, which are available as either atomistic or coarse-grained models. A unique feature of TMPL is the possibility for users to update the database by uploading, through an intuitive web interface, the membrane assignments they can obtain with our recent OREMPRO web server.

Computational Approaches for Revealing the Structure of Membrane Transporters: Case Study on Bilitranslocase

The structural and functional details of transmembrane proteins are vastly underexplored, mostly due to experimental difficulties regarding their solubility and stability. Currently, the majority of transmembrane protein structures are still unknown and this present a huge experimental and computational challenge. Nowadays, thanks to X-ray crystallography or NMR spectroscopy over 3000 structures of membrane proteins have been solved, among them only a few hundred unique ones. Due to the vast biological and pharmaceutical interest in the elucidation of the structure and the functional mechanisms of transmembrane proteins, several computational methods have been developed to overcome the experimental gap. If combined with experimental data the computational information enables rapid, low cost and successful predictions of the molecular structure of unsolved proteins. The reliability of the predictions depends on the availability and accuracy of experimental data associated with structural information. In this review, the following methods are proposed for in silico structure elucidation: sequence-dependent predictions of transmembrane regions, predictions of transmembrane helix–helix interactions, helix arrangements in membrane models, and testing their stability with molecular dynamics simulations. We also demonstrate the usage of the computational methods listed above by proposing a model for the molecular structure of the transmembrane protein bilitranslocase. Bilitranslocase is bilirubin membrane transporter, which shares similar tissue distribution and functional properties with some of the members of the Organic Anion Transporter family and is the only member classified in the Bilirubin Transporter Family. Regarding its unique properties, bilitranslocase is a potentially interesting drug target.

TSTMP: target selection for structural genomics of human transmembrane proteins.

The TSTMP database is designed to help the target selection of human transmembrane proteins for structural genomics projects and structure modeling studies. Currently, there are only 60 known 3D structures among the polytopic human transmembrane proteins and about a further 600 could be modeled using existing structures. Although there are a great number of human transmembrane protein structures left to be determined, surprisingly only a small fraction of these proteins have ‘selected’ (or above) status according to the current version the TargetDB/TargetTrack database. This figure is even worse regarding those transmembrane proteins that would contribute the most to the structural coverage of the human transmembrane proteome. The database was built by sorting out proteins from the human transmembrane proteome with known structure and searching for suitable model structures for the remaining proteins by combining the results of a state-of-the-art transmembrane specific fold recognition algorithm and a sequence similarity search algorithm. Proteins were searched for homologues among the human transmembrane proteins in order to select targets whose successful structure determination would lead to the best structural coverage of the human transmembrane proteome. The pipeline constructed for creating the TSTMP database guarantees to keep the database up-to-date. The database is available at http://tstmp.enzim.ttk.mta.hu.

Structural characterization reveals a novel bilobed architecture for the ectodomains of insect stage expressed Trypanosoma brucei PSSA-2 and Trypanosoma congolense ISA.

African trypanosomiasis, caused by parasites of the genus Trypanosoma, is a complex of devastating vector-borne diseases of humans and livestock in sub-Saharan Africa. Central to the pathogenesis of African trypanosomes is their transmission by the arthropod vector, Glossina spp. (tsetse fly). Intriguingly, the efficiency of parasite transmission through the vector is reduced following depletion of Trypanosoma brucei Procyclic-Specific Surface Antigen-2 (TbPSSA-2). To investigate the underlying molecular mechanism of TbPSSA-2, we determined the crystal structures of its ectodomain and that of its homolog T. congolense Insect Stage Antigen (TcISA) to resolutions of 1.65 Å and 2.45 Å, respectively using single wavelength anomalous dispersion. Both proteins adopt a novel bilobed architecture with the individual lobes displaying rotational flexibility around the central tether that suggest a potential mechanism for coordinating a binding partner. In support of this hypothesis, electron density consistent with a bound peptide was observed in the inter-lob cleft of a TcISA monomer. These first reported structures of insect stage transmembrane proteins expressed by African trypanosomes provide potentially valuable insight into the interface between parasite and tsetse vector.