Transmembrane Beta-barrel Proteins Research Articles

Efficient reconstitution of beta-barrel proteins for single-molecule experiments

The outer membrane of Gram-negative bacteria is populated with vital beta-barrel transmembrane proteins, so-called outer-membrane proteins (OMPs). These proteins fold and insert into the outer membrane from the periplasm in absence of any known energy source. Unlike the widely examined process of alpha-helical transmembrane protein folding, the complex steps involved in OMP biogenesis remain obscure. However, in recent years, the development of single-molecule techniques (such as force spectroscopy and single-molecule FRET) has allowed researchers to markedly advance the observation of OMP folding and insertion.

The Functional Significance of Hydrophobic Residue Distribution in Bacterial Beta-Barrel Transmembrane Proteins.

β-barrel membrane proteins have several important biological functions, including transporting water and solutes across the membrane. They are active in the highly hydrophobic environment of the lipid membrane, as opposed to soluble proteins, which function in a more polar, aqueous environment. Globular soluble proteins typically have a hydrophobic core and a polar surface that interacts favorably with water. In the fuzzy oil drop (FOD) model, this distribution is represented by the 3D Gauss function (3DG). In contrast, membrane proteins expose hydrophobic residues on the surface, and, in the case of ion channels, the polar residues face inwards towards a central pore. The distribution of hydrophobic residues in membrane proteins can be characterized by means of 1–3DG, a complementary 3D Gauss function. Such an analysis was carried out on the transmembrane proteins of bacteria, which, despite the considerable similarities of their super-secondary structure (β-barrel), have highly differentiated properties in terms of stabilization based on hydrophobic interactions. The biological activity and substrate specificity of these proteins are determined by the distribution of the polar and nonpolar amino acids. The present analysis allowed us to compare the ways in which the different proteins interact with antibiotics and helped us understand their relative importance in the development of the resistance mechanism. We showed that beta barrel membrane proteins with a hydrophobic core interact less strongly with the molecules they transport.

Cascading classifier application for topology prediction of transmembrane beta-barrel proteins.

Membrane proteins are a major focus for new drug discovery. Transmembrane beta-barrel (TMB) proteins play key roles in the translocation machinery, pore formation, membrane anchoring and ion exchange. Given their key roles and the difficulty in membrane protein structure determination, the use of computational modeling is essential. This paper focuses on the topology prediction of TMB proteins. In the field of bioinformatics, many years of research has been spent on the topology prediction of transmembrane alpha-helices. The efforts to TMB proteins topology prediction have been overshadowed and the prediction accuracy could be improved with further research. Various methodologies have been developed in the past for the prediction of TMB protein topology, however, the use of cascading classifier has never been fully explored. This research presents a novel approach to TMB topology prediction with the use of a cascading classifier. The MATLAB computer simulation results show that the proposed methodology predicts TMB proteins topologies with high accuracy for randomly selected proteins. By using the cascading classifier approach, the best overall accuracy is 76.3% with a precision of 0.831 and recall or probability of detection of 0.799 for TMB topology prediction. The accuracy of 76.3% is achieved using a two-layers cascading classifier.

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.

A fast and accurate method for large-scale transmembrane beta barrel topology prediction

Transmembrane beta barrel proteins (TMBs) play a major role in the normal functioning of the cell and are an important constituent of the translocation machinery of the outer membrane proteins in bacteria, mitochondria and chloroplast. Currently there are only around 36 experimental 3D structures available for TMBs in PDB (at 30% sequence identity). Furthermore, with large amounts of sequence data available from high-throughput methods, it is imperative to develop accurate and fast computational methods for their identification and topology prediction. Here, we present a concept for a fast method to identify TMBs and predict their topology. The method uses sparse encoded amino acid data as input and employs a Support Vector Machine (SVM) and a Hidden Markov Model (HMM) to generate accurate topologies. The topologies in the training phase are divided into pre-barrel state, outer-loop state, inner-loop state and the transmembrane beta-strand state. In the first stage, 4 separate SVMs are used to predict the local state preference for each residue. A profile generated from the probabilities thus obtained is used as input to the HMM stage to determine the overall topology. If the number of predicted strands is between 8 and 24, then the given sequence is identified as a TMB. We see the application of our method in the proteome-wide topology prediction of TMBs, where current methods might have a limitation due to the time consuming homologous sequence search step. Funding Christoph Peters: Travel Fellowship awarded by ISCB Student Council with financial support from EMBO, 2012 J-A Ekstroms travel stipend, 2012 A fast and accurate method for large-scale transmembrane beta barrel topology prediction

A general and Efficient All-Atom Simulation Method to Determine the Equilibrium Orientation of Transmembrane Proteins in Membranes

Knowledge of the spatial orientation of transmembrane (TM) proteins is broadly useful in both in-vitro and in-silico approaches that aim to elucidate the dynamic and functional properties of TM proteins. Such approaches include site-directed spin labeling, cys-scanning mutagenesis, and atomistic molecular dynamics (MD) simulations using lipid bilayers. Because three-dimensional structures of TM proteins are generally obtained in a detergent solvent, these structures do not reveal the native orientation of the protein in the lipid membrane. For this reason, we aimed to develop a general and computationally-efficient MD approach to predict the most favorable orientation of TM proteins in a lipid membrane. In order to avoid the long relaxation time scales characterizing protein and lipid bilayer dynamics, our method treats the TM protein as a rigid body in a membrane-mimetic hydrated octane slab, allowing the protein to reach a stable orientation within 10 ns. The method was systematically tested on alpha-helical and beta-barrel TM proteins, each with different starting orientations in the hydrated octane slab. Each protein attained a consistent orientation irrespective of its starting orientation. Furthermore, the converged orientations are in good agreement with the known orientations of these test proteins in lipid bilayers. These results indicate that this method is reliable as a general protocol that can be used to determine the orientation of TM proteins of known structure.

The Orientation of a Tandem POTRA Domain Pair, of the Beta-Barrel Assembly Protein BamA, Determined by PELDOR Spectroscopy

The outer membrane beta-barrel trans-membrane proteins in gram-negative bacteria are folded into the membrane with the aid of polypeptide transport-associated (POTRA) domains. These domains occur, and probably function, as a tandem array situated on the periplasmic side of the outer membrane. Two crystal structures and one NMR study have attempted to define the structure and articulation of the POTRA domains of the Escherichia coli, prototypic Omp85 protein BamA. We have used pulsed electron paramagnetic resonance (EPR) to determine the distance and distance distribution between (1-Oxyl-2,2,5,5-tetramethylpyrroline-3-methyl) methanethiosulfonate spin labels (MTSSL), placed across the domain interface of the first two POTRA domains of BamA. Our results show tightly defined interdomain distance distributions that indicate a well-defined domain orientation. Examination of the known structures revealed that none of them fitted the EPR data. A combination of EPR and NMR data was used to generate converged structures with defined domain-domain orientation.

Membrane Proteins Solubilized Intact in Lipid Containing Nanoparticles Bounded by Styrene Maleic Acid Copolymer

One-third of eukaryotic proteins are integrated within membranes, as are the targets of 40% of approved drugs. However, the lack of a general means of solubilizing, stabilizing and structurally characterizing active membrane proteins has frustrated efforts to understand their mechanisms and exploit their potential value. Here we report that bilayer disks formed by phospholipids and styrene maleic anhydride copolymer preserve the functional and structural integrity of alpha-helical and beta-barrel transmembrane proteins. They form 11 nm particles that are monodispersed, biocompatible, thermostable, and water-soluble, allowing diverse membrane proteins to be simply and rapidly presented for virtually any in vitro analysis.

Fold and function of polypeptide transport‐associated domains responsible for delivering unfolded proteins to membranes

Membranes of Gram-negative bacteria, mitochondria and chloroplasts receive and fold beta-barrel transmembrane proteins through the action of polypeptide transport-associated (POTRA) domains. In Escherichia coli, folding substrates are inserted into the outer membrane by the essential protein YaeT, a prototypic Omp85 protein. Here, the articulation between tandem POTRA domains in solution is defined by nuclear magnetic resonance (NMR) spectroscopy, indicating an unprecedented juxtaposition. The novel solution orientations of all five POTRA domains are revealed by small-angle X-ray scattering of the entire 46 kDa periplasmic region. NMR titration studies show that strands from YaeT's canonical folding substrate, PhoE, bind non-specifically along alternating sides of its mixed beta sheets, thus providing an ideal platform for helping to fold nascent outer-membrane proteins. Together, this provides the first structural model of how multiple POTRA domains recruit substrates from the periplasmic solution into the outer membrane.

A method for discovering transmembrane beta-barrel proteins in Gram-negative bacterial proteomes

Transmembrane β-barrel (TMB) proteins play pivotal roles in many aspects of bacterial functions. This paper presents a k-nearest neighbor ( K-NN) method for discriminating TMB and non-TMB proteins. We start with a method that makes predictions based on a distance computed from residue composition and gradually improve the prediction performance by including homologous sequences and searching for a set of residues and di-peptides for calculating the distance. The final method achieves an accuracy of 97.1%, with 0.876 MCC, 86.4% sensitivity and 98.8% specificity. A web server based on the proposed method is available at http://yanbioinformatics.cs.usu.edu:8080/TMBKNNsubmit.

Modeling ensembles of transmembrane β‐barrel proteins

Transmembrane beta-barrel (TMB) proteins are embedded in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. Despite their importance, very few nonhomologous TMB structures have been determined by X-ray diffraction because of the experimental difficulty encountered in crystallizing transmembrane proteins. We introduce the program partiFold to investigate the folding landscape of TMBs. By computing the Boltzmann partition function, partiFold estimates inter-beta-strand residue interaction probabilities, predicts contacts and per-residue X-ray crystal structure B-values, and samples conformations from the Boltzmann low energy ensemble. This broad range of predictive capabilities is achieved using a single, parameterizable grammatical model to describe potential beta-barrel supersecondary structures, combined with a novel energy function of stacked amino acid pair statistical potentials. PartiFold outperforms existing programs for inter-beta-strand residue contact prediction on TMB proteins, offering both higher average predictive accuracy as well as more consistent results. Moreover, the integration of these contact probabilities inside a stochastic contact map can be used to infer a more meaningful picture of the TMB folding landscape, which cannot be achieved with other methods. Partifold's predictions of B-values are competitive with recent methods specifically designed for this problem. Finally, we show that sampling TMBs from the Boltzmann ensemble matches the X-ray crystal structure better than single structure prediction methods. A webserver running partiFold is available at http://partiFold.csail.mit.edu/.

TMB Finding Pipeline: Novel Approach for Detecting β-Barrel Membrane Proteins in Genomic Sequences

We have developed a novel approach for dissecting transmembrane beta-barrel proteins (TMBs) in genomic sequences. The features include (i) the identification of TMBs using the preference of residue pairs in globular, transmembrane helical (TMH) and TMBs, (ii) elimination of globular/TMH proteins that show sequence identity of more than 70% for the coverage of 80% residues with known structures, (iii) elimination of globular/TMH proteins that have sequence identity of more than 60% with known sequences in SWISS-PROT, and (iv) exclusion of TMH proteins using SOSUI, a prediction system for TMH proteins. Our approach picked up 7% TMBs in all the considered genomes. The comparison between the identified TMBs in E. coli genome and available experimental data demonstrated that the new approach could correctly identify all the 11 known TMBs, whose crystal structures are available. Further, it revealed the presence of 19 TMBs, homology with known structures, 60 TMBs similar to well annotated sequences, and 54 TMBs that have high sequence similarity with Escherichia coli beta-barrel proteins deposited in Transport Classification Database (TCDB). Interestingly, the present approach identified TMBs from all 15 families in TCDB. In human genome, the occurrence of TMBs varies from 0 to 3% in different chromosomes. We suggest that our approach could lead to a step forward in the advancement of structural and functional genomics.

Predicting transmembrane β‐barrels and interstrand residue interactions from sequence

Transmembrane beta-barrel (TMB) proteins are embedded in the outer membrane of Gram-negative bacteria, mitochondria, and chloroplasts. The cellular location and functional diversity of beta-barrel outer membrane proteins (omps) makes them an important protein class. At the present time, very few nonhomologous TMB structures have been determined by X-ray diffraction because of the experimental difficulty encountered in crystallizing transmembrane proteins. A novel method using pairwise interstrand residue statistical potentials derived from globular (nonouter membrane) proteins is introduced to predict the supersecondary structure of transmembrane beta-barrel proteins. The algorithm transFold employs a generalized hidden Markov model (i.e., multitape S-attribute grammar) to describe potential beta-barrel supersecondary structures and then computes by dynamic programming the minimum free energy beta-barrel structure. Hence, the approach can be viewed as a "wrapping" component that may capture folding processes with an initiation stage followed by progressive interaction of the sequence with the already-formed motifs. This approach differs significantly from others, which use traditional machine learning to solve this problem, because it does not require a training phase on known TMB structures and is the first to explicitly capture and predict long-range interactions. TransFold outperforms previous programs for predicting TMBs on smaller (<or=200 residues) proteins and matches their performance for straightforward recognition of longer proteins. An exception is for multimeric porins where the algorithm does perform well when an important functional motif in loops is initially identified. We verify our simulations of the folding process by comparing them with experimental data on the functional folding of TMBs. A Web server running transFold is available and outputs contact predictions and locations for sequences predicted to form TMBs.

PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins

PROFtmb predicts transmembrane beta-barrel (TMB) proteins in Gram-negative bacteria. For each query protein, PROFtmb provides both a Z-value indicating that the protein actually contains a membrane barrel, and a four-state per-residue labeling of upward- and downward-facing strands, periplasmic hairpins and extracellular loops. While most users submit individual proteins known to contain TMBs, some groups submit entire proteomes to screen for potential TMBs. Response time is about 4 min for a 500-residue protein. PROFtmb is a profile-based Hidden Markov Model (HMM) with an architecture mirroring the structure of TMBs. The per-residue accuracy on the 8-fold cross-validated testing set is 86% while whole-protein discrimination accuracy was 70 at 60% coverage. The PROFtmb web server includes all source code, training data and whole-proteome predictions from 78 Gram-negative bacterial genomes and is available freely and without registration at .

TransFold: a web server for predicting the structure and residue contacts of transmembrane beta-barrels

Transmembrane β-barrel (TMB) proteins are embedded in the outer membrane of Gram-negative bacteria, mitochondria and chloroplasts. The cellular location and functional diversity of β-barrel outer membrane proteins makes them an important protein class. At the present time, very few non-homologous TMB structures have been determined by X-ray diffraction because of the experimental difficulty encountered in crystallizing transmembrane (TM) proteins. The transFold web server uses pairwise inter-strand residue statistical potentials derived from globular (non-outer-membrane) proteins to predict the supersecondary structure of TMB. Unlike all previous approaches, transFold does not use machine learning methods such as hidden Markov models or neural networks; instead, transFold employs multi-tape S-attribute grammars to describe all potential conformations, and then applies dynamic programming to determine the global minimum energy supersecondary structure. The transFold web server not only predicts secondary structure and TMB topology, but is the only method which additionally predicts the side-chain orientation of transmembrane β-strand residues, inter-strand residue contacts and TM β-strand inclination with respect to the membrane. The program transFold currently outperforms all other methods for accuracy of β-barrel structure prediction. Available at .

TMB-Hunt: An amino acid composition based method to screen proteomes for beta-barrel transmembrane proteins

BackgroundBeta-barrel transmembrane (bbtm) proteins are a functionally important and diverse group of proteins expressed in the outer membranes of bacteria (both gram negative and acid fast gram positive), mitochondria and chloroplasts. Despite recent publications describing reasonable levels of accuracy for discriminating between bbtm proteins and other proteins, screening of entire genomes remains troublesome as these molecules only constitute a small fraction of the sequences screened. Therefore, novel methods are still required capable of detecting new families of bbtm protein in diverse genomes.ResultsWe present TMB-Hunt, a program that uses a k-Nearest Neighbour (k-NN) algorithm to discriminate between bbtm and non-bbtm proteins on the basis of their amino acid composition. By including differentially weighted amino acids, evolutionary information and by calibrating the scoring, an accuracy of 92.5% was achieved, with 91% sensitivity and 93.8% positive predictive value (PPV), using a rigorous cross-validation procedure.A major advantage of this approach is that because it does not rely on beta-strand detection, it does not require resolved structures and thus larger, more representative, training sets could be used. It is therefore believed that this approach will be invaluable in complementing other, physicochemical and homology based methods. This was demonstrated by the correct reassignment of a number of proteins which other predictors failed to classify. We have used the algorithm to screen several genomes and have discussed our findings.ConclusionTMB-Hunt achieves a prediction accuracy level better than other approaches published to date. Results were significantly enhanced by use of evolutionary information and a system for calibrating k-NN scoring. Because the program uses a distinct approach to that of other discriminators and thus suffers different liabilities, we believe it will make a significant contribution to the development of a consensus approach for bbtm protein detection.

Association of spin-labeled lipids with beta-barrel proteins from the outer membrane of Escherichia coli.

The interaction of spin-labeled lipids with beta-barrel transmembrane proteins has been studied by the electron spin resonance (ESR) methods developed for alpha-helical integral proteins. The outer membrane protein OmpA and the ferrichrome-iron receptor FhuA from the outer membrane of Escherichia coli were reconstituted in bilayers of dimyristoylphosphatidylglycerol. The ESR spectra from phosphatidylglycerol spin labeled on the 14-C atom of the sn-2 chain contain a second component from motionally restricted lipids contacting the intramembranous surface of the beta-barrel, in addition to that from the fluid bilayer lipids. The stoichiometry of motionally restricted lipids, 11 and 32 lipids/monomer for OmpA and FhuA, respectively, is constant irrespective of the total lipid/protein ratio. It is proportional to the number of transmembrane beta-strands, eight for OmpA and 22 for FhuA, and correlates reasonably well with the intramembranous perimeter of the protein. Spin-labeled lipids with different polar headgroups display a differential selectivity of interaction with the two proteins. The more pronounced pattern of lipid selectivity for FhuA than for OmpA correlates with the preponderance of positively charged residues facing the lipids in the extensions of the beta-sheet and shorter interconnecting loops on the extracellular side of FhuA.

TMRPres2D: high quality visual representation of transmembrane protein models.

The 'TransMembrane protein Re-Presentation in 2-Dimensions' (TMRPres2D) tool, automates the creation of uniform, two-dimensional, high analysis graphical images/models of alpha-helical or beta-barrel transmembrane proteins. Protein sequence data and structural information may be acquired from public protein knowledge bases, emanate from prediction algorithms, or even be defined by the user. Several important biological and physical sequence attributes can be embedded in the graphical representation.

Predicting transmembrane beta-barrels in proteomes

Very few methods address the problem of predicting beta-barrel membrane proteins directly from sequence. One reason is that only very few high-resolution structures for transmembrane beta-barrel (TMB) proteins have been determined thus far. Here we introduced the design, statistics and results of a novel profile-based hidden Markov model for the prediction and discrimination of TMBs. The method carefully attempts to avoid over-fitting the sparse experimental data. While our model training and scoring procedures were very similar to a recently published work, the architecture and structure-based labelling were significantly different. In particular, we introduced a new definition of beta- hairpin motifs, explicit state modelling of transmembrane strands, and a log-odds whole-protein discrimination score. The resulting method reached an overall four-state (up-, down-strand, periplasmic-, outer-loop) accuracy as high as 86%. Furthermore, accurately discriminated TMB from non-TMB proteins (45% coverage at 100% accuracy). This high precision enabled the application to 72 entirely sequenced Gram-negative bacteria. We found over 164 previously uncharacterized TMB proteins at high confidence. Database searches did not implicate any of these proteins with membranes. We challenge that the vast majority of our 164 predictions will eventually be verified experimentally. All proteome predictions and the PROFtmb prediction method are available at http://www.rostlab.org/ services/PROFtmb/.