-Barrel transmembrane proteins: Geometric modelling, detection of transmembrane region, and structural properties

Predicting the outer membrane proteome of Pasteurella multocida based on consensus prediction enhanced by results integration and manual confirmation

Background

Outer membrane proteins (OMPs) of Pasteurella multocida have various functions related to virulence and pathogenesis and represent important targets for vaccine development. Various bioinformatic algorithms can predict outer membrane localization and discriminate OMPs by structure or function. The designation of a confident prediction framework by integrating different predictors followed by consensus prediction, results integration and manual confirmation will improve the prediction of the outer membrane proteome.

Results

In the present study, we used 10 different predictors classified into three groups (subcellular localization, transmembrane β-barrel protein and lipoprotein predictors) to identify putative OMPs from two available P. multocida genomes: those of avian strain Pm70 and porcine non-toxigenic strain 3480. Predicted proteins in each group were filtered by optimized criteria for consensus prediction: at least two positive predictions for the subcellular localization predictors, three for the transmembrane β-barrel protein predictors and one for the lipoprotein predictors. The consensus predicted proteins were integrated from each group into a single list of proteins. We further incorporated a manual confirmation step including a public database search against PubMed and sequence analyses, e.g. sequence and structural homology, conserved motifs/domains, functional prediction, and protein-protein interactions to enhance the confidence of prediction. As a result, we were able to confidently predict 98 putative OMPs from the avian strain genome and 107 OMPs from the porcine strain genome with 83% overlap between the two genomes.

Conclusions

The bioinformatic framework developed in this study has increased the number of putative OMPs identified in P. multocida and allowed these OMPs to be identified with a higher degree of confidence. Our approach can be applied to investigate the outer membrane proteomes of other Gram-negative bacteria.

Predicting three-dimensional structures of transmembrane domains of β-barrel membrane proteins

β-Barrel membrane proteins are found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They are important for pore formation, membrane anchoring, and enzyme activity. These proteins are also often responsible for bacterial virulence. Due to difficulties in experimental structure determination, they are sparsely represented in the protein structure databank. We have developed a computational method for predicting structures of the transmembrane (TM) domains of β-barrel membrane proteins. Based on physical principles, our method can predict structures of the TM domain of β-barrel membrane proteins of novel topology, including those from eukaryotic mitochondria. Our method is based on a model of physical interactions, a discrete conformational state space, an empirical potential function, as well as a model to account for interstrand loop entropy. We are able to construct three-dimensional atomic structure of the TM domains from sequences for a set of 23 nonhomologous proteins (resolution 1.8-3.0 Å). The median rmsd of TM domains containing 75-222 residues between predicted and measured structures is 3.9 Å for main chain atoms. In addition, stability determinants and protein-protein interaction sites can be predicted. Such predictions on eukaryotic mitochondria outer membrane protein Tom40 and VDAC are confirmed by independent mutagenesis and chemical cross-linking studies. These results suggest that our model captures key components of the organization principles of β-barrel membrane protein assembly.

Prediction of the exposure status of transmembrane beta barrel residues from protein sequence

We present BTMX (Beta barrel TransMembrane eXposure), a computational method to predict the exposure status (i.e. exposed to the bilayer or hidden in the protein structure) of transmembrane residues in transmembrane beta barrel proteins (TMBs). BTMX predicts the exposure status of known TM residues with an accuracy of 84.2% over 2,225 residues and provides a confidence score for all predictions. Predictions made are in concert with the fact that hydrophobic residues tend to be more exposed to the bilayer. The biological relevance of the input parameters is also discussed. The highest prediction accuracy is obtained when a sliding window comprising three residues with similar C(α)-C(β) vector orientations is employed. The prediction accuracy of the BTMX method on a separate unseen non-redundant test dataset is 78.1%. By employing out-pointing residues that are exposed to the bilayer, we have identified various physico-chemical properties that show statistically significant differences between the beta strands located at the oligomeric interfaces compared to the non-oligomeric strands. The BTMX web server generates colored, annotated snake-plots as part of the prediction results and is available under the BTMX tab at http://service.bioinformatik.uni-saarland.de/tmx-site/. Exposure status prediction of TMB residues may be useful in 3D structure prediction of TMBs.

Statistical analysis and exposure status classification of transmembrane beta barrel residues

Several computational methods exist for the identification of transmembrane beta barrel proteins (TMBs) from sequence. Some of these methods also provide the transmembrane (TM) boundaries of the putative TMBs. The aim of this study is to (1) derive the propensities of the TM residues to be exposed to the lipid bilayer and (2) to predict the exposure status (i.e. exposed to the bilayer or hidden in protein structure) of TMB residues. Three novel propensity scales namely, BTMC, BTMI and HTMI were derived for the TMB residues at the hydrophobic core region of the outer membrane (OM), the lipid-water interface regions of the OM, and for the helical membrane proteins (HMPs) residues at the lipid-water interface regions of the inner membrane (IM), respectively. Separate propensity scales were derived for monomeric and functionally oligomeric TMBs. The derived propensities reflect differing physico-chemical properties of the respective membrane bilayer regions and were employed in a computational method for the prediction of the exposure status of TMB residues. Based on the these propensities, the conservation indices and the frequency profile of the residues, the transmembrane residues were classified into buried/exposed with an accuracy of 77.91% and 80.42% for the residues at the membrane core and the interface regions, respectively. The correlation of the derived scales with different physico-chemical properties obtained from the AAIndex database are also discussed. Knowledge about the residue propensities and burial status will be useful in annotating putative TMBs with unknown structure.

Probing designability via a generalized model of helical bundle geometry

Because the space of folded protein structures is highly degenerate, with recurring secondary and tertiary motifs, methods for representing protein structure in terms of collective physically relevant coordinates are of great interest. By collapsing structural diversity to a handful of parameters, such methods can be used to delineate the space of designable structures (i.e., conformations that can be stabilized with a large number of sequences)-a crucial task for de novo protein design. We first demonstrate this on natural α-helical coiled coils using the Crick parameterization. We show that over 95% of known coiled-coil structures are within  1-Å C(α) root mean square deviation of a Crick-ideal backbone. Derived parameters show that natural geometric space of coiled coils is highly restricted and can be represented by "allowed" conformations amidst a potential continuum of conformers. Allowed structures have (1) restricted axial offsets between helices, which differ starkly between parallel and anti-parallel structures; (2) preferred superhelical radii, which depend linearly on the oligomerization state; (3) pronounced radius-dependent a- and d-position amino acid propensities; and (4) discrete angles of rotation of helices about their axes, which are surprisingly independent of oligomerization state or orientation. In all, we estimate the space of designable coiled-coil structures to be reduced at least 160-fold relative to the space of geometrically feasible structures. To extend the benefits of structural parameterization to other systems, we developed a general mathematical framework for parameterizing arbitrary helical structures, which reduces to the Crick parameterization as a special case. The method is successfully validated on a set of non-coiled-coil helical bundles, frequent in channels and transporter proteins, which show significant helix bending but not supercoiling. Programs for coiled-coil parameter fitting and structure generation are provided via a web interface at http://www.gevorggrigoryan.com/cccp/, and code for generalized helical parameterization is available upon request.

Genomic inventory and transcriptional analysis of Medicago truncatula transporters

Transporters move hydrophilic substrates across hydrophobic biological membranes and play key roles in plant nutrition, metabolism, and signaling and, consequently, in plant growth, development, and responses to the environment. To initiate and support systematic characterization of transporters in the model legume Medicago truncatula, we identified 3,830 transporters and classified 2,673 of these into 113 families and 146 subfamilies. Analysis of gene expression data for 2,611 of these transporters identified 129 that are expressed in an organ-specific manner, including 50 that are nodule specific and 36 specific to mycorrhizal roots. Further analysis uncovered 196 transporters that are induced at least 5-fold during nodule development and 44 in roots during arbuscular mycorrhizal symbiosis. Among the nodule- and mycorrhiza-induced transporter genes are many candidates for known transport activities in these beneficial symbioses. The data presented here are a unique resource for the selection and functional characterization of legume transporters.

Molecular modeling of proteinlike inclusions in lipid bilayers: lipid-mediated interactions

We investigated the insertion of transmembrane structures in a lipid bilayer and their interactions using self-consistent field theory. The lipids are coarse-grained on a united-atom level and consist of a phosphatidylcholinelike headgroup and two hydrophobic tails. The inclusions, acting as simple models for proteins that span biological membranes, are rigid rods (radius R ) with a hydrophobic surface and hydrophilic end caps. The insertion free energy Omega of an individual rod is strongly regulated by the affinity between its hydrophobic surface and the lipid tails. This affinity also controls the best match of the hydrophobic length of the rod with that of the bilayer. The line tension tau(=Omega/2piR) is practically independent of R . The perturbations in the bilayer as a function of distance from the inclusion, have the shape of a damped oscillation. The wavelength and decay length are related to the elastic properties of the bilayer and do not depend on R . These results are used to analyze how the lipid matrix affects the interaction between transmembrane objects, for computational reasons considering the limit of R-->infinity . Contributions on different length scales can be distinguished: (i) a long-range elastic interaction, which is an exponentially decaying oscillation; (ii) an exponentially decaying repulsion on an intermediate length scale, resulting from the loss of conformational entropy of the lipid tails; and (iii) a short-range interaction due to the finite compressibility of the lipid tails, which manifests either as a depletion attraction if there is no affinity between the tails and the inclusions' surface or, otherwise, as an oscillatory structural force.

Coarse-grained MD simulations of membrane protein-bilayer self-assembly

Complete determination of a membrane protein structure requires knowledge of the protein position within the lipid bilayer. As the number of determined structures of membrane proteins increases so does the need for computational methods which predict their position in the lipid bilayer. Here we present a coarse-grained molecular dynamics approach to lipid bilayer self-assembly around membrane proteins. We demonstrate that this method can be used to predict accurately the protein position in the bilayer for membrane proteins with a range of different sizes and architectures.