Brucella Omp2a and Omp2b porins: single channel measurements and topology prediction

Brucella usually carry two highly homologous genes (omp2a and omp2b) for porin-like proteins. In several B. abortus biovars the omp2a gene has a large deletion compared to other Brucella omp2's. In this study we have measured Omp2 pore activity in planar bilayers. Omp2b exhibits well-defined trimeric channel activity whilst Omp2a forms monomeric pores of variable size which are smaller than Omp2b. No sequence homology exists between Omp2 and porins of known structure, so hydrophobic moment analysis has been used to model their membrane topology. From this it appears likely that the deletion removes the crucial L3 internal loop.     

Critical assessment of high-throughput standalone methods for secondary structure prediction

Sequence-based prediction of protein secondary structure (SS) enjoys wide-spread and increasing use for the analysis and prediction of numerous structural and functional characteristics of proteins. The lack of a recent comprehensive and large-scale comparison of the numerous prediction methods results in an often arbitrary selection of a SS predictor. To address this void, we compare and analyze 12 popular, standalone and high-throughput predictors on a large set of 1975 proteins to provide in-depth, novel and practical insights. We show that there is no universally best predictor and thus detailed comparative studies are needed to support informed selection of SS predictors for a given application. Our study shows that the three-state accuracy (Q3) and segment overlap (SOV3) of the SS prediction currently reach 82% and 81%, respectively. We demonstrate that carefully designed consensus-based predictors improve the Q3 by additional 2% and that homology modeling-based methods are significantly better by 1.5% Q3 than ab initio approaches. Our empirical analysis reveals that solvent exposed and flexible coils are predicted with a higher quality than the buried and rigid coils, while inverse is true for the strands and helices. We also show that longer helices are easier to predict, which is in contrast to longer strands that are harder to find. The current methods confuse 1-6% of strand residues with helical residues and vice versa and they perform poorly for residues in the β- bridge and 3(10)-helix conformations. Finally, we compare predictions of the standalone implementations of four well-performing methods with their corresponding web servers.     

Simultaneous prediction of protein secondary structure and transmembrane spans

Prediction of transmembrane spans and secondary structure from the protein sequence is generally the first step in the structural characterization of (membrane) proteins. Preference of a stretch of amino acids in a protein to form secondary structure and being placed in the membrane are correlated. Nevertheless, current methods predict either secondary structure or individual transmembrane states. We introduce a method that simultaneously predicts the secondary structure and transmembrane spans from the protein sequence. This approach not only eliminates the necessity to create a consensus prediction from possibly contradicting outputs of several predictors but bears the potential to predict conformational switches, i.e., sequence regions that have a high probability to change for example from a coil conformation in solution to an α‐helical transmembrane state. An artificial neural network was trained on databases of 177 membrane proteins and 6048 soluble proteins. The output is a 3 × 3 dimensional probability matrix for each residue in the sequence that combines three secondary structure types (helix, strand, coil) and three environment types (membrane core, interface, solution). The prediction accuracies are 70.3% for nine possible states, 73.2% for three‐state secondary structure prediction, and 94.8% for three‐state transmembrane span prediction. These accuracies are comparable to state‐of‐the‐art predictors of secondary structure (e.g., Psipred) or transmembrane placement (e.g., OCTOPUS). The method is available as web server and for download at www.meilerlab.org . Proteins 2013; 81:1127–1140. © 2013 Wiley Periodicals, Inc.     

TMBpro: secondary structure, beta-contact and tertiary structure prediction of transmembrane beta-barrel proteins

MOTIVATION: Transmembrane beta-barrel (TMB) proteins are embedded in the outer membranes of mitochondria, Gram-negative bacteria and chloroplasts. These proteins perform critical functions, including active ion-transport and passive nutrient intake. Therefore, there is a need for accurate prediction of secondary and tertiary structure of TMB proteins. Traditional homology modeling methods, however, fail on most TMB proteins since very few non-homologous TMB structures have been determined. Yet, because TMB structures conform to specific construction rules that restrict the conformational space drastically, it should be possible for methods that do not depend on target-template homology to be applied successfully. RESULTS: We develop a suite (TMBpro) of specialized predictors for predicting secondary structure (TMBpro-SS), beta-contacts (TMBpro-CON) and tertiary structure (TMBpro-3D) of transmembrane beta-barrel proteins. We compare our results to the recent state-of-the-art predictors transFold and PRED-TMBB using their respective benchmark datasets, and leave-one-out cross-validation. Using the transFold dataset TMBpro predicts secondary structure with per-residue accuracy (Q(2)) of 77.8%, a correlation coefficient of 0.54, and TMBpro predicts beta-contacts with precision of 0.65 and recall of 0.67. Using the PRED-TMBB dataset, TMBpro predicts secondary structure with Q(2) of 88.3% and a correlation coefficient of 0.75. All of these performance results exceed previously published results by 4% or more. Working with the PRED-TMBB dataset, TMBpro predicts the tertiary structure of transmembrane segments with RMSD <6.0 A for 9 of 14 proteins. For 6 of 14 predictions, the RMSD is <5.0 A, with a GDT_TS score greater than 60.0. AVAILABILITY: http://www.igb.uci.edu/servers/psss.html.     

Molecular, antigenic, and functional analyses of Omp2b porin size variants of Brucella spp

Omp2a and Omp2b are highly homologous porins present in the outer membrane of the bacteria from the genus Brucella, a facultative intracellular pathogen. The genes coding for these proteins are closely linked in the Brucella genome and oriented in opposite directions. In this work, we present the cloning, purification, and characterization of four Omp2b size variants found in various Brucella species, and we compare their antigenic and functional properties to the Omp2a and Omp2b porins of Brucella melitensis reference strain 16M. The variation of the Omp2a and Omp2b porin sequences among the various strains of the genus Brucella seems to result mostly from multiple gene conversions between the two highly homologous genes. As shown in this study, this phenomenon has led to the creation of natural Omp2a and Omp2b chimeric proteins in Omp2b porin size variants. The comparison by liposome swelling assay of the porins sugar permeability suggested a possible functional differences between Omp2a and Omp2b, with Omp2a showing a more efficient pore in sugar diffusion. The sequence variability in the Omp2b size variants was located in the predicted external loops of the porin. Several epitopes recognized by anti-Omp2b monoclonal antibodies were mapped by comparison of the Omp2b size variants antigenicity, and two of them were located in the most exposed surface loops. However, since variations are mostly driven by simple exchanges of conserved motifs between the two genes (except for an Omp2b version from an atypical strain of Brucella suis biovar 3), the porin variability does not result in major antigenic variability of the Brucella surface that could help the bacteria during the reinfection of a host. Porin variation in Brucella seems to result mainly in porin conductivity modifications.     

Identification of the essential Brucella melitensis porin Omp2b as a suppressor of Bax-induced cell death in yeast in a genome-wide screening

Background

Inhibition of apoptosis is one of the mechanisms selected by numerous intracellular pathogenic bacteria to control their host cell. Brucellae, which are the causative agent of a worldwide zoonosis, prevent apoptosis of infected cells, probably to support survival of their replication niche.

Methodology/Principal Findings

In order to identify Brucella melitensis anti-apoptotic effector candidates, we performed a genome-wide functional screening in yeast. The B. melitensis ORFeome was screened to identify inhibitors of Bax-induced cell death in S. cerevisiae. B. melitensis porin Omp2b, here shown to be essential, prevents Bax lethal effect in yeast, unlike its close paralog Omp2a. Our results based on Omp2b size variants characterization suggest that signal peptide processing is required for Omp2b effect in yeast.

Conclusion/Significance

We report here the first application to a bacterial genome-wide library of coding sequences of this “yeast-rescue” screening strategy, previously used to highlight several new apoptosis regulators. Our work provides B. melitensis proteins that are candidates for an anti-apoptotic function, and can be tested in mammalian cells in the future. Hypotheses on possible molecular mechanisms of Bax inhibition by the B. melitensis porin Omp2b are discussed.     

Evaluation and improvement of multiple sequence methods for protein secondary structure prediction

A new dataset of 396 protein domains is developed and used to evaluate the performance of the protein secondary structure prediction algorithms DSC, PHD, NNSSP, and PREDATOR. The maximum theoretical Q3 accuracy for combination of these methods is shown to be 78%. A simple consensus prediction on the 396 domains, with automatically generated multiple sequence alignments gives an average Q3 prediction accuracy of 72.9%. This is a 1% improvement over PHD, which was the best single method evaluated. Segment Overlap Accuracy (SOV) is 75.4% for the consensus method on the 396-protein set. The secondary structure definition method DSSP defines 8 states, but these are reduced by most authors to 3 for prediction. Application of the different published 8- to 3-state reduction methods shows variation of over 3% on apparent prediction accuracy. This suggests that care should be taken to compare methods by the same reduction method. Two new sequence datasets (CB513 and CB251) are derived which are suitable for cross-validation of secondary structure prediction methods without artifacts due to internal homology. A fully automatic World Wide Web service that predicts protein secondary structure by a combination of methods is available via http://barton.ebi.ac.uk/.     

The secondary structure of myelin P2 protein derived by secondary structure prediction methods, circular dichroism, and 400-MHz 1H-NMR spectroscopy: implications for tertiary structure

The various methods used to study the secondary and tertiary structure of myelin P2 protein, and one of its peptides (CN1), in aqueous solution, indicate that the native protein contains a significant fraction of alpha-helix, suggesting that the current prediction of an all-beta tertiary structure requires revision.     

Structure and function prediction of the Brucella abortus P39 protein by comparative modeling with marginal sequence similarities

A methodology is proposed to solve a difficult modeling problem related to the recently sequenced P39 protein. This sequence shares no similarity with any known 3D structure, but a fold is proposed by several threading tools. The difficulty in aligning the target sequence on one of the proposed template structures is overcome by combining the results of several available prediction methods and by refining a rational consensus between them. In silico validation of the obtained model and a preliminary cross-check with experimental features allow us to state that this borderline prediction is at least reasonable. This model raises relevant hypotheses on the main structural features of the protein and allows the design of site-directed mutations. Knowing the genetic context of the P39 reading frame, we are now able to suggest a function for the P39 protein: it would act as a periplasmic substrate-binding protein.     

Omp25‐dependent engagement of SLAMF1 by Brucella abortus in dendritic cells limits acute inflammation and favours bacterial persistence in vivo

The strategies by which intracellular pathogenic bacteria manipulate innate immunity to establish chronicity are poorly understood. Here, we show that Brucella abortus outer membrane protein Omp25 specifically binds the immune cell receptor SLAMF1 in vitro. The Omp25‐dependent engagement of SLAMF1 by B. abortus limits NF‐κB translocation in dendritic cells (DCs) with no impact on Brucella intracellular trafficking and replication. This in turn decreases pro‐inflammatory cytokine secretion and impairs DC activation. The Omp25‐SLAMF1 axis also dampens the immune response without affecting bacterial replication in vivo during the acute phase of Brucella infection in a mouse model. In contrast, at the chronic stage of infection, the Omp25/SLAMF1 engagement is essential for Brucella persistence. Interaction of a specific bacterial protein with an immune cell receptor expressed on the DC surface at the acute stage of infection is thus a powerful mechanism to support microbe settling in its replicative niche and progression to chronicity.     

Purification, refolding and characterization of the trimeric Omp2a outer membrane porin from Brucella melitensis

Brucella melitensis is a gram-negative bacteria known to cause brucellosis and to produce severe infections in humans. Whilst brucella's outer membrane proteins have been extensively studied due to their potential role as antigens or virulence factors, their function is still poorly understood at the structural level, as the 3D structure of Brucella β-barrel membrane proteins are still unknown. In this context, the B. melitensis trimeric Omp2a porin has been overexpressed and refolded in n-dodecyl-β-d-maltopyranoside. We here show that this refolding process is insensitive to urea but is temperature- and ionic strength-dependent. Reassembled species were characterized by fluorescence, size-exclusion chromatography and circular dichroism. A refolding mechanism is proposed, suggesting that Omp2a first refolds under a monomeric form and then self-associates into a trimeric state. This first complete in vitro refolding of a membrane protein from B. melitensis shall eventually lead to functional and 3D structure determination.     

A critical assessment of the secondary structure alpha-helices and their termini in proteins

Secondary structure prediction from amino acid sequence is a key component of protein structure prediction, with current accuracy at approximately 75%. We analysed two state-of-the-art secondary structure prediction methods, PHD and JPRED, comparing predictions with secondary structure assigned by the algorithms DSSP and STRIDE. The specific focus of our study was alpha-helix N-termini, as empirical free energy scales are available for residue preferences at N-terminal positions. Although these prediction methods perform well in general at predicting the alpha-helical locations and length distributions in proteins, they perform less well at predicting the correct helical termini. For example, although most predicted alpha-helices overlap a real alpha-helix (with relatively few completely missed or extra predicted helices), only one-third of JPRED and PHD predictions correctly identify the N-terminus. Analysis of neighbouring N-terminal sequences to predicted helical N-termini shows that the correct N-terminus is often within one or two residues. More importantly, the true N-terminal motif is, on average, more favourable as judged by our experimentally measured free energies. This suggests a simple, but powerful, strategy to improve secondary structure prediction using empirically derived energies to adjust the predicted output to a more favourable N-terminal sequence.     

A modified definition of Sov, a segment-based measure for protein secondary structure prediction assessment

We present a measure for the evaluation of secondary structure prediction methods that is based on secondary structure segments rather than individual residues. The algorithm is an extension of the segment overlap measure Sov, originally defined by Rost et al. (J Mol Biol 1994;235:13-26). The new definition of Sov corrects the normalization procedure and improves Sov's ability to discriminate between similar and dissimilar segment distributions. The method has been comprehensively tested during the second Critical Assessment of Techniques for Protein Structure Prediction (CASP2). Here, we describe the underlying concepts, modifications to the original definition, and their significance.     

A consensus prediction of the secondary structure for the 6-phospho-β-D-galactosidase superfamily

Two separate unrefined models for the secondary structure of two subfamilies of the 6-phospho-β-D -galactosidase superfamily were independently constructed by examining patterns of variation and conservation within homologous protein sequences, assigning surface, interior, parsing, and active site residues to positions in the alignment, and identifying periodicities in these. A consensus model for the secondary structure of the entire superfamily was then built. The prediction tests the limits of an unrefined prediction made using this approach in a large protein with substantial functional and sequence divergence within the family. The protein belongs to the (α–β class), with the core β strands aligned parallel. The supersecondary structural elements that are readily identified in this model is a parallel β sheet built by strands C, D, and E, with helices 2 and 3 connecting strands (C + D) and (D + E), respectively, and an analogous α–β unit (strand G and helix 7) toward the end of the sequence. The resemblance of the supersecondary model to the tertiary structure formed by 8-fold α–β barrel proteins is almost certainly not coincidental. © 1995 Wiley-Liss, Inc.     

Ultrastructure of Brucella abortus L-forms induced by penicillin in a liquid and in a semisolid medium

Brucella abortus L-forms were induced by 5.0 or 10.0 mug of penicillin/ml in a broth medium containing 0.3 m sucrose, and in a semisolid medium containing 10% calf serum and 20.0, 40.0, or 60.0 mug of penicillin/ml. After 96 hr of incubation, L-forms of various sizes and shapes were observed. Basic structures of the L-forms were similar whether induced in liquid or semisolid medium. L-forms had two "unit" membranes, each consisting of two outer dense layers separated by a lucent layer. A few large, irregularly shaped organisms in penicillin-treated broth cultures had additional surface material and were referred to as "transitional" forms. In contrast with L-forms, the bacterial cells were fairly uniform in size and shape, were smaller, and had a more complex cell wall structure. Small bodies limited by a "unit" membrane were present within and around numerous L-forms from liquid and semisolid medium cultures. Other internal membranous structures were also seen in some L-forms. Most Brucella L-forms described in this paper reverted to bacteria in the absence of penicillin and were structurally characteristic of unstable L-forms.     

MUPRED: a tool for bridging the gap between template based methods and sequence profile based methods for protein secondary structure prediction

Predicting secondary structures from a protein sequence is an important step for characterizing the structural properties of a protein. Existing methods for protein secondary structure prediction can be broadly classified into template based or sequence profile based methods. We propose a novel framework that bridges the gap between the two fundamentally different approaches. Our framework integrates the information from the fuzzy k-nearest neighbor algorithm and position-specific scoring matrices using a neural network. It combines the strengths of the two methods and has a better potential to use the information in both the sequence and structure databases than existing methods. We implemented the framework into a software system MUPRED. MUPRED has achieved three-state prediction accuracy (Q3) ranging from 79.2 to 80.14%, depending on which benchmark dataset is used. A higher Q3 can be achieved if a query protein has a significant sequence identity (>25%) to a template in PDB. MUPRED also estimates the prediction accuracy at the individual residue level more quantitatively than existing methods. The MUPRED web server and executables are freely available at http://digbio.missouri.edu/mupred.     

NMR structure and dynamics of the second transmembrane domain of the neuronal acetylcholine receptor beta 2 subunit

Structure and backbone dynamics of a selectively [(15)N]Leu-labeled 28-residue segment of the extended second transmembrane domain (TM2e) of the human neuronal nicotinic acetylcholine receptor (nAChR) beta(2) subunit were studied by (1)H and (15)N solution-state NMR in dodecylphosphocholine micelles. The TM2e structure was determined on the basis of the nuclear Overhauser effects (NOEs) and the hydrogen bond restraints, which were inferred from the presence of H(alpha)(i)-H(N)(i+3), H(alpha)(i)-H(beta)(i+3), and H(alpha)(i)-H(N)(i+4) NOE connectivity and from the slow amide hydrogen exchange with D(2)O. The TM2e structure of the nAChR beta(2) subunit contains a helical region between T4 and K22. Backbone dynamics were calculated using the model-free approach based on the (15)N relaxation rate constants, R(1) and R(2), and on the (15)N-[(1)H] NOE. The data acquired at 9.4 and 14.1 T and calculations using different dynamic models demonstrated no conformational exchange and internal motions on the nanosecond time scale. The global tumbling time of TM2e in micelles was 14.4 +/- 0.2 ns; the NOE values were greater than 0.63 at 9.4 T, and the order parameter, S(2), was 0.83-0.96 for all (15)N-labeled leucine residues, suggesting a restricted internal motion. This is the first report of NMR structure and backbone dynamics of the second transmembrane domain of the human nAChR beta(2) subunit in a membrane-mimetic environment, providing the basis for subsequent studies of subunit interactions in the transmembrane domain complex of the neuronal nAChR.     

Position-specific residue preference features around the ends of helices and strands and a novel strategy for the prediction of secondary structures

It has been many years since position-specific residue preference around the ends of a helix was revealed. However, all the existing secondary structure prediction methods did not exploit this preference feature, resulting in low accuracy in predicting the ends of secondary structures. In this study, we collected a relatively large data set consisting of 1860 high-resolution, non-homology proteins from the PDB, and further analyzed the residue distributions around the ends of regular secondary structures. It was found that there exist position-specific residue preferences (PSRP) around the ends of not only helices but also strands. Based on the unique features, we proposed a novel strategy and developed a tool named E-SSpred that treats the secondary structure as a whole and builds models to predict entire secondary structure segments directly by integrating relevant features. In E-SSpred, the support vector machine (SVM) method is adopted to model and predict the ends of helices and strands according to the unique residue distributions around them. A simple linear discriminate analysis method is applied to model and predict entire secondary structure segments by integrating end-prediction results, tri-peptide composition, and length distribution features of secondary structures, as well as the prediction results of the most famous program PSIPRED. The results of fivefold cross-validation on a widely used data set demonstrate that the accuracy of E-SSpred in predicting ends of secondary structures is about 10% higher than PSIPRED, and the overall prediction accuracy (Q(3) value) of E-SSpred (82.2%) is also better than PSIPRED (80.3%). The E-SSpred web server is available at http://bioinfo.hust.edu.cn/bio/tools/E-SSpred/index.html.