Components Subcellular Localization: Cell Surface Exposure

Surface-exposed proteins of Gram-negative bacteria are represented by integral outer membrane β-barrel proteins and lipoproteins. There are no computational methods to predict surface-exposed lipoproteins, and therefore lipoprotein topology must be experimentally tested. This chapter describes several distinct but complementary methods for detection of surface-exposed proteins: cell surface protein labeling, accessibility to extracellular protease or antibodies, and SpyTag/SpyCatcher system.

Protein Sorting Prediction

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global property-based, and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches are described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

Sequence-structure-function relationships in the microbial protein universe

For the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain parts of the protein universe, it disregards spaces that don't rely on this assumption. Here we explore areas of the protein universe where similar protein functions can be achieved by different sequences and different structures. We predict ~200,000 structures for diverse protein sequences from 1,003 representative genomes across the microbial tree of life and annotate them functionally on a per-residue basis. Structure prediction is accomplished using the World Community Grid, a large-scale citizen science initiative. The resulting database of structural models is complementary to the AlphaFold database, with regards to domains of life as well as sequence diversity and sequence length. We identify 148 novel folds and describe examples where we map specific functions to structural motifs. We also show that the structural space is continuous and largely saturated, highlighting the need for a shift in focus across all branches of biology, from obtaining structures to putting them into context and from sequence-based to sequence-structure-function based meta-omics analyses.

ProtRAP: Predicting Lipid Accessibility Together with Solvent Accessibility of Proteins in One Run

Solvent accessibility has been extensively used to characterize and predict the chemical properties of the surface residues of soluble proteins. However, there is not yet a widely accepted quantity of the same dimension for the study of lipid-accessible residues of membrane proteins. In this study, we propose that lipid accessibility, defined in a similar way to solvent accessibility, can be used to characterize the lipid-accessible residues of membrane proteins. Moreover, we developed a deep learning-based method, ProtRAP (Protein Relative Accessibility Predictor), to predict the relative lipid accessibility and relative solvent accessibility of residues from a given protein sequence, which can infer which residues are likely accessible to lipids, accessible to solvent, or buried in the protein interior in one run.

TopProperty: Robust Metaprediction of Transmembrane and Globular Protein Features Using Deep Neural Networks

Transmembrane proteins (TMPs) are critical components of cellular life. However, due to experimental challenges, the number of experimentally resolved TMP structures is severely underrepresented in databases compared to their cellular abundance. Prediction of (per-residue) features such as transmembrane topology, membrane exposure, secondary structure, and solvent accessibility can be a useful starting point for experimental design or protein structure prediction but often requires different computational tools for different features or types of proteins. We present TopProperty, a metapredictor that predicts all of these features for TMPs or globular proteins. TopProperty is trained on datasets without bias toward a high number of sequence homologs, and the predictions are significantly better than the evaluated state-of-the-art primary predictors on all quality metrics. TopProperty eliminates the need for protein type- or feature-tailored tools, specifically for TMPs. TopProperty is freely available as a web server and standalone at https://cpclab.uni-duesseldorf.de/topsuite/.

TopDomain: Exhaustive Protein Domain Boundary Metaprediction Combining Multisource Information and Deep Learning

Protein domains are independent, functional, and stable structural units of proteins. Accurate protein domain boundary prediction plays an important role in understanding protein structure and evolution, as well as for protein structure prediction. Current domain boundary prediction methods differ in terms of boundary definition, methodology, and training databases resulting in disparate performance for different proteins. We developed TopDomain, an exhaustive metapredictor, that uses deep neural networks to combine multisource information from sequence- and homology-based features of over 50 primary predictors. For this purpose, we developed a new domain boundary data set termed the TopDomain data set, in which the true annotations are informed by SCOPe annotations, structural domain parsers, human inspection, and deep learning. We benchmark TopDomain against 2484 targets with 3354 boundaries from the TopDomain test set and achieve F1 scores of 78.4% and 73.8% for multidomain boundary prediction within ±20 residues and ±10 residues of the true boundary, respectively. When examined on targets from CASP11-13 competitions, TopDomain achieves F1 scores of 47.5% and 42.8% for multidomain proteins. TopDomain significantly outperforms 15 widely used, state-of-the-art ab initio and homology-based domain boundary predictors. Finally, we implemented TopDomain_TMC, which accurately predicts whether domain parsing is necessary for the target protein.

Insertion of plastidic β-barrel proteins into the outer envelopes of plastids involves an intermembrane space intermediate formed with Toc75-V/OEP80

The insertion of organellar membrane proteins with the correct topology requires the following: First, the proteins must contain topogenic signals for translocation across and insertion into the membrane. Second, proteinaceous complexes in the cytoplasm, membrane, and lumen of organelles are required to drive this process. Many complexes required for the intracellular distribution of membrane proteins have been described, but the signals and components required for the insertion of plastidic β-barrel-type proteins into the outer membrane are largely unknown. The discovery of common principles is difficult, as only a few plastidic β-barrel proteins exist. Here, we provide evidence that the plastidic outer envelope β-barrel proteins OEP21, OEP24, and OEP37 from pea (Pisum sativum) and Arabidopsis thaliana contain information defining the topology of the protein. The information required for the translocation of pea proteins across the outer envelope membrane is present within the six N-terminal β-strands. This process requires the action of translocon of the outer chloroplast (TOC) membrane. After translocation into the intermembrane space, β-barrel proteins interact with TOC75-V, as exemplified by OEP37 and P39, and are integrated into the membrane. The membrane insertion of plastidic β-barrel proteins is affected by mutation of the last β-strand, suggesting that this strand contributes to the insertion signal. These findings shed light on the elements and complexes involved in plastidic β-barrel protein import.

Current production by non-methanotrophic bacteria enriched from an anaerobic methane-oxidizing microbial community

In recent years, the externalization of electrons as part of respiratory metabolic processes has been discovered in many different bacteria and some archaea. Microbial extracellular electron transfer (EET) plays an important role in many anoxic natural or engineered ecosystems. In this study, an anaerobic methane-converting microbial community was investigated with regard to its potential to perform EET. At this point, it is not well-known if or how EET confers a competitive advantage to certain species in methane-converting communities. EET was investigated in a two-chamber electrochemical system, sparged with methane and with an applied potential of +400 mV versus standard hydrogen electrode. A biofilm developed on the working electrode and stable low-density current was produced, confirming that EET indeed did occur. The appearance and presence of redox centers at −140 to −160 mV and at −230 mV in the biofilm was confirmed by cyclic voltammetry scans. Metagenomic analysis and fluorescence in situ hybridization of the biofilm showed that the anaerobic methanotroph ‘Candidatus Methanoperedens BLZ2’ was a significant member of the biofilm community, but its relative abundance did not increase compared to the inoculum. On the contrary, the relative abundance of other members of the microbial community significantly increased (up to 720-fold, 7.2% of mapped reads), placing these microorganisms among the dominant species in the bioanode community. This group included Zoogloea sp., Dechloromonas sp., two members of the Bacteroidetes phylum, and the spirochete Leptonema sp. Genes encoding proteins putatively involved in EET were identified in Zoogloea sp., Dechloromonas sp. and one member of the Bacteroidetes phylum. We suggest that instead of methane, alternative carbon sources such as acetate were the substrate for EET. Hence, EET in a methane-driven chemolithoautotrophic microbial community seems a complex process in which interactions within the microbial community are driving extracellular electron transfer to the electrode.

Computational prediction of secreted proteins in gram-negative bacteria

Gram-negative bacteria harness multiple protein secretion systems and secrete a large proportion of the proteome. Proteins can be exported to periplasmic space, integrated into membrane, transported into extracellular milieu, or translocated into cytoplasm of contacting cells. It is important for accurate, genome-wide annotation of the secreted proteins and their secretion pathways. In this review, we systematically classified the secreted proteins according to the types of secretion systems in Gram-negative bacteria, summarized the known features of these proteins, and reviewed the algorithms and tools for their prediction.

Integrative approach for detecting membrane proteins

Background

Membrane proteins are key gates that control various vital cellular functions. Membrane proteins are often detected using transmembrane topology prediction tools. While transmembrane topology prediction tools can detect integral membrane proteins, they do not address surface-bound proteins. In this study, we focused on finding the best techniques for distinguishing all types of membrane proteins.

Results

This research first demonstrates the shortcomings of merely using transmembrane topology prediction tools to detect all types of membrane proteins. Then, the performance of various feature extraction techniques in combination with different machine learning algorithms was explored. The experimental results obtained by cross-validation and independent testing suggest that applying an integrative approach that combines the results of transmembrane topology prediction and position-specific scoring matrix (Pse-PSSM) optimized evidence-theoretic k nearest neighbor (OET-KNN) predictors yields the best performance.

Conclusion

The integrative approach outperforms the state-of-the-art methods in terms of accuracy and MCC, where the accuracy reached a 92.51% in independent testing, compared to the 89.53% and 79.42% accuracies achieved by the state-of-the-art methods.

BetAware-Deep: An Accurate Web Server for Discrimination and Topology Prediction of Prokaryotic Transmembrane β-barrel Proteins

TransMembrane β-Barrel (TMBB) proteins located in the outer membranes of Gram-negative bacteria are crucial for many important biological processes and primary candidates as drug targets. Structure determination of TMBB proteins is challenging and hence computational methods devised for the analysis of TMBB proteins are important for complementing experimental approaches. Here, we present a novel web server called BetAware-Deep that is able to accurately identify the topology of TMBB proteins (i.e. the number and orientation of membrane-spanning segments along the protein sequence) and to discriminate them from other protein types. The method in BetAware-Deep defines new features by exploiting a non-canonical computation of the hydrophobic moment and by adopting sequence-profile weighting of the White&Wimley hydrophobicity scale. These features are processed using a two-step approach based on deep learning and probabilistic graphical models. BetAware-Deep has been trained on a dataset comprising 58 TMBBs and benchmarked on a novel set of 15 TMBB proteins. Results showed that BetAware-Deep outperforms two recently released state-of-the-art methods for topology prediction, predicting correct topologies of 10 out of 15 proteins. TMBB detection was also assessed on a larger dataset comprising 1009 TMBB proteins and 7571 non-TMBB proteins. Even in this benchmark, BetAware-Deep scored at the level of top-performing methods. A web server has been developed allowing users to analyze input protein sequences and providing topology prediction together with a rich set of information including a graphical representation of the residue-level annotations and prediction probabilities. BetAware-Deep is available at https://busca.biocomp.unibo.it/betaware2.

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.

Prediction of Ion Channels and their Types from Protein Sequences: Comprehensive Review and Comparative Assessment

BACKGROUND

Ion channels are a large and growing protein family. Many of them are associated with diseases, and consequently, they are targets for over 700 drugs. Discovery of new ion channels is facilitated with computational methods that predict ion channels and their types from protein sequences. However, these methods were never comprehensively compared and evaluated.

OBJECTIVE

We offer first-of-its-kind comprehensive survey of the sequence-based predictors of ion channels. We describe eight predictors that include five methods that predict ion channels, their types, and four classes of the voltage-gated channels. We also develop and use a new benchmark dataset to perform comparative empirical analysis of the three currently available predictors.

RESULTS

While several methods that rely on different designs were published, only a few of them are currently available and offer a broad scope of predictions. Support and availability after publication should be required when new methods are considered for publication. Empirical analysis shows strong performance for the prediction of ion channels and modest performance for the prediction of ion channel types and voltage-gated channel classes. We identify a substantial weakness of current methods that cannot accurately predict ion channels that are categorized into multiple classes/types.

CONCLUSION

Several predictors of ion channels are available to the end users. They offer practical levels of predictive quality. Methods that rely on a larger and more diverse set of predictive inputs (such as PSIONplus) are more accurate. New tools that address multi-label prediction of ion channels should be developed.

A Brief History of Protein Sorting Prediction

Ever since the signal hypothesis was proposed in 1971, the exact nature of signal peptides has been a focus point of research. The prediction of signal peptides and protein subcellular location from amino acid sequences has been an important problem in bioinformatics since the dawn of this research field, involving many statistical and machine learning technologies. In this review, we provide a historical account of how position-weight matrices, artificial neural networks, hidden Markov models, support vector machines and, lately, deep learning techniques have been used in the attempts to predict where proteins go. Because the secretory pathway was the first one to be studied both experimentally and through bioinformatics, our main focus is on the historical development of prediction methods for signal peptides that target proteins for secretion; prediction methods to identify targeting signals for other cellular compartments are treated in less detail.

Effect of environmental salt concentration on the Helicobacter pylori exoproteome

Helicobacter pylori infection and a high salt diet are each risk factors for gastric cancer. In this study, we tested the hypothesis that environmental salt concentration influences the composition of the H. pylori exoproteome. H. pylori was cultured in media containing varying concentrations of sodium chloride, and aliquots were fractionated and analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). We identified proteins that were selectively released into the extracellular space, and we identified selectively released proteins that were differentially abundant in culture supernatants, depending on the environmental salt concentration. We also used RNA-seq analysis to identify genes that were differentially expressed in response to environmental salt concentration. The salt-responsive proteins identified by proteomic analysis and salt-responsive genes identified by RNA-seq analysis were mostly non-concordant, but the secreted toxin VacA was salt-responsive in both analyses. Western blot analysis confirmed that VacA levels in the culture supernatant were increased in response to high salt conditions, and quantitative RT-qPCR experiments confirmed that vacA transcription was upregulated in response to high salt conditions. These results indicate that environmental salt concentration influences the composition of the H. pylori exoproteome, which could contribute to the increased risk of gastric cancer associated with a high salt diet. SIGNIFICANCE: Helicobacter pylori-induced alterations in the gastric mucosa have been attributed, at least in part, to the actions of secreted H. pylori proteins. In this study, we show that H. pylori growth in high salt concentrations leads to increased levels of a secreted VacA toxin. Salt-induced alterations in the composition of the H. pylori exoproteome is relevant to the increased risk of gastric cancer associated with consumption of a high salt diet.

Deprivation of the Periplasmic Chaperone SurA Reduces Virulence and Restores Antibiotic Susceptibility of Multidrug-Resistant Pseudomonas aeruginosa

Pseudomonas aeruginosa is one of the main causative agents of nosocomial infections and the spread of multidrug-resistant strains is rising. Therefore, novel strategies for therapy are urgently required. The outer membrane composition of Gram-negative pathogens and especially of Pa restricts the efficacy of antibiotic entry into the cell and determines virulence. For efficient outer membrane protein biogenesis, the β-barrel assembly machinery (BAM) complex in the outer membrane and periplasmic chaperones like Skp and SurA are crucial. Previous studies indicated that the importance of individual proteins involved in outer membrane protein biogenesis may vary between different Gram-negative species. In addition, since multidrug-resistant Pa strains pose a serious global threat, the interference with both virulence and antibiotic resistance by disturbing outer membrane protein biogenesis might be a new strategy to cope with this challenge. Therefore, deletion mutants of the non-essential BAM complex components bamB and bamC, of the skp homolog hlpA as well as a conditional mutant of surA were investigated. The most profound effects for both traits were associated with reduced levels of SurA, characterized by increased membrane permeability, enhanced sensitivity to antibiotic treatment and attenuation of virulence in a Galleria mellonella infection model. Strikingly, the depletion of SurA in a multidrug-resistant clinical bloodstream isolate re-sensitized the strain to antibiotic treatment. From our data we conclude that SurA of Pa serves as a promising target for developing a drug that shows antiinfective activity and re-sensitizes multidrug-resistant strains to antibiotics.

Protein Sorting Prediction

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global-property-based and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches is described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

Statistically derived asymmetric membrane potentials from α-helical and β-barrel membrane proteins

Modeling membrane protein (MP) folding, insertion, association and their interactions with other proteins, lipids, and drugs requires accurate transfer free energies (TFEs). Various TFE scales have been derived to quantify the energy required or released to insert an amino acid or protein into the membrane. Experimental measurement of TFEs is challenging, and only few scales were extended to depth-dependent energetic profiles. Statistical approaches can be used to derive such potentials; however, this requires a sufficient number of MP structures. Furthermore, MPs are tightly coupled to bilayers that are heterogeneous in terms of lipid composition, asymmetry, and protein content between organisms and organelles. Here we derived asymmetric implicit membrane potentials from β-barrel and α-helical MPs and use them to predict topology, depth and orientation of proteins in the membrane. Our data confirm the ‘charge-outside’ and ‘positive-inside’ rules for β-barrels and α-helical proteins, respectively. We find that the β-barrel profiles have greater asymmetry than the ones from α-helical proteins, as a result of the different membrane architecture of gram-negative bacterial outer membranes and the existence of lipopolysaccharide in the outer leaflet. Our data further suggest that pore-facing residues in β-barrels have a larger contribution to membrane insertion and stability than previously suggested.

The ropAe gene encodes a porin-like protein involved in copper transit in Rhizobium etli CFN42

Copper (Cu) is an essential micronutrient for all aerobic forms of life. Its oxidation states (Cu⁺/Cu²⁺) make this metal an important cofactor of enzymes catalyzing redox reactions in essential biological processes. In gram‐negative bacteria, Cu uptake is an unexplored component of a finely regulated trafficking network, mediated by protein–protein interactions that deliver Cu to target proteins and efflux surplus metal to avoid toxicity. Rhizobium etliCFN42 is a facultative symbiotic diazotroph that must ensure its appropriate Cu supply for living either free in the soil or as an intracellular symbiont of leguminous plants. In crop fields, rhizobia have to contend with copper‐based fungicides. A detailed deletion analysis of the pRet42e (505 kb) plasmid from an R. etli mutant with enhanced CuCl₂ tolerance led us to the identification of the ropAe gene, predicted to encode an outer membrane protein (OMP) with a β–barrel channel structure that may be involved in Cu transport. In support of this hypothesis, the functional characterization of ropAe revealed that: (I) gene disruption increased copper tolerance of the mutant, and its complementation with the wild‐type gene restored its wild‐type copper sensitivity; (II) the ropAe gene maintains a low basal transcription level in copper overload, but is upregulated when copper is scarce; (III) disruption of ropAe in an actP (copA) mutant background, defective in copper efflux, partially reduced its copper sensitivity phenotype. Finally, BLASTP comparisons and a maximum likelihood phylogenetic analysis highlight the diversification of four RopA paralogs in members of the Rhizobiaceae family. Orthologs of RopAe are highly conserved in the Rhizobiales order, poorly conserved in other alpha proteobacteria and phylogenetically unrelated to characterized porins involved in Cu or Mn uptake.

Topology of membrane proteins-predictions, limitations and variations

Transmembrane proteins perform a variety of important biological functions necessary for the survival and growth of the cells. Membrane proteins are built up by transmembrane segments that span the lipid bilayer. The segments can either be in the form of hydrophobic alpha-helices or beta-sheets which create a barrel. A fundamental aspect of the structure of transmembrane proteins is the membrane topology, that is, the number of transmembrane segments, their position in the protein sequence and their orientation in the membrane. Along these lines, many predictive algorithms for the prediction of the topology of alpha-helical and beta-barrel transmembrane proteins exist. The newest algorithms obtain an accuracy close to 80% both for alpha-helical and beta-barrel transmembrane proteins. However, lately it has been shown that the simplified picture presented when describing a protein family by its topology is limited. To demonstrate this, we highlight examples where the topology is either not conserved in a protein superfamily or where the structure cannot be described solely by the topology of a protein. The prediction of these non-standard features from sequence alone was not successful until the recent revolutionary progress in 3D-structure prediction of proteins.

The major outer sheath protein forms distinct conformers and multimeric complexes in the outer membrane and periplasm of Treponema denticola

The major outer sheath protein (MOSP) is a prominent constituent of the cell envelope of Treponema denticola (TDE) and one of its principal virulence determinants. Bioinformatics predicts that MOSP consists of N- and C-terminal domains, MOSP^N and MOSP^C. Biophysical analysis of constructs refolded in vitro demonstrated that MOSP^C, previously shown to possess porin activity, forms amphiphilic trimers, while MOSP^N forms an extended hydrophilic monomer. In TDE and E. coli expressing MOSP with a PelB signal sequence (PelB-MOSP), MOSP^C is OM-embedded and surface-exposed, while MOSP^N resides in the periplasm. Immunofluorescence assay, surface proteolysis, and novel cell fractionation schemes revealed that MOSP in TDE exists as outer membrane (OM) and periplasmic trimeric conformers; PelB-MOSP, in contrast, formed only OM-MOSP trimers. Although both conformers form hetero-oligomeric complexes in TDE, only OM-MOSP associates with dentilisin. Mass spectrometry (MS) indicated that OM-MOSP interacts with proteins in addition to dentilisin, most notably, oligopeptide-binding proteins (OBPs) and the β-barrel of BamA. MS also identified candidate partners for periplasmic MOSP, including TDE1658, a spirochete-specific SurA/PrsA ortholog. Collectively, our data suggest that MOSP destined for the TDE OM follows the canonical BAM pathway, while formation of a stable periplasmic conformer involves an export-related, folding pathway not present in E. coli.

Cell Surface Exposure

Surface-exposed proteins of Gram-negative bacteria are represented by integral outer membrane beta-barrel proteins and lipoproteins. No computational methods exist for predicting surface-exposed lipoproteins, and therefore lipoprotein topology must be experimentally tested. This chapter describes three distinct but complementary methods for the detection of surface-exposed proteins: cell surface protein labeling, accessibility to extracellular protease and antibodies.

Computing structure-based lipid accessibility of membrane proteins with mp_lipid_acc in RosettaMP

Background

Membrane proteins are underrepresented in structural databases, which has led to a lack of computational tools and the corresponding inappropriate use of tools designed for soluble proteins. For membrane proteins, lipid accessibility is an essential property. Although programs are available for sequence-based prediction of lipid accessibility and structure-based identification of solvent-accessible surface area, the latter does not distinguish between water accessible and lipid accessible residues in membrane proteins.

Results

Here we present mp_lipid_acc, the first method to identify lipid accessible residues from the protein structure, implemented in the RosettaMP framework and available as a webserver. Our method uses protein structures transformed in membrane coordinates, for instance from PDBTM or OPM databases, and a defined membrane thickness to classify lipid accessibility of residues. mp_lipid_acc is applicable to both α-helical and β-barrel membrane proteins of diverse architectures with or without water-filled pores and uses a concave hull algorithm for surface-residue classification. We further provide a manually curated benchmark dataset that can be used for further method development.

Conclusions

We present a novel tool to classify lipid accessibility from the protein structure, which is applicable to proteins of diverse architectures and achieves prediction accuracies of 90% on a manually curated database. mp_lipid_acc is part of the Rosetta software suite, available at www.rosettacommons.org. The webserver is available at http://rosie.graylab.jhu.edu/mp_lipid_acc/submit and the benchmark dataset is available at http://tinyurl.com/mp-lipid-acc-dataset.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1541-z) contains supplementary material, which is available to authorized users.

Predicting Beta Barrel Transmembrane Proteins Using HMMs

Transmembrane beta-barrels (TMBBs) constitute an important structural class of membrane proteins located in the outer membrane of gram-negative bacteria, and in the outer membrane of chloroplasts and mitochondria. They are involved in a wide variety of cellular functions and the prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes is of great importance as they are promising targets for antimicrobial drugs and vaccines. Several methods have been applied for the prediction of the transmembrane segments and the topology of beta barrel transmembrane proteins utilizing different algorithmic techniques. Hidden Markov Models (HMMs) have been efficiently used in the development of several computational methods used for this task. In this chapter we give a brief review of different available prediction methods for beta barrel transmembrane proteins pointing out sequence and structural features that should be incorporated in a prediction method. We then describe the procedure of the design and development of a Hidden Markov Model capable of predicting the transmembrane beta strands of TMBBs and discriminating them from globular proteins.

Enhancing the Prediction of Transmembrane β-Barrel Segments with Chain Learning and Feature Sparse Representation

Transmembrane β-barrels (TMBs) are one important class of membrane proteins that play crucial functions in the cell. Membrane proteins are difficult wet-lab targets of structural biology, which call for accurate computational prediction approaches. Here, we developed a novel method named MemBrain-TMB to predict the spanning segments of transmembrane β-barrel from amino acid sequence. MemBrain-TMB is a statistical machine learning-based model, which is constructed using a new chain learning algorithm with input features encoded by the image sparse representation approach. We considered the relative status information between neighboring residues for enhancing the performance, and the matrix of features was translated into feature image by sparse coding algorithm for noise and dimension reduction. To deal with the diverse loop length problem, we applied a dynamic threshold method, which is particularly useful for enhancing the recognition of short loops and tight turns. Our experiments demonstrate that the new protocol designed in MemBrain-TMB effectively helps improve prediction performance.

Structure of the bacterial plant-ferredoxin receptor FusA

Iron is a limiting nutrient in bacterial infection putting it at the centre of an evolutionary arms race between host and pathogen. Gram-negative bacteria utilize TonB-dependent outer membrane receptors to obtain iron during infection. These receptors acquire iron either in concert with soluble iron-scavenging siderophores or through direct interaction and extraction from host proteins. Characterization of these receptors provides invaluable insight into pathogenesis. However, only a subset of virulence-related TonB-dependent receptors have been currently described. Here we report the discovery of FusA, a new class of TonB-dependent receptor, which is utilized by phytopathogenic Pectobacterium spp. to obtain iron from plant ferredoxin. Through the crystal structure of FusA we show that binding of ferredoxin occurs through specialized extracellular loops that form extensive interactions with ferredoxin. The function of FusA and the presence of homologues in clinically important pathogens suggests that small iron-containing proteins represent an iron source for bacterial pathogens.

Many bacteria use TonB-dependent outer membrane receptors to scavenge iron from their host during infection. Here, the authors report on the structure and function of FusA, which is a bacterial receptor that is used to obtain iron from plants.

OMPcontact: An Outer Membrane Protein Inter-Barrel Residue Contact Prediction Method

In the two transmembrane protein types, outer membrane proteins (OMPs) perform diverse important biochemical functions, including substrate transport and passive nutrient uptake and intake. Hence their 3D structures are expected to reveal these functions. Because experimental structures are scarce, predicted 3D structures are more adapted to OMP research instead, and the inter-barrel residue contact is becoming one of the most remarkable features, improving prediction accuracy by describing the structural information of OMPs. To predict OMP structures accurately, we explored an OMP inter-barrel residue contact prediction method: OMPcontact. Multiple OMP-specific features were integrated in the method, including residue evolutionary covariation, topology-based transmembrane segment relative residue position, OMP lipid layer accessibility, and residue evolution conservation. These features describe the properties of a residue pair in different respects: sequential, structural, evolutionary, and biochemical. Within a 3-residues slide window, a Support Vector Machine (SVM) could accurately determinate the inter-barrel contact residue pair using above features. A 5-fold cross-valuation process was applied in testing the OMPcontact performance against a non-redundant OMP set with 75 samples inside. The tests compared four evolutionary covariation methods and screen analyzed the adaptive ones for inter-barrel contact prediction. The results showed our method not only efficiently realized the prediction, but also scored the possibility for residue pairs reliably. This is expected to improve OMP tertiary structure prediction. Therefore, OMPcontact will be helpful in compiling a structural census of outer membrane protein.

Characterization of the targeting signal in mitochondrial β-barrel proteins

Mitochondrial β-barrel proteins are synthesized on cytosolic ribosomes and must be specifically targeted to the organelle before their integration into the mitochondrial outer membrane. The signal that assures such precise targeting and its recognition by the organelle remained obscure. In the present study we show that a specialized β-hairpin motif is this long searched for signal. We demonstrate that a synthetic β-hairpin peptide competes with the import of mitochondrial β-barrel proteins and that proteins harbouring a β-hairpin peptide fused to passenger domains are targeted to mitochondria. Furthermore, a β-hairpin motif from mitochondrial proteins targets chloroplast β-barrel proteins to mitochondria. The mitochondrial targeting depends on the hydrophobicity of the β-hairpin motif. Finally, this motif interacts with the mitochondrial import receptor Tom20. Collectively, we reveal that β-barrel proteins are targeted to mitochondria by a dedicated β-hairpin element, and this motif is recognized at the organelle surface by the outer membrane translocase.

Mitochondrial β-barrel proteins are synthesized in the cytosol before being targeted to the organelle. Here, Jores et al. show that a specialized hydrophobic β-hairpin motif is the previously undefined targeting sequence and is recognized by the mitochondrial outer membrane translocase.

Inclusion of dyad-repeat pattern improves topology prediction of transmembrane β-barrel proteins

: Accurate topology prediction of transmembrane β-barrels is still an open question. Here, we present BOCTOPUS2, an improved topology prediction method for transmembrane β-barrels that can also identify the barrel domain, predict the topology and identify the orientation of residues in transmembrane β-strands. The major novelty of BOCTOPUS2 is the use of the dyad-repeat pattern of lipid and pore facing residues observed in transmembrane β-barrels. In a cross-validation test on a benchmark set of 42 proteins, BOCTOPUS2 predicts the correct topology in 69% of the proteins, an improvement of more than 10% over the best earlier method (BOCTOPUS) and in addition, it produces significantly fewer erroneous predictions on non-transmembrane β-barrel proteins.

AVAILABILITY AND IMPLEMENTATION

BOCTOPUS2 webserver along with full dataset and source code is available at http://boctopus.bioinfo.se/

CONTACT

: arne@bioinfo.se

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Atomic model of a nonenveloped virus reveals pH sensors for a coordinated process of cell entry

Viruses sense environmental cues (such as pH) to engage in membrane interactions for cell entry during infection but how non-enveloped viruses sense pH is largely undefined. Here, we report the structures — at high and low pH conditions — of bluetongue virus (BTV), which enters cells via a two-stage endosomal process. The receptor-binding protein VP2 possesses a zinc-finger and a conserved His866, which may function to maintain VP2 in a metastable state and to sense early-endosomal pH, respectively. The membrane penetration protein VP5 has three domains: dagger, unfurling, and anchoring. Notably, the β-meander motif of the anchoring domain contains a histidine cluster that could sense the late-endosomal pH and four putative membrane-interaction elements. Exposing BTV to low pH detaches VP2 and dramatically refolds the dagger and unfurling domains of VP5. Our biochemical and structure-guided mutagenesis studies support these coordinated pH-sensing mechanisms.

Contact- and Protein Transfer-Dependent Stimulation of Assembly of the Gliding Motility Machinery in Myxococcus xanthus

Bacteria engage in contact-dependent activities to coordinate cellular activities that aid their survival. Cells of Myxococcus xanthus move over surfaces by means of type IV pili and gliding motility. Upon direct contact, cells physically exchange outer membrane (OM) lipoproteins, and this transfer can rescue motility in mutants lacking lipoproteins required for motility. The mechanism of gliding motility and its stimulation by transferred OM lipoproteins remain poorly characterized. We investigated the function of CglC, GltB, GltA and GltC, all of which are required for gliding. We demonstrate that CglC is an OM lipoprotein, GltB and GltA are integral OM β-barrel proteins, and GltC is a soluble periplasmic protein. GltB and GltA are mutually stabilizing, and both are required to stabilize GltC, whereas CglC accumulate independently of GltB, GltA and GltC. Consistently, purified GltB, GltA and GltC proteins interact in all pair-wise combinations. Using active fluorescently-tagged fusion proteins, we demonstrate that GltB, GltA and GltC are integral components of the gliding motility complex. Incorporation of GltB and GltA into this complex depends on CglC and GltC as well as on the cytoplasmic AglZ protein and the inner membrane protein AglQ, both of which are components of the gliding motility complex. Conversely, incorporation of AglZ and AglQ into the gliding motility complex depends on CglC, GltB, GltA and GltC. Remarkably, physical transfer of the OM lipoprotein CglC to a ΔcglC recipient stimulates assembly of the gliding motility complex in the recipient likely by facilitating the OM integration of GltB and GltA. These data provide evidence that the gliding motility complex in M. xanthus includes OM proteins and suggest that this complex extends from the cytoplasm across the cell envelope to the OM. These data add assembly of gliding motility complexes in M. xanthus to the growing list of contact-dependent activities in bacteria.

Motility facilitates a wide variety of processes such as virulence, biofilm formation and development in bacteria. Bacteria have evolved at least three mechanisms for motility on surfaces: swarming motility, twitching motility and gliding motility. Mechanistically, gliding motility is poorly understood. Here, we focused on four proteins in Myxococcus xanthus that are essential for gliding. We show that CglC is an outer membrane (OM) lipoprotein, GltB and GltA are integral OM β-barrel proteins, and GltC is a soluble periplasmic protein. GltB, GltA and GltC are components of the gliding motility complex, and CglC likely stimulates the integration of GltB and GltA into the OM. Moreover, CglC, in a cell-cell contact-dependent manner, can be transferred from a cglC⁺ donor to a ΔcglC mutant leading to stimulation of gliding motility in the recipient. We show that upon physical transfer of CglC, CglC stimulates the assembly of the gliding motility complex in the recipient. The data presented here adds to the growing list of cell-cell contact-dependent activities in bacteria by demonstrating that gliding motility can be stimulated in a contact-dependent manner by transfer of a protein that stimulates assembly of the gliding motility complexes.

All-atom 3D structure prediction of transmembrane β-barrel proteins from sequences

Transmembrane β-barrels (TMBs) carry out major functions in substrate transport and protein biogenesis but experimental determination of their 3D structure is challenging. Encouraged by successful de novo 3D structure prediction of globular and α-helical membrane proteins from sequence alignments alone, we developed an approach to predict the 3D structure of TMBs. The approach combines the maximum-entropy evolutionary coupling method for predicting residue contacts (EVfold) with a machine-learning approach (boctopus2) for predicting β-strands in the barrel. In a blinded test for 19 TMB proteins of known structure that have a sufficient number of diverse homologous sequences available, this combined method (EVfold_bb) predicts hydrogen-bonded residue pairs between adjacent β-strands at an accuracy of ∼70%. This accuracy is sufficient for the generation of all-atom 3D models. In the transmembrane barrel region, the average 3D structure accuracy [template-modeling (TM) score] of top-ranked models is 0.54 (ranging from 0.36 to 0.85), with a higher (44%) number of residue pairs in correct strand-strand registration than in earlier methods (18%). Although the nonbarrel regions are predicted less accurately overall, the evolutionary couplings identify some highly constrained loop residues and, for FecA protein, the barrel including the structure of a plug domain can be accurately modeled (TM score = 0.68). Lower prediction accuracy tends to be associated with insufficient sequence information and we therefore expect increasing numbers of β-barrel families to become accessible to accurate 3D structure prediction as the number of available sequences increases.

Small-Molecule Transport by CarO, an Abundant Eight-Stranded β-Barrel Outer Membrane Protein from Acinetobacter baumannii

Outer membrane (OM) β-barrel proteins composed of 12-18 β-strands mediate cellular entry of small molecules in Gram-negative bacteria. Small OM proteins with barrels of 10 strands or less are not known to transport small molecules. CarO (carbapenem-associated outer membrane protein) from Acinetobacter baumannii is a small OM protein that has been implicated in the uptake of ornithine and carbapenem antibiotics. Here we report crystal structures of three isoforms of CarO. The structures are very similar and show a monomeric eight-stranded barrel lacking an open channel. CarO has a substantial extracellular domain resembling a glove that contains all the divergent residues between the different isoforms. Liposome swelling experiments demonstrate that full-length CarO and a "loop-less" truncation mutant mediate small-molecule uptake at low levels but that they are unlikely to mediate passage of carbapenem antibiotics. These results are confirmed by biased molecular dynamics simulations that allowed us to quantitatively model the transport of selected small molecules.

The observation of evolutionary interaction pattern pairs in membrane proteins

Background

Over the last two decades, many approaches have been developed in bioinformatics that aim at one of the most promising, yet unsolved problems in modern life sciences - prediction of structural features of a protein. Such tasks addressed to transmembrane protein structures provide valuable knowledge about their three-dimensional structure. For this reason, the analysis of membrane proteins is essential in genomic and proteomic-wide investigations. Thus, many in-silico approaches have been utilized extensively to gain crucial advances in understanding membrane protein structures and functions.

Results

It turned out that amino acid covariation within interacting sequence parts, extracted from a evolutionary sequence record of α-helical membrane proteins, can be used for structure prediction. In a recent study we discussed the significance of short membrane sequence motifs widely present in nature that act as stabilizing ’building blocks’ during protein folding and in retaining the three-dimensional fold. In this work, we used motif data to define evolutionary interaction pattern pairs. These were obtained from different pattern alignments and were used to evaluate which coupling mechanisms the evolution provides. It can be shown that short interaction patterns of homologous sequence records are membrane protein family-specific signatures. These signatures can provide valuable information for structure prediction and protein classification. The results indicate a good agreement with recent studies.

Conclusions

Generally, it can be shown how the evolution contributes to realize covariation within discriminative interaction patterns to maintain structure and function. This points to their general importance for α-helical membrane protein structure formation and interaction mediation. In the process, no fundamentally energetic approaches of previous published works are considered. The low-cost rapid computational methods postulated in this work provides valuable information to classify unknown α-helical transmembrane proteins and to determine their structural similarity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12900-015-0033-5) contains supplementary material, which is available to authorized users.

A novel Mitosomal β-barrel Outer Membrane Protein in Entamoeba

Entamoeba possesses a highly divergent mitochondrion-related organelle known as the mitosome. Here, we report the discovery of a novel protein in Entamoeba, which we name Mitosomal β-barrel Outer Membrane Protein of 30 kDa (MBOMP30). Initially identified through in silico analysis, we experimentally confirmed that MBOMP30 is indeed a β-barrel protein. Circular dichroism analysis showed MBOMP30 has a predominant β-sheet structure. Localization to Entamoeba histolytica mitosomes was observed through Percoll-gradient fractionation and immunofluorescence assay. Mitosomal membrane integration was demonstrated by carbonate fractionation, proteinase K digestion, and immunoelectron microscopy. Interestingly, the deletion of the putative β-signal, a sequence believed to guide β-barrel outer membrane protein (BOMP) assembly, did not affect membrane integration, but abolished the formation of a ~240 kDa complex. MBOMP30 represents only the seventh subclass of eukaryotic BOMPs discovered to date and lacks detectable homologs outside Entamoeba, suggesting that it may be unique to Entamoeba mitosomes.

Strategies for the Analysis of Bam Recognition Motifs in Outer Membrane Proteins

Well-structured proteins interact with other proteins through surface-surface interactions. In such cases, the residues that form the interacting surface are not necessarily neighboring residues on the level of protein sequence. In contrast, unfolded or partially unfolded proteins can interact with other proteins through defined linear motifs. In the case of the β-barrel assembly machinery (BAM) in the outer membrane of Gram-negative bacteria, unfolded β-barrel proteins are recognized through a C-terminal linear motif, and are inserted into the membrane. While the exact mechanism of recognition is still under investigation, it has been shown that mutations in the recognition motif can partially or completely abolish membrane insertion. In this chapter, we demonstrate the workflow for motif discovery, motif extraction, and motif visualization on the example of the C-terminal motifs in transmembrane β-barrel proteins.

Computational modeling of membrane proteins

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1-2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans-membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α-helical MPs as well as β-barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge-based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade.

The pCri System: a vector collection for recombinant protein expression and purification

A major bottleneck in structural, biochemical and biophysical studies of proteins is the need for large amounts of pure homogenous material, which is generally obtained by recombinant overexpression. Here we introduce a vector collection, the pCri System, for cytoplasmic and periplasmic/extracellular expression of heterologous proteins that allows the simultaneous assessment of prokaryotic and eukaryotic host cells (Escherichia coli, Bacillus subtilis, and Pichia pastoris). By using a single polymerase chain reaction product, genes of interest can be directionally cloned in all vectors within four different rare restriction sites at the 5'end and multiple cloning sites at the 3'end. In this way, a number of different fusion tags but also signal peptides can be incorporated at the N- and C-terminus of proteins, facilitating their expression, solubility and subsequent detection and purification. Fusion tags can be efficiently removed by treatment with site-specific peptidases, such as tobacco etch virus proteinase, thrombin, or sentrin specific peptidase 1, which leave only a few extra residues at the N-terminus of the protein. The combination of different expression systems in concert with the cloning approach in vectors that can fuse various tags makes the pCri System a valuable tool for high throughput studies.

The Xanthomonas Ax21 protein is processed by the general secretory system and is secreted in association with outer membrane vesicles

Pattern recognition receptors (PRRs) play an important role in detecting invading pathogens and mounting a robust defense response to restrict infection. In rice, one of the best characterized PRRs is XA21, a leucine rich repeat receptor-like kinase that confers broad-spectrum resistance to multiple strains of the bacterial pathogen Xanthomonas oryzae pv. oryzae (Xoo). In 2009 we reported that an Xoo protein, called Ax21, is secreted by a type I-secretion system and that it serves to activate XA21-mediated immunity. This report has recently been retracted. Here we present data that corrects our previous model. We first show that Ax21 secretion does not depend on the predicted type I secretion system and that it is processed by the general secretion (Sec) system. We further show that Ax21 is an outer membrane protein, secreted in association with outer membrane vesicles. Finally, we provide data showing that ax21 knockout strains do not overcome XA21-mediated immunity.

Ranking models of transmembrane β-barrel proteins using Z-coordinate predictions

Motivation: Transmembrane β-barrels exist in the outer membrane of gram-negative bacteria as well as in chloroplast and mitochondria. They are often involved in transport processes and are promising antimicrobial drug targets. Structures of only a few β-barrel protein families are known. Therefore, a method that could automatically generate such models would be valuable. The symmetrical arrangement of the barrels suggests that an approach based on idealized geometries may be successful.

Results: Here, we present tobmodel; a method for generating 3D models of β-barrel transmembrane proteins. First, alternative topologies are obtained from the BOCTOPUS topology predictor. Thereafter, several 3D models are constructed by using different angles of the β-sheets. Finally, the best model is selected based on agreement with a novel predictor, ZPRED3, which predicts the distance from the center of the membrane for each residue, i.e. the Z-coordinate. The Z-coordinate prediction has an average error of 1.61 Å. Tobmodel predicts the correct topology for 75% of the proteins in the dataset which is a slight improvement over BOCTOPUS alone. More importantly, however, tobmodel provides a Cα template with an average RMSD of 7.24 Å from the native structure.

Availability: Tobmodel is freely available as a web server at: http://tobmodel.cbr.su.se/. The datasets used for training and evaluations are also available from this site.

Contact:arne@bioinfo.se

The phosphatidic acid binding site of the Arabidopsis trigalactosyldiacylglycerol 4 (TGD4) protein required for lipid import into chloroplasts

Chloroplast membrane lipid synthesis relies on the import of glycerolipids from the ER. The TGD (TriGalactosylDiacylglycerol) proteins are required for this lipid transfer process. The TGD1, -2, and -3 proteins form a putative ABC (ATP-binding cassette) transporter transporting ER-derived lipids through the inner envelope membrane of the chloroplast, while TGD4 binds phosphatidic acid (PtdOH) and resides in the outer chloroplast envelope. We identified two sequences in TGD4, amino acids 1-80 and 110-145, which are necessary and sufficient for PtdOH binding. Deletion of both sequences abolished PtdOH binding activity. We also found that TGD4 from 18:3 plants bound specifically and with increased affinity PtdOH. TGD4 did not interact with other proteins and formed a homodimer both in vitro and in vivo. Our results suggest that TGD4 is an integral dimeric β-barrel lipid transfer protein that binds PtdOH with its N terminus and contains dimerization domains at its C terminus.

TMBB-DB: a transmembrane β-barrel proteome database

MOTIVATION

We previously reported the development of a highly accurate statistical algorithm for identifying β-barrel outer membrane proteins or transmembrane β-barrels (TMBBs), from genomic sequence data of Gram-negative bacteria (Freeman,T.C. and Wimley,W.C. (2010) Bioinformatics, 26, 1965-1974). We have now applied this identification algorithm to all available Gram-negative bacterial genomes (over 600 chromosomes) and have constructed a publicly available, searchable, up-to-date, database of all proteins in these genomes.

RESULTS

For each protein in the database, there is information on (i) β-barrel membrane protein probability for identification of β-barrels, (ii) β-strand and β-hairpin propensity for structure and topology prediction, (iii) signal sequence score because most TMBBs are secreted through the inner membrane translocon and, thus, have a signal sequence, and (iv) transmembrane α-helix predictions, for reducing false positive predictions. This information is sufficient for the accurate identification of most β-barrel membrane proteins in these genomes. In the database there are nearly 50 000 predicted TMBBs (out of 1.9 million total putative proteins). Of those, more than 15 000 are 'hypothetical' or 'putative' proteins, not previously identified as TMBBs. This wealth of genomic information is not available anywhere else.

AVAILABILITY

The TMBB genomic database is available at http://beta-barrel.tulane.edu/.

CONTACT

wwimley@tulane.edu.