Evaluation of methods for predicting the topology of beta-barrel outer membrane proteins and a consensus prediction method

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.

Outer membrane β-barrel structure prediction through the lens of AlphaFold2

Most proteins found in the outer membrane of gram-negative bacteria share a common domain: the transmembrane β-barrel. These outer membrane β-barrels (OMBBs) occur in multiple sizes and different families with a wide range of functions evolved independently by amplification from a pool of homologous ancestral ββ-hairpins. This is part of the reason why predicting their three-dimensional (3D) structure, especially by homology modeling, is a major challenge. Recently, DeepMind's AlphaFold v2 (AF2) became the first structure prediction method to reach close-to-experimental atomic accuracy in CASP even for difficult targets. However, membrane proteins, especially OMBBs, were not abundant during their training, raising the question of how accurate the predictions are for these families. In this study, we assessed the performance of AF2 in the prediction of OMBBs and OMBB-like folds of various topologies using an in-house-developed tool for the analysis of OMBB 3D structures, and barrOs. In agreement with previous studies on other membrane protein classes, our results indicate that AF2 predicts transmembrane β-barrel structures at high accuracy independently of the use of templates, even for novel topologies absent from the training set. These results provide confidence on the models generated by AF2 and open the door to the structural elucidation of novel transmembrane β-barrel topologies identified in high-throughput OMBB annotation studies or designed de novo.

Computational approaches for molecular characterization and structure-based functional elucidation of a hypothetical protein from Mycobacterium tuberculosis

Adaptation of infections and hosts has resulted in several metabolic mechanisms adopted by intracellular pathogens to combat the defense responses and the lack of fuel during infection. Human tuberculosis caused by Mycobacterium tuberculosis (MTB) is the world's first cause of mortality tied to a single disease. This study aims to characterize and anticipate potential antigen characteristics for promising vaccine candidates for the hypothetical protein of MTB through computational strategies. The protein is associated with the catalyzation of dithiol oxidation and/or disulfide reduction because of the protein's anticipated disulfide oxidoreductase properties. This investigation analyzed the protein's physicochemical characteristics, protein-protein interactions, subcellular locations, anticipated active sites, secondary and tertiary structures, allergenicity, antigenicity, and toxicity properties. The protein has significant active amino acid residues with no allergenicity, elevated antigenicity, and no toxicity.

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.

Prediction of Transmembrane Regions, Cholesterol, and Ganglioside Binding Sites in Amyloid-Forming Proteins Indicate Potential for Amyloid Pore Formation

Besides amyloid fibrils, amyloid pores (APs) represent another mechanism of amyloid induced toxicity. Since hypothesis put forward by Arispe and collegues in 1993 that amyloid-beta makes ion-conducting channels and that Alzheimer's disease may be due to the toxic effect of these channels, many studies have confirmed that APs are formed by prefibrillar oligomers of amyloidogenic proteins and are a common source of cytotoxicity. The mechanism of pore formation is still not well-understood and the structure and imaging of APs in living cells remains an open issue. To get closer to understand AP formation we used predictive methods to assess the propensity of a set of 30 amyloid-forming proteins (AFPs) to form transmembrane channels. A range of amino-acid sequence tools were applied to predict AP domains of AFPs, and provided context on future experiments that are needed in order to contribute toward a deeper understanding of amyloid toxicity. In a set of 30 AFPs we predicted their amyloidogenic propensity, presence of transmembrane (TM) regions, and cholesterol (CBM) and ganglioside binding motifs (GBM), to which the oligomers likely bind. Noteworthy, all pathological AFPs share the presence of TM, CBM, and GBM regions, whereas the functional amyloids seem to show just one of these regions. For comparative purposes, we also analyzed a few examples of amyloid proteins that behave as biologically non-relevant AFPs. Based on the known experimental data on the β-amyloid and α-synuclein pore formation, we suggest that many AFPs have the potential for pore formation. Oligomerization and α-TM helix to β-TM strands transition on lipid rafts seem to be the common key events.

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.

Semi-supervised learning of Hidden Markov Models for biological sequence analysis

MOTIVATION

Hidden Markov Models (HMMs) are probabilistic models widely used in applications in computational sequence analysis. HMMs are basically unsupervised models. However, in the most important applications, they are trained in a supervised manner. Training examples accompanied by labels corresponding to different classes are given as input and the set of parameters that maximize the joint probability of sequences and labels is estimated. A main problem with this approach is that, in the majority of the cases, labels are hard to find and thus the amount of training data is limited. On the other hand, there are plenty of unclassified (unlabeled) sequences deposited in the public databases that could potentially contribute to the training procedure. This approach is called semi-supervised learning and could be very helpful in many applications.

RESULTS

We propose here, a method for semi-supervised learning of HMMs that can incorporate labeled, unlabeled and partially labeled data in a straightforward manner. The algorithm is based on a variant of the Expectation-Maximization (EM) algorithm, where the missing labels of the unlabeled or partially labeled data are considered as the missing data. We apply the algorithm to several biological problems, namely, for the prediction of transmembrane protein topology for alpha-helical and beta-barrel membrane proteins and for the prediction of archaeal signal peptides. The results are very promising, since the algorithms presented here can significantly improve the prediction performance of even the top-scoring classifiers.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comparative Analysis of TM and Cytoplasmic β-barrel Conformations Using Joint Descriptor

Macroscopic descriptors have become valuable as coarse-grained features of complex proteins and are complementary to microscopic descriptors. Proteins macroscopic geometric features provide effective clues in the quantification of distant similarity and close dissimilarity searches for structural comparisons. In this study, we performed a systematic comparison of β-barrels, one of the important classes of protein folds in various transmembrane (TM) proteins against cytoplasmic barrels to estimate the conformational features using a joint-based descriptor. The approach uses joint coordinates and dihedral angles (β and γ) based on the β-strand joints and loops to determine the arrangements and propensities at the local and global levels. We then confirmed that there is a clear preference in the overall β and γ distribution, arrangements of β-strands and loops, signature patterns, and the number of strand effects between TM and cytoplasmic β-barrel geometries. As a robust and simple approach, we determine that the joint-based descriptor could provide a reliable static structural comparison aimed at macroscopic level between complex protein conformations.

Efflux Pumps Represent Possible Evolutionary Convergence onto the β-Barrel Fold

There are around 100 varieties of outer membrane proteins in each Gram-negative bacteria. All of these proteins have the same fold-an up-down β-barrel. It has been suggested that all membrane β-barrels excluding lysins are homologous. Here we suggest that β-barrels of efflux pumps have converged on this fold as well. By grouping structurally solved outer membrane β-barrels (OMBBs) by sequence we find that the membrane environment may have led to convergent evolution of the barrel fold. Specifically, the lack of sequence linkage to other barrels coupled with distinctive structural differences, such as differences in strand tilt and barrel radius, suggest that the outer membrane factor of efflux pumps evolutionarily converged on the barrel. Rather than being related to other OMBBs, sequence and structural similarity in the periplasmic region of the outer membrane factor of efflux pumps suggests an evolutionary link to the periplasmic subunit of the same pump complex.

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.

Unusual Self-Assembly of the Recombinant Chlamydia trachomatis Major Outer Membrane Protein-Based Fusion Antigen CTH522 Into Protein Nanoparticles

Sexually transmitted Chlamydia trachomatis infects more than 100 million people annually, and untreated chlamydia infections can cause severe complications. Therefore, there is an urgent need for a chlamydia vaccine. The Ctrachomatis major outer membrane protein (MOMP) is highly immunogenic but is a challenging vaccine candidate by being an integral membrane protein, and the immunogenicity depends on a correctly folded structure. We investigated the biophysical properties of the recombinant MOMP-based fusion antigen CTH522, which is tested in early human clinical trials. It consists of a truncated and cysteine-free version of MOMP fused to 4 variable domains from serovars D-G. In the native state, CTH522 did not exist as a monomer but showed an unusual self-assembly into nanoparticles with a negative zeta potential. In contrast to the β-barrel structure of MOMP, native CTH522 contained no well-defined secondary structural elements, and no thermal transitions were measurable. Chemical unfolding resulted in monomers that upon removal of the denaturant self-assembled into higher order structures, comparable to the structure of the native protein. The conformation of CTH522 in nanoparticles is thus not entirely random and contains structural elements stabilized via denaturant-disruptable hydrophobic interactions. In conclusion, CTH522 has an unusual quaternary structure of supramolecular self-assemblies.

Predicting Alpha Helical Transmembrane Proteins Using HMMs

Alpha helical transmembrane (TM) proteins constitute an important structural class of membrane proteins involved in a wide variety of cellular functions. The prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes, is of great importance for the elucidation of their structure and function. Several methods have been applied for the prediction of the transmembrane segments and the topology of alpha helical 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 alpha helical 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 alpha helices in proteins and discriminating them from globular proteins.

Identification and characterization of a cell wall porin from Gordonia jacobaea

Gordonia jacobaea is a bacterium belonging to the mycolata group characterized by its ability to produce carotenoids. Mycolic acids in the cell wall contribute to reducing the permeability of their envelopes requiring the presence of channel-forming proteins to allow the exchange of hydrophilic molecules with the surrounding medium. Identification and purification of the channel-forming proteins was accomplished by SDS-PAGE, Mass spectrometry and Mass peptide fingerprinting and the channel-forming activity was studied by reconstitution in lipid bilayers. Here, we describe for the first time the presence of a cell-wall protein from G. jacobaea with channel-forming activity. Our results suggest that this protein bears a low similarity to other hypothetical proteins from the genus Gordonia of uncharacterized functions. The channel has an average single-channel conductance of 800 pS in 1 M KCl, is moderately anion-selective, and does not show any voltage dependence for voltages between +100 and -100 mV. The channel characteristics suggest that this protein could be of relevance in the import and export of negatively charged molecules across the cell wall. This could contribute to design treatments for mycobacterial infections, as well as being of interest in biotechnology applications.

Measuring the Conformational Distance of GPCR-related Proteins Using a Joint-based Descriptor

Joint-based descriptor is a new level of macroscopic descriptor for protein structure using joints of secondary structures as a basic element. Here, we propose how the joint-based descriptor can be applied to examine the conformational distances or differences of transmembrane (TM) proteins. Specifically, we performed three independent studies that measured the global and conformational distances between GPCR A family and its related structures. First, the conformational distances of GPCR A family and other 7TM proteins were evaluated. This provided the information on the distant and close families or superfamilies to GPCR A family and permitted the identification of conserved local conformations. Second, computational models of GPCR A family proteins were validated, which enabled us to estimate how much they reproduce the native conformation of GPCR A proteins at global and local conformational level. Finally, the conformational distances between active and inactive states of GPCR proteins were estimated, which identified the difference of local conformation. The proposed macroscopic joint-based approach is expected to allow us to investigate structural features, evolutionary relationships, computational models and conformational changes of TM proteins in a more simplistic manner.

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.

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

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

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.

PredβTM: A Novel β-Transmembrane Region Prediction Algorithm

Predicting the transmembrane regions is an important aspect of understanding the structures and architecture of different β-barrel membrane proteins. Despite significant efforts, currently available β-transmembrane region predictors are still limited in terms of prediction accuracy, especially in precision. Here, we describe PredβTM, a transmembrane region prediction algorithm for β-barrel proteins. Using amino acid pair frequency information in known β-transmembrane protein sequences, we have trained a support vector machine classifier to predict β-transmembrane segments. Position-specific amino acid preference data is incorporated in the final prediction. The predictor does not incorporate evolutionary profile information explicitly, but is based on sequence patterns generated implicitly by encoding the protein segments using amino acid adjacency matrix. With a benchmark set of 35 β-transmembrane proteins, PredβTM shows a sensitivity and precision of 83.71% and 72.98%, respectively. The segment overlap score is 82.19%. In comparison with other state-of-art methods, PredβTM provides a higher precision and segment overlap without compromising with sensitivity. Further, we applied PredβTM to analyze the β-barrel membrane proteins without defined transmembrane regions and the uncharacterized protein sequences in eight bacterial genomes and predict possible β-transmembrane proteins. PredβTM can be freely accessed on the web at http://transpred.ki.si/.

Evolution of the Translocation and Assembly Module (TAM)

Bacterial outer membrane proteins require the beta-barrel assembly machinery (BAM) for their correct folding and function. The central component of this machinery is BamA, an Omp85 protein that is essential and found in all Gram-negative bacteria. An additional feature of the BAM is the translocation and assembly module (TAM), comprised TamA (an Omp85 family protein) and TamB. We report that TamA and a closely related protein TamL are confined almost exclusively to Proteobacteria and Bacteroidetes/Chlorobi respectively, whereas TamB is widely distributed across the majority of Gram-negative bacterial lineages. A comprehensive phylogenetic and secondary structure analysis of the TamB protein family revealed that TamB was present very early in the evolution of bacteria. Several sequence characteristics were discovered to define the TamB protein family: A signal-anchor linkage to the inner membrane, beta-helical structure, conserved domain architecture and a C-terminal region that mimics outer membrane protein beta-strands. Taken together, the structural and phylogenetic analyses suggest that the TAM likely evolved from an original combination of BamA and TamB, with a later gene duplication event of BamA, giving rise to an additional Omp85 sequence that evolved to be TamA in Proteobacteria and TamL in Bacteroidetes/Chlorobi.

HMMpTM: improving transmembrane protein topology prediction using phosphorylation and glycosylation site prediction

During the last two decades a large number of computational methods have been developed for predicting transmembrane protein topology. Current predictors rely on topogenic signals in the protein sequence, such as the distribution of positively charged residues in extra-membrane loops and the existence of N-terminal signals. However, phosphorylation and glycosylation are post-translational modifications (PTMs) that occur in a compartment-specific manner and therefore the presence of a phosphorylation or glycosylation site in a transmembrane protein provides topological information. We examine the combination of phosphorylation and glycosylation site prediction with transmembrane protein topology prediction. We report the development of a Hidden Markov Model based method, capable of predicting the topology of transmembrane proteins and the existence of kinase specific phosphorylation and N/O-linked glycosylation sites along the protein sequence. Our method integrates a novel feature in transmembrane protein topology prediction, which results in improved performance for topology prediction and reliable prediction of phosphorylation and glycosylation sites. The method is freely available at http://bioinformatics.biol.uoa.gr/HMMpTM.

In silico determination and validation of baumannii acinetobactin utilization a structure and ligand binding site

Acinetobacter baumannii is a deadly nosocomial pathogen. Iron is an essential element for the pathogen. Under iron-restricted conditions, the bacterium expresses iron-regulated outer membrane proteins (IROMPs). Baumannii acinetobactin utilization (BauA) is the most important member of IROMPs in A. baumannii. Determination of its tertiary structure could help deduction of its functions and its interactions with ligands. The present study unveils BauA 3D structure via in silico approaches. Apart from ab initio, other rational methods such as homology modeling and threading were invoked to achieve the purpose. For homology modeling, BLAST was run on the sequence in order to find the best template. The template was then served to model the 3D structure. All the models built were evaluated qualitatively. The best model predicted by LOMETS was selected for analyses. Refinement of 3D structure as well as determination of its clefts and ligand binding sites was carried out on the structure. In contrast to the typical trimeric arrangement found in porins, BauA is monomeric. The barrel is formed by 22 antiparallel transmembrane β -strands. There are short periplasmic turns and longer surface-located loops. An N-terminal domain referred to either as the cork, the plug, or the hatch domain occludes the β -barrel.

A 3-dimensional trimeric β-barrel model for Chlamydia MOMP contains conserved and novel elements of Gram-negative bacterial porins

Chlamydia trachomatis is the most prevalent cause of bacterial sexually transmitted diseases and the leading cause of preventable blindness worldwide. Global control of Chlamydia will best be achieved with a vaccine, a primary target for which is the major outer membrane protein, MOMP, which comprises ~60% of the outer membrane protein mass of this bacterium. In the absence of experimental structural information on MOMP, three previously published topology models presumed a16-stranded barrel architecture. Here, we use the latest β-barrel prediction algorithms, previous 2D topology modeling results, and comparative modeling methodology to build a 3D model based on the 16-stranded, trimeric assumption. We find that while a 3D MOMP model captures many structural hallmarks of a trimeric 16-stranded β-barrel porin, and is consistent with most of the experimental evidence for MOMP, MOMP residues 320-334 cannot be modeled as β-strands that span the entire membrane, as is consistently observed in published 16-stranded β-barrel crystal structures. Given the ambiguous results for β-strand delineation found in this study, recent publications of membrane β-barrel structures breaking with the canonical rule for an even number of β-strands, findings of β-barrels with strand-exchanged oligomeric conformations, and alternate folds dependent upon the lifecycle of the bacterium, we suggest that although the MOMP porin structure incorporates canonical 16-stranded conformations, it may have novel oligomeric or dynamic structural changes accounting for the discrepancies observed.

Inmembrane, a bioinformatic workflow for annotation of bacterial cell-surface proteomes

BACKGROUND

The annotation of surface exposed bacterial membrane proteins is an important step in interpretation and validation of proteomic experiments. In particular, proteins detected by cell surface protease shaving experiments can indicate exposed regions of membrane proteins that may contain antigenic determinants or constitute vaccine targets in pathogenic bacteria.

RESULTS

Inmembrane is a tool to predict the membrane proteins with surface-exposed regions of polypeptide in sets of bacterial protein sequences. We have re-implemented a protocol for Gram-positive bacterial proteomes, and developed a new protocol for Gram-negative bacteria, which interface with multiple predictors of subcellular localization and membrane protein topology. Through the use of a modern scripting language, inmembrane provides an accessible code-base and extensible architecture that is amenable to modification for related sequence annotation tasks.

CONCLUSIONS

Inmembrane easily integrates predictions from both local binaries and web-based queries to help gain an overview of likely surface exposed protein in a bacterial proteome. The program is hosted on the Github repository http://github.com/boscoh/inmembrane.

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

Supersecondary structure prediction of transmembrane beta-barrel proteins

We introduce a graph-theoretic model for predicting the supersecondary structure of transmembrane β-barrel proteins--a particular class of proteins that performs diverse important functions but it is difficult to determine their structure with experimental methods. This ab initio model resolves the protein folding problem based on pseudo-energy minimization with the aid of a simple probabilistic filter. It also allows for determining structures whose barrel follows a given permutation on the arrangement of β-strands, and allows for rapidly discriminating the transmembrane β-barrels from other kinds of proteins. The model is fairly accurate, robust and can be run very efficiently on PC-like computers, thus proving useful for genome screening.

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.

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.

A graph-theoretic approach for classification and structure prediction of transmembrane β-barrel proteins

Background

Transmembrane β-barrel proteins are a special class of transmembrane proteins which play several key roles in human body and diseases. Due to experimental difficulties, the number of transmembrane β-barrel proteins with known structures is very small. Over the years, a number of learning-based methods have been introduced for recognition and structure prediction of transmembrane β-barrel proteins. Most of these methods emphasize on homology search rather than any biological or chemical basis.

Results

We present a novel graph-theoretic model for classification and structure prediction of transmembrane β-barrel proteins. This model folds proteins based on energy minimization rather than a homology search, avoiding any assumption on availability of training dataset. The ab initio model presented in this paper is the first method to allow for permutations in the structure of transmembrane proteins and provides more structural information than any known algorithm. The model is also able to recognize β-barrels by assessing the pseudo free energy. We assess the structure prediction on 41 proteins gathered from existing databases on experimentally validated transmembrane β-barrel proteins. We show that our approach is quite accurate with over 90% F-score on strands and over 74% F-score on residues. The results are comparable to other algorithms suggesting that our pseudo-energy model is close to the actual physical model. We test our classification approach and show that it is able to reject α-helical bundles with 100% accuracy and β-barrel lipocalins with 97% accuracy.

Conclusions

We show that it is possible to design models for classification and structure prediction for transmembrane β-barrel proteins which do not depend essentially on training sets but on combinatorial properties of the structures to be proved. These models are fairly accurate, robust and can be run very efficiently on PC-like computers. Such models are useful for the genome screening.

Mutational analyses reveal overall topology and functional regions of NilB, a bacterial outer membrane protein required for host association in a model of animal-microbe mutualism

The gammaproteobacterium Xenorhabdus nematophila is a mutualistic symbiont that colonizes the intestine of the nematode Steinernema carpocapsae. nilB (nematode intestine localization) is essential for X. nematophila colonization of nematodes and is predicted to encode an integral outer membrane beta-barrel protein, but evidence supporting this prediction has not been reported. The function of NilB is not known, but when expressed with two other factors encoded by nilA and nilC, it confers upon noncognate Xenorhabdus spp. the ability to colonize S. carpocapsae nematodes. We present evidence that NilB is a surface-exposed outer membrane protein whose expression is repressed by NilR and growth in nutrient-rich medium. Bioinformatic analyses reveal that NilB is the only characterized member of a family of proteins distinguished by N-terminal region tetratricopeptide repeats (TPR) and a conserved C-terminal domain of unknown function (DUF560). Members of this family occur in diverse bacteria and are prevalent in the genomes of mucosal pathogens. Insertion and deletion mutational analyses support a beta-barrel structure model with an N-terminal globular domain, 14 transmembrane strands, and seven extracellular surface loops and reveal critical roles for the globular domain and surface loop 6 in nematode colonization. Epifluorescence microscopy of these mutants demonstrates that NilB is necessary at early stages of colonization. These findings are an important step in understanding the function of NilB and, by extension, its homologs in mucosal pathogens.

RECOGNITION OF TRANSMEMBRANE SEGMENTS IN PROTEINS: REVIEW AND CONSISTENCY-BASED BENCHMARKING OF INTERNET SERVERS

Membrane proteins perform a number of crucial functions as transporters, receptors, and components of enzyme complexes. Identification of membrane proteins and prediction of their topology is thus an important part of genome annotation. We present here an overview of transmembrane segments in protein sequences, summarize data from large-scale genome studies, and report results of benchmarking of several popular internet servers.

Pattern of amino acid substitutions in transmembrane domains of β-barrel membrane proteins for detecting remote homologs in bacteria and mitochondria

-barrel membrane proteins play an important role in controlling the exchange and transport of ions and organic molecules across bacterial and mitochondrial outer membranes. They are also major regulators of apoptosis and are important determinants of bacterial virulence. In contrast to -helical membrane proteins, their evolutionary pattern of residue substitutions has not been quantified, and there are no scoring matrices appropriate for their detection through sequence alignment. Using a Bayesian Monte Carlo estimator, we have calculated the instantaneous substitution rates of transmembrane domains of bacterial -barrel membrane proteins. The scoring matrices constructed from the estimated rates, called bbTM for -barrel Transmembrane Matrices, improve significantly the sensitivity in detecting homologs of -barrel membrane proteins, while avoiding erroneous selection of both soluble proteins and other membrane proteins of similar composition. The estimated evolutionary patterns are general and can detect -barrel membrane proteins very remote from those used for substitution rate estimation. Furthermore, despite the separation of 2–3 billion years since the proto-mitochondrion entered the proto-eukaryotic cell, mitochondria outer membrane proteins in eukaryotes can also be detected accurately using these scoring matrices derived from bacteria. This is consistent with the suggestion that there is no eukaryote-specific signals for translocation. With these matrices, remote homologs of -barrel membrane proteins with known structures can be reliably detected at genome scale, allowing construction of high quality structural models of their transmembrane domains, at the rate of 131 structures per template protein. The scoring matrices will be useful for identification, classification, and functional inference of membrane proteins from genome and metagenome sequencing projects. The estimated substitution pattern will also help to identify key elements important for the structural and functional integrity of -barrel membrane proteins, and will aid in the design of mutagenesis studies.

Outer membrane proteins can be simply identified using secondary structure element alignment

Background

Outer membrane proteins (OMPs) are frequently found in the outer membranes of gram-negative bacteria, mitochondria and chloroplasts and have been found to play diverse functional roles. Computational discrimination of OMPs from globular proteins and other types of membrane proteins is helpful to accelerate new genome annotation and drug discovery.

Results

Based on the observation that almost all OMPs consist of antiparallel β-strands in a barrel shape and that their secondary structure arrangements differ from those of other types of proteins, we propose a simple method called SSEA-OMP to identify OMPs using secondary structure element alignment. Through intensive benchmark experiments, the proposed SSEA-OMP method is better than some well-established OMP detection methods.

Conclusions

The major advantage of SSEA-OMP is its good prediction performance considering its simplicity. The web server implements the method is freely accessible at http://protein.cau.edu.cn/SSEA-OMP/index.html.

Legionella pneumophila LbtU acts as a novel, TonB-independent receptor for the legiobactin siderophore

Gram-negative Legionella pneumophila produces a siderophore (legiobactin) that promotes lung infection. We previously determined that lbtA and lbtB are required for the synthesis and secretion of legiobactin. DNA sequence and reverse transcription-PCR (RT-PCR) analyses now reveal the presence of an iron-repressed gene (lbtU) directly upstream of the lbtAB-containing operon. In silico analysis predicted that LbtU is an outer membrane protein consisting of a 16-stranded transmembrane β-barrel, multiple extracellular domains, and short periplasmic tails. Immunoblot analysis of cell fractions confirmed an outer membrane location for LbtU. Although replicating normally in standard media, lbtU mutants, like lbtA mutants, were impaired for growth on iron-depleted agar media. While producing typical levels of legiobactin, lbtU mutants were unable to use supplied legiobactin to stimulate growth on iron-depleted media and displayed an inability to take up iron. Complemented lbtU mutants behaved as the wild type did. The lbtU mutants were also impaired for infection in a legiobactin-dependent manner. Together, these data indicate that LbtU is involved in the uptake of legiobactin and, based upon its location, is most likely the Legionella siderophore receptor. The sequence and predicted two-dimensional (2D) and 3D structures of LbtU were distinct from those of all known siderophore receptors, which generally contain a 22-stranded β-barrel and an extended N terminus that binds TonB in order to transduce energy from the inner membrane. This observation coupled with the fact that L. pneumophila does not encode TonB suggests that LbtU is a new type of receptor that participates in a form of iron uptake that is mechanistically distinct from the existing paradigm.

OMPdb: a database of {beta}-barrel outer membrane proteins from Gram-negative bacteria

We describe here OMPdb, which is currently the most complete and comprehensive collection of integral β-barrel outer membrane proteins from Gram-negative bacteria. The database currently contains 69,354 proteins, which are classified into 85 families, based mainly on structural and functional criteria. Although OMPdb follows the annotation scheme of Pfam, many of the families included in the database were not previously described or annotated in other publicly available databases. There are also cross-references to other databases, references to the literature and annotation for sequence features, like transmembrane segments and signal peptides. Furthermore, via the web interface, the user can not only browse the available data, but submit advanced text searches and run BLAST queries against the database protein sequences or domain searches against the collection of profile Hidden Markov Models that represent each family's domain organization as well. The database is freely accessible for academic users at http://bioinformatics.biol.uoa.gr/OMPdb and we expect it to be useful for genome-wide analyses, comparative genomics as well as for providing training and test sets for predictive algorithms regarding transmembrane β-barrels.

Surface immunolabeling and consensus computational framework to identify candidate rare outer membrane proteins of Treponema pallidum

Treponema pallidum reacts poorly with the antibodies present in rabbit and human syphilitic sera, a property attributed to the paucity of proteins in its outer membrane. To better understand the basis for the syphilis spirochete's "stealth pathogenicity," we used a dual-label, 3-step amplified assay in which treponemes encapsulated in gel microdroplets were probed with syphilitic sera in parallel with anti-FlaA antibodies. A small (approximately 5 to 10%) but reproducible fraction of intact treponemes bound IgG and/or IgM antibodies. Three lines of evidence supported the notion that the surface antigens were likely β-barrel-forming outer membrane proteins (OMPs): (i) surface labeling with anti-lipoidal (VDRL) antibodies was not observed, (ii) immunoblot analysis confirmed prior results showing that T. pallidum glycolipids are not immunoreactive, and (iii) labeling of intact organisms was not appreciably affected by proteinase K (PK) treatment. With this method, we also demonstrate that TprK (TP0897), an extensively studied candidate OMP, and TP0136, a lipoprotein recently reported to be surface exposed, are both periplasmic. Consistent with the immunolabeling studies, TprK was also found to lack amphiphilicity, a characteristic property of β-barrel-forming proteins. Using a consensus computational framework that combined subcellular localization and β-barrel structural prediction tools, we generated ranked groups of candidate rare OMPs, the predicted T. pallidum outer membrane proteome (OMPeome), which we postulate includes the surface-exposed molecules detected by our enhanced gel microdroplet assay. In addition to underscoring the syphilis spirochete's remarkably poor surface antigenicity, our findings help to explain the complex and shifting balance between pathogen and host defenses that characterizes syphilitic infection.

From protein sequences to 3D-structures and beyond: the example of the UniProt knowledgebase

With the dramatic increase in the volume of experimental results in every domain of life sciences, assembling pertinent data and combining information from different fields has become a challenge. Information is dispersed over numerous specialized databases and is presented in many different formats. Rapid access to experiment-based information about well-characterized proteins helps predict the function of uncharacterized proteins identified by large-scale sequencing. In this context, universal knowledgebases play essential roles in providing access to data from complementary types of experiments and serving as hubs with cross-references to many specialized databases. This review outlines how the value of experimental data is optimized by combining high-quality protein sequences with complementary experimental results, including information derived from protein 3D-structures, using as an example the UniProt knowledgebase (UniProtKB) and the tools and links provided on its website ( http://www.uniprot.org/ ). It also evokes precautions that are necessary for successful predictions and extrapolations.

CoBaltDB: Complete bacterial and archaeal orfeomes subcellular localization database and associated resources

BACKGROUND

The functions of proteins are strongly related to their localization in cell compartments (for example the cytoplasm or membranes) but the experimental determination of the sub-cellular localization of proteomes is laborious and expensive. A fast and low-cost alternative approach is in silico prediction, based on features of the protein primary sequences. However, biologists are confronted with a very large number of computational tools that use different methods that address various localization features with diverse specificities and sensitivities. As a result, exploiting these computer resources to predict protein localization accurately involves querying all tools and comparing every prediction output; this is a painstaking task. Therefore, we developed a comprehensive database, called CoBaltDB, that gathers all prediction outputs concerning complete prokaryotic proteomes.

DESCRIPTION

The current version of CoBaltDB integrates the results of 43 localization predictors for 784 complete bacterial and archaeal proteomes (2.548.292 proteins in total). CoBaltDB supplies a simple user-friendly interface for retrieving and exploring relevant information about predicted features (such as signal peptide cleavage sites and transmembrane segments). Data are organized into three work-sets ("specialized tools", "meta-tools" and "additional tools"). The database can be queried using the organism name, a locus tag or a list of locus tags and may be browsed using numerous graphical and text displays.

CONCLUSIONS

With its new functionalities, CoBaltDB is a novel powerful platform that provides easy access to the results of multiple localization tools and support for predicting prokaryotic protein localizations with higher confidence than previously possible. CoBaltDB is available at http://www.umr6026.univ-rennes1.fr/english/home/research/basic/software/cobalten.

Spectral density ratio based clustering methods for the binary segmentation of protein sequences: a comparative study

We compare several spectral domain based clustering methods for partitioning protein sequence data. The main instrument for this exercise is the spectral density ratio model, which specifies that the logarithmic ratio of two or more unknown spectral density functions has a parametric linear combination of cosines. Maximum likelihood inference is worked out in detail and it is shown that its output yields several distance measures among independent stationary time series. These similarity indices are suitable for clustering time series data based on their second order properties. Other spectral domain based distances are investigated as well; and we compare all methods and distances to the problem of producing segmentations of bacterial outer membrane proteins consistent with their transmembrane topology. Protein sequences are transformed to time series data by employing numerical scales of physicochemical parameters. We also present interesting results on the prediction of transmembrane beta-strands, based on the clustering outcome, for a representative set of bacterial outer membrane proteins with given three-dimensional structure.