Prediction of β-barrel membrane proteins by searching for restricted domains

BACKGROUND: The identification of beta-barrel membrane proteins out of a genomic/proteomic background is one of the rapidly developing fields in bioinformatics. Our main goal is the prediction of such proteins in genome/proteome wide analyses. RESULTS: For the prediction of beta-barrel membrane proteins within prokaryotic proteomes a set of parameters was developed. We have focused on a procedure with a low false positive rate beside a procedure with lowest false prediction rate to obtain a high certainty for the predicted sequences. We demonstrate that the discrimination between beta-barrel membrane proteins and other proteins is improved by analyzing a length limited region. The developed set of parameters is applied to the proteome of E. coli and the results are compared to four other described procedures. CONCLUSION: Analyzing the beta-barrel membrane proteins revealed the presence of a defined membrane inserted beta-barrel region. This information can now be used to refine other prediction programs as well. So far, all tested programs fail to predict outer membrane proteins in the proteome of the prokaryote E. coli with high reliability. However, the reliability of the prediction is improved significantly by a combinatory approach of several programs. The consequences and usability of the developed scores are discussed

Discrimination of outer membrane proteins with improved performance

<p>Abstract</p> <p>Background</p> <p>Outer membrane proteins (OMPs) perform diverse functional roles in Gram-negative bacteria. Identification of outer membrane proteins is an important task.</p> <p>Results</p> <p>This paper presents a method for distinguishing outer membrane proteins (OMPs) from non-OMPs (that is, globular proteins and inner membrane proteins (IMPs)). First, we calculated the average residue compositions of OMPs, globular proteins and IMPs separately using a training set. Then for each protein from the test set, its distances to the three groups were calculated based on residue composition using a weighted Euclidean distance (WED) approach. Proteins from the test set were classified into OMP versus non-OMP classes based on the least distance. The proposed method can distinguish between OMPs and non-OMPs with 91.0% accuracy and 0.639 Matthews correlation coefficient (MCC). We then improved the method by including homologous sequences into the calculation of residue composition and using a feature-selection method to select the single residue and di-peptides that were useful for OMP prediction. The final method achieves an accuracy of 96.8% with 0.859 MCC. In direct comparisons, the proposed method outperforms previously published methods.</p> <p>Conclusion</p> <p>The proposed method can identify OMPs with improved performance. It will be very helpful to the discovery of OMPs in a genome scale.</p

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

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

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

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

Translational Retinal Research and Therapies.

The following review summarizes the state of the art in representative aspects of gene therapy/translational medicine and evolves from a symposium held at the School of Veterinary Medicine, University of Pennsylvania on November 16, 2017 honoring Dr. Gustavo Aguirre, recipient of ARVO's 2017 Proctor Medal. Focusing on the retina, speakers highlighted current work on moving therapies for inherited retinal degenerative diseases from the laboratory bench to the clinic

In silico local structure approach: A case study on Outer Membrane Proteins.

International audienceThe detection of Outer Membrane Proteins (OMP) in whole genomes is an actual question, their sequence characteristics have thus been intensively studied. This class of protein displays a common beta-barrel architecture, formed by adjacent antiparallel strands. However, due to the lack of available structures, few structural studies have been made on this class of proteins. Here we propose a novel OMP local structure investigation, based on a structural alphabet approach, i.e., the decomposition of 3D structures using a library of four-residue protein fragments. The optimal decomposition of structures using hidden Markov model results in a specific structural alphabet of 20 fragments, six of them dedicated to the decomposition of beta-strands. This optimal alphabet, called SA20-OMP, is analyzed in details, in terms of local structures and transitions between fragments. It highlights a particular and strong organization of beta-strands as series of regular canonical structural fragments. The comparison with alphabets learned on globular structures indicates that the internal organization of OMP structures is more constrained than in globular structures. The analysis of OMP structures using SA20-OMP reveals some recurrent structural patterns. The preferred location of fragments in the distinct regions of the membrane is investigated. The study of pairwise specificity of fragments reveals that some contacts between structural fragments in beta-sheets are clearly favored whereas others are avoided. This contact specificity is stronger in OMP than in globular structures. Moreover, SA20-OMP also captured sequential information. This can be integrated in a scoring function for structural model ranking with very promising results. Proteins 2007. (c) 2007 Wiley-Liss, Inc

NN approach and its comparison with NN-SVM to beta-barrel prediction

This paper is concerned with applications of a dual Neural Network (NN) and Support Vector Machine (SVM) to prediction and analysis of beta barrel trans membrane proteins. The prediction and analysis of beta barrel proteins usually offer a host of challenges to the research community, because of their low presence in genomes. Current beta barrel prediction methodologies present intermittent misclassifications resulting in mismatch in the number of membrane spanning regions within amino-acid sequences. To address the problem, this research embarks upon a NN technique and its comparison with hybrid- two-level NN-SVM methodology to classify inter-class and intra-class transitions to predict the number and range of beta membrane spanning regions. The methodology utilizes a sliding-window-based feature extraction to train two different class transitions entitled symmetric and asymmetric models. In symmet- ric modelling, the NN and SVM frameworks train for sliding window over the same intra-class areas such as inner-to-inner, membrane(beta)-to-membrane and outer-to-outer. In contrast, the asymmetric transi- tion trains a NN-SVM classifier for inter-class transition such as outer-to-membrane (beta) and membrane (beta)-to-inner, inner-to-membrane and membrane-to-outer. For the NN and NN-SVM to generate robust outcomes, the prediction methodologies are analysed by jack-knife tests and single protein tests. The computer simulation results demonstrate a significant impact and a superior performance of NN-SVM tests with a 5 residue overlap for signal protein over NN with and without redundant proteins for pre- diction of trans membrane beta barrel spanning regions

Directed Evolution of Stabilized Peptides with Bacterial Display

Interactions between proteins govern cellular and the body’s states, including aberrant interactions found in diseases such as in cancers and infections. Small molecule drugs are not ideal in targeting these interactions as their size generally prevents efficient blocking of contacts over large surface areas. Antibodies and related biologics have seen clinical success in the past few decades and can block large surfaces but are typically limited to extracellular targets. Intermediate-size peptides have the potential to bridge this gap, with the ability to target large surface areas inside the cell. Peptide stapling, by chemically linking two or more amino acid residues, can confer affinity improvements, resistance to degradation, and better biological transport properties. As such, stapled peptides show promise as next-generation therapeutics. Unfortunately, existing methods to screen sequence and stapling locations suffer from numerous disadvantages including limited search space, lack of real-time monitoring of selections, and difficulty in incorporating the non-canonical amino acids used for amino acid stapling. In this dissertation, I describe my research on stapled peptide discovery with bacterial incorporation of non-canonical amino acids. To screen stapled peptides of the type desired, we incorporated azidohomoalanine (AHA) into surface displayed peptides, enabling an in situ ‘click’ chemistry reaction to bridge two turns of an alpha helical (i, i+7) amino acid library for directed evolution. Using the p53-MDM2 interaction as a model target, we developed peptides that block MDM2 degradation of the tumor suppressor protein p53, an interaction that is dysregulated in a sizeable fraction of cancers. We generated and displayed a stapled peptide library on the bacterial cell surface with fixed residues for stabilization and binding requirements, while randomizing the remaining amino acids. After multiple rounds of selection, clones were sequenced and characterized. The dissociation constants of the peptide-MDM2 interaction were measured on both the bacterial cell surface by flow cytometry and in solution by bio-layer interferometry. The highest affinity variant, named SPD-M6-V1 with sequence VCDFXCYWNDLXGY (dissociation constant = 1.8 nM; X = azidohomoalanine) was selected for structural characterization by NMR spectroscopy, revealing a bicyclic disulfide and double click-constrained peptide. Sequencing showed that peptides with two cysteines were highly enriched, further suggesting that the MDM2-binding conformation was enforced with a disulfide bond. In addition, SPD-M6-V1 was the most protease-resistant peptide from the library that we tested. Next, we stapled the displayed peptide library with chemically distinct linkers and screened each library separately. We performed deep sequencing to better understand the relationship between amino acid sequence and linker identity in contributing to high affinity MDM2 binding. We found that both linker-specific and linker-agnostic (i.e. MDM2-specific) mutations were enhanced. Finally, we developed a dual-channel, sequential labeling selection strategy to discriminate between high-display, low-affinity peptides and low-display, high-affinity peptides, two categories that would ordinarily overlap in a typical one-color screen in the absence of an independent display marker. In summary, this thesis develops the chemical tools to screen libraries of stabilized peptides on the bacterial cell surface and applies these techniques to select stabilized alpha helices that disrupt the p53-MDM2 interaction.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163094/1/tejasn_1.pd

Advances and challenges in barcoding pathogenic and environmental Leptospira

Leptospirosis is a zoonotic bacterial disease of global importance. A large spectrum of asymptomatic animal hosts can carry the infection and contribute to the burden of human disease. Environmental sources of human contamination also point to the importance of a hydrotelluric reservoir. Leptospirosis can be caused by as many as 15 different pathogenic or intermediate Leptospira species. However, classification of these bacteria remains complicated through the use of both serological and genetic classification systems that show poor correlation. With the advent of molecular techniques, DNA-based barcoding offers a conceptual framework that can be used for leptospirosis surveillance as well as source tracking. In this review, we summarize some of the current techniques, highlight significant successes and weaknesses and point to the future opportunities and challenges to successfully establish a widely applicable barcoding scheme for Leptospira

Structure of the proton-gated urea channel from the gastric pathogen Helicobacter pylori.

Half the world's population is chronically infected with Helicobacter pylori, causing gastritis, gastric ulcers and an increased incidence of gastric adenocarcinoma. Its proton-gated inner-membrane urea channel, HpUreI, is essential for survival in the acidic environment of the stomach. The channel is closed at neutral pH and opens at acidic pH to allow the rapid access of urea to cytoplasmic urease. Urease produces NH(3) and CO(2), neutralizing entering protons and thus buffering the periplasm to a pH of roughly 6.1 even in gastric juice at a pH below 2.0. Here we report the structure of HpUreI, revealing six protomers assembled in a hexameric ring surrounding a central bilayer plug of ordered lipids. Each protomer encloses a channel formed by a twisted bundle of six transmembrane helices. The bundle defines a previously unobserved fold comprising a two-helix hairpin motif repeated three times around the central axis of the channel, without the inverted repeat of mammalian-type urea transporters. Both the channel and the protomer interface contain residues conserved in the AmiS/UreI superfamily, suggesting the preservation of channel architecture and oligomeric state in this superfamily. Predominantly aromatic or aliphatic side chains line the entire channel and define two consecutive constriction sites in the middle of the channel. Mutation of Trp 153 in the cytoplasmic constriction site to Ala or Phe decreases the selectivity for urea in comparison with thiourea, suggesting that solute interaction with Trp 153 contributes specificity. The previously unobserved hexameric channel structure described here provides a new model for the permeation of urea and other small amide solutes in prokaryotes and archaea