The signaling helix: a common functional theme in diverse signaling proteins

BACKGROUND: The mechanism by which the signals are transmitted between receptor and effector domains in multi-domain signaling proteins is poorly understood. RESULTS: Using sensitive sequence analysis methods we identify a conserved helical segment of around 40 residues in a wide range of signaling proteins, including numerous sensor histidine kinases such as Sln1p, and receptor guanylyl cyclases such as the atrial natriuretic peptide receptor and nitric oxide receptors. We term this helical segment the signaling (S)-helix and present evidence that it forms a novel parallel coiled-coil element, distinct from previously known helical segments in signaling proteins, such as the Dimerization-Histidine phosphotransfer module of histidine kinases, the intra-cellular domains of the chemotaxis receptors, inter-GAF domain helical linkers and the α-helical HAMP module. Analysis of domain architectures allowed us to reconstruct the domain-neighborhood graph for the S-helix, which showed that the S-helix almost always occurs between two signaling domains. Several striking patterns in the domain neighborhood of the S-helix also became evident from the graph. It most often separates diverse N-terminal sensory domains from various C-terminal catalytic signaling domains such as histidine kinases, cNMP cyclase, PP2C phosphatases, NtrC-like AAA+ ATPases and diguanylate cyclases. It might also occur between two sensory domains such as PAS domains and occasionally between a DNA-binding HTH domain and a sensory domain. The sequence conservation pattern of the S-helix revealed the presence of a unique constellation of polar residues in the dimer-interface positions within the central heptad of the coiled-coil formed by the S-helix. CONCLUSION: Combining these observations with previously reported mutagenesis studies on different S-helix-containing proteins we suggest that it functions as a switch that prevents constitutive activation of linked downstream signaling domains. However, upon occurrence of specific conformational changes due to binding of ligand or other sensory inputs in a linked upstream domain it transmits the signal to the downstream domain. Thus, the S-helix represents one of the most prevalent functional themes involved in the flow of signals between modules in diverse prokaryote-type multi-domain signaling proteins. REVIEWERS: This article was reviewed by Frank Eisenhaber, Arcady Mushegian and Sandor Pongor

MiST: a microbial signal transduction database

Signal transduction pathways control most cellular activities in living cells ranging from regulation of gene expression to fine-tuning enzymatic activity and controlling motile behavior in response to extracellular and intracellular signals. Because of their extreme sequence variability and extensive domain shuffling, signal transduction proteins are difficult to identify, and their current annotation in most leading databases is often incomplete or erroneous. To overcome this problem, we have developed the microbial signal transduction (MiST) database (), a comprehensive library of the signal transduction proteins from completely sequenced bacterial and archaeal genomes. By searching for domain profiles that implicate a particular protein as participating in signal transduction, we have systematically identified 69 270 two- and one-component proteins in 365 bacterial and archaeal genomes. We have designed a user-friendly website to access and browse the predicted signal transduction proteins within various organisms. Further capabilities include gene/protein sequence retrieval, visualized domain architectures, interactive chromosomal views for exploring gene neighborhood, advanced querying options and cross-species comparison. Newly available, complete genomes are loaded into the database each month. MiST is the only comprehensive and up-to-date electronic catalog of the signaling machinery in microbial genomes

A word of caution about biological inference - Revisiting cysteine covalent state predictions

The success of methods for predicting the redox state of cysteine residues from the sequence environment seemed to validate the basic assumption that this state is mainly determined locally. However, the accuracy of predictions on randomized sequences or of non-cysteine residues remained high, suggesting that these predictions rather capture global features of proteins such as subcellular localization, which depends on composition. This illustrates that even high prediction accuracy is insufficient to validate implicit assumptions about a biological phenomenon. Correctly identifying the relevant underlying biochemical reasons for the success of a method is essential to gain proper biological insights and develop more accurate and novel bioinformatics tools. 2014 The Authors. Published by Elsevier B.V. on behalf of the Federation of European Biochemical Societies. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/)

Evolutionary descent of prion genes from a ZIP metal ion transport ancestor

In the more than 20 years since its discovery, both the phylogenetic origin and cellular function of the prion protein (PrP) have remained enigmatic. Insights into the function of PrP may be obtained through a characterization of its molecular neighborhood. Quantitative interactome data revealed the spatial proximity of a subset of metal ion transporters of the ZIP family to mammalian prion proteins. A subsequent bioinformatic analysis revealed the presence of a prion-like protein sequence within the N-terminal, extracellular domain of a phylogenetic branch of ZIPs. Additional structural threading and ortholog sequence alignment analyses consolidated the conclusion that the prion protein gene family is phylogenetically derived from a ZIP-like ancestor molecule. Our data explain structural and functional features found within mammalian prion proteins as elements of an ancient involvement in the transmembrane transport of divalent cations. The connection to ZIP proteins is expected to open new avenues to elucidate the biology of the prion protein in health and disease

Evolutionary Descent of Prion Genes from the ZIP Family of Metal Ion Transporters

In the more than twenty years since its discovery, both the phylogenetic origin and cellular function of the prion protein (PrP) have remained enigmatic. Insights into a possible function of PrP may be obtained through the characterization of its molecular neighborhood in cells. Quantitative interactome data demonstrated the spatial proximity of two metal ion transporters of the ZIP family, ZIP6 and ZIP10, to mammalian prion proteins in vivo. A subsequent bioinformatic analysis revealed the unexpected presence of a PrP-like amino acid sequence within the N-terminal, extracellular domain of a distinct sub-branch of the ZIP protein family that includes ZIP5, ZIP6 and ZIP10. Additional structural threading and orthologous sequence alignment analyses argued that the prion gene family is phylogenetically derived from a ZIP-like ancestral molecule. The level of sequence homology and the presence of prion protein genes in most chordate species place the split from the ZIP-like ancestor gene at the base of the chordate lineage. This relationship explains structural and functional features found within mammalian prion proteins as elements of an ancient involvement in the transmembrane transport of divalent cations. The phylogenetic and spatial connection to ZIP proteins is expected to open new avenues of research to elucidate the biology of the prion protein in health and disease

Machine learning applications for the topology prediction of transmembrane beta-barrel proteins

The research topic for this PhD thesis focuses on the topology prediction of beta-barrel transmembrane proteins. Transmembrane proteins adopt various conformations that are about the functions that they provide. The two most predominant classes are alpha-helix bundles and beta-barrel transmembrane proteins. Alpha-helix proteins are present in larger numbers than beta-barrel transmembrane proteins in structure databases. Therefore, there is a need to find computational tools that can predict and detect the structure of beta-barrel transmembrane proteins. Transmembrane proteins are used for active transport across the membrane or signal transduction. Knowing the importance of their roles, it becomes essential to understand the structures of the proteins. Transmembrane proteins are also a significant focus for new drug discovery. Transmembrane beta-barrel proteins play critical roles in the translocation machinery, pore formation, membrane anchoring, and ion exchange. In bioinformatics, many years of research have been spent on the topology prediction of transmembrane alpha-helices. The efforts to TMB (transmembrane beta-barrel) proteins topology prediction have been overshadowed, and the prediction accuracy could be improved with further research. Various methodologies have been developed in the past to predict TMB proteins topology. Methods developed in the literature that are available include turn identification, hydrophobicity profiles, rule-based prediction, HMM (Hidden Markov model), ANN (Artificial Neural Networks), radial basis function networks, or combinations of methods. The use of cascading classifier has never been fully explored. This research presents and evaluates approaches such as ANN (Artificial Neural Networks), KNN (K-Nearest Neighbors, SVM (Support Vector Machines), and a novel approach to TMB topology prediction with the use of a cascading classifier. Computer simulations have been implemented in MATLAB, and the results have been evaluated. Data were collected from various datasets and pre-processed for each machine learning technique. A deep neural network was built with an input layer, hidden layers, and an output. Optimisation of the cascading classifier was mainly obtained by optimising each machine learning algorithm used and by starting using the parameters that gave the best results for each machine learning algorithm. The cascading classifier results show that the proposed methodology predicts transmembrane beta-barrel proteins topologies with high accuracy for randomly selected proteins. 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. By constructing and using various machine-learning frameworks, systems were developed to analyse the TMB topologies with significant robustness. We have presented several experimental findings that may be useful for future research. Using the cascading classifier, we used a novel approach for the topology prediction of TMB proteins

New Methods to Study Proline-Rich Disordered Regions and Their Structural Ensembles in Protein Signaling Pathways

Ph.DDOCTOR OF PHILOSOPH

Functional and evolutionary significance of unknown genes from uncultivated taxa

Most microbes on our planet remain uncultured and poorly studied. Recent efforts to catalog their genetic diversity have revealed that a significant fraction of the observed microbial genes are functional and evolutionary untraceable, lacking homologs in reference databases. Despite their potential biological value, these apparently unrelated orphan genes from uncultivated taxa have been routinely discarded in metagenomics surveys. Here, we analyzed a global multi-habitat dataset covering 151,697 medium and high-quality metagenome assembled genomes (MAGs), 5,969 single-amplified genomes (SAGs), and 19,642 reference genomes, and identified 413,335 highly curated novel protein families under strong purifying selection out of previously considered orphan genes. These new protein families, representing a three-fold increase over the total number of prokaryotic orthologous groups described to date, spread out across the prokaryote phylogeny, can span multiple habitats, and are notably overrepresented in recently discovered taxa. By genomic context analysis, we pinpointed thousands of unknown protein families to phylogenetically conserved operons linked to energy production, xenobiotic metabolism and microbial resistance. Most remarkably, we found 980 previously neglected protein families that can accurately distinguish entire uncultivated phyla, classes, and orders, likely representing synapomorphic traits that fostered their divergence. The systematic curation and evolutionary analysis of the unique genetic repertoire of uncultivated taxa opens new avenues for understanding the biology and ecological roles of poorly explored lineages at a global scale

Computational systems biology methods for functional classification of membrane proteins and modeling of quorum sensing in Pseudomonas aeruginosa

Due to the function of membrane proteins and the effort required for experimental annotations, bioinformatical approaches to functionally classify uncharacterized sequences are desirable. For this, the similarity between sequences of different membrane proteins was statistically analyzed based on several amino acid compositions. To discriminate between functional classes, a ranking method was developed. We showed that including further information in the amino acid composition and filtering into different sequence regions improved the classification quality. Subsets based on function achieved sensitivities of about 80%, whereas those of random subsets are in the range of 30--35%. The pathogen Pseudomonas aeruginosa produces many virulence factors that are regulated by Quorum sensing. The number of infecting strains with antibiotic resistance is growing. Thus, new strategies focus on Quorum sensing inhibitors that target the regulatory pathways of virulence factors. Pseudomonas aeruginosa contains three Quorum sensing systems that were simulated with an extended multi--level logical formalism to study the influence of Quorum sensing inhibitors on the autoinducer and virulence factor formation. A topology analysis suggested that the proteins PqsR and PqsE act as receptors. Both are required together with an autoinducer to form pyocyanin. Enzyme inhibitors were more useful to block the autoinducer formation, whereas PqsR antagonists inhibited the pyocyanin biosynthesis stronger.Aufgrund der Funktionen von Membranproteinen und dem Aufwand experimenteller Charakterisierungen sind bioinformatische Ansätze zur Klassifizierung unbekannter Sequenzen sinnvoll. Daher wurde deren Ähnlichkeit basierend auf verschiedenen Aminosäurenkompositionen bestimmt und statistisch analysiert. Eine Ranking--Methode wurde zur Einteilung in funktionelle Klassen entwickelt. Wir konnten zeigen, dass die Vorhersagegenauigkeit durch Hinzunahme weiterer Informationen und durch Unterscheidung verschiedener Sequenzregionen verbessert werden kann. Proteingruppen mit derselben Funktion erzielten Sensitivitäten von etwa 80%, während zufällig zusammengestellte Gruppen nur 30--35% erreichten. Der Krankheitserreger Pseudomonas aeruginosa produziert viele durch Quorum Sensing regulierte Virulenzfaktoren. Wegen der wachsenden Anzahl Antibiotika--resistenter Stämme greifen neue antibakterielle Strategien gezielt diese Regulationsmechanismen an. Die drei Quorum Sensing--Systeme von Pseudomonas aeruginosa wurden mit einem erweiterten logischen Formalismus modelliert um den Einfluss von Quorum Sensing--Inhibitoren auf die Bildung von Autoinducern und Virulenzfaktoren zu untersuchen. Eine Topologie--Analyse zeigte, dass die Proteine PqsR und PqsE anscheinend als Rezeptoren zusammen mit einem Autoinducer Pyocyanin regulieren. Enzym--Hemmstoffe waren besser geeignet, die Bildung von Autoinducern zu blockieren, während PqsR--Antagonisten die Pyocyanin--Biosynthese besser hemmten