Homology-Based Functional Proteomics By Mass Spectrometry and Advanced Informatic Methods

Functional characterization of biochemically-isolated proteins is a central task in the biochemical and genetic description of the biology of cells and tissues. Protein identification by mass spectrometry consists of associating an isolated protein with a specific gene or protein sequence in silico, thus inferring its specific biochemical function based upon previous characterizations of that protein or a similar protein having that sequence identity. By performing this analysis on a large scale in conjunction with biochemical experiments, novel biological knowledge can be developed. The study presented here focuses on mass spectrometry-based proteomics of organisms with unsequenced genomes and corresponding developments in biological sequence database searching with mass spectrometry data. Conventional methods to identify proteins by mass spectrometry analysis have employed proteolytic digestion, fragmentation of resultant peptides, and the correlation of acquired tandem mass spectra with database sequences, relying upon exact matching algorithms; i.e. the analyzed peptide had to previously exist in a database in silico to be identified. One existing sequence-similarity protein identification method was applied (MS BLAST, Shevchenko 2001) and one alternative novel method was developed (MultiTag), for searching protein and EST databases, to enable the recognition of proteins that are generally unrecognizable by conventional softwares but share significant sequence similarity with database entries (~60-90%). These techniques and available database sequences enabled the characterization of the Xenopus laevis microtubule-associated proteome and the Dunaliella salina soluble salt-induced proteome, both organisms with unsequenced genomes and minimal database sequence resources. These sequence-similarity methods extended protein identification capabilities by more than two-fold compared to conventional methods, making existing methods virtually superfluous. The proteomics of Dunaliella salina demonstrated the utility of MS BLAST as an indispensable method for characterization of proteins in organisms with unsequenced genomes, and produced insight into Dunaliella?s inherent resilience to high salinity. The Xenopus study was the first proteomics project to simultaneously use all three central methods of representation for peptide tandem mass spectra for protein identification: sequence tags, amino acids sequences, and mass lists; and it is the largest proteomics study in Xenopus laevis yet completed, which indicated a potential relationship between the mitotic spindle of dividing cells and the protein synthesis machinery. At the beginning of these experiments, the identification of proteins was conceptualized as using ?conventional? versus ?sequence-similarity? techniques, but through the course of experiments, a conceptual shift in understanding occurred along with the techniques developed and employed to encompass variations in mass spectrometry instrumentation, alternative mass spectrum representation forms, and the complexities of database resources, producing a more systematic description and utilization of available resources for the characterization of proteomes by mass spectrometry and advanced informatic approaches. The experiments demonstrated that proteomics technologies are only as powerful in the field of biology as the biochemical experiments are precise and meaningful

Undetected antisense tRNAs in mitochondrial genomes?

<p>Abstract</p> <p>Background</p> <p>The hypothesis that both mitochondrial (mt) complementary DNA strands of tRNA genes code for tRNAs (sense-antisense coding) is explored. This could explain why mt tRNA mutations are 6.5 times more frequently pathogenic than in other mt sequences. Antisense tRNA expression is plausible because tRNA punctuation signals mt sense RNA maturation: both sense and antisense tRNAs form secondary structures potentially signalling processing. Sense RNA maturation processes by default 11 antisense tRNAs neighbouring sense genes. If antisense tRNAs are expressed, processed antisense tRNAs should have adapted more for translational activity than unprocessed ones. Four tRNA properties are examined: antisense tRNA 5' and 3' end processing by sense RNA maturation and its accuracy, cloverleaf stability and misacylation potential.</p> <p>Results</p> <p>Processed antisense tRNAs align better with standard tRNA sequences with the same cognate than unprocessed antisense tRNAs, suggesting less misacylations. Misacylation increases with cloverleaf fragility and processing inaccuracy. Cloverleaf fragility, misacylation and processing accuracy of antisense tRNAs decrease with genome-wide usage of their predicted cognate amino acid.</p> <p>Conclusions</p> <p>These properties correlate as if they adaptively coevolved for translational activity by some antisense tRNAs, and to avoid such activity by other antisense tRNAs. Analyses also suggest previously unsuspected particularities of aminoacylation specificity in mt tRNAs: combinations of competition between tRNAs on tRNA synthetases with competition between tRNA synthetases on tRNAs determine specificities of tRNA amino acylations. The latter analyses show that alignment methods used to detect tRNA cognates yield relatively robust results, even when they apparently fail to detect the tRNA's cognate amino acid and indicate high misacylation potential.</p> <p>Reviewers</p> <p>This article was reviewed by Dr Juergen Brosius, Dr Anthony M Poole and Dr Andrei S Rodin (nominated by Dr Rob Knight).</p

Fine-Tuning of Alanyl-tRNA Synthetase Quality Control Alleviates Global Dysregulation of the Proteome

One integral step in the transition from a nucleic acid encoded-genome to functional proteins is the aminoacylation of tRNA molecules. To perform this activity, aminoacyl-tRNA synthetases (aaRSs) activate free amino acids in the cell forming an aminoacyl-adenylate before transferring the amino acid on to its cognate tRNA. These newly formed aminoacyl-tRNA (aa-tRNA) can then be used by the ribosome during mRNA decoding. In Escherichia coli, there are twenty aaRSs encoded in the genome, each of which corresponds to one of the twenty proteinogenic amino acids used in translation. Given the shared chemicophysical properties of many amino acids, aaRSs have evolved mechanisms to prevent erroneous aa-tRNA formation with non-cognate amino acid substrates. Of particular interest is the post-transfer proofreading activity of alanyl-tRNA synthetase (AlaRS) which prevents the accumulation of Ser-tRNAAla and Gly-tRNAAla in the cell. We have previously shown that defects in AlaRS proofreading of Ser-tRNAAla lead to global dysregulation of the E. coli proteome, subsequently causing defects in growth, motility, and antibiotic sensitivity. Here we report second-site AlaRS suppressor mutations that alleviate the aforementioned phenotypes, revealing previously uncharacterized residues within the AlaRS proofreading domain that function in quality control

Discovering RNA-Protein Interactome by Using Chemical Context Profiling of the RNA-Protein Interface

SummaryRNA-protein (RNP) interactions generally are required for RNA function. At least 5% of human genes code for RNA-binding proteins. Whereas many approaches can identify the RNA partners for a specific protein, finding the protein partners for a specific RNA is difficult. We present a machine-learning method that scores a protein’s binding potential for an RNA structure by utilizing the chemical context profiles of the interface from known RNP structures. Our approach is applicable even when only a single RNP structure is available. We examined 801 mammalian proteins and find that 37 (4.6%) potentially bind transfer RNA (tRNA). Most are enzymes involved in cellular processes unrelated to translation and were not known to interact with RNA. We experimentally tested six positive and three negative predictions for tRNA binding in vivo, and all nine predictions were correct. Our computational approach provides a powerful complement to experiments in discovering new RNPs

Codon usage variability determines the correlation between proteome and transcriptome fold changes

Background: The availability of high throughput experimental methods has made possible to observe the relationships between proteome and transcirptome. The protein abundances show a positive but weak correlation with the concentrations of their cognate mRNAs. This weak correlation implies that there are other crucial effects involved in the regulation of protein translation, different from the sole availability of mRNA. It is well known that ribosome and tRNA concentrations are sources of variation in protein levels. Thus, by using integrated analysis of omics data, genomic information, transcriptome and proteome, we aim to unravel important variables affecting translation. Results: We identified how much of the variability in the correlation between protein and mRNA concentrations can be attributed to the gene codon frequencies. We propose the hypothesis that the influence of codon frequency is due to the competition of cognate and near-cognate tRNA binding; which in turn is a function of the tRNA concentrations. Transcriptome and proteome data were combined in two analytical steps; first, we used Self-Organizing Maps (SOM) to identify similarities among genes, based on their codon frequencies, grouping them into different clusters; and second, we calculated the variance in the protein mRNA correlation in the sampled genes from each cluster. This procedure is justified within a mathematical framework. Conclusions: With the proposed method we observed that in all the six studied cases most of the variability in the relation protein-transcript could be explained by the variation in codon composition

Correlation of gene expression and protein production rate - a system wide study

<p>Abstract</p> <p>Background</p> <p>Growth rate is a major determinant of intracellular function. However its effects can only be properly dissected with technically demanding chemostat cultivations in which it can be controlled. Recent work on <it>Saccharomyces cerevisiae </it>chemostat cultivations provided the first analysis on genome wide effects of growth rate. In this work we study the filamentous fungus <it>Trichoderma reesei </it>(<it>Hypocrea jecorina</it>) that is an industrial protein production host known for its exceptional protein secretion capability. Interestingly, it exhibits a low growth rate protein production phenotype.</p> <p>Results</p> <p>We have used transcriptomics and proteomics to study the effect of growth rate and cell density on protein production in chemostat cultivations of <it>T. reesei</it>. Use of chemostat allowed control of growth rate and exact estimation of the extracellular specific protein production rate (SPPR). We find that major biosynthetic activities are all negatively correlated with SPPR. We also find that expression of many genes of secreted proteins and secondary metabolism, as well as various lineage specific, mostly unknown genes are positively correlated with SPPR. Finally, we enumerate possible regulators and regulatory mechanisms, arising from the data, for this response.</p> <p>Conclusions</p> <p>Based on these results it appears that in low growth rate protein production energy is very efficiently used primarly for protein production. Also, we propose that flux through early glycolysis or the TCA cycle is a more fundamental determining factor than growth rate for low growth rate protein production and we propose a novel eukaryotic response to this i.e. the lineage specific response (LSR).</p

High-Dimensional Mutant and Modular Thermodynamic Cycles, Molecular Switching, and Free Energy Transduction

Understanding how distinct parts of proteins produce coordinated behavior has driven and continues to drive advances in protein science and enzymology. However, despite consensus about the conceptual basis for allostery, the idiosyncratic nature of allosteric mechanisms resists general approaches. Computational methods can identify conformational transition states from structural changes, revealing common switching mechanisms that impose multistate behavior. Thermodynamic cycles use factorial perturbations to measure coupling energies between side chains in molecular switches that mediate shear during domain motion. Such cycles have now been complemented by modular cycles that measure energetic coupling between separable domains. For one model system, energetic coupling between domains has been shown to be quantitatively equivalent to that between dynamic side chains. Linkages between domain motion, switching residues, and catalysis make nucleoside triphosphate hydrolysis conditional on domain movement, confirming an essential yet neglected aspect of free energy transduction and suggesting the potential generality of these studies

Transcriptional and translational dynamics of the human heart

Die Genexpression wurde bisher hauptsächlich auf Transkriptions- und Proteinebene untersucht, wobei der Einfluss der Translation, die die Proteinhäufigkeit direkt beeinflusst, weitgehend außer Acht gelassen wurde. Um diese Rolle besser zu verstehen, habe ich Ribosomen-Profiling-Daten (Ribo-seq) verwendet, um die Translationsregulation zu untersuchen und neue Translationsvorgänge in 65 linksventrikulären Proben von DCM-Patienten im Endstadium und 15 Nicht-DCM-Kontrollen zu identifizieren. Dieser Datensatz half dabei, die Transkriptions- und Translationsregulation zwischen erkrankten und nicht betroffenen menschlichen Herzen zu sezieren und enthüllte Gene und Prozesse, die rein unter Translationskontrolle stehen. Darüber hinaus habe ich neue kardiale Proteine vorhergesagt, die von langen nicht-kodierenden RNAs (lncRNAs) und zirkulären RNAs (circRNAs) translatiert werden. Computergestützte Analysen dieser evolutionär jungen Proteine legten eine Beteiligung an verschiedenen molekularen Prozessen nahe, mit einer besonderen Anreicherung für den mitochondrialen Energiestoffwechsel. Schließlich identifizierte ich RNA-bindende Proteine (RBPs), deren Expression die Menge der Ziel-mRNA oder die Frequenz der Translationseffizienz (TE) beeinflusst. Für eine Untergruppe von 21 RBPs habe ich die Regulation auf beiden quantitativen Merkmalen beobachtet, was zu einer unterschiedlichen mechanistischen Basis der Expressionskontrolle für unabhängige Gensätze führte. Obwohl die genaue Umschaltung der RBP-Funktion wahrscheinlich durch eine Kombination von mehreren Faktoren erreicht wird, haben wir für drei Kandidaten eine starke Abhängigkeit von der Zielgenlänge und der 5'-UTR-Struktur beobachtet. Diese Arbeit präsentiert einen Katalog von neu identifizierten Translationsereignissen und einen quantitativen Ansatz zur Untersuchung der Translationsregulation im gesunden und kranken menschlichen Herzen.Gene expression has primarily been studied on transcriptional and protein levels, largely disregarding the extent of translational regulation that directly influences protein abundance. To elucidate its role, I used ribosome profiling (Ribo-seq) data, obtained through ribosome profiling, to study translational regulation and identify novel translation events in 65 left ventricular samples of end-stage DCM patients and 15 non-DCM controls. This dataset helped dissect transcriptional and translational regulation between diseased and unaffected human hearts, revealing genes and processes purely under translational control. These would have remained undetected by only looking at the transcriptional level. Furthermore, I predicted novel cardiac proteins translated from long non-coding RNAs (lncRNAs) and circRNAs. Computational analysis of these evolutionary young proteins suggested involvement in diverse molecular processes with a particular enrichment for mitochondrial processes. Finally, I identified RNA-binding proteins (RBPs) whose expression influences target mRNA abundance or translational efficiency (TE) rates. For a subset of 21 RBPs, I have observed regulation on both quantitative traits, which resulted in different mechanistic basis expression control for independent sets of genes. Though the precise switch in RBP function is likely achieved by a combination of multiple factors, for three candidates we have observed a strong dependency on target length and 5’ UTR structure. This work presents a catalogue of newly identified translation events and a quantitative approach to study translational regulation in the healthy and failing human heart

Patterns and Signals of Biology: An Emphasis On The Role of Post Translational Modifications in Proteomes for Function and Evolutionary Progression

After synthesis, a protein is still immature until it has been customized for a specific task. Post-translational modifications (PTMs) are steps in biosynthesis to perform this customization of protein for unique functionalities. PTMs are also important to protein survival because they rapidly enable protein adaptation to environmental stress factors by conformation change. The overarching contribution of this thesis is the construction of a computational profiling framework for the study of biological signals stemming from PTMs associated with stressed proteins. In particular, this work has been developed to predict and detect the biological mechanisms involved in types of stress response with PTMs in mitochondrial (Mt) and non-Mt protein. Before any mechanism can be studied, there must first be some evidence of its existence. This evidence takes the form of signals such as biases of biological actors and types of protein interaction. Our framework has been developed to locate these signals, distilled from “Big Data” resources such as public databases and the the entire PubMed literature corpus. We apply this framework to study the signals to learn about protein stress responses involving PTMs, modification sites (MSs). We developed of this framework, and its approach to analysis, according to three main facets: (1) by statistical evaluation to determine patterns of signal dominance throughout large volumes of data, (2) by signal location to track down the regions where the mechanisms must be found according to the types and numbers of associated actors at relevant regions in protein, and (3) by text mining to determine how these signals have been previously investigated by researchers. The results gained from our framework enable us to uncover the PTM actors, MSs and protein domains which are the major components of particular stress response mechanisms and may play roles in protein malfunction and disease