Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

p53 and little brother p53/47: linking IRES activities with protein functions

The tumor suppressor p53 represents a paradigm for gene regulation. Its rapid induction in response to DNA damage conditions has been attributed to both increased half-life of p53 protein and also increased translation of p53 mRNA. Recent advances in our understanding of the post-transcriptional regulation of p53 include the discovery of internal ribosome entry sites (IRESs) within the p53 mRNA. These IRES elements regulate the translation of the full length as well as the N-terminally truncated isoform, p53/47. The p53/47 isoform is generated by alternative initiation at an internal AUG codon present within the p53 ORF. The aim of this review is to summarize the role of translational control mechanisms in regulating p53 functions. We discuss here in detail how diverse cellular stress pathways trigger alterations in the cap-dependent and cap-independent translation of p53 mRNA and how changes in the relative expression levels of p53 isoforms result in more differentiated p53 activity.

PP1 interactomes as a means of characterizing protein functions

A maioria das funções celulares, incluindo expressão de genes, crescimento e proliferação celulares, metabolismo, morfologia, motilidade, comunicação intercelular e apoptose, é regulada por interações proteína-proteína (IPP). A célula responde a uma variedade de estímulos, como tal a expressão de proteínas é um processo dinâmico e os complexos formados são constituídos transitoriamente mudando de acordo com o seu ciclo funcional, adicionalmente, muitas proteínas são expressas de uma forma dependente do tipo de célula. Em qualquer instante a célula pode conter cerca de centenas de milhares de IPPs binárias, e encontrar os companheiros de interação de uma proteína é um meio de inferir a sua função. Alterações em redes de IPP podem também fornecer informações acerca de mecanismos de doença. O método de identificação binário mais frequentemente usado é o sistema Dois Hibrido de Levedura, adaptado para rastreio em larga escala. Esta metodologia foi aqui usada para identificar os interactomas específicos de isoforma da Proteína Fosfatase 1 (PP1), em cérebro humano. A PP1 é uma proteína fosfatase de Ser/Thr envolvida numa grande variedade de vias e eventos celulares. É uma proteína conservada codificada por três genes, que originam as isoformas α, β, e γ, com a última a originar γ1 e γ2 por splicing alternativo. As diferentes isoformas da PP1 são reguladas pelos companheiros de interação – proteínas que interagem com a PP1 (PIPs). A natureza modular dos complexos da PP1, bem como a sua associação combinacional, gera um largo reportório de complexos reguladores e papéis em circuitos de sinalização celular. Os interactomas da PP1 específicos de isofoma, em cérebro, foram aqui descritos, com um total de 263 interações identificadas e integradas com os dados recolhidos de várias bases de dados de IPPs. Adicionalmente, duas PIPs foram selecionadas para uma caracterização mais aprofundada da interação: Taperina e Sinfilina-1A. A Taperina é uma proteína ainda pouco descrita, descoberta recentemente como sendo uma PIP. A sua interação com as diferentes isoformas da PP1 e localização celulares foram analisadas. Foi descoberto que a Taperina é clivada e que está presente no citoplasma, membrana e núcleo e que aumenta os níveis de PP1, em células HeLa. Na membrana ela co-localiza com a PP1 e a actina e uma forma mutada da Taperina, no motivo de ligação à PP1, está enriquecida no núcleo, juntamente com a actina. Mais, foi descoberto que a Taperina é expressa em testículo e localiza-se na região acrossómica da cabeça do espermatozoide, uma estrutura onde a PP1 e a actina estão também presentes. A Sinfilina-1A, uma isoforma da Sinfilina-1, é uma proteína com tendência para agregar e tóxica, envolvida na doença de Parkinson. Foi mostrado que a Sinfilina-1A liga às isoformas da PP1, por co-transformação em levedura, e que mutação do seu motivo de ligação à PP1 diminuiu significativamente a interação, num ensaio de overlay. Quando sobre-expressa em células Cos-7, a Sinfilina-1A formou corpos de inclusão onde a PP1 estava presente, no entanto a forma mutada da Sinfilina-1A também foi capaz de agregar, indicando que a formação de inclusões não foi dependente de ligação à PP1. Este trabalho dá uma nova perspetiva dos interactomas da PP1, incluindo a identificação de dezenas de companheiros de ligação específicos de isoforma, e enfatiza a importância das PIPs, não apenas na compreensão das funções celulares da PP1 mas também, como alvos de intervenção terapêutica.

Unveiling Protein Functions through the Dynamics of the Interaction Network

Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes

Cysteine String Protein Functions Directly in Regulated Exocytosis

Cysteine string protein (Csp) is essential for neurotransmitter release in Drosophila. It has been suggested that Csp functions by regulating the activity of presynaptic Ca2+ channels, thus controlling exocytosis. We have examined the effect of overexpressing Csp1 in PC12 cells, a neuroendocrine cell line. PC12 cell clones overexpressing Csp1 did not show any changes in morphology, granule number or distribution, or in the levels of other key exocytotic proteins. This overexpression did not affect intracellular Ca2+ signals after depolarization, suggesting that Csp1 has no gross effect on Ca2+ channel activity in PC12 cells. In contrast, we show that Csp1 overexpression enhances the extent of exocytosis from permeabilized cells in response to Ca2+ or GTPγS in the absence of Ca2+. Because secretion from permeabilized cells is not influenced by Ca2+ channel activity, this represents the first demonstration that Csp has a direct role in regulated exocytosis.

Nutritional regulation of the fatty acid synthase promoter in vivo: Sterol regulatory element binding protein functions through an upstream region containing a sterol regulatory element

The transcription of fatty acid synthase (FAS), a central enzyme in de novo lipogenesis, is dramatically induced by fasting/refeeding and insulin. We reported that upstream stimulatory factor binding to the −65 E-box is required for induction of the FAS transcription by insulin in 3T3-L1 adipocytes. On the other hand, we recently found that two upstream 5′ regions are required for induction in vivo by fasting/refeeding and insulin; one at −278 to −131 albeit at a low level, and the other at −444 to −278 with an E-box at −332 where upstream stimulatory factor functions for maximal induction. Here, we generated double transgenic mice carrying the chloramphenicol acetyltransferase reporter driven by the various 5′ deletions of the FAS promoter region and a truncated active form of the sterol regulatory element (SRE) binding protein (SREBP)-1a. We found that SREBP participates in the nutritional regulation of the FAS promoter and that the region between −278 and −131 bp is required for SREBP function. We demonstrate that SREBP binds the −150 canonical SRE present between −278 and −131, and SREBP can function through the −150 SRE in cultured cells. These in vivo and in vitro results indicate that SREBP is involved in the nutritional induction of the FAS promoter via the −278/−131 region and that the −150 SRE is the target sequence.

Mso1p: A yeast protein that functions in secretion and interacts physically and genetically with Sec1p

The yeast Sec1p protein functions in the docking of secretory transport vesicles to the plasma membrane. We previously have cloned two yeast genes encoding syntaxins, SSO1 and SSO2, as suppressors of the temperature-sensitive sec1–1 mutation. We now describe a third suppressor of sec1–1, which we call MSO1. Unlike SSO1 and SSO2, MSO1 is specific for sec1 and does not suppress mutations in any other SEC genes. MSO1 encodes a small hydrophilic protein that is enriched in a microsomal membrane fraction. Cells that lack MSO1 are viable, but they accumulate secretory vesicles in the bud, indicating that the terminal step in secretion is partially impaired. Moreover, loss of MSO1 shows synthetic lethality with mutations in SEC1, SEC2, and SEC4, and other synthetic phenotypes with mutations in several other late-acting SEC genes. We further found that Mso1p interacts with Sec1p both in vitro and in the two-hybrid system. These findings suggest that Mso1p is a component of the secretory vesicle docking complex whose function is closely associated with that of Sec1p.

Ion pairs in non-redundant protein structures

Ion pairs contribute to several functions including the activity of catalytic triads, fusion of viral membranes, stability in thermophilic proteins and solvent-protein interactions. Furthermore, they have the ability to affect the stability of protein structures and are also a part of the forces that act to hold monomers together. This paper deals with the possible ion pair combinations and networks in 25% and 90% non-redundant protein chains. Different types of ion pairs present in various secondary structural elements are analysed. The ion pairs existing between different subunits of multisubunit protein structures are also computed and the results of various analyses are presented in detail. The protein structures used in the analysis are solved using X-ray crystallography, whose resolution is better than or equal to 1.5 angstrom and R-factor better than or equal to 20%. This study can, therefore, be useful for analyses of many protein functions. It also provides insights into the better understanding of the architecture of protein structure.

Observation of a power-law memory kernel for fluctuations within a single protein molecule

The fluctuation of the distance between a fluorescein-tyrosine pair within a single protein complex was directly monitored in real time by photoinduced electron transfer and found to be a stationary, time-reversible, and non-Markovian Gaussian process. Within the generalized Langevin equation formalism, we experimentally determine the memory kernel K(t), which is proportional to the autocorrelation function of the random fluctuating force. K(t) is a power-law decay, t(-0.51 +/- 0.07) in a broad range of time scales (10(-3)-10 s). Such a long-time memory effect could have implications for protein functions.

Cloning and characterization of the Burkholderia vietnamiensis norM gene encoding a multi-drug efflux protein

Polymyxin B-sensitive mutants in Burkholderia vietnamiensis (Burkholderia cepacia genomovar V) were generated with a mini-Tn5 encoding tetracycline resistance. One of the transposon mutants had an insertion in the norM gene encoding a multi-drug efflux protein. Expression of B. vietnamiensis norM in an Escherichia coli acrAB deletion mutant complemented its norfloxacin hypersensitivity, indicating that the protein functions in drug efflux. However, no effect on antibiotic sensitivity other than sensitivity to polymyxin B was observed in the B. vietnamiensis norM mutant. We demonstrate that increased polymyxin sensitivity in B. vietnamiensis was associated with the presence of tetracycline in the growth medium, a phenotype that was partially suppressed by expression of the norM gene.

A structural analysis of M protein in coronavirus assembly and morphology

The M protein of coronavirus plays a central role in virus assembly, turning cellular membranes into workshops where virus and host factors come together to make new virus particles. We investigated how M structure and organization is related to virus shape and size using cryo-electron microscopy, tomography and statistical analysis. We present evidence that suggests M can adopt two conformations and that membrane curvature is regulated by one M conformer. Elongated M protein is associated with rigidity, clusters of spikes and a relatively narrow range of membrane curvature. In contrast, compact M protein is associated with flexibility and low spike density. Analysis of several types of virus-like particles and virions revealed that S protein, N protein and genomic RNA each help to regulate virion size and variation, presumably through interactions with M. These findings provide insight into how M protein functions to promote virus assembly.

Automatic classification of enzyme family in protein annotation

Most of the tasks in genome annotation can be at least partially automated. Since this annotation is time-consuming, facilitating some parts of the process - thus freeing the specialist to carry out more valuable tasks - has been the motivation of many tools and annotation environments. In particular, annotation of protein function can benefit from knowledge about enzymatic processes. The use of sequence homology alone is not a good approach to derive this knowledge when there are only a few homologues of the sequence to be annotated. The alternative is to use motifs. This paper uses a symbolic machine learning approach to derive rules for the classification of enzymes according to the Enzyme Commission (EC). Our results show that, for the top class, the average global classification error is 3.13%. Our technique also produces a set of rules relating structural to functional information, which is important to understand the protein tridimensional structure and determine its biological function. © 2009 Springer Berlin Heidelberg.

NETWORK TOPOLOGY IN HUMAN PROTEIN INTERACTION DATA PREDICTS FUNCTIONAL ASSOCIATION

High-throughput assays, such as yeast two-hybrid system, have generated a huge amount of protein-protein interaction (PPI) data in the past decade. This tremendously increases the need for developing reliable methods to systematically and automatically suggest protein functions and relationships between them. With the available PPI data, it is now possible to study the functions and relationships in the context of a large-scale network. To data, several network-based schemes have been provided to effectively annotate protein functions on a large scale. However, due to those inherent noises in high-throughput data generation, new methods and algorithms should be developed to increase the reliability of functional annotations. Previous work in a yeast PPI network (Samanta and Liang, 2003) has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional associations between proteins, and hence suggest their functions. One advantage of the work is that their algorithm is not sensitive to noises (false positives) in high-throughput PPI data. In this study, we improved their prediction scheme by developing a new algorithm and new methods which we applied on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting functionally associated proteins. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as independent and unbiased benchmarks to evaluate our algorithms and methods within the human PPI network. We showed that, compared with the previous work from Samanta and Liang, our algorithm and methods developed in this study improved the overall quality of functional inferences for human proteins. By applying the algorithms to the human PPI network, we obtained 4,233 significant functional associations among 1,754 proteins. Further comparisons of their KEGG and GO annotations allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made pathway analysis to identify several subclusters that are highly enriched in certain signaling pathways. Particularly, we performed a detailed analysis on a subcluster enriched in the transforming growth factor β signaling pathway (P<10-50) which is important in cell proliferation and tumorigenesis. Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotations in this post-genomic era.

In vitro properties of the first ORF protein from mouse LINE-1 support its role in ribonucleoprotein particle formation during retrotransposition

LINEs are transposable elements, widely distributed among eukaryotes, that move via reverse transcription of an RNA intermediate. Mammalian LINEs have two ORFs (ORF1 and ORF2). The proteins encoded by these ORFs play important roles in the retrotransposition process. Although the predicted amino acid sequence of ORF1 is not closely related to any known proteins, it is highly basic; thus, it has long been hypothesized that ORF1 protein functions to bind LINE-1 (L1) RNA during retrotransposition. Cofractionation of ORF1 protein and L1 RNA in extracts from both mouse and human embryonal carcinoma cells indicated that ORF1 protein binds L1 RNA, forming a ribonucleoprotein particle. Based on UV crosslinking and electrophoretic mobility-shift assays using purified components, we demonstrate here that the ORF1 protein encoded by mouse L1 binds nucleic acids with a strong preference for RNA and other single-stranded nucleic acids. Furthermore, multiple copies of ORF1 protein appear to bind single-stranded nucleic acid in a manner suggesting positive cooperativity; such binding characteristics are likely to be facilitated by the protein–protein interactions detected among molecules of ORF1 polypeptide by coimmunoprecipitation. These observations are consistent with the formation of ribonucleoprotein particles containing L1 RNA and ORF1 protein and provide additional evidence for the role of ORF1 protein during retrotransposition of L1.