Rediseño de la eficiencia catalítica y de la termorresistencia de la [Beta](1-->3)(1-->4)glucanasa de Bacillus licheniformis

Consultable des del TDXTítol obtingut de la portada digitalitzadaIntroducción Las b(lŸ3)(lŸ4) glucanasas son enzimas de gran interés biotecnólogico tanto en la industria cervezera como en la de fabricación de piensos. Los objetivos de esta tesis han sido: rediseñar la glucanasa de bacillus licheniformis con objeto de tratar de mejorar la actividad enzimática y determinar la función del lazo mayor del centro activo en dicho proceso, así como analizar y rediseñar la estabilidad térmica del enzima, en particular determinar la influencia del lazo mayor en la desnaturalización térmica, relacionándolo con el proceso y tipo de plegamiento. Análisis de los mutantes de saturación en la posición 58 de glucanasas 1. La reducción de volumen de la cadena lateral aumenta la actividad, aumentado la constante catalítica kcat, si bien disminuye la Km. El mutante M58G presenta la actividad más alta, hasta 6 veces más que el silvestre, y el mutante M58A presenta más de 4 veces la actividad del silvestre, ambos, en términos de kcat/Km. 2. Los residuos hidrófilos disminuyen la actividad. 3. Unicamente en mutantes aromáticos como M58F y M58W, se aprecia una disminución de valores de Km, respecto al enzima silvestre. El análisis de la termorresistencia 1. Con respecto al medio, serían claves el pH (por pérdida de interacciones pH dependientes), el tampón (por su posible papel quelante),y la presencia de sustrato (porque estabilizaría). 2. Con respecto a la estructura serían claves los aminoácidos del lazo y la coordinación del Ca++. La coordinación del Ca++ puede ser mejorada introduciendo una nueva interacción entre el catión y la beta 15, como en el caso del mutante N236D. 3. Finalmente, la termoinactivación de la glucanasa de Bacillus licheniformis, en todos los mutantes estudiados, siguen un patrón bifásico. Su análisis cinético permite justificar el efecto de diversos mutantes y el comportamiento del enzima frente a la temperatura.Introduction The (l-3)(l-4) glucanases are enzymes of great biotechnologyc interest in the brewery industry and in the manufacture of fodder. One objective of this thesis is to redesign the glucanase of bacillus licheniformis trying to improve its enzymatic activity to determine the function that the big loop, situated in the active centre, in this process. Another objective is to analyse and redesign the thermal stability of the enzyme, determining, in particular, the influence of the big loop in the thermal denaturation, and the relation with the process and type of folding. Analysis of the saturation at position 58 of glucanase 1. The reduction of volume of the lateral chain increases the activity, increasing the catalytic constant kcat, although the Km diminishes. Mutant M58G shows the highest activity, up to 6 times more than the wild type, and mutant M58A shows more than 4 times the activity of the wild type, both, in terms of kcat/Km. 2. Hydrophilic residues diminish the activity. 3. Only in the aromatic mutants, like M58F and M58W, we can observe that the Km values diminish, with respect to the wild enzyme. The analysis of the thermoresistance 1. Some factors of the buffer are essential: pH (by loss of pH dependent interactions ), the buffer composition (by its possible chelant action) and the substrate presence (it would stabilize the enzyme). 2. Regarding to structure, the amino acids of the loop and the coordination of the Ca++ would be key elements. The coordination of the Ca++ can be improved introducing a new interaction between the cation and beta 15, as in the case of mutant N236D. 3. Finally the thermoinactivation, of the glucanase of Bacillus licheniformis, in all of the mutants studied, follows a two-phases pattern. The kinetic analysis justifies the effect of several mutants and the behaviour of the enzyme versus the temperature

How hydrophobicity shapes the architecture of protein assemblies

Altres ajuts: acords transformatius de la UABThe interactions that give rise to protein self-assembly are basically electrical and hydrophobic in origin. The electrical interactions are approached in this study as the interaction between electrostatic dipoles originated by the asymmetric distribution of their charged amino acids. However, hydrophobicity is not easily derivable from basic physicochemical principles. Its treatment is carried out here considering a hydrophobic force field originated by "hydrophobic charges". These charges are indices obtained experimentally from the free energies of transferring amino acids from polar to hydrophobic media. Hydrophobic dipole moments are used here in a manner analogous to electric dipole moments, and an empirical expression of interaction energy between hydrophobic dipoles is derived. This methodology is used with two examples of self-assembly systems of different complexity. It was found that the hydrophobic dipole moments of proteins tend to interact in such a way that they align parallel to each other in a completely analogous way to how phospholipids are oriented in biological membranes to form the well-known double layer. In this biological membrane model (BM model), proteins tend to interact in a similar way, although in this case this alignment is modulated by the tendency of the corresponding electrostatic dipoles to counter-align

DockAnalyse : an application for the analysis of protein-protein interactions

Background: Is it possible to identify what the best solution of a docking program is? The usual answer to this question is the highest score solution, but interactions between proteins are dynamic processes, and many times the interaction regions are wide enough to permit protein-protein interactions with different orientations and/or interaction energies. In some cases, as in a multimeric protein complex, several interaction regions are possible among the monomers. These dynamic processes involve interactions with surface displacements between the proteins to finally achieve the functional configuration of the protein complex. Consequently, there is not a static and single solution for the interaction between proteins, but there are several important configurations that also have to be analyzed. Results: To extract those representative solutions from the docking output datafile, we have developed an unsupervised and automatic clustering application, named DockAnalyse. This application is based on the already existing DBscan clustering method, which searches for continuities among the clusters generated by the docking output data representation. The DBscan clustering method is very robust and, moreover, solves some of the inconsistency problems of the classical clustering methods like, for example, the treatment of outliers and the dependence of the previously defined number of clusters. Conclusions: DockAnalyse makes the interpretation of the docking solutions through graphical and visual representations easier by guiding the user to find the representative solutions. We have applied our new approach to analyze several protein interactions and model the dynamic protein interaction behavior of a protein complex. DockAnalyse might also be used to describe interaction regions between proteins and, therefore, guide future flexible dockings. The application (implemented in the R package) is accessible

Studying the complex expression dependences between sets of coexpressed genes

Organisms simplify the orchestration of gene expression by coregulating genes whose products function together in the cell. The use of clustering methods to obtain sets of coexpressed genes from expression arrays is very common; nevertheless there are no appropriate tools to studge the expression networks among these sets of coexpressed genes. The aim of the developed tools is to allow studying the complex expression dependences that exist between sets of coexpressed genes. For this purpose, we start detecting the nonlinear expression relationships between pairs of genes, plus the coexpressed genes. Next, we form networks among sets of coexpressed genes that maintain nonlinear expression dependences between all of them. The expression relationship between the sets of coexpressed genes is defined by the expression relationship between the skeletons of these sets, where this skeleton represents the coexpressed genes with a well-defined nonlinear expression relationship with the skeleton of the other sets. As a result, we can study the nonlinear expression relationships between a target gene and other sets of coexpressed genes, or start the study from the skeleton of the sets, to study the complex relationships of activation and deactivation between the sets of coexpressed genes that carry out the different cellular processes present in the expression experiment

Can bioinformatics help in the identification of moonlighting proteins?

Protein multitasking or moonlighting is the capability of certain proteins to execute two or more unique biological functions. This ability to perform moonlighting functions helps us to understand one of the ways used by cells to perform many complex functions with a limited number of genes. Usually, moonlighting proteins are revealed experimentally by serendipity, and the proteins described probably represent just the tip of the iceberg. It would be helpful if bioinformatics could predict protein multifunctionality, especially because of the large amounts of sequences coming from genome projects. In the present article, we describe several approaches that use sequences, structures, interactomics and current bioinformatics algorithms and programs to try to overcome this problem. The sequence analysis has been performed: (i) by remote homology searches using PSI-BLAST, (ii) by the detection of functionalmotifs, and (iii) by the co-evolutionary relationship between amino acids. Programs designed to identify functional motifs/domains are basically oriented to detect the main function, but usually fail in the detection of secondary ones. Remote homology searches such as PSI-BLAST seem to be more versatile in this task, and it is a good complement for the information obtained from protein-protein interaction (PPI) databases. Structural information and mutation correlation analysis can help us to map the functional sites. Mutation correlation analysis can be used only in very restricted situations, but can suggest how the evolutionary process of the acquisition of the second function took plac

MGDB : crossing the marker genes of a user microarray with a database of public-microarrays marker genes

AVAILABILITY: Marker-gene database tool: http://ibb.uab.es/mgdbSummary: The microarrays performed by scientific teams grow exponentially. These microarray data could be useful for researchers around the world, but unfortunately they are underused. To fully exploit these data, it is necessary (i) to extract these data from a repository of the high-throughput gene expression data like Gene Expression Omnibus (GEO) and (ii) to make the data from different microarrays comparable with tools easy to use for scientists. We have developed these two solutions in our server, implementing a database of microarray marker genes (Marker Genes Data Base). This database contains the marker genes of all GEO microarray datasets and it is updated monthly with the new microarrays from GEO. Thus, researchers can see whether the marker genes of their microarray are marker genes in other microarrays in the database, expanding the analysis of their microarray to the rest of the public microarrays. This solution helps not only to corroborate the conclusions regarding a researcher's microarray but also to identify the phenotype of different subsets of individuals under investigation, to frame the results with microarray experiments from other species, pathologies or tissues, to search for drugs that promote the transition between the studied phenotypes, to detect undesirable side effects of the treatment applied, etc. Thus, the researcher can quickly add relevant information to his/her studies from all of the previous analyses performed in other studies as long as they have been deposited in public repositories

Role of Moonlighting Proteins in Disease : Analyzing the Contribution of Canonical and Moonlighting Functions in Disease Progression

The term moonlighting proteins refers to those proteins that present alternative functions performed by a single polypeptide chain acquired throughout evolution (called canonical and moonlighting, respectively). Over 78% of moonlighting proteins are involved in human diseases, 48% are targeted by current drugs, and over 25% of them are involved in the virulence of pathogenic microorganisms. These facts encouraged us to study the link between the functions of moonlighting proteins and disease. We found a large number of moonlighting functions activated by pathological conditions that are highly involved in disease development and progression. The factors that activate some moonlighting functions take place only in pathological conditions, such as specific cellular translocations or changes in protein structure. Some moonlighting functions are involved in disease promotion while others are involved in curbing it. The disease-impairing moonlighting functions attempt to restore the homeostasis, or to reduce the damage linked to the imbalance caused by the disease. The disease-promoting moonlighting functions primarily involve the immune system, mesenchyme cross-talk, or excessive tissue proliferation. We often find moonlighting functions linked to the canonical function in a pathological context. Moonlighting functions are especially coordinated in inflammation and cancer. Wound healing and epithelial to mesenchymal transition are very representative. They involve multiple moonlighting proteins with a different role in each phase of the process, contributing to the current-phase phenotype or promoting a phase switch, mitigating the damage or intensifying the remodeling. All of this implies a new level of complexity in the study of pathology genesis, progression, and treatment. The specific protein function involved in a patient's progress or that is affected by a drug must be elucidated for the correct treatment of diseases

NCR-PCOPGene : An Exploratory Tool for Analysis of Sample-Classes Effect on Gene-Expression Relationships

Background. Microarray technology is so expensive and powerful that it is essential to extract maximum value from microarray data. Our tools allow researchers to test and formulate from a hypothesis to entire models. Results. The objective of the NCRPCOPGene is to study the relationships among gene expressions under different conditions, to classify these conditions, and to study their effect on the different relationships. The web application makes it easier to define the sample classes, grouping the microarray experiments either by using (a) biological, statistical, or any other previous knowledge or (b) their effect on the expression relationship maintained among specific genes of interest. By means of the type (a) class definition, the researcher can add biological information to the gene-expression relationships. The type (b) class definition allows for linking genes correlated neither linearly nor nonlinearly. Conclusions. The PCOPGene tools are especially suitable for microarrays with large sample series. This application helps to identify cellular states and the genes involved in it in a flexible way. The application takes advantage of the ability of our system to relate gene expressions; even when these relationships are noncontinuous and cannot be found using linear or nonlinear analytical methods

A hypothesis explaining why so many pathogen virulence proteins are moonlighting proteins

Moonlighting or multitasking proteins refer to those proteins with two or more functions performed by a single polypeptide chain. Proteins that belong to key ancestral functions and metabolic pathways such as primary metabolism typically exhibit moonlighting phenomenon. We have collected 698 moonlighting proteins in MultitaskProtDB-II database. A survey shows that 25% of the proteins of the database correspond to moonlighting functions related to pathogens virulence activity. Why is the canonical function of these virulence proteins mainly from ancestral key biological functions (especially of primary metabolism)? Our hypothesis is that these proteins present a high conservation between the pathogen protein and the host counterparts. Therefore, the host immune system will not elicit protective antibodies against pathogen proteins. The fact of sharing epitopes with host proteins (known as epitope mimicry) might be the cause of autoimmune diseases. Although many pathogen proteins can be antigenic, only a few of them would elicit a protective immune response. This would also explain the lack of successful vaccines based in these conserved moonlighting proteins. This review looks at why so many pathogen virulence proteins are from the primary metabolism and are conserved between pathogen and host

Sheltering DNA in self-organizing, protein-only nano-shells as artificial viruses for gene delivery

By recruiting functional domains supporting DNA condensation, cell binding, internalization, endosomal escape and nuclear transport, modular single-chain polypeptides can be tailored to associate with cargo DNA for cell-targeted gene therapy. Recently, an emerging architectonic principle at the nanoscale has permitted tagging protein monomers for self-organization as protein-only nanoparticles. We have studied here the accommodation of plasmid DNA into protein nanoparticles assembled with the synergistic assistance of end terminal poly-arginines (R9) and poly-histidines (H6). Data indicate a virus-like organization of the complexes, in which a DNA core is surrounded by a solvent-exposed protein layer. This finding validates end-terminal cationic peptides as pleiotropic tags in protein building blocks for the mimicry of viral architecture in artificial viruses, representing a promising alternative to the conventional use of viruses and virus-like particles for nanomedicine and gene therapy