Characterization of pathogenic germline mutations in human Protein Kinases

Background: Protein Kinases are a superfamily of proteins involved in crucial cellular processes such as cell cycle regulation and signal transduction. Accordingly, they play an important role in cancer biology. To contribute to the study of the relation between kinases and disease we compared pathogenic mutations to neutral mutations as an extension to our previous analysis of cancer somatic mutations. First, we analyzed native and mutant proteins in terms of amino acid composition. Secondly, mutations were characterized according to their potential structural effects and finally, we assessed the location of the different classes of polymorphisms with respect to kinase-relevant positions in terms of subfamily specificity, conservation, accessibility and functional sites.Results: Pathogenic Protein Kinase mutations perturb essential aspects of protein function, including disruption of substrate binding and/or effector recognition at family-specific positions. Interestingly these mutations in Protein Kinases display a tendency to avoid structurally relevant positions, what represents a significant difference with respect to the average distribution of pathogenic mutations in other protein families.Conclusions: Disease-associated mutations display sound differences with respect to neutral mutations: several amino acids are specific of each mutation type, different structural properties characterize each class and the distribution of pathogenic mutations within the consensus structure of the Protein Kinase domain is substantially different to that for non-pathogenic mutations. This preferential distribution confirms previous observations about the functional and structural distribution of the controversial cancer driver and passenger somatic mutations and their use as a proxy for the study of the involvement of somatic mutations in cancer development. © 2011 Izarzugaza et al; licensee BioMed Central Ltd

Characterization of pathogenic germline mutations in human Protein Kinases

Background Protein Kinases are a superfamily of proteins involved in crucial cellular processes such as cell cycle regulation and signal transduction. Accordingly, they play an important role in cancer biology. To contribute to the study of the relation between kinases and disease we compared pathogenic mutations to neutral mutations as an extension to our previous analysis of cancer somatic mutations. First, we analyzed native and mutant proteins in terms of amino acid composition. Secondly, mutations were characterized according to their potential structural effects and finally, we assessed the location of the different classes of polymorphisms with respect to kinase-relevant positions in terms of subfamily specificity, conservation, accessibility and functional sites.<p></p> Results Pathogenic Protein Kinase mutations perturb essential aspects of protein function, including disruption of substrate binding and/or effector recognition at family-specific positions. Interestingly these mutations in Protein Kinases display a tendency to avoid structurally relevant positions, what represents a significant difference with respect to the average distribution of pathogenic mutations in other protein families.<p></p> Conclusions Disease-associated mutations display sound differences with respect to neutral mutations: several amino acids are specific of each mutation type, different structural properties characterize each class and the distribution of pathogenic mutations within the consensus structure of the Protein Kinase domain is substantially different to that for non-pathogenic mutations. This preferential distribution confirms previous observations about the functional and structural distribution of the controversial cancer driver and passenger somatic mutations and their use as a proxy for the study of the involvement of somatic mutations in cancer development.<p></p&gt

wKinMut: An integrated tool for the analysis and interpretation of mutations in human protein kinases

BACKGROUND: Protein kinases are involved in relevant physiological functions and a broad number of mutations in this superfamily have been reported in the literature to affect protein function and stability. Unfortunately, the exploration of the consequences on the phenotypes of each individual mutation remains a considerable challenge. RESULTS: The wKinMut web-server offers direct prediction of the potential pathogenicity of the mutations from a number of methods, including our recently developed prediction method based on the combination of information from a range of diverse sources, including physicochemical properties and functional annotations from FireDB and Swissprot and kinase-specific characteristics such as the membership to specific kinase groups, the annotation with disease-associated GO terms or the occurrence of the mutation in PFAM domains, and the relevance of the residues in determining kinase subfamily specificity from S3Det. This predictor yields interesting results that compare favourably with other methods in the field when applied to protein kinases. Together with the predictions, wKinMut offers a number of integrated services for the analysis of mutations. These include: the classification of the kinase, information about associations of the kinase with other proteins extracted from iHop, the mapping of the mutations onto PDB structures, pathogenicity records from a number of databases and the classification of mutations in large-scale cancer studies. Importantly, wKinMut is connected with the SNP2L system that extracts mentions of mutations directly from the literature, and therefore increases the possibilities of finding interesting functional information associated to the studied mutations. CONCLUSIONS: wKinMut facilitates the exploration of the information available about individual mutations by integrating prediction approaches with the automatic extraction of information from the literature (text mining) and several state-of-the-art databases. wKinMut has been used during the last year for the analysis of the consequences of mutations in the context of a number of cancer genome projects, including the recent analysis of Chronic Lymphocytic Leukemia cases and is publicly available at http://wkinmut.bioinfo.cnio.es

Investigating endothelial cell pim kinase as a novel anti-thrombotic target

Cardiovascular disease is the most common cause of mortality worldwide, presenting a significant burden on healthcare costs globally. Atherothrombosis, the development of an occlusive clot in an artery, is triggered by atherosclerotic plaque rupture/erosion, and involves the interaction of multiple cell types in the blood and vasculature, with endothelial cells and platelets playing significant roles. Blood clots formed during thrombotic events are rich in platelets, making them a suitable target for anti-thrombotic therapies. However, the most widely prescribed anti-platelet drugs for arterial thrombosis prevention, aspirin and clopidogrel, only provide ~20% protection against cardiovascular disease related events. Simultaneously targeting platelets and the endothelium could provide an effective novel anti-thrombotic therapeutic approach. Pim kinase, a family of serine/threonine kinases, have shown to modulate platelet function, and whilst their expression is confirmed in endothelial cells, their role in the endothelium remains unknown. This project aimed to investigate the role of Pim kinase in regulating the inflammatory pathways involved in endothelial cell control of thrombus formation. The role for Pim kinase in endothelial cells in response to cigarette smoke extract and Tumour Necrosis Factor alpha, initiators of endothelial cell damage, was determined using human umbilical vein endothelial cells as a model platform of endothelial cell function. Human umbilical vein endothelial cells were treated for 24 hours with cigarette smoke extract and/or Tumour Necrosis Factor alpha +/- pan Pim kinase inhibitor AZD1208, and techniques including enzyme-linked immunosorbent assay, fluorescence microscopy, qPCR, and Western Blotting used to monitor gene and protein expression of Pim kinases and mediators of thrombo-inflammation. mRNA expression of all three Pim kinase isoforms, and protein expression of Pim-1 was confirmed. Human umbilical vein endothelial cells treated with cigarette smoke extract and Tumour Necrosis Factor alpha combined demonstrated a decrease in endothelial nitric oxide synthase levels, a protective mediator of cardiovascular homeostasis. Human umbilical vein endothelial cells treated with AZD1208 demonstrated a decrease in the expression of von Willebrand factor, a pro-coagulant mediator, and release of inflammatory markers, Interleukin-6, and Page | 7 Interleukin-8 were observed. Collectively, these findings identify a potential role for Pim kinase in atherothrombosis and indicate that Pim kinase inhibitors could be repurposed for use alongside other anti-thrombotic agents for the prevention of cardiovascular-related events

Desarrollo de métodos computacionales basados en co-evolución para la predicción de interacciones entre proteínas

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología MolecularLa co-evolución es el proceso por el que las interacciones de agentes evolutivos (especies, proteínas, etc.) evolucionan acumulando cambios dirigidos por la selección natural en dichos agentes. Por tanto la co-evolución es un componente clave de la teoría de la evolución y es esencial para comprender las redes de interacciones de los agentes evolutivos. A menudo la co-evolución se manifiesta en la acumulación (casi) simultánea de cambios en los agentes que interaccionan. Esta dinámica evolutiva resulta en la evolución paralela en ambos agentes de los caracteres heredados responsables de la interacción y, ocasionalmente, en la de los agentes mismos. En consecuencia, el rastro de la co-evolución puede inferirse de las similitudes entre los árboles filogenéticos de los caracteres o agentes que interaccionan. A nivel molecular, se han desarrollado métodos basados en co-evolución para predecir interacciones entre proteínas y contactos entre residuos de aminoácidos. En concreto, se han utilizado los parecidos entre árboles de proteínas para detectar interacciones entre ellas. Esta aproximación, denominada MirrorTree (MT), detecta una asociación significativa entre parecidos de árboles e interacciones de proteínas, pero también recupera similitudes altas para muchos pares de proteínas que no interaccionan. El objetivo principal de esta tesis es desarrollar métodos computacionales basados en los parecidos entre árboles capaces de predecir interacciones entre proteínas con alta fiabilidad. Para ello se diseñaron estrategias para analizar el proteoma completo. En particular, se diseñaron análisis de correlaciones parciales para recuperar señales de parecidos evolutivos predictivos de interacciones funcionales. Así, cada similitud entre dos árboles se evaluó frente a los árboles del resto del proteoma. En esta aproximación, estos “otros árboles” se constituyen como variables externas portadoras de señales engañosas potencialmente responsables de la similitud entre árboles observada. Es este marco se desarrollaron dos métodos diferentes: ContextMirror (CM) y ContextMirror Global (CMG). CM se diseñó para extraer similitudes compartidas por un pequeño número de proteínas que fueran potencialmente informativas de coevolución de varias proteínas en grupo. La evaluación del rendimiento predictivo de CM en Escherichia coli muestra una clara mejoría comparada con MT. Más aún, el análisis de estos resultados demuestra un mayor potencial predictivo en grupos funcionales de proteínas, como complejos o rutas metabólicas. En cambio, CMG se diseñó para extraer parecidos de árboles específicos de cada par de proteínas. Una evaluación en 23 especies bacterianas muestra que CMG supera claramente a CM y recupera predicciones más fiables. La comparación entre predicciones acertadas de CM y CMG muestra un solapamiento pequeño. Además, el solapamiento de predicciones acertadas de CMG en diferentes especies es también modesto. Sin embargo se observa un mayor solapamiento entre anotaciones funcionales más generales. En concreto la fosforilación oxidativa, el transporte de membrana y el flagelo están entre los procesos y estructuras más habitualmente señalados por las predicciones de CMG en diferentes especies. En conjunto, tanto CM como CMG muestran una capacidad predictiva muy buena en especies bacterianas. Estos buenos resultados demuestran que el proteoma completo es un marco adecuado para analizar la co-evolución entre proteínas. Es más, el éxito de estas estrategias complementarias sugiere que la co-evolución ocurre a diferentes niveles de la organización funcional de las proteínas. Finalmente, CM y CMG son una combinación potente para la predicción de interacciones funcionales entre proteínas y la exploración de los procesos co-evolutivos en bacterias.Evolution describes the natural selection driven accumulation of changes in evolutionary agents such as species and proteins. Co-evolution is the process by which interactions between these evolutionary agents evolve. Co-evolution is a key component of the theory of evolution and is essential for understanding the interaction networks of evolutionary agents. Often, co-evolution shows up as the (close to) simultaneous accumulation of changes in the interacting agents and results in the parallel evolution of inherited features responsible for the interaction in both agents. As a consequence, co-evolutionary traces can be inferred from similarities between the phylogenetic trees of the interacting features or agents. At the molecular level, a wealth of co-evolution-based methods have been developed for predicting protein interactions and amino acid residue contacts. In particular, protein tree similarities have been used for detecting protein interactions. This approach, known as MirrrorTree (MT), detects a significant association between tree similarities and protein interaction, but it also retrieves high tree similarities for many non-interacting protein pairs. The main goal of this thesis is the development of tree similarity based computational methods that are able to generate high quality protein interaction predictions. For this, strategies to take advantage of analysing the whole proteome were designed. More specifically, partial correlation analyses were designed to retrieve those tree similarity signals predictive of protein functional interactions. In fact, every tree-tree similarity was evaluated in the context of the protein trees for all proteins in a reference proteome. This approach regards 'other trees' as external variables carrying deceptive signals that are potentially responsible for the observed tree-tree similarity. In this conceptual framework, two different methodologies were developed: ContextMirror (CM) and ContextMirror Global (CMG). CM was designed to extract tree similarities shared by a small number of proteins that were potentially informative of protein group coevolution. Evaluation of the predictive performance of CM in Escherichia coli shows clear improvement when compared to MT. Moreover, analyses of these results show higher predictive power for functional groups of proteins, such as protein complexes or metabolic pathways. In contrast, CMG was designed to extract tree similarities that were strictly specific to every protein pair. Evaluation of the performance of the CMG predictions in 23 bacterial species shows that CMG outperforms CM. Comparison of successful CM and CMG predictions in E. coli shows a small overlap between predictions. Similarly, successful predictions of CMG for different species also show modest overlaps. However, a higher overlap was observed at the level of more general functional annotations. Concretely, Oxidative Phosphorylation, transmembrane transport and flagellum are among the most commonly targeted processes and structures by CMG predictions in different species. As a whole, both CM and CMG show very good predictive power for bacterial species. These results show that the whole proteome is a more suitable framework for analysing protein co-evolution. Moreover, the success of these complementary strategies suggests that co-evolution occurs at different levels of protein functional organization. Finally, CM and CMG are a powerful combination for the prediction of functional interactions and the exploration of co-evolutionary processes in bacterial species

Structural analysis of single amino acid polymorphisms

Understanding genetic variation is the basis for prevention and diagnosis of inherited disease. In the ‘next generation sequencing’ era with rapidly accumulating variation data, the focus has shifted from population-level analyses to individuals. This thesis is centred on the problem of gathering, storing and analysing mutation data to understand and predict the effects single amino acid mutations will have on protein structure and function. I present analysis of a subset of mutations and a new predictive method implemented to expand the coverage of the structural effects by our pipeline. I characterised a subset of pathogenic mutations: ‘compensated pathogenic deviations’. These are mutations which cause disease in humans, but the mutant residues are found as native residues in other species. During evolution, they are presumed to spread through populations by co-evolving with another, neutralising mutation. When compared with uncompensated mutations, they often cause milder structural disruptions, prefer less conserved structural environments and are often found on the protein surface. I describe the development of a new analysis to test the effects of mutations by predicting residues involved in protein-protein interfaces where the structure of the complex is unknown. Two machine learning methods (multilayer perceptrons and, in particular, random forests) show an improvement over previously published protein-protein interface predictors. This new method further increases the ability of the SAAPdb analysis pipeline to show the effects of mutations on protein structure and function. Furthermore, it is a template for building prediction-based structural analysis methods for the pipeline, where available structural data are insufficient. In summary this thesis examines mutations from both an evolutionary and a disease perspective. In addition, a novel method for predicting protein interaction regions is developed thus expanding the existing pipeline and furthering our ability to understand mutations and use them in a predictive context

Mutations in the protein kinase superfamily

Tesis doctoral inédita. Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología Molecular. Fecha de lectura: 25-11-201