Mutations in the protein kinase superfamily

• Autores: José María González Izarzugaza
• Directores de la Tesis: Alfonso Valencia Herrera (dir. tes.)
• Lectura: En la Universidad Autónoma de Madrid ( España ) en 2011
• Idioma: inglés
• Tribunal Calificador de la Tesis: Xavier de la Cruz Montserrat (presid.), Florencio Pazos (secret.), Francesco Luigi Gervasio (voc.), Rita Casadio (voc.), Ana María Rojas Mendoza (voc.), Marc A. Marti-Renom (voc.), Guillermo Montoya Blanco (voc.)
• Materias:
  • Ciencias de la vida
    • Biología molecular
• Enlaces
  • Tesis en acceso abierto en: digitool-uam.greendata.es
• Resumen

  • Protein Kinases constitute a promising pharmaceutical target since they are involved in a large number of tumorigenic functions such as immune evasion, proliferation, anti-apoptosis, metastasis and angiogenesis. Although a small number of single-nucleotide kinase aberrations are causally associated with human diseases, most of the many protein kinase mutations published in the literature are tolerated and therefore, they are neutral in terms of protein and cell activity.

    The mechanisms by which mutations elicit aberrant phenotypes have been studied and characterized in some relevant cases for which cause-eect relationships are now well understood.

    Nevertheless, the biochemical characterization of mutations cannot keep up with the pace of current high-throughput mutation discovery technologies, since the detailed study of each single mutation requires an enormous amount of eort, time and resources.

    Thus, there is a clear need to broaden our understanding of mutations in the protein kinase superfamily and, in particular, the mechanisms by which these alter protein function and cause disease. This will help to develop cost-ecient protocols to identify, annotate, characterize and prioritize mutations, such that communal eorts can focus on those most likely to play a direct role in human disease.

    The aims of this thesis are to extend our understanding of the mechanisms by which pathogenic mutations disrupt the structure and function of protein kinases, and to design a reliable pipeline to identify mutations likely to be causally implicated in human disease.

    Focusing our eorts on protein kinases makes possible the use of methods and ideas regarding their specic evolution and organization in protein families. This exclusive information can yield better results than those achieved by general-purpose methods.

    Such challenging biological problems will be tackled in this doctoral thesis from the perspective of Computational Biology, a discipline that provides a powerful framework for the integrated analysis of complex information from multiple sources. Current advances in biostatistics and automatic machine learning technologies enable generalized rules to be established based on prior observation, which can then be used to assess the probability of newly discovered mutation being harmful.