Evolutionary algorithms and de novo peptide design

• Autores: Ignacio Belda Reig
• Directores de la Tesis: Ernest Giralt Lledó (dir. tes.), Francesc Xavier Llorá Fábrega (dir. tes.)
• Lectura: En la Universitat Politècnica de Catalunya (UPC) ( España ) en 2008
• Idioma: español
• Tribunal Calificador de la Tesis: Juan Jesús Pérez González (presid.), Lluis Antoni Belanche Muñoz (secret.), Natalio Krasnogor (voc.), Xavier Salvatella (voc.), Xavier Messeguer Peypoch (voc.)
• Materias:
  • Matemáticas
    • Ciencia de los ordenadores
      • Inteligencia artificial
      • Diseño con ayuda de ordenador
  • Química
    • Bioquímica
      • Péptidos
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• Resumen

  • The present thesis addresses the specific biomedical problem of the automated design of peptide ligands that bind therapeutic protein targets, To achieve this, we use evolutionary algorithms that evolve peptide populations. Evolutionary algorithms start the search with random peptides-individuals-, and then, by applying evolutionary rules-survival of fitness, genotypic inheritance, etc. -explore the space in an implicitly parallel manner. The fitness function that determines the fitness of each individual-in other words, the function to be optimized-is the free energy of binding between the peptide ligand being evaluated and the target protein. This energy is obtained through peptide-protein docking simulations.

    In this thesis I study several implementations of evolutionary algorithms and some extensions that amplify evolutionary computational capacities, such as parallel evolutionary algorithms, multimodal evolutionary algorithms, fitness inheritance techniques, and variable length individuals evolution. Finally, the methodology-ENPDA (Evolutionary de Novo Peptide Design Algorithm)-is applied to the design of peptides that can recognize biomedical important targets, such as, the proteins p53, prolyl oligopeptidase, DNA gyrase, and MHC H-2Kb, as well as a model of amyloid-ß (1-42) fibril.

    Among the developed and tested extensions of the evolutionary algorithms, there is the two-leveled parallelization performed on the evolutionary algorithms, with an almost linear scalability; the multimodal evolutionary algorithms developed, which lead the evolutionary process towards a molecular diverse search; the fitness inheritance techniques, that, theoretically, are expected to speed up in a great manner the evolutionary process, but it does not happen in ENPDA, due to the hypothesis explained later on; and the variable length individuals evolution, which prepares ENPDA to dynamically adapt peptide size to each protein surface patch.

    I also develop a data mining technolog