{"id":108083,"date":"2018-03-11T10:32:43","date_gmt":"2018-03-11T10:32:43","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/computational-modeling-applications-protein-protein-docking-hot-spots-prediciton-and-alloteric-effects-aplicaciones-de-modelizacion-computacional-interaccion-proteina-proteina-prediccion-de-r\/"},"modified":"2018-03-11T10:32:43","modified_gmt":"2018-03-11T10:32:43","slug":"computational-modeling-applications-protein-protein-docking-hot-spots-prediciton-and-alloteric-effects-aplicaciones-de-modelizacion-computacional-interaccion-proteina-proteina-prediccion-de-r","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/barcelona\/computational-modeling-applications-protein-protein-docking-hot-spots-prediciton-and-alloteric-effects-aplicaciones-de-modelizacion-computacional-interaccion-proteina-proteina-prediccion-de-r\/","title":{"rendered":"Computational modeling applications: protein – protein docking, hot spots prediciton and alloteric effects (aplicaciones de modelizaci\u00f3n computacional: interacci\u00f3n proteina – proteina, predicci\u00f3n de residuos responsables de esas interacciones y efectos alost\u00e9ricos)."},"content":{"rendered":"

Tesis doctoral de Sol\u00e9ne Grosdidier <\/strong><\/h2>\n

English summary: protein-protein interactions (ppi) are key events in most of the essential processes that occur in living organisms. For example, signals coming from the exterior of a cell are sensed through ppi involving many signaling molecules. This cellular communication is very important in a large number of biological processes and can lead to disease when misregulated (as in cancer for example). Because of the importance of ppi, lots of efforts have been made, for many decades, to understand the rules governing these associations and several methods are currently available for their 3d structure determination such as x-ray crystallography, nmr and em. However, only about 8800 structures of heterogeneous macromolecular complexes have been solved so far because of their difficulty. consequently, computational approaches as docking are needed to model ppi in order to understand protein-protein association as a detailed energetic and structural knowledge of ppi, with the ultimate goal of designing drugs to modulate interactions of therapeutic interest. We participated to capri (critical assessment of predicted interactions), an international protein-protein docking experiment with our docking protocol called pydock. Capri is a blind test to evaluate the ability of protein-protein docking algorithms to predict the binding-mode between two interacting proteins before the public release of their complexed structure and pydock gave excellent success rates as compared to the most competitive docking methods participating in capri. the pharmaceutical industry interest in ppi is huge because they are involved in virtually all essential biological processes and the potential applications to modulate specific ppi with small compounds spans over a large range. Thus, targeting ppi with small molecules is becoming the holy grail of drug discovery. The pharmaceutical industry has shown many examples of small molecules successfully designed to fit into enzyme active sites, but there are still very few cases of small compounds designed to target ppi. However, two strategies to modulate ppi are emerging: targeting protein-protein hot-spots and taking advantage of allosteric effects. indeed, it is believed that targeting protein-protein hot-spots (residues directly responsible for the interaction from the energetic point of view) could help to the long-awaited goal of disrupting complexes with small molecules. We used our docking protocol called pydock to predict hot-spots by calculating the normalized interface propensities (nip) of the 100 lowest energy poses on different datasets of complexes. We reached a quite good positive predictive value (78%) but a limited sensitivity. Interestingly our approach does not need any previous knowledge of the complex structure whereas most of the other does. a likely allosteric effect that could be used to develop new therapeutic strategies has been shown recently for the androgen receptor (ar), a transcriptional factor implicated in different human diseases as prostate cancer. We studied this nuclear receptor as well as 9 ar mutants (found in prostate cancer or androgen insensitivity syndromes) by molecular dynamics and described for the first time a clear allosteric effect between two different pockets: af-2 and bf-3 that could explain the effects observed in vitro of several mutants. resumen en castellano: la mayor parte de prote\u00ednas en la c\u00e9lula act\u00faan en asociaci\u00f3n con otras prote\u00ednas, formando complejos espec\u00edficos. Las interacciones prote\u00edna-prote\u00edna (ipp) est\u00e1n implicadas en la mayor\u00eda de los procesos esenciales que ocurren en los seres vivos. Por ejemplo, todas las se\u00f1ales que provienen del exterior se transmiten al interior de la c\u00e9lula mediante interacciones prote\u00edna-prote\u00edna de mol\u00e9culas se\u00f1alizadoras. Esta comunicaci\u00f3n es muy importante en muchos procesos biol\u00f3gicos y puede desencadenar enfermedades cuando no est\u00e1 regulada. Se dispone actualmente de una serie de m\u00e9todos para la determinaci\u00f3n estructural de complejos, como la cristalograf\u00eda de rayos x, la resonancia magn\u00e9tica nuclear (rmn) y la microscop\u00eda electr\u00f3nica (em). Sin embargo, solo hay alrededor de 8800 estructuras de complejos macromoleculares en el pdb dada la dificultad de resolverlos. en consecuencia, m\u00e9todos computacionales de docking son necesarios para modelizar los ipp y entender la asociaci\u00f3n entre dos prote\u00ednas con el objetivo \u00faltimo de dise\u00f1ar f\u00e1rmacos para modificar interacciones de inter\u00e9s terap\u00e9utico. Hemos participado en capri (critical assessment of predicted interactions), un experimento internacional de evaluaci\u00f3n de m\u00e9todos de predicci\u00f3n de ipp con nuestro m\u00e9todo de docking llamado pydock. Este experimento permite la comparaci\u00f3n directa y objetiva del rendimiento de los diferentes m\u00e9todos de docking y pydock dio resultados altamente competitivos comparados con los de otros m\u00e9todos. la industria farmac\u00e9utica esta muy interesada por las ipp dado que estan implicadas virtualmente en todos los procesos biol\u00f3gicos y las aplicaciones posibles para modificar ipp de inter\u00e9s terap\u00e9utico con peque\u00f1as mol\u00e9culas son ilimitadas. La industria farmac\u00e9utica presenta numerosos ejemplos de mol\u00e9culas de bajo peso molecular dise\u00f1adas para unirse al sitio activo de enzimas, pero hay todav\u00eda muy pocos casos de compuestos capaces de modificar interacciones prote\u00edna-prote\u00edna, a pesar de que el inter\u00e9s en este campo es inmenso. Sin embargo, dos estrategias para modificar las ipp est\u00e1n disponibles: usar los residuos hot-spots y aprovechar los efectos alost\u00e9ricos. los residuos directamente responsables de la uni\u00f3n entre prote\u00ednas (llamados hot-spots) podr\u00edan ayudar a modificar complejos mediante mol\u00e9culas peque\u00f1as, lo que podr\u00eda tener enormes aplicaciones biol\u00f3gicas y terap\u00e9uticas. Hemos explorado el uso de nuestros valores llamados nip (por propensidad normalizada a la interfase), derivados de la distribuci\u00f3n de orientaciones de docking mediante nuestro programa pydock, para la predicci\u00f3n de hot-spots. La principal ventaja de nuestro m\u00e9todo es que, al contrario de otros previamente descritos, basta la estructura de las prote\u00ednas individuales. Permite predecir hot-spots con un valor predictivo positivo de 78% pero una sensitividad limitada. recientemente se ha descubierto un efecto alost\u00e9rico que podr\u00eda ayudar al desarrollo de nuevas estrategias terap\u00e9uticas en el receptor de andr\u00f3genos, un factor de transcripci\u00f3n que tiene un papel significativo en una gran multitud de patolog\u00edas humanas (por ejemplo en el c\u00e1ncer de pr\u00f3stata). Hemos estudiado este receptor nuclear junto con 9 de sus mutantes (encontrados en patolog\u00edas humanas) por din\u00e1mica molecular y hemos descrito por primera vez un efecto alost\u00e9rico claro entre dos bolsillos af-2 y bf-3 que podr\u00eda explicar los efectos observado in vitro por algunos mutantes.<\/p>\n

 <\/p>\n

Datos acad\u00e9micos de la tesis doctoral \u00abComputational modeling applications: protein – protein docking, hot spots prediciton and alloteric effects (aplicaciones de modelizaci\u00f3n computacional: interacci\u00f3n proteina – proteina, predicci\u00f3n de residuos responsables de esas interacciones y efectos alost\u00e9ricos).<\/strong>\u00ab<\/h3>\n
    \n
  • T\u00edtulo de la tesis:<\/strong>\u00a0 Computational modeling applications: protein – protein docking, hot spots prediciton and alloteric effects (aplicaciones de modelizaci\u00f3n computacional: interacci\u00f3n proteina – proteina, predicci\u00f3n de residuos responsables de esas interacciones y efectos alost\u00e9ricos). <\/li>\n
  • Autor:<\/strong>\u00a0 Sol\u00e9ne Grosdidier <\/li>\n
  • Universidad:<\/strong>\u00a0 Barcelona<\/li>\n
  • Fecha de lectura de la tesis:<\/strong>\u00a0 26\/04\/2011<\/li>\n<\/ul>\n

     <\/p>\n

    Direcci\u00f3n y tribunal<\/h3>\n
      \n
    • Director de la tesis<\/strong>\n
        \n
      • Juan Fern\u00e1ndez Recio<\/li>\n<\/ul>\n<\/li>\n
      • Tribunal<\/strong>\n
          \n
        • Presidente del tribunal: modesto Orozco l\u00f3pez <\/li>\n
        • anne Imberty (vocal)<\/li>\n
        • (vocal)<\/li>\n
        • (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

           <\/p>\n","protected":false},"excerpt":{"rendered":"

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