¿Puede la Inteligencia Artificial reemplazar la experimentación en animales?

¿Puede la Inteligencia Artificial reemplazar la experimentación en animales?

Las proteínas son esenciales para la vida, apoyando prácticamente todas sus funciones. Son moléculas grandes y complejas, formadas por cadenas de aminoácidos, y lo que hace una proteína depende en gran medida de su estructura 3D única . Averiguar en qué formas se pliegan las proteínas se conoce como el «problema del plegamiento de proteínas» , y ha sido un gran desafío en biología durante los últimos 50 años. En un gran avance científico, la última versión de la inteligencia artificial AlphaFold ha sido reconocida como una solución a este gran desafío por los organizadores de la Evaluación Crítica de la Predicción de la Estructura de las proteínas. Este avance demuestra el impacto que la IA (Inteligencia Artificial) puede tener en los descubrimientos científicos y su potencial para acelerar drásticamente el progreso en algunos de los campos más fundamentales que explican y dan forma a nuestro mundo.

La complejidad del problema del plegamiento de proteínas

En su discurso de aceptación del Premio Nobel de Química de 1972, Christian Anfinsen  postuló  que, en teoría, la secuencia de aminoácidos de una proteína  debería determinar completamente su estructura. Esta hipótesis provocó una búsqueda de cinco décadas para poder predecir computacionalmente la estructura 3D de una proteína basándose únicamente en su secuencia de aminoácidos 1D como una alternativa complementaria a estos métodos experimentales costosos y que consumen mucho tiempo. Sin embargo, un desafío importante es que la cantidad de formas en que una proteína podría doblarse teóricamente antes de asentarse en su estructura 3D final es astronómica. En 1969, Cyrus Levinthal señaló que llevaría más tiempo que la edad del universo conocido enumerar todas las configuraciones posibles de una proteína típica mediante el cálculo de la fuerza bruta: Levinthal estimó ¡10³ºº conformaciones posibles  para una proteína típica! Sin embargo, en la naturaleza, las proteínas se pliegan espontáneamente, algunas en milisegundos, una dicotomía a la que a veces se hace referencia como  la paradoja de Levinthal.

Este trabajo computacional representa un avance asombroso en el problema del plegamiento de proteínas, un gran desafío de 50 años en biología. Ha ocurrido décadas antes de que muchas personas en el campo lo hubieran predicho. Será emocionante ver las muchas formas en que cambiará fundamentalmente la investigación biológica.

PROFESOR VENKI RAMAKRISHNAN

Premio Nobel y Presidente de la Royal Society

Método de resolución

El método fue primero estudiado cuidadosamente y publicado en la revista Nature. La idea es obtener, a partir de la secuencia de la proteína, un nombre particular, trasladarla a redes neuronales, luego elaborar una representación matricial de la misma para obtener un gráfico en 3D, iterar este proceso, y finalmente obtener su resultado.

El primer método se basó en técnicas comúnmente utilizadas en biología estructural y reemplazó repetidamente partes de una estructura de proteína con nuevos fragmentos de proteína. Se entrenó una red neuronal generativa para inventar nuevos fragmentos, que se utilizaron para mejorar continuamente la puntuación de la estructura de la proteína propuesta.

El segundo método optimizó las puntuaciones a través del descenso de gradientes, una técnica matemática comúnmente utilizada en el aprendizaje automático para realizar pequeñas mejoras incrementales, lo que resultó en estructuras altamente precisas. Esta técnica se aplicó a cadenas de proteínas completas en lugar de a piezas que deben doblarse por separado antes de ensamblarse en una estructura más grande, para simplificar el proceso de predicción.

La versión AlphaFold utilizada en CASP13 está disponible en Github para cualquier persona interesada en aprender más o replicar nuestros resultados de plegamiento de proteínas.

Impactos en la ciencia

Los científicos solo han mapeado las estructuras de aproximadamente la mitad de todas las proteínas producidas por las células humanas. Algunas enfermedades raras involucran mutaciones en un solo gen, lo que resulta en una proteína malformada que puede tener efectos profundos en la salud de todo un organismo. A medida que los científicos adquieren más conocimiento sobre las formas de las proteínas y cómo operan a través de simulaciones y modelos, este método puede eventualmente ayudarnos a contribuir al descubrimiento eficiente de fármacos, al tiempo que reduce los costos asociados con la experimentación. Nuestra esperanza es que la IA sea útil para la investigación de enfermedades y, en última instancia, mejore la calidad de vida de millones de pacientes en todo el mundo.

Pero los beneficios potenciales no se limitan solo a la salud: comprender el plegamiento de proteínas ayudará en el diseño de proteínas, lo que podría desbloquear una gran cantidad de beneficios. Por ejemplo, los avances en las enzimas biodegradables, que pueden ser habilitadas por el diseño de proteínas, podrían ayudar a manejar contaminantes como el plástico y el aceite, ayudándonos a descomponer los desechos de maneras más amigables con nuestro medio ambiente. De hecho, los investigadores ya han comenzado a diseñar bacterias para que secreten proteínas que harán que los desechos sean biodegradables y más fáciles de procesar.

Otro gran beneficio sería el coste que implica la experimentación en animales. Ya en el marco ético hay muchas discusiones. Además, existe siempre un gran margen de error si no se considera una buena cantidad de especímenes. De nuevo, habría que estudiar qué pasa con los humanos. En cambio, si una IA tiene un pequeño margen de error, no expone al sufrimiento a ningún individuo, y sus resultados pueden obtenerse en horas, entonces estamos ante un avance sin precedentes en medicina y biología.

Esto aún sigue en proceso, pero no sorprendería que este procedimiento se lleve a cabo en pocos años.

Daniel Dávalos

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