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Diseño de novo de las luciferasas utilizando el aprendizaje profundo
Andy Hsien-Wei Yeh1,2,3, Christoffer Norn4,5, Yakov Kipnis4,5,6
1Department of Biochemistry, University of Washington, Seattle, WA, USA. hsyeh@ucsc.edu.
Nature
|February 22, 2023
Resumen
Los investigadores desarrollaron un método de aprendizaje profundo para diseñar nuevas enzimas, creando luciferasas artificiales con alta actividad y especificidad para aplicaciones biomédicas.
Área de la Ciencia:
- La bioquímica
- Biología computacional
- Ingeniería de proteínas
Sus antecedentes:
- El diseño de enzimas de novo tradicionalmente enfrentaba limitaciones debido a las escasas estructuras de proteínas y las complejas relaciones secuencia-estructura.
- Los métodos anteriores lucharon para crear enzimas con las funciones catalíticas deseadas y bolsas específicas de unión al sustrato.
Objetivo del estudio:
- Desarrollar un enfoque de aprendizaje profundo para generar andamios de proteínas idealizados con diversas formas de bolsillo.
- Diseñar nuevas luciferasas artificiales con alta actividad catalítica y especificidad de sustrato utilizando estos andamios de ingeniería.
Principales métodos:
- Empleó una estrategia de "alucinación en toda la familia" de aprendizaje profundo para generar numerosas estructuras de proteínas idealizadas.
- Las luciferasas artificiales diseñadas para atacar sustratos sintéticos de luciferina: difenilterazina y 2-deoxicoelenterazina.
- Geometría optimizada del sitio activo para el posicionamiento preciso de los residuos catalíticos y la interacción con el sustrato.
Principales resultados:
- Se han diseñado con éxito luciferasas artificiales que presentan quimioluminiscencia oxidativa selectiva tanto para la difenilterazina como para la 2-deoxicoelenterazina.
- Se ha desarrollado una luciferasa artificial altamente activa (kcat/Km = 10^6 M^-1s^-1) y térmicamente estable (Tm > 95°C).
- Se obtiene una especificidad de sustrato significativamente mayor en comparación con las luciferasas nativas.
Conclusiones:
- El enfoque de alucinación de aprendizaje profundo permite la creación de biocatalizadores altamente activos y específicos desde cero.
- Este hito avanza en el diseño de enzimas computacionales, allanando el camino para generar diversas enzimas, incluidas las nuevas luciferasas.
- Las enzimas diseñadas tienen un amplio potencial para aplicaciones en biomedicina y biotecnología.
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