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Sitios funcionales de las proteínas de andamiaje utilizando aprendizaje profundo

Jue Wang^1,2, Sidney Lisanza^1,2,3, David Juergens^1,2,4

¹Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.

Science (New York, N.Y.)

|July 21, 2022

Resumen

Este resumen es generado por máquina.

El aprendizaje profundo diseña nuevos andamios de proteínas optimizando secuencias para sitios funcionales. Estos métodos crean diversas proteínas como enzimas e inmunógenos, validados computacionalmente y experimentalmente.

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Área de la Ciencia:

Ingeniería de proteínas
Biología computacional
Biotecnología

Sus antecedentes:

La función de las proteínas depende de residuos específicos dentro de una estructura estable.
Diseñar nuevas proteínas con las funciones deseadas es un desafío significativo.
Los métodos actuales a menudo requieren pliegues o estructuras de proteínas predefinidas.

Objetivo del estudio:

Desarrollar métodos de aprendizaje profundo para el diseño de andamios de proteínas de novo.
Para crear sitios de proteínas funcionales sin predecir la arquitectura del andamio.
Para generar diversos diseños de proteínas, incluidos inmunógenos, enzimas y proteínas de unión.

Principales métodos:

Se introdujo la "alucinación restringida" para optimizar las secuencias para los sitios funcionales deseados.
Desarrollado 'inpainting' para construir andamios alrededor de sitios funcionales utilizando RoseTTAFold.
Aplicó estos métodos para diseñar varias proteínas funcionales.

Principales resultados:

Se han diseñado con éxito inmunógenos candidatos, trampas receptoras, metaloproteínas, enzimas y proteínas de unión a proteínas.
Diseños validados mediante una combinación de predicciones in silico y pruebas experimentales.
Demostró la capacidad de crear andamios de proteínas funcionales sin pliegues predefinidos.

Conclusiones:

El aprendizaje profundo ofrece herramientas poderosas para el diseño de andamios de proteínas de novo.
Estos métodos permiten la creación de nuevas proteínas con funciones a medida.
Los enfoques desarrollados tienen amplias aplicaciones en biotecnología e ingeniería de proteínas.