Learning the shape of protein microenvironments with a holographic convolutional neural network.
Proc Natl Acad Sci U S A
; 121(6): e2300838121, 2024 Feb 06.
Article
en En
| MEDLINE
| ID: mdl-38300863
ABSTRACT
Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from its sequence or structure remains a major challenge. Here, we introduce holographic convolutional neural network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Redes Neurales de la Computación
Tipo de estudio:
Prognostic_studies
Idioma:
En
Año:
2024
Tipo del documento:
Article