Neural networks to learn protein sequence-function relationships from deep mutational scanning data.
Proc Natl Acad Sci U S A
; 118(48)2021 11 30.
Article
em En
| MEDLINE
| ID: mdl-34815338
ABSTRACT
The mapping from protein sequence to function is highly complex, making it challenging to predict how sequence changes will affect a protein's behavior and properties. We present a supervised deep learning framework to learn the sequence-function mapping from deep mutational scanning data and make predictions for new, uncharacterized sequence variants. We test multiple neural network architectures, including a graph convolutional network that incorporates protein structure, to explore how a network's internal representation affects its ability to learn the sequence-function mapping. Our supervised learning approach displays superior performance over physics-based and unsupervised prediction methods. We find that networks that capture nonlinear interactions and share parameters across sequence positions are important for learning the relationship between sequence and function. Further analysis of the trained models reveals the networks' ability to learn biologically meaningful information about protein structure and mechanism. Finally, we demonstrate the models' ability to navigate sequence space and design new proteins beyond the training set. We applied the protein G B1 domain (GB1) models to design a sequence that binds to immunoglobulin G with substantially higher affinity than wild-type GB1.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Sequência de Aminoácidos
/
Análise de Sequência de Proteína
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article