Neural networks to learn protein sequence-function relationships from deep mutational scanning data.

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 seque

SUBMITTER: Gelman S 

PROVIDER: S-EPMC8640744 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature