Paper Info
Title
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 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.
Year
DOI
Venue
2021
10.1073/pnas.2104878118
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Keywords
DocType
Volume
protein engineering, deep learning, convolutional neural network
Journal
118
Issue
ISSN
Citations 
48
0027-8424
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Sam Gelman100.34
Sarah A Fahlberg200.34
Pete Heinzelman300.34
Philip A Romero400.34
Anthony Gitter500.34