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Generative models for graph-based protein design
Name
NeurIPS-2019-generative-models-for-graph-based-protein-design-Paper.pdf
Description
Published version
Size
2.5 MB
Format
Adobe PDF
Checksum (MD5)
e89736532d1608d43f7e2f69708b36aa
Author(s)
Ingraham, John
Garg, Vikas Kamur
Barzilay, Regina
Jaakkola, Tommi S
Journal
Advances in Neural Information Processing Systems 32 (NeurIPS 2019)
Publisher
Neural Information Processing Systems Foundation, Inc.
Citation
Ingraham, John et al. "Generative models for graph-based protein design." Advances in Neural Information Processing Systems 32 (NeurIPS 2019), December 2019, Vancouver, Canada, Neural Information Processing Systems Foundation, 2019. © 2019 Neural Information Processing Systems Foundation
Version
Final published version
Abstract
Engineered proteins offer the potential to solve many problems in biomedicine, energy, and materials science, but creating designs that succeed is difficult in practice. A significant aspect of this challenge is the complex coupling between protein sequence and 3D structure, with the task of finding a viable design often referred to as the inverse protein folding problem. In this work, we introduce a conditional generative model for protein sequences given 3D structures based on graph representations. Our approach efficiently captures the complex dependencies in proteins by focusing on those that are long-range in sequence but local in 3D space. This graph-based approach improves in both speed and reliability over conventional and other neural network-based approaches, and takes a step toward rapid and targeted biomolecular design with the aid of deep generative models.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/2019
