Generative models for graph-based protein design

John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi Jaakkola

Research output: Contribution to journalConference articleScientificpeer-review

69 Citations (Scopus)

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.

Original languageEnglish
JournalADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
Volume32
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventConference on Neural Information Processing Systems - Vancouver, Canada
Duration: 8 Dec 201914 Dec 2019
Conference number: 33
https://neurips.cc

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