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, and the task of finding a viable design is often referred to as the inverse protein folding problem. We develop generative models for protein sequences conditioned on a graph-structured specification of the design target. Our approach efficiently captures the complex dependencies in proteins by focusing on those that are long-range in sequence but local in 3D space. Our framework significantly improves upon prior parametric models of protein sequences given structure, and takes a step toward rapid and targeted biomolecular design with the aid of deep generative models.
Original language | English |
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Publication status | Published - 2019 |
MoE publication type | Not Eligible |
Event | Deep Generative Models for Highly Structured Data - New Orleans, United States Duration: 6 May 2019 → 6 May 2019 |
Workshop
Workshop | Deep Generative Models for Highly Structured Data |
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Abbreviated title | DGS@ICLR Workshop |
Country/Territory | United States |
City | New Orleans |
Period | 06/05/2019 → 06/05/2019 |
Other | DGS@ICLR Workshop |