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.
|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||Deep Generative Models for Highly Structured Data|
|Abbreviated title||DGS@ICLR Workshop|
|Period||06/05/2019 → 06/05/2019|