Graph

Protein design

Metric (unit)

Generative Design

Bioinformatics

Biomedicine

Biology

Gene

Machine learning

Engineering

Artificial intelligence

Protein folding

Operations management

Sequence (biology)

Computer science

Protein structure

Theoretical computer science

Generative grammar

Genetics

Biochemistry

Robustness (evolution)

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. We develop relational language models for protein sequences that directly condition on a graph specification of the target structure. 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 in both speed and robustness over conventional and deep-learning-based methods for structure-based protein sequence design, and takes a step toward rapid and targeted biomolecular design with the aid of deep generative models.

Generative models for graph-based protein design