Deep Generative Models of Protein Structure Uncover Distant Relationships Across a Continuous Fold Space

Abstract

Unresolved questions about the discrete/continuous dichotomy of protein fold space permeate structural and evolutionary biology. From protein structure comparison and classification to evolutionary analyses and function prediction, our views of fold space implicitly rest upon many assumptions that impact how we analyze, interpret and come to understand biological systems. Discrete views of fold space categorize similar folds into separate groups; unfortunately, such a ‘binning’ process inherently fails to capture many remote relationships. While hierarchical databases such as CATH, SCOP, and ECOD represent major steps forward in protein classification, we believe that a scalable, objective and conceptually flexible method that is less reliant upon assumptions and heuristics could enable a more systematic and thorough exploration of fold space and evolutionary-distant relationships. Here, we develop a structure-guided, comparative analysis of proteins, leveraging embeddings derived from deep generative models, which represent a highly-compressed, lower-dimensional space of a given protein and its sequence, structure and biophysical properties. Building upon a recent ‘Urfold’ model of protein structure, the deep generative approach developed here, termed ‘DeepUrfold’, suggests a new, mostly-continuous view of fold space—a view that extends beyond simple 3D structural/geometric similarity, towards the realm of integrated sequence↔structure↔function properties. We find that such an approach can quantitatively represent and detect evolutionarily-remote relationships that are not captured by existing methods.