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Designing novel protein sequences for a desired 3D topological fold is afundamental yet non-trivial task in protein engineering. Challenges exist dueto the complex sequence--fold relationship, as well as the difficulties tocapture the diversity of the sequences (therefore structures and functions)within a fold. To overcome these challenges, we propose Fold2Seq, a noveltransformer-based generative framework for designing protein sequencesconditioned on a specific target fold. To model the complex sequence--structurerelationship, Fold2Seq jointly learns a sequence embedding using a transformerand a fold embedding from the density of secondary structural elements in 3Dvoxels. On test sets with single, high-resolution and complete structure inputsfor individual folds, our experiments demonstrate improved or comparableperformance of Fold2Seq in terms of speed, coverage, and reliability forsequence design, when compared to existing state-of-the-art methods thatinclude data-driven deep generative models and physics-based RosettaDesign. Theunique advantages of fold-based Fold2Seq, in comparison to a structure-baseddeep model and RosettaDesign, become more evident on three additionalreal-world challenges originating from low-quality, incomplete, or ambiguousinput structures. Source code and data are available athttps://github.com/IBM/fold2seq.

Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design