  Designing novel protein sequences for a desired 3D topological fold is a
fundamental yet non-trivial task in protein engineering. Challenges exist due
to the complex sequence--fold relationship, as well as the difficulties to
capture the diversity of the sequences (therefore structures and functions)
within a fold. To overcome these challenges, we propose Fold2Seq, a novel
transformer-based generative framework for designing protein sequences
conditioned on a specific target fold. To model the complex sequence--structure
relationship, Fold2Seq jointly learns a sequence embedding using a transformer
and a fold embedding from the density of secondary structural elements in 3D
voxels. On test sets with single, high-resolution and complete structure inputs
for individual folds, our experiments demonstrate improved or comparable
performance of Fold2Seq in terms of speed, coverage, and reliability for
sequence design, when compared to existing state-of-the-art methods that
include data-driven deep generative models and physics-based RosettaDesign. The
unique advantages of fold-based Fold2Seq, in comparison to a structure-based
deep model and RosettaDesign, become more evident on three additional
real-world challenges originating from low-quality, incomplete, or ambiguous
input structures. Source code and data are available at
https://github.com/IBM/fold2seq.
