Protein sequence design with deep generative models

These macromolecules are encoded as linear chains of amino acids, which then fold into dynamic 3-dimensional structures that accomplish a staggering variety of functions. To improve proteins for human purposes, protein engineers have developed a variety of experimental and computational methods for designing sequences that fold to desired structures or perform desired functions [1, 2, 3, 4]. A developing paradigm, machine learning-guided protein engineering, promises to leverage the information obtained from wet-lab experiments with data-driven models to more efficiently find desirable proteins [5, 6, 7]. Much of the early work has focused on incorporating discriminative models trained on measured sequence-fitness pairs to guide protein engineering [5]. However, methods that can take advantage of unlabeled protein sequences are improving the protein engineering paradigm.