RT Journal Article
SR Electronic
T1 Function-guided protein design by deep manifold sampling
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.12.22.473759
DO 10.1101/2021.12.22.473759
A1 Vladimir Gligorijević
A1 Daniel Berenberg
A1 Stephen Ra
A1 Andrew Watkins
A1 Simon Kelow
A1 Kyunghyun Cho
A1 Richard Bonneau
YR 2021
UL http://biorxiv.org/content/early/2021/12/23/2021.12.22.473759.abstract
AB Protein design is challenging because it requires searching through a vast combinatorial space that is only sparsely functional. Self-supervised learning approaches offer the potential to navigate through this space more effectively and thereby accelerate protein engineering. We introduce a sequence denoising autoencoder (DAE) that learns the manifold of protein sequences from a large amount of potentially unlabelled proteins. This DAE is combined with a function predictor that guides sampling towards sequences with higher levels of desired functions. We train the sequence DAE on more than 20M unlabeled protein sequences spanning many evolutionarily diverse protein families and train the function predictor on approximately 0.5M sequences with known function labels. At test time, we sample from the model by iteratively denoising a sequence while exploiting the gradients from the function predictor. We present a few preliminary case studies of protein design that demonstrate the effectiveness of this proposed approach, which we refer to as “deep manifold sampling”, including metal binding site addition, function-preserving diversification, and global fold change.Competing Interest StatementThe authors have declared no competing interest.