RT Journal Article
SR Electronic
T1 Unified rational protein engineering with sequence-only deep representation learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 589333
DO 10.1101/589333
A1 Alley, Ethan C.
A1 Khimulya, Grigory
A1 Biswas, Surojit
A1 AlQuraishi, Mohammed
A1 Church, George M.
YR 2019
UL http://biorxiv.org/content/early/2019/03/26/589333.abstract
AB Rational protein engineering requires a holistic understanding of protein function. Here, we apply deep learning to unlabelled amino acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily, and biophysically grounded. We show that the simplest models built on top of this unified representation (UniRep) are broadly applicable and generalize to unseen regions of sequence space. Our data-driven approach reaches near state-of-the-art or superior performance predicting stability of natural and de novo designed proteins as well as quantitative function of molecularly diverse mutants. UniRep further enables two orders of magnitude cost savings in a protein engineering task. We conclude UniRep is a versatile protein summary that can be applied across protein engineering informatics.