PT  - JOURNAL ARTICLE
AU  - Alley, Ethan C.
AU  - Khimulya, Grigory
AU  - Biswas, Surojit
AU  - AlQuraishi, Mohammed
AU  - Church, George M.
TI  - Unified rational protein engineering with sequence-only deep representation learning
AID  - 10.1101/589333
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 589333
4099  - http://biorxiv.org/content/early/2019/03/26/589333.short
4100  - http://biorxiv.org/content/early/2019/03/26/589333.full
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.