Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 64,995 bioRxiv papers from 288,040 authors.

Unified rational protein engineering with sequence-only deep representation learning

By Ethan C Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, George M Church

Posted 26 Mar 2019
bioRxiv DOI: 10.1101/589333

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.

Download data

  • Downloaded 4,847 times
  • Download rankings, all-time:
    • Site-wide: 443 out of 64,995
    • In synthetic biology: 10 out of 618
  • Year to date:
    • Site-wide: 87 out of 64,995
  • Since beginning of last month:
    • Site-wide: None out of 64,995

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News