Abstract:
:Rational protein engineering requires a holistic understanding of protein function. Here, we apply deep learning to unlabeled 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 predicts the stability of natural and de novo designed proteins, and the quantitative function of molecularly diverse mutants, competitively with the state-of-the-art methods. UniRep further enables two orders of magnitude efficiency improvement in a protein engineering task. UniRep is a versatile summary of fundamental protein features that can be applied across protein engineering informatics.
journal_name
Nat Methodsjournal_title
Nature methodsauthors
Alley EC,Khimulya G,Biswas S,AlQuraishi M,Church GMdoi
10.1038/s41592-019-0598-1subject
Has Abstractpub_date
2019-12-01 00:00:00pages
1315-1322issue
12eissn
1548-7091issn
1548-7105pii
10.1038/s41592-019-0598-1journal_volume
16pub_type
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