Design of metalloproteins and novel protein folds using variational autoencoders View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Joe G. Greener, Lewis Moffat, David T Jones

ABSTRACT

The design of novel proteins has many applications but remains an attritional process with success in isolated cases. Meanwhile, deep learning technologies have exploded in popularity in recent years and are increasingly applicable to biology due to the rise in available data. We attempt to link protein design and deep learning by using variational autoencoders to generate protein sequences conditioned on desired properties. Potential copper and calcium binding sites are added to non-metal binding proteins without human intervention and compared to a hidden Markov model. In another use case, a grammar of protein structures is developed and used to produce sequences for a novel protein topology. One candidate structure is found to be stable by molecular dynamics simulation. The ability of our model to confine the vast search space of protein sequences and to scale easily has the potential to assist in a variety of protein design tasks. More... »

PAGES

16189

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-34533-1

DOI

http://dx.doi.org/10.1038/s41598-018-34533-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107871703

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30385875


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curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-34533-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-34533-1'

RDF/XML is a standard XML format for linked data.

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