RT Journal Article
SR Electronic
T1 ProGen: Language Modeling for Protein Generation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.03.07.982272
DO 10.1101/2020.03.07.982272
A1 Madani, Ali
A1 McCann, Bryan
A1 Naik, Nikhil
A1 Keskar, Nitish Shirish
A1 Anand, Namrata
A1 Eguchi, Raphael R.
A1 Huang, Po-Ssu
A1 Socher, Richard
YR 2020
UL http://biorxiv.org/content/early/2020/03/08/2020.03.07.982272.abstract
AB Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. We pose protein engineering as an unsupervised sequence generation problem in order to leverage the exponentially growing set of proteins that lack costly, structural annotations. We train a 1.2B-parameter language model, ProGen, on ∼280M protein sequences conditioned on taxonomic and keyword tags such as molecular function and cellular component. This provides ProGen with an unprecedented range of evolutionary sequence diversity and allows it to generate with fine-grained control as demonstrated by metrics based on primary sequence similarity, secondary structure accuracy, and conformational energy.