ProGen: Language Modeling for Protein Generation
This addresses protein engineering for synthetic biology and medicine by enabling controlled generation from unannotated data, though it is incremental as it applies language modeling to a known problem.
The authors tackled protein engineering by training a 1.2B-parameter language model, ProGen, on 280M protein sequences to generate proteins with control over properties, achieving results demonstrated by metrics like sequence similarity and structural accuracy.
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.