ProGen2: Exploring the Boundaries of Protein Language Models
This work addresses the challenge of optimizing protein design for researchers in computational biology and AI, though it is incremental as it builds on existing attention-based models by scaling up size and data.
The authors tackled the problem of understanding the role of large-scale models and data in protein language models by introducing ProGen2, a suite of models scaled up to 6.4B parameters trained on over a billion proteins, which achieved state-of-the-art performance in capturing evolutionary sequences, generating novel viable sequences, and predicting protein fitness without finetuning.
Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein design. However, we lack a sufficient understanding of how very large-scale models and data play a role in effective protein model development. We introduce a suite of protein language models, named ProGen2, that are scaled up to 6.4B parameters and trained on different sequence datasets drawn from over a billion proteins from genomic, metagenomic, and immune repertoire databases. ProGen2 models show state-of-the-art performance in capturing the distribution of observed evolutionary sequences, generating novel viable sequences, and predicting protein fitness without additional finetuning. As large model sizes and raw numbers of protein sequences continue to become more widely accessible, our results suggest that a growing emphasis needs to be placed on the data distribution provided to a protein sequence model. We release the ProGen2 models and code at https://github.com/salesforce/progen.