Protein Family Sequence Generation Through ProGen2 Fine-Tuning

Proteins are biomolecules involved in virtually all biological processes, making the design of novel proteins with specific functions crucial for advancing drug development and biological research. Large protein sequence databases allow for training language models adapted from natural language processing, treating amino acid sequences as a biological “language”. However, these generative protein language models lack a straightforward, user-friendly method for prompting them to generate specific sequences with desired properties. In this work, we demonstrate how the pre-trained protein language model ProGen2 can be effectively fine-tuned for controllable generation of protein sequences from several distinct protein families. We validate the generated sequences using various in-silico metrics and show that the model is able to generate viable protein sequences that exhibit low similarity to existing proteins.