# De novo functional protein sequence generation: overcoming data scarcity through regeneration and large language models

## TL;DR

This paper introduces a new model for generating functional protein sequences using large language models, even with limited data.

## Contribution

The novel hierarchical model, ProteinRG, improves protein sequence generation with small datasets and outperforms existing methods.

## Key findings

- ProteinRG generates sequences similar to original ones while maintaining functional consistency.
- The model outperforms other generative models in sequence generation tasks.
- Generated sequences align well with original sequences in multiple evaluation methods.

## Abstract

Proteins are essential components of all living organisms and play a critical role in cellular survival. They have a broad range of applications, from clinical treatments to material engineering. This versatility has spurred the development of protein design, with amino acid sequence design being a crucial step in the process. Recent advancements in deep generative models have shown promise for protein sequence design. However, the scarcity of functional protein sequence data for certain types can hinder the training of these models, which often require large datasets. To address this challenge, we propose a hierarchical model named ProteinRG that can generate functional protein sequences using relatively small datasets. ProteinRG begins by generating a representation of a protein sequence, leveraging existing large protein sequence models, before producing a functional protein sequence. We have tested our model on various functional protein sequences and evaluated the results from three perspectives: multiple sequence alignment, t-SNE distribution analysis, and 3D structure prediction. The findings indicate that our generated protein sequences maintain both similarity to the original sequences and consistency with the desired functions. Moreover, our model demonstrates superior performance compared twith other generative models for protein sequence generation.