Controllable Protein Design with Language Models

TL;DR

This paper explores how transformer-based language models can be fine-tuned and controlled to generate novel, functional protein sequences, potentially transforming protein design and understanding folding principles.

Contribution

It introduces the concept of using NLP transformer models for controllable protein sequence generation, highlighting recent advances and future potential in the field.

Findings

Transformer models can generate diverse protein sequences.

Control tags enable specific protein function design.

Model interpretability can reveal folding principles.

Abstract

The 21st century is presenting humankind with unprecedented environmental and medical challenges. The ability to design novel proteins tailored for specific purposes could transform our ability to respond timely to these issues. Recent advances in the field of artificial intelligence are now setting the stage to make this goal achievable. Protein sequences are inherently similar to natural languages: Amino acids arrange in a multitude of combinations to form structures that carry function, the same way as letters form words and sentences that carry meaning. Therefore, it is not surprising that throughout the history of Natural Language Processing (NLP), many of its techniques have been applied to protein research problems. In the last few years, we have witnessed revolutionary breakthroughs in the field of NLP. The implementation of Transformer pre-trained models has enabled text generation with human-like capabilities, including texts with specific properties such as style or subject. Motivated by its considerable success in NLP tasks, we expect dedicated Transformers to dominate custom protein sequence generation in the near future. Finetuning pre-trained models on protein families will enable the extension of their repertoires with novel sequences that could be highly divergent but still potentially functional. The combination of control tags such as cellular compartment or function will further enable the controllable design of novel protein functions. Moreover, recent model interpretability methods will allow us to open the 'black box' and thus enhance our understanding of folding principles. While early initiatives show the enormous potential of generative language models to design functional sequences, the field is still in its infancy. We believe that protein language models are a promising and largely unexplored field and discuss their foreseeable impact on protein design.