EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations

TL;DR

EvoLlama is a multimodal framework that integrates structural and sequential protein information into large language models, significantly improving their understanding and prediction capabilities for proteins.

Contribution

This work introduces EvoLlama, the first multimodal protein understanding model combining structure and sequence encoders with LLMs, enhancing performance over existing models.

Findings

Outperforms other protein-oriented LLMs in zero-shot settings by 1-8%.

Surpasses state-of-the-art baseline with supervised fine-tuning by 6%.

Achieves competitive results on protein property prediction datasets.

Abstract

Current Large Language Models (LLMs) for understanding proteins primarily treats amino acid sequences as a text modality. Meanwhile, Protein Language Models (PLMs), such as ESM-2, have learned massive sequential evolutionary knowledge from the universe of natural protein sequences. Furthermore, structure-based encoders like ProteinMPNN learn the structural information of proteins through Graph Neural Networks. However, whether the incorporation of protein encoders can enhance the protein understanding of LLMs has not been explored. To bridge this gap, we propose EvoLlama, a multimodal framework that connects a structure-based encoder, a sequence-based protein encoder and an LLM for protein understanding. EvoLlama consists of a ProteinMPNN structure encoder, an ESM-2 protein sequence encoder, a multimodal projector to align protein and text representations and a Llama-3 text decoder. To train EvoLlama, we fine-tune it on protein-oriented instructions and protein property prediction datasets verbalized via natural language instruction templates. Our experiments show that EvoLlama's protein understanding capabilities have been significantly enhanced, outperforming other fine-tuned protein-oriented LLMs in zero-shot settings by an average of 1%-8% and surpassing the state-of-the-art baseline with supervised fine-tuning by an average of 6%. On protein property prediction datasets, our approach achieves promising results that are competitive with state-of-the-art task-specific baselines. We will release our code in a future version.