  Large Language Models (LLMs) have revolutionized the field of natural
language processing, but they fall short in comprehending biological sequences
such as proteins. To address this challenge, we propose InstructProtein, an
innovative LLM that possesses bidirectional generation capabilities in both
human and protein languages: (i) taking a protein sequence as input to predict
its textual function description and (ii) using natural language to prompt
protein sequence generation. To achieve this, we first pre-train an LLM on both
protein and natural language corpora, enabling it to comprehend individual
languages. Then supervised instruction tuning is employed to facilitate the
alignment of these two distinct languages. Herein, we introduce a knowledge
graph-based instruction generation framework to construct a high-quality
instruction dataset, addressing annotation imbalance and instruction deficits
in existing protein-text corpus. In particular, the instructions inherit the
structural relations between proteins and function annotations in knowledge
graphs, which empowers our model to engage in the causal modeling of protein
functions, akin to the chain-of-thought processes in natural languages.
Extensive experiments on bidirectional protein-text generation tasks show that
InstructProtein outperforms state-of-the-art LLMs by large margins. Moreover,
InstructProtein serves as a pioneering step towards text-based protein function
prediction and sequence design, effectively bridging the gap between protein
and human language understanding.
