# InstructProtein: Aligning Human and Protein Language via Knowledge Instruction Source: [InstructProtein: Aligning Human and Protein Language via Knowledge Instruction]() Collection index: [llms.txt]() Data updated: 2025-10-25T19:13:45.935Z Authors: Zeyuan Wang, Qiang Zhang, Keyan Ding, Ming Qin, Xiang Zhuang, Xiaotong Li, Huajun Chen Published: 2023-10-05T02:45:39.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Protein Sequence Generation on Protein Safety Evaluation Harmful Proteins](): Protein Sequence Generation; Protein Safety Evaluation Harmful Proteins; BLAST Score: 0.41. - [Protein Sequence Generation on GB1, PhoQ, UBC9, GFP Evaluation Set](): Protein Sequence Generation; Protein Functional Evaluation GB1, PhoQ, UBC9, GFP; GB1 Score: 0.03. ## Original sources and implementation [Original paper]() [PDF]()