InstructProtein：通过知识指令对齐人类语言与蛋白质语言
InstructProtein: Aligning Human and Protein Language via Knowledge Instruction
AI总结：
针对大语言模型难以理解蛋白质序列的问题，研究提出InstructProtein模型，通过知识图谱构建指令数据集并经预训练与指令微调实现蛋白质与人类语言的双向生成，性能显著优于现有模型，为相关研究开辟新方向。
摘要：
大语言模型（LLMs）已彻底改变自然语言处理领域，但它们在理解蛋白质等生物序列方面存在不足。为应对这一挑战，我们提出InstructProtein——一种具备人类语言与蛋白质语言双向生成能力的创新型大语言模型：（i）以蛋白质序列为输入，预测其文本功能描述；（ii）利用自然语言提示生成蛋白质序列。为实现这一目标，我们首先在蛋白质与自然语言语料上预训练大语言模型，使其能够理解两种独立的语言。随后采用监督指令微调，推动这两种不同语言的对齐。在此过程中，我们引入一种基于知识图谱的指令生成框架，以构建高质量指令数据集，解决现有蛋白质-文本语料中存在的标注不平衡与指令缺失问题。特别地，这些指令继承了知识图谱中蛋白质与功能注释之间的结构关系，使我们的模型能够进行蛋白质功能的因果建模，类似于自然语言中的思维链过程。在双向蛋白质-文本生成任务上的大量实验表明，InstructProtein以大幅优势超越了当前最先进的大语言模型。此外，InstructProtein是迈向基于文本的蛋白质功能预测与序列设计的开创性一步，有效弥合了蛋白质与人类语言理解之间的差距。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
