Protein research is crucial in various fundamental disciplines, but understanding their intricate structure-function relationships remains challenging. Recent Large Language Models (LLMs) have made significant strides in comprehending task-specific knowledge, suggesting the potential for ChatGPT-like systems specialized in protein to facilitate basic research. In this work, we introduce ProtChatGPT, which aims at learning and understanding protein structures via natural languages. ProtChatGPT enables users to upload proteins, ask questions, and engage in interactive conversations to produce comprehensive answers. The system comprises protein encoders, a Protein-Language Pertaining Transformer (PLP-former), a projection adapter, and an LLM. The protein first undergoes protein encoders and PLP-former to produce protein embeddings, which are then projected by the adapter to conform with the LLM. The LLM finally combines user questions with projected embeddings to generate informative answers. Experiments show that ProtChatGPT can produce promising responses to proteins and their corresponding questions. We hope that ProtChatGPT could form the basis for further exploration and application in protein research. Code and our pre-trained model will be publicly available.

翻译：蛋白质研究在诸多基础学科中至关重要，但其复杂结构与功能关系的理解仍具挑战。近年来，大型语言模型（LLM）在理解特定任务知识方面取得了显著进展，这预示了类似ChatGPT的蛋白质专用系统具备促进基础研究的潜力。本研究提出ProtChatGPT，旨在通过自然语言学习并理解蛋白质结构。该系统支持用户上传蛋白质、提出问题并进行交互式对话，从而生成全面的答案。ProtChatGPT包含蛋白质编码器、蛋白质-语言适配Transformer（PLP-former）、投影适配器及LLM。蛋白质首先通过蛋白质编码器和PLP-former生成蛋白质嵌入表示，随后经适配器投影以适配LLM的输入格式。LLM最终将用户问题与投影后的嵌入表示结合，生成信息性答案。实验表明，ProtChatGPT能够针对蛋白质及其相关问题输出具有前景的回应。我们期望ProtChatGPT能为蛋白质研究的进一步探索与应用奠定基础。代码及预训练模型将公开提供。