TourSynbio：连接文本与蛋白质序列以服务于蛋白质工程的多模态大模型与智能体框架
TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering
AI总结：
针对大语言模型在蛋白质工程中依赖外部编码器导致性能次优的问题，提出无外部编码器的多模态大模型TourSynbio-7B及智能体框架，在多项任务上超越GPT-4，并通过湿实验验证了其有效性。
摘要：
蛋白质序列与自然语言之间的结构相似性，推动了这两个领域在深度学习方面的并行发展。尽管大语言模型（LLMs）在自然语言处理领域取得了长足进步，但其在蛋白质工程中的潜力在很大程度上仍未被探索。以往的方法通过引入外部蛋白质编码器赋予LLMs蛋白质理解能力，但这未能充分利用蛋白质序列与自然语言之间的内在相似性，导致性能次优且增加了模型复杂度。为了填补这一空白，我们提出了TourSynbio-7B，这是首个专为蛋白质工程任务设计的多模态大模型，且无需外部蛋白质编码器。TourSynbio-7B证明LLMs能够本质上学会将蛋白质理解为一种语言。该模型基于InternLM2-7B进行后训练和指令微调，使用的数据集为ProteinLMDataset，该数据集包含174.6亿个用于自监督预训练的文本和蛋白质序列token，以及用于监督微调的89.3万条指令。TourSynbio-7B在ProteinLMBench上超越了GPT-4，该基准包含944道人工验证的选择题，其准确率达到62.18%。利用TourSynbio-7B增强的蛋白质序列理解能力，我们引入了TourSynbio-Agent，这是一个创新框架，能够执行包括突变分析、逆折叠、蛋白质折叠和可视化在内的各种蛋白质工程任务。TourSynbio-Agent整合了蛋白质工程领域此前互不相连的深度学习模型，提供了统一的对话式用户界面以提升可用性。最后，我们通过在香草兰关键酶修饰和类固醇化合物催化方面的两项湿实验案例研究，证明了TourSynbio-7B和TourSynbio-Agent的有效性。
英文摘要：
The structural similarities between protein sequences and natural languages have led to parallel advancements in deep learning across both domains. While large language models (LLMs) have achieved much progress in the domain of natural language processing, their potential in protein engineering remains largely unexplored. Previous approaches have equipped LLMs with protein understanding capabilities by incorporating external protein encoders, but this fails to fully leverage the inherent similarities between protein sequences and natural languages, resulting in sub-optimal performance and increased model complexity. To address this gap, we present TourSynbio-7B, the first multi-modal large model specifically designed for protein engineering tasks without external protein encoders. TourSynbio-7B demonstrates that LLMs can inherently learn to understand proteins as language. The model is post-trained and instruction fine-tuned on InternLM2-7B using ProteinLMDataset, a dataset comprising 17.46 billion tokens of text and protein sequence for self-supervised pretraining and 893K instructions for supervised fine-tuning. TourSynbio-7B outperforms GPT-4 on the ProteinLMBench, a benchmark of 944 manually verified multiple-choice questions, with 62.18% accuracy. Leveraging TourSynbio-7B's enhanced protein sequence understanding capability, we introduce TourSynbio-Agent, an innovative framework capable of performing various protein engineering tasks, including mutation analysis, inverse folding, protein folding, and visualization. TourSynbio-Agent integrates previously disconnected deep learning models in the protein engineering domain, offering a unified conversational user interface for improved usability. Finally, we demonstrate the efficacy of TourSynbio-7B and TourSynbio-Agent through two wet lab case studies on vanilla key enzyme modification and steroid compound catalysis.
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