利用大语言模型设计蛋白质：增强效果与对比分析
Design Proteins Using Large Language Models: Enhancements and Comparative Analyses
AI总结：
本研究利用42000条人类蛋白质序列微调四款公开预训练LLM，在有限数据下实现了与百万级数据训练的专用蛋白质生成模型相当的性能，并公开模型以推动计算生物学发展。
摘要：
预训练大语言模型（LLM）已在摘要生成、实体识别等一系列传统自然语言处理（NLP）任务中展现出强大能力。本文探究了LLM在生成高质量蛋白质序列方面的应用。具体而言，我们采用了一套预训练LLM，包括Mistral-7B、Llama-2-7B、Llama-3-8B和gemma-7B，来生成有效的蛋白质序列，所有这些模型均为公开可用。与该领域以往的研究不同，我们的方法使用了一个规模相对较小的数据集，其中包含42000条不同的人类蛋白质序列。我们对这些模型进行再训练以处理蛋白质相关数据，确保生成具有生物学可行性的蛋白质结构。研究结果表明，即便数据有限，经过适配的模型仍展现出与成熟的蛋白质专用模型（如ProGen系列、ProtGPT2和ProLLaMA，这些模型均在数百万条蛋白质序列上训练）相当的效率。为验证并量化模型的性能，我们采用pLDDT、RMSD、TM-score和REU等标准指标开展了对比分析。此外，我们承诺将所有四个模型的训练后版本公开，以促进计算生物学领域更高的透明度与协作。
英文摘要：
Pre-trained LLMs have demonstrated substantial capabilities across a range of conventional natural language processing (NLP) tasks, such as summarization and entity recognition. In this paper, we explore the application of LLMs in the generation of high-quality protein sequences. Specifically, we adopt a suite of pre-trained LLMs, including Mistral-7B1, Llama-2-7B2, Llama-3-8B3, and gemma-7B4, to produce valid protein sequences. All of these models are publicly available.5 Unlike previous work in this field, our approach utilizes a relatively small dataset comprising 42,000 distinct human protein sequences. We retrain these models to process protein-related data, ensuring the generation of biologically feasible protein structures. Our findings demonstrate that even with limited data, the adapted models exhibit efficiency comparable to established protein-focused models such as ProGen varieties, ProtGPT2, and ProLLaMA, which were trained on millions of protein sequences. To validate and quantify the performance of our models, we conduct comparative analyses employing standard metrics such as pLDDT, RMSD, TM-score, and REU. Furthermore, we commit to making the trained versions of all four models publicly available, fostering greater transparency and collaboration in the field of computational biology.
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