Decoding viral protein sequences by large language models

Tianyi Fei

doi:10.1093/bib/bbag377

2026-07-17

DOI：10.1093/bib/bbag377

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摘要

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大型语言模型（LLMs）正应用于生物序列，推动计算生物学发展，有助于深入理解蛋白质和核苷酸序列数据。近期模型包括ESM2、ESM3、AlphaGenome、Evo-1和Evo-2等，通过学习高维隐含表征以捕捉进化约束、结构模式和功能基序，将自然语言处理原理应用于生物领域。本文综述了这些模型的研发与应用进展，重点强调病毒蛋白分析。我们重点介绍了利用基于序列的LLMs（即蛋白质语言模型，PLMs）在病毒蛋白注释、变异效应预测和免疫逃逸表征等重要应用任务中的研究。此外，我们对这些前沿蛋白质语言模型进行了基准评估，以评价其捕捉病毒蛋白序列间进化关系的基本能力。通过讨论PLMs的机遇与挑战，本文为LLMs在赋能病毒学研究及病原体监测的潜在应用提供了路线图。

期刊

Briefings in Bioinformatics

IF:

7.7

论文数:

5.8K

被引数:

2.7W

机构

Guangzhou Medical University

学者数:

632

论文数: 164

被引数: 0

guangzhou national laboratory

学者数:

论文数: 17

被引数: 0

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