评估预训练的蛋白质语言模型嵌入作为功能相似性的代理
Robert Shaw1, Samuel D Love1, Claire D McWhite2
1Department of Molecular and Cellular Biology, The University of Arizona, Tucson, AZ, USA.
Journal of molecular evolution
|November 22, 2025
概括
蛋白质语言模型 (PLMs) 生成数值嵌入,可以表示蛋白质结构和功能. 这些嵌入显示了检测微妙的功能差异和与功能保存相关的潜力.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 蛋白质科学 蛋白质科学
背景情况:
- 蛋白质语言模型 (PLM) 越来越多地用于蛋白质序列表示.
- 预训练的PLM嵌入有潜力作为蛋白质结构和功能的直接数值代理.
- 使用这些嵌入式不需要额外的培训或微调.
研究的目的:
- 探索预训练的PLM嵌入的实用性,作为蛋白质结构和功能的直接数值代理.
- 评估PLM嵌入器使用跨物种互补三胞胎区分微妙的功能差异的能力.
- 调查嵌入相似性和功能性保护在 ортология关系中的相关性.
主要方法:
- 使用了ESM-C 600M PLM中的嵌入式.
- 应用聚合切片瓦斯斯坦嵌入来总结氨基酸嵌入.
- 运用最佳运输理论来比较嵌入分布.
- 分析了22个跨物种互补三胞胎的功能相似性.
- 探索了正统学关系和正统学假设.
主要成果:
- 聚合的切片瓦斯斯坦嵌入显示了对补充三胞胎中微妙的功能差异的高度歧视.
- 初步结果表明,嵌入相似性可能与ortologs的功能保存相关 (约. 在三分之二的案件中).
- 在对等序列分离时,在一对一的正义词和不对等词之间没有观察到嵌入相似性的明显差异.
结论:
- PLM嵌入显示了蛋白质功能分析和检测功能分歧的前景.
- 建议采用混合方法,将PLM与遗传学方法相结合,以提高同质检测和进化精度.
- 嵌入树与基因树不一致性被引入作为功能分歧检测的度量.
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