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A Practical Guide to Phylogenetics for Nonexperts
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蛋白质语言模型是否可以学习原生学?
Sanjana Tule1, Gabriel Foley1, Mikael Bodén1
1School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD 4072, Australia.
Briefings in bioinformatics
|February 23, 2025
概括
像ESM2这样的蛋白质语言模型 (pLMs) 可以从蛋白质序列中推断进化关系,并反映出经典的遗传学方法. 这些模型以分离的序列表现出色,并提供了对遗传学的补充方法,特别是对于复杂的进化历史.
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.
- 机器学习 机器学习
背景情况:
- 深度机器学习模型,特别是蛋白质语言模型 (pLMs),在分析蛋白质序列方面表现有前途.
- 经典的家族遗传树推断依赖于从序列数据中获得的进化关系.
- 机器学习与传统遗传学的整合是一个新兴的研究领域.
研究的目的:
- 评估蛋白质语言模型 (pLMs) 在没有明确培训的情况下辨别家族遗传关系的能力.
- 为了比较pLMs (ESM2,ProtTrans,MSA-Transformer) 与经典的遗传学方法的性能.
- 为了研究序列插入和删除 (indels) 对类遗传学分析中的pLM性能的影响.
主要方法:
- 在114个Pfam数据集上评估ESM2,ProtTrans和MSA-Transformer.
- 与已建立的遗传学推断技术进行比较.
- 对不同级别的序列插入和删除 (indels) 的性能分析.
主要成果:
- 最大的ESM2模型在恢复跨不同数据集和indel级别的家族遗传关系方面表现出卓越的表现.
- pLMs通常与古典方法一致,在蛋白质家族中观察到更高的一致性,这些蛋白质家族表现出更少的indels.
- pLM捕捉了更广泛的进化关系,ESM2在分析高度分歧的序列方面表现出特别强大的力量.
结论:
- 蛋白质语言模型,特别是ESM2,可以有效地推断进化关系,并作为传统遗传学方法的宝贵补充.
- 序列内涵代表了影响基于pLM和古典遗传学方法之间的差异的一个关键因素.
- 在pLM中,仅有一小部分神经元就足以近似测定基因学距离,这表明了进化信息的高效表现.

