通过基于同类学和表示的层次聚类来探索大蛋白序列空间
John Z Chen1,2, Barnabas Gall1,3, Sacha B Pulsford1,3
1Research School of Chemistry, Australian National University, Canberra, Australia.
Molecular biology and evolution
|June 4, 2025
概括
我们开发了一个可扩展的蛋白质序列分析管道,以探索蛋白质序列功能关系. 我们的方法使用分层可视化和蛋白质语言模型来改进同质检测,帮助科学发现.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- 探索蛋白质序列空间对于理解蛋白质的功能和关系至关重要.
- 传统的序列相似性网络面临着可扩展性和层次同样性可视化方面的局限性.
- 目前的方法难以分析非常大的蛋白质序列数据集.
研究的目的:
- 提出一个创新的序列分析管道,解决传统方法的局限性.
- 为了使大蛋白序列数据集的可扩展探索.
- 为了增强对蛋白质序列功能关系的理解.
主要方法:
- 开发了一种对同类学的层次可视化方法.
- 利用蛋白质语言模型嵌入作为BLAST的替代同类度量.
- 使用HMM或矢量表示来进行无偏向的代表性序列采样.
- 将管道应用于FMN/F420结合式分裂桶和核运输因子2类超级家族.
主要成果:
- 层次可视化捕捉了蛋白质超级家族的完整同质范围.
- 蛋白质语言模型嵌入提供了与BLAST相似的结果,用于识别异功能家族.
- 无偏的序列采样改善了家族遗传学分析.
- 该管道可扩展到桌面计算机上的大约445,000个序列.
结论:
- 开发的管道为探索大型蛋白质序列数据集提供了一个可扩展的解决方案.
- 视觉化和同质度量的创新增强了蛋白质序列功能分析.
- 公开可用的代码 (ProteinClusterTools) 促进了更广泛的研究应用.
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