评估基于嵌入的蛋白质序列对齐与集群和双动态编程对于远程同类学的重要性
Robert Spicer1, Nilanjana Raychawdhary2, Shivaram Danwada1
1Department of Computer Science & Computer Information Systems, Auburn University at Montgomery, Goodwyn Hall, 7400 East Dr, Montgomery, AL, 36117, USA.
Scientific reports
|November 12, 2025
概括
这项研究引入了一种改进的蛋白质序列对齐方法,使用蛋白质语言模型嵌入,K-means集群和双动态编程. 这种精细的方法增强了远程同质检测,优于现有的方法.
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
- 结构生物信息学 结构生物信息学
背景情况:
- 精确的蛋白质序列同质检测对于进化和功能分析至关重要.
- 传统方法在"暮色区" (相似度为20-35%) 中与远程同质性作斗争.
- 蛋白质语言模型 (pLMs) 显示出改善远程同质检测的前景.
研究的目的:
- 评估一种基于嵌入的新型序列对齐方法,用于增强远程同质检测.
- 从目前基于pLM的对齐方法来解决相似度矩阵中的噪声.
- 为了提高识别蛋白质之间的远程进化关系的准确性.
主要方法:
- 开发了一种基于嵌入的序列对齐方法.
- 纳入的K-意味着集群,以完善残留水平的嵌入相似性.
- 使用双动态编程 (DDP) 进行精细对齐.
- 根据远程同类检测的既定基准评估性能.
主要成果:
- 这种结合聚类和DDP的新方法始终改善了远程同质检测.
- 实验结果显示,与传统的基于序列的方法相比,性能优越.
- 该方法的性能优于现有的基于嵌入的最先进的对齐技术.
- 在识别具有较低序列相似性的蛋白质方面取得了显著的收益.
结论:
- 基于嵌入的对齐,通过聚类和DDP进行精细化,是远程同质检测的强大策略.
- 这种方法比目前的方法有了显著的进步,特别是在黄昏区.
- 这项工作为进一步进步提供了基础,随着蛋白质语言模型的发展.
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