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PC_ali:基于混合蛋白序列和结构相似度得分的改进多重对齐和进化推断的工具
Ugo Bastolla1, David Abia2, Oscar Piette1
1Centro de Biologia Molecular "Severo Ochoa" (CBMSO), CSIC-UAM Cantoblanco, 28049 Madrid, Spain.
Bioinformatics (Oxford, England)
|October 17, 2023
概括
对蛋白质进行准确的进化推断需要强大的多重序列对齐 (MSA). 一个新的混合评分,PC_sim,及其衍生工具,PC_ali,通过整合序列和结构数据来提高MSA质量,以便更好地分析进化分歧.
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
- 进化生物学 进化生物学
背景情况:
- 多个序列对齐 (MSA) 对于进化推断至关重要,但对于远距离相关的蛋白质来说具有挑战性.
- 蛋白质结构比序列更保守,这表明基于结构的对齐可以帮助远程同类分析.
- 现有的相似性测量通常缺乏对蛋白质保存的完整,公正的看法.
研究的目的:
- 为蛋白质序列和结构相似性开发一种新的混合评分.
- 创建一个工具 (PC_ali) 来构建改进的多重序列对齐 (MSA).
- 为了提高对蛋白质的进化分歧推断的准确性.
主要方法:
- 检查了四个基于序列和结构的蛋白质相似度.
- 使用主要组件分析开发了一种混合相似度 (PC_sim) 和分歧度 (PC_div).
- 使用PC_sim和PC_div.实现了PC_ali程序,用于使用PC_sim和PC_div.进行新的或修改的MSA构建.
主要成果:
- PC_sim和PC_div显示出与进化差异有很强的相关性.
- 与最先进的工具相比,PC_ali实现了更高的对齐分数和结构得分.
- PC_ali与参考MSA具有很高的相似性,并改善了序列识别分数.
结论:
- PC_sim评分和PC_ali工具为蛋白质MSA结构提供了更准确的方法.
- 通过PC_sim集成序列和结构信息可以增强进化推理.
- PC_ali代表了进化分析的生物信息学工具的重大进步.
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