在已知的蛋白质宇宙的规模上对预测结构进行聚类
Inigo Barrio-Hernandez1, Jingi Yeo2, Jürgen Jänes3
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK.
Nature
|September 13, 2023
概括
这项研究介绍了Foldseek集群,这是一个集群数百万个蛋白质结构的高效算法. 它在AlphaFold数据库中揭示了许多新的蛋白质结构, 帮助进化和功能研究.
科学领域:
- 结构生物学
- 生物信息学
- 计算生物学
背景情况:
- 蛋白质是细胞过程的基础,理解它们的结构对于功能和进化至关重要.
- 现在AlphaFold数据库包含超过21400万个预测的蛋白质结构,需要有效的分析方法.
- 在如此大规模的蛋白质结构研究需要先进的计算工具.
研究的目的:
- 开发和应用一个高效的算法来聚类大规模的蛋白质结构数据.
- 在AlphaFold数据库中识别新型蛋白质结构并分析它们的进化起源.
- 证明结构比较对预测蛋白质域家族和远程同质性的有用性.
主要方法:
- 开发Foldseek集群,一个基于结构调整的集群算法.
- 在AlphaFold数据库中的所有结构的集群.
- 已识别的结构集群的进化分析.
- 识别远程结构相似性和潜在的新型蛋白质家族.
主要成果:
- 通过对2140万个蛋白质结构进行聚类,发现了230万个非单元结构聚类.
- 31%的这些群体缺乏注释,表明潜在的新型蛋白质结构.
- 进化分析表明大多数群是古老的,其中4%是特定物种的.
- 确定了远程的结构相似性,包括人类免疫蛋白和 prokaryotic 物种之间的潜在联系.
结论:
- Foldseek集群为分析大型蛋白质结构数据集提供了一种有效的方法.
- 这项研究揭示了大量未经注释的蛋白质结构, 扩大了我们对蛋白质多样性的了解.
- 结构比较提供了对不同物种蛋白质进化,功能和同质性的宝贵见解.
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