用分层方法对蛋白质功能家族进行大规模聚类。

IF 4.5 3区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Protein Science Pub Date : 2024-09-01 DOI:10.1002/pro.5140
Nicola Bordin, Harry Scholes, Clemens Rauer, Joel Roca-Martínez, Ian Sillitoe, Christine Orengo
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引用次数: 0

摘要

蛋白质是细胞活动的基础,通过其结构和序列揭示其功能和进化。CATH 功能家族(FunFams)是蛋白质结构域序列的连贯群集,其成员之间的功能是一致的。随着 MGnify 或 AlphaFold 数据库等大型资源库带来的蛋白质数据量和复杂性的不断增加,需要更强大的方法来扩展这些新资源的规模。在这项工作中,我们介绍了 MARC 和 FRAN,这两种算法是在 GeMMA/FunFHMMER 的基础上开发的,并解决了 GeMMA/FunFHMMER 的局限性。我们还介绍了 CATH-eMMA,它使用嵌入或 Foldseek 距离从距离矩阵中形成关系树,从而降低了计算要求并有效处理各种数据类型。CATH-eMMA 为蛋白质功能的大规模聚类提供了一种高度稳健且速度更快的工具,为未来蛋白质功能和进化研究提供了一种新工具。
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Clustering protein functional families at large scale with hierarchical approaches.

Proteins, fundamental to cellular activities, reveal their function and evolution through their structure and sequence. CATH functional families (FunFams) are coherent clusters of protein domain sequences in which the function is conserved across their members. The increasing volume and complexity of protein data enabled by large-scale repositories like MGnify or AlphaFold Database requires more powerful approaches that can scale to the size of these new resources. In this work, we introduce MARC and FRAN, two algorithms developed to build upon and address limitations of GeMMA/FunFHMMER, our original methods developed to classify proteins with related functions using a hierarchical approach. We also present CATH-eMMA, which uses embeddings or Foldseek distances to form relationship trees from distance matrices, reducing computational demands and handling various data types effectively. CATH-eMMA offers a highly robust and much faster tool for clustering protein functions on a large scale, providing a new tool for future studies in protein function and evolution.

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来源期刊
Protein Science
Protein Science 生物-生化与分子生物学
CiteScore
12.40
自引率
1.20%
发文量
246
审稿时长
1 months
期刊介绍: Protein Science, the flagship journal of The Protein Society, is a publication that focuses on advancing fundamental knowledge in the field of protein molecules. The journal welcomes original reports and review articles that contribute to our understanding of protein function, structure, folding, design, and evolution. Additionally, Protein Science encourages papers that explore the applications of protein science in various areas such as therapeutics, protein-based biomaterials, bionanotechnology, synthetic biology, and bioelectronics. The journal accepts manuscript submissions in any suitable format for review, with the requirement of converting the manuscript to journal-style format only upon acceptance for publication. Protein Science is indexed and abstracted in numerous databases, including the Agricultural & Environmental Science Database (ProQuest), Biological Science Database (ProQuest), CAS: Chemical Abstracts Service (ACS), Embase (Elsevier), Health & Medical Collection (ProQuest), Health Research Premium Collection (ProQuest), Materials Science & Engineering Database (ProQuest), MEDLINE/PubMed (NLM), Natural Science Collection (ProQuest), and SciTech Premium Collection (ProQuest).
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