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Propedia: a database for protein–peptide identification based on a hybrid clustering algorithm
BMC Bioinformatics ( IF 3 ) Pub Date : 2021-01-02 , DOI: 10.1186/s12859-020-03881-z
Pedro M Martins 1 , Lucianna H Santos 1 , Diego Mariano 1 , Felippe C Queiroz 2 , Luana L Bastos 1 , Isabela de S Gomes 2 , Pedro H C Fischer 3 , Rafael E O Rocha 1 , Sabrina A Silveira 2 , Leonardo H F de Lima 3 , Mariana T Q de Magalhães 4 , Maria G A Oliveira 5 , Raquel C de Melo-Minardi 1
Affiliation  

Protein–peptide interactions play a fundamental role in a wide variety of biological processes, such as cell signaling, regulatory networks, immune responses, and enzyme inhibition. Peptides are characterized by low toxicity and small interface areas; therefore, they are good targets for therapeutic strategies, rational drug planning and protein inhibition. Approximately 10% of the ethical pharmaceutical market is protein/peptide-based. Furthermore, it is estimated that 40% of protein interactions are mediated by peptides. Despite the fast increase in the volume of biological data, particularly on sequences and structures, there remains a lack of broad and comprehensive protein–peptide databases and tools that allow the retrieval, characterization and understanding of protein–peptide recognition and consequently support peptide design. We introduce Propedia, a comprehensive and up-to-date database with a web interface that permits clustering, searching and visualizing of protein–peptide complexes according to varied criteria. Propedia comprises over 19,000 high-resolution structures from the Protein Data Bank including structural and sequence information from protein–peptide complexes. The main advantage of Propedia over other peptide databases is that it allows a more comprehensive analysis of similarity and redundancy. It was constructed based on a hybrid clustering algorithm that compares and groups peptides by sequences, interface structures and binding sites. Propedia is available through a graphical, user-friendly and functional interface where users can retrieve, and analyze complexes and download each search data set. We performed case studies and verified that the utility of Propedia scores to rank promissing interacting peptides. In a study involving predicting peptides to inhibit SARS-CoV-2 main protease, we showed that Propedia scores related to similarity between different peptide complexes with SARS-CoV-2 main protease are in agreement with molecular dynamics free energy calculation. Propedia is a database and tool to support structure-based rational design of peptides for special purposes. Protein–peptide interactions can be useful to predict, classifying and scoring complexes or for designing new molecules as well. Propedia is up-to-date as a ready-to-use webserver with a friendly and resourceful interface and is available at: https://bioinfo.dcc.ufmg.br/propedia

中文翻译:

Propedia:基于混合聚类算法的蛋白质-肽鉴定数据库

蛋白质-肽相互作用在多种生物过程中发挥着重要作用,例如细胞信号传导、调节网络、免疫反应和酶抑制。肽的特点是毒性低、界面面积小;因此,它们是治疗策略、合理药物规划和蛋白质抑制的良好靶点。大约 10% 的道德药品市场是基于蛋白质/肽的。此外,据估计 40% 的蛋白质相互作用是由肽介导的。尽管生物数据量迅速增加,特别是序列和结构方面的数据,但仍然缺乏广泛且全面的蛋白质肽数据库和工具,无法检索、表征和理解蛋白质肽识别,从而支持肽设计。我们推出 Propedia,这是一个全面且最新的数据库,具有网络界面,允许根据不同的标准对蛋白质-肽复合物进行聚类、搜索和可视化。Propedia 包含来自蛋白质数据库的 19,000 多个高分辨率结构,包括来自蛋白质-肽复合物的结构和序列信息。与其他肽数据库相比,Propedia 的主要优点是它允许对相似性和冗余进行更全面的分析。它是基于混合聚类算法构建的,该算法根据序列、界面结构和结合位点对肽进行比较和分组。Propedia 可通过图形化、用户友好的功能性界面使用,用户可以在其中检索和分析复合物并下载每个搜索数据集。我们进行了案例研究,并验证了 Propedia 评分对有前景的相互作用肽进行排名的效用。在一项涉及预测抑制 SARS-CoV-2 主要蛋白酶的肽的研究中,我们表明与 SARS-CoV-2 主要蛋白酶的不同肽复合物之间的相似性相关的 Propedia 评分与分子动力学自由能计算一致。Propedia 是一个数据库和工具,支持基于结构的特殊用途肽的合理设计。蛋白质-肽相互作用可用于预测、分类和评分复合物或设计新分子。Propedia 是一款最新的即用型网络服务器,具有友好且资源丰富的界面,可从以下网址获取:https://bioinfo.dcc.ufmg.br/propedia
更新日期:2021-01-02
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