Data from proteome analysis of Lasiodiplodia theobromae (Botryosphaeriaceae)

来自毛色二孢菌(Botryosphaeriaceae)蛋白质组分析的数据

阅读:9
作者:Carla C Uranga, Majid Ghassemian, Rufina Hernández-Martínez

Abstract

Trunk disease fungi are a global problem affecting many economically important fruiting trees. The Botryosphaeriaceae are a family of trunk disease fungi that require detailed biochemical characterization in order to gain insight into their pathogenicity. The application of a modified Folch extraction to protein extraction from the Botryosphaeriaceae Lasiodiplodia theobromae generated an unprecedented data set of protein identifications from fragmentation analysis and de novo peptide sequencing of its proteome. This article contains data from protein identifications obtained from a database-dependent fragmentation analysis using three different proteomics algorithms (MSGF, Comet and X! Tandem via the SearchGUI proteomics pipeline program) and de novo peptide sequencing. Included are data sets of gene ontology annotations using an all-Uniprot ontology database, as well as a Saccharomyces cerevisiae-only and a Candida albicans-only ontology database, in order to discern between those proteins involved in common functions with S. cerevisiae and those in common with the pathogenic yeast C. albicans. Our results reveal the proteome of L. theobromae contains more ontological categories in common to C. albicans, yet possesses a much wider metabolic repertoire than any of the yeasts studied in this work. Many novel proteins of interest were identified for further biochemical characterization and annotation efforts, as further discussed in the article referencing this article (1). Interactive Cytoscape networks of molecular functions of identified peptides using an all-Uniprot ontological database are included. Data, including raw data, are available via ProteomeXchange with identifier PXD005283.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。