five

Improvement of Peptide Separation for Exploring the Missing Proteins Localized on Membranes

收藏
Figshare2018-10-26 更新2026-04-29 收录
下载链接:
https://figshare.com/articles/dataset/Improvement_of_Peptide_Separation_for_Exploring_the_Missing_Proteins_Localized_on_Membranes/7259078
下载链接
链接失效反馈
官方服务:
资源简介:
Following an enormous effort by the global scientific community coordinated by HUPO’s Human Proteome Project, the number of proteins without high-quality MS or other evidence (colloquially termed missing proteins) has substantially decreased; however, some highly hydrophobic MPs remain on the list. We believe that efficient peptide separation is an approach that can be used to improve the identification of these hydrophobic MPs. We propose that peptides prepared from the membrane fractions of human cell lines and placental tissue can be well separated from hydrophilic peptides in organic solvents at high concentrations due to the precipitation of hydrophilic peptides with lower solubility. Using a combination strategy of peptide separation in 98% acetonitrile prior to traditional 2D reverse-phase liquid chromatography, more hydrophobic peptides were detected in the supernatants of the organic solvent extractions than were found in the pellets. When this strategy was adopted, 30 MPs (≥2 non-nested unique peptides with ≥9 amino acids) with 114 unique peptides were identified at protein false discovery rate (FDR) < 1%, including 7, 12, and 13 MPs obtained from membrane preparations derived from K562, HeLa cells, and human placenta, respectively. Of the 30 MPs identified in this study, 19 were categorized as membrane proteins or extracellular matrix proteins. Furthermore, 20 were verified to possess two non-nested unique peptides through parallel reaction monitoring with the corresponding chemically synthesized peptides. The use of organic solvents at high concentrations was shown to be an efficient way to improve the exploration of hydrophobic MPs. The data obtained in this study are available via ProteomeXchange (PXD010630) and PeptideAtlas (PASS01218).
创建时间:
2018-10-26
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作