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Published on: June 28, 2019
Improvement of Peptide Separation for Exploring the Missing Proteins Localized on Membranes
Yuanliang Zhang1,2, Zhilong Lin1,2, Piliang Hao3
1BGI-Shenzhen , Beishan Industrial Zone 11th building , Yantian District, Shenzhen , Guangdong 518083 , China.
Abstract:
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).
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