Using enrichment index for quality control of secretory protein sample and identification of secretory proteins.
Analysis of secretory proteins is an important area in proteomic research. We propose that a good secretory protein sample should be enriched with known secretory proteins, and a secretory protein should be enriched in the secretory protein sample compared with its corresponding soluble cell lysate. Positive identifications of proteins were subjected to quantitation of spectral counts, which reflect relative protein abundance. Enrichment index of the sample (EIS) and the enrichment index for protein (EIP) were obtained by comparing proteins identified in the secretory protein sample and those in the soluble cell lysate sample. The quality of the secretory protein sample can be represented by EIS. EIP was used to identify the secretory proteins.The secretory proteins from mouse dendritic cell sarcoma (DCS) were analyzed by MS. The EISs of two samples were 75.4 and 84.65, respectively. 72 proteins were significantly enriched in secretory protein samples, of which 42 proteins were either annotated in Swiss-Prot and/or predicted by signal peptides to be secretory. In the remaining 30 proteins, 12 and 15 proteins were positively predicted by SecretomeP and ProP, respectively, and 5 proteins were positive by both methods. Furthermore, 11 proteins were found to be present in exosome in other studies that involved mice dendritic cell lines. We suggest that this assessment method is helpful for systemic research of secretory proteins and biomarker discovery for diseases such as cancer.
Chen
Yong
Y
Department of Physiology and Pathophysiology, Core Instrument Facility, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences/Peking Union Medical College, Tsinghua University, Beijing, China.
Gu
Bei
B
Wu
Shuzhen
S
Sun
Wei
W
Ma
Sucan
S
Liu
Yuqin
Y
Gao
Youhe
Y
D000818:Animals; D045744:Cell Line, Tumor; D002853:Chromatography, Liquid; D030562:Databases, Protein; D005189:False Positive Reactions; D051379:Mice; D021382:Protein Sorting Signals; D011506:Proteins / Q000737:chemistry / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D012984:Software; D012995:Solubility; D013223:Statistics as Topic / Q000379:methods*; D053719:Tandem Mass Spectrometry
D021382:Protein Sorting Signals; D011506:Proteins