| Primary information |
|---|
| SALID | SAL_12865 |
| Biomarker name | Protein S100-A11 (Calgizzarin) (Metastatic lymph node gene 70 protein) (MLN 70) (Protein S100-C) (S100 calcium-binding protein A11) [Cleaved into: Protein S100-A11, N-terminally processed] |
| Biomarker Type | NA |
| Sampling Method | Adult |
| Collection Method | NA |
| Analysis Method | NA |
| Collection Site | Whole Saliva |
| Disease Category | Healthy |
| Disease/Condition | Healthy |
| Disease Subtype | NA |
| Fold Change/ Concentration | NA |
| Up/Downregulated | NA |
| Exosomal | NA |
| Organism | Homo sapiens |
| PMID | 16103422 |
| Year of Publication | 2005 |
| Biomarker ID | P31949 |
| Biomarker Category | Protein |
| Sequence | MAKISSPTETERCIESLIAVFQKYAGKDGYNYTLSKTEFLSFMNTELAAFTKNQKDPGVLDRMMKKLDTNSDGQLDFSEFLNLIGGLAMACHDSFLKAVPSQKRT |
| Title of study | A catalogue of human saliva proteins identified by free flow electrophoresis-based peptide separation and tandem mass spectrometry |
| Abstract of study | Human saliva has great potential for clinical disease diagnostics. Constructing a comprehensive catalogue of saliva proteins using proteomic approaches is a necessary first step to identifying potential protein biomarkers of disease. However, because of the challenge presented in cataloguing saliva proteins with widely varying abundance, new proteomic approaches are needed. To this end, we used a newly developed approach coupling peptide separation using free flow electrophoresis with linear ion trap tandem mass spectrometry to identify proteins in whole human saliva. We identified 437 proteins with high confidence (false positive rate below 1%), producing the largest catalogue of proteins from a single saliva sample to date and providing new information on the composition and potential diagnostic utility of this fluid. The statistically validated, transparently presented, and annotated dataset provides a model for presenting large scale proteomic data of this type, which should facilitate better dissemination and easier comparisons of proteomic datasets from future studies in saliva. |