SILAC-based quantitative proteomics (SILAQ)

1. SILAC-based Proteomics Analysis SILAC-based quantitative proteomics (SILAQ) is an innovative technology used in high throughput quantitative analysis of large protein complexes, protein-protein and protein-small molecule interactions. SILAC service of Creative Proteomics provides an unbiased strategy that can reveal how specifically either inhibitors, or other perturbations, affect the dynamic properties and cellular distributions of proteins. It can also be used as a sensitive and effective method to determine the specific interaction partners of proteins in the cell. SILAC Workflow: SILAC relies on metabolic incorporation of a given 'light' or 'heavy' form of the amino acid into two samples. As the two isotopically labeled amino acids are essentially chemically identical, their incorporation does not interfere with normal cell growth, while leading to proteins/peptides that are distinguishable by mass and thus are ideal for mass spectrometric analysis. The method relies on the incorporation of amino acids with substituted stable isotopic nuclei. Thus in an experiment, two cell populations are grown in culture media that are identical except that one of them contains a 'light' and the other a 'heavy' form of a particular amino acid (e. g. 12C and 13C labeled L-lysine, respectively). The SILAC samples are then subjected to enzymatic digestion and LC/MS analysis. The protein quantification is carried out on the protein level. In addition, SILAC approaches are well suited for monitoring

2. changes in post-translational modifications. Examples for these applications include the measurement of changes in protein phosphorylation and methylation. Advantage of our SILAC technique: Cutting-edge facilities & optimized protocols Harmless in vivo labeling Compare up to three samples at one time High sensitivity Suitable for study of protein-protein-interaction Post-translational modification is detectable Bioinformatic Analysis: Functional annotation and enrichment analysis Clustering analysis Network analysis Statistical analysis          