自動化蛋白質定量系統 Automatic Protein Quantitation System 生物資訊實驗室 計畫主持人 許聞廉 特聘研究員 宋定懿 研究員 Relative quantitative proteomics Labeling Label-free MS MS/MS 14 N / 15 N SILAC ICAT 16 O / 18 O iTRAQ TM IDEAL-Q MaXIC-Q We have developed the following software to support various experiments Multi-Q MS-based shotgun proteomics M G M K N V Q W E D S L G G L L V W G M G E G A I H R V E D VA G G Q E V L F L K T P H E G E L K F D K F K HL K E S D M E K K H A S ED L K A T H N G V L T L G G I L K K F G E L G Q PVIK Q S A H G L H E A E L T P H A T K I Q V L Q S Y E A E L F K I I S R F A L EL G D F P G A H M Q S G D A A K N M D AAK Y K Digested peptides Digestion Proteins LC Separation MS(MS/MS) MS, MS/MS spectrum State A State B Peptide identification LC-MS data Protein & Peptide ID list Protein 1…….. Peptide 1…… Peptide 25…… Peptide 65…… Protein 2…….. Peptide 25…… Peptide 65…… Protein 1…….. Peptide 1…… Peptide 25…… Peptide 65…… Protein 2…….. Peptide 25…… Peptide 65…… Elution time m/z MS/MS spectra Protein & Peptide ratio Protein 1……..0.5 Peptide 1……0.3 Peptide 25……0.6 Peptide 65……0.7 Protein 2……..1.2 Peptide 25……1.3 Peptide 65……1.2 Protein 1……..0.5 Peptide 1……0.3 Peptide 25……0.6 Peptide 65……0.7 Protein 2……..1.2 Peptide 25……1.3 Peptide 65……1.2 XIC of State A XIC of State B MS analysis Protein 5…….. Peptide 1…… Peptide 25…… Peptide 65…… Protein 2…….. Peptide 25…… Peptide 65…… Protein 5…….. Peptide 1…… Peptide 25…… Peptide 65…… Protein 2…….. Peptide 25…… Peptide 65…… Alignment and cross assignment … Workflow of label-free quantitation approach  Multi-Q: (JPR 2006; NAR 2007.)  MaXIC-Q: (NAR 2009)  IDEAL-Q: (MCP 2009)  Challenges 1.Peptide elutes at different time across experiments 2.peptide identified randomly across experiments IDEAL-Q Purpose: Provide a automated quantitation software tool for label- free proteomics analysis. Methods: IDEAL algorithm to predict the elution time of peptides and SCI peptide validation to accurately profile protein expression. Results: IDEAL-Q can accurately quantify protein expressions and significantly increase the number of quantifiable proteins/peptides. Purpose: Provide a automated quantitation software tool for label- free proteomics analysis. Methods: IDEAL algorithm to predict the elution time of peptides and SCI peptide validation to accurately profile protein expression. Results: IDEAL-Q can accurately quantify protein expressions and significantly increase the number of quantifiable proteins/peptides. IDEAL-Q is a fully automated software tool for label-free quantitation analysis. Unlike other label-free programs, IDEAL-Q uses an efficient algorithm, called IDEAL (ID-based Elution time Alignment based on Linear regression), to predict the elution time of unidentified peptides (i.e., peptides unidentified in this LC-MS run, but identified in other LC-MS runs). It also utilizes computational and statistical methods to evaluate the validity of detected peptide clusters and then quantify validated peptide clusters. IDEAL-Q is designed to adapt to different fractionation strategies for correct quantitation analysis. Furthermore, it provides graphical visualizations of peptide/protein information and statistical charts for quantitation results. Elution time prediction XIC and PIMS constructions SCI validation (based on PIMS) Peptide fraction/sample abundance The developed tools Please visit the developed tools: