Informatics on MS-Based Proteomics

Transcription

1 Informatics on MS-Based Proteomics Yet-Ran Chen 陳逸然 Associate Research Fellow / Associate Professor Agricultural Biotech. Research Center / Institute of Biotechnology & Genomics and Systems Biology Academia Sinica / National Taiwan University 2015/11/19 Yet-Ran Chen 陳逸然 Assistant Research Fellow / Assistant Professor Agricultural Biotech. Research Center / Institute of Biotechnology & Genomics and Systems Biology Academia Sinica / National Taiwan University Study of the Cellular Signaling Events using MS-Based OMICS Approach Elucidation of Important Intracellular Signaling Events Spermatozoa Capacitation T-Cell Early Signaling Peptide Hormone Signaling Characterization of Novel Extracellular Signaling Molecules Involved in the Development and Stress Defense Peptide Hormone Signaling in Stress Tolerance Biology Technical Development to Facilitate the Research Strategy for OMICs Study Proteomics / Metabolomics Membrane Proteomics Protein PTMs Signaling Peptide Identification MS-Based OMICs New OMICs Quantitation Strategy In-Situ Quantitation / Imaging Bioinformatics for High-Throughput OMICs Signal Processing / Spectra Interpretation Platform Quantitation 1

2 Protein Analysis in Plasma J. Biomed. Biotech Biological Threads Cause Significant Crop Loss The Major Crop Loss in World Wide is Due to Disease Cause ~ % Crop Loss Insect Cause ~ 15 % Crop Loss Weeds Cause ~ 15 % Crop Loss ~ 40% Crop Loss /Yr eq. to 800 Billion USD /Year Pesticide Usages (2007) Global ~2.5 Billion kg/yr $ 40 Billion /Yr US ~0.5 Billion kg/yr $ 12.5 Billion/Yr Source: 2011 US EPA report Pesticides Advances in Chemical and Botanical Pesticides (2012) DOP /

3 A Novel Peptide Produced from PR 1b was Named as CAPE1 (CAP Derived PEptide 1) CAP (cysteine rich secretory proteins, antigen 5, and pathogenesis related 1) Validation of Endogenous CAPE1 in Tomato Plant Cell (2014) October Issue 3

4 Tomato Plant Sprayed with 10 ml (A) Water or (B) 100 nm CAPE1 in Water for 2 hrs and Challenged by Pseudomonas syringae DC3000 (Pst DC3000, OD 600 =0.04 in % silwet ) (A) Water Pretreated (B) CAPE 1 Pretreated 1 g Crude Synthetic Peptide (> 90% Purity) ~ USD 200 ~ 9000 Liters of 100 nm Solution plants Agrochemical ~ 1000 ppm, CAPE1 ~ 10 4 ppm General OMICS Approach Biological Question Acquire Biological Sample Specific Physiological Status Experimental Control Collect the Specific Cells or Tissue etc.. Subcellular Fractionation Genomics Transcriptomics Proteomics Peptidomics DNA RNA Protein Metabolomics Transcription Translation Level and PTMs Biological Function 4

5 Current Tech. for the OMICS Research Genomics Transcriptomics Proteomics Peptidomics Metabolomics Nature Biotechnology Vol 26 No. 10, OCTOBER 2008 MS Based Approach What is Mass Spectrometry (MS)? A mass spectrometer is an instrument that measures the masses and abundances of individual molecules that have been converted to ions. MS measures the mass to charge ratio (m/z) of the ion. 5

6 Molecule Fragmentation Analysis using MS Structure Elucidation and Characterization Tandem Mass Spectrometry 6

7 The Merits of Mass Spectrometry 1. Compound Identification and Quantitation at Trace Level. 2. Good Orthogonality to Most of Chromatography Separation Methods (based on exact mass or feature fragmentation reaction). *GC/HRMS or GC/MS/MS has much higher resolving power than GC. 3. Improving Molecular Detection and Quantitation Specificity. 3. Speed of analysis * MS/MS vs GC/MS 4. Applicable to Most of Compound Types Ability of analyzing small as well as large molecules. Ability of analyzing nonpolar as well as polar molecules. Current Tech. for the OMICS Research Genomics Transcriptomics Proteomics Peptidomics Metabolomics Nature Biotechnology Vol 26 No. 10, OCTOBER 2008 MS Based Approach 7

8 The Proteomics Study Genomics Transcriptomics Proteomics DNA RNA Protein Post Translational Modifications ~3.3 billion BPs in Human 22,165 Genes (Aug 2009) > 220,000 Proteins 5x ~ 50x Functional Links Per Protein Proteins Measured Clinically in Plasma Span > 10 Orders of Magnitude in Abundance (199 proteins, literature values) Copyright 8

9 10 10 Really Is Wide Dynamic Range (Here on a linear scale) Copyright General Proteomics Approach Proteome Reduction of Complexity Analysis 9

10 10

11 Quantitative Approach for LC MS Based Approach Journal of Proteome Research 2008, 7, Protein Identification by Mass Spectrometry Peptide Fragmentation a,b,c N-terminal side x,y,z C-terminal side 11

12 MS/MS Ion Search I * Protein mixture II * Peptides Time (min) 1D, 2D, 3D chromatographic peptide separation Q1 Q2 CID Collision Cell Q m/z Tandem Mass Spectrum III Correlative sequence database searching Find Peptide Partial Sequence m/z Theoretical Protein identification m/z Acquired Protein identification Issues to be Considered * Q2 Q1 Collision Cell Selection of search algorithm (s) Selection of Search Param. interrogate protein/genomic DB Correlative sequence database searching m/z Theoretical Q m/z m/z Acquired Peptide ID Verification of Peptide ID Tandem mass spectrum Peak Extraction Protein ID Verification of Protein ID Apportionment of Proteins from ID peptides Evaluation of false positive rate of ID protein Degenerate peptide Degenerate peptides are more prevalent with databases of higher eukaryotes due to the presence of: related protein family members alternative splice forms partial sequences DB redundancy Single hit 12

13 Peak Extraction RKKR C24 H48 N12 O4 Abundance Profile /4/ :54:35 PM Simulation RKKR Profile Resolution: Daltons 0.25 at 5% height Charges 1 Chrg dist 0 Ions 5334 M in Ion Ab. 1e-020 M in Ions 5000 M ax Ions m/z RKKR 9/4/ :55:04 PM C24 H48 N12 O4 Centroid Simulation RKKR Centroid 15 Resolution: Daltons 0.25 at 5% height Charges 1 10 Chrg dist 0 Ions Min Ion Ab. 1e Min Ions 5000 Max Ions m/z Abundance How we extract the m/z info from raw spectra?? % 90 % % % +TO F P roduct (600.4): Experim ent 2, m in from BS A_050726_04.wiff a= e-004, t0 = e+001 M ax counts m/z, amu m /z, a m u 13

14 Matching Accuracy Using Different Centroid Settings 15 % Height 50 % Height 95 % Height Calibration without centroid (Calibrate by the Peak Apex) Calibration with centroid (Centroid Peak Height: 50%) (Can we use raw data??) Interpretation of Search Result SEQUEST: Xcorr > 2.0 C n > 0.1 sort by search score MASCOT: Score > 45 Threshold? 14

15 Distribution of Identification Score Distribution Curve Fitting (Gaussian or.) Significant Threshold 1 p = 0.05 Significant Threshold 2 p << 0.05 Random Hit Significant Hit? Selection of Search Algorithm MASCOT Probability Based MOWSE Scoring ASMS Workshop and User Meeting

16 The MASCOT Search Engine Probability-Based implementation of the MOWSE algorithm The total score is the absolute probability (P) that the observed score is a random evant The Mascot score is given as -10xlog(P) Score Distribution Small and Large Scale 1~10 proteins >100 proteins ASMS Workshop and User Meeting

17 Large Scale Proteomics Data Analysis incorrect hit --- Normal Distribution/Possein Fitting Mascot X!Tandem correct hit--- Statistical Model of Peptide Prophet entire dataset: Spectrum Peptide Score Spectrum 1 LGEYGH Spectrum 2 FQSEEQ Spectrum 3 FLYQE Spectrum N EIQKKF probability incorrect incorrect --- p=0.5 correct correct --- unsupervised learning EM mixture model algorithm learns the most likely distributions among correct and incorrect peptide assignments given the observed data Keller at al. Anal. Chem

18 The Decoy DB Decoy DB (Randomized or Reversed) Randomized PMF Applicable Peptide number changed after randomized (alter the trial number) Degree of Random Reversed Not suitable for PMF Real KQTALVELLKH K.QTALVELLK.H MW=1013 Reversed HKLLEVLATQK K.LLEVLATQK.- MW=1013 Not suitable for no enzyme restricted =-18 Testing the Significant in Protein Assignment 18

19 Search Against Normal + Decoy DB Threshold X Mascot v.s. SEQUEST Proteomics 2004, 4,

20 Probability Accuracy with Different Algorithms 1 Mascot Calculated Probability X!Tandem SEQUEST Combination Model Combination Model X!Tandem Model SEQUEST Model Mascot Model Ideal Actual Probability Brian C. Searle, Proteome Software MS-Based Proteomics: 2D v.s. shotgun Alexey Nesvizhskii, Institute of Systems Biology 20

21 Analysis of Complex Protein Mixture Evaluation of Protein Probability Anal. Chem.2003, 75,

22 Protein Assignment from Identified Peptide(s) Alexey Nesvizhskii, Institute of Systems Biology Apportionment of Degenerate Peptides using Peptide Prophet Alexey Nesvizhskii, Institute of Systems Biology 22

23 Example for the Protein Apportionment Alexey Nesvizhskii, Institute of Systems Biology Example for the Protein Apportionment Alexey Nesvizhskii, Institute of Systems Biology 23

24 Example for the Protein Apportionment Alexey Nesvizhskii, Institute of Systems Biology Example for the Protein Apportionment Alexey Nesvizhskii, Institute of Systems Biology 24

25 Example for the Protein Apportionment Alexey Nesvizhskii, Institute of Systems Biology Example for the Protein Apportionment Alexey Nesvizhskii, Institute of Systems Biology 25

26 Issues to be Considered * Q2 Q1 Collision Cell Selection of search algorithm (s) Selection of Search Param. interrogate protein/genomic DB Correlative sequence database searching m/z Theoretical Q m/z m/z Acquired Peptide ID Verification of Peptide ID Tandem mass spectrum Peak Extraction Protein ID Verification of Protein ID Apportionment of Proteins from ID peptides Evaluation of false positive rate of ID protein Degenerate peptide Degenerate peptides are more prevalent with databases of higher eukaryotes due to the presence of: related protein family members alternative splice forms partial sequences DB redundancy Single hit Peak Extraction RKKR C24 H48 N12 O4 Abundance Profile /4/ :54:35 PM Simulation RKKR Profile Resolution: Daltons 0.25 at 5% height Charges 1 Chrg dist 0 Ions 5334 M in Ion Ab. 1e-020 M in Ions 5000 M ax Ions m/z RKKR 9/4/ :55:04 PM C24 H48 N12 O4 Centroid Simulation RKKR Centroid 15 Resolution: Daltons 0.25 at 5% height Charges 1 10 Chrg dist 0 Ions Min Ion Ab. 1e Min Ions 5000 Max Ions m/z Abundance 26

27 Data Workflow for General MS Based Proteomics 6300 Series Ion Trap LC/MS amazon X microtof Q II LCT premire XE 6500 Series Accurate (Q TOF) LC/MS 6400 Series Triple Quadrupole LC/MS? Triple quadrupole solarixtm Maxis Xevo TQ MS Raw Data Loading??? Quantitation MS/MS Peak List Extraction Peptide Identification Peptide Validation Protein Validation Result Interpretation NATURE BIOTECHNOLOGY VOLUME 22 NUMBER 11 NOVEMBER 2004 The Common Data Format for the MS Data: mzxml 27

28 Proteomics Data Workflow for General MS Based Proteomics MS Raw Data amazon X microtof Q II LCT premire XE Converter for Different Instrumentations Triple quadrupole solarixtm Maxis Xevo TQ 6300 Series Ion Trap LC/MS mzxml / mzml Data Quantitation ASAPRatio, Libra, etc Series Triple 6500 Series Accurate Quadrupole LC/MS (Q TOF) LC/MS MS/MS Spectra Extraction Peptide Identification MASCOT SEQUEST etc. Peptide Validation Peptide Prophet etc. Protein Validation Protein Prophet etc. Result Interpretation GO, DAVID, etc. NATURE BIOTECHNOLOGY VOLUME 22 NUMBER 11 NOVEMBER 2004 Some of the MS Type Must Preprocessed Before Performing Proteomics Data Analysis Low Resolution Centroid MS Spectrum mzxml Data Analysis 28

29 Some of the MS Type Must Preprocessed Before Performing Proteomics Data Analysis MS Type 1 MS Type 2 MS Type 3 mzxml Data Analysis Proteomics Data Workflow for General MS Based Proteomics MS/MS Peak Extraction MS Raw Data RT Info? mzxml / mzml Data Conversion RT and m/z Info? Quantitation ASAPRatio, Libra, etc.. RT Info? RT Info? Peak Lists of MS/MS Spectra Peptide Identification MASCOT SEQUEST etc. Peptide Validation Peptide Prophet etc. Protein Validation Protein Prophet etc. Result Interpretation GO, DAVID, etc. 29

30 The Common Data Format for the MS Data: mzxml MS Type 1 MS Type 2 MS Type 3 mzxml mzxml mzxml Selection of Correct Signal Processing Method Processed mzxml Data Analysis Proteomics Data Workflow for General MS Based Proteomics MS Raw Data to XML Format Conversion mzxml/mzml Processing UniQua Quantitation ASAPRatio, Libra, etc.. MS/MS Peak Lists Extraction Peptide Identification MASCOT SEQUEST etc. Peptide Validation Peptide Prophet etc. Protein Validation Protein Prophet etc. Result Interpretation GO, DAVID, etc. 30

31 Flowchart of the UniQua for protein Qualitative and Quantitative Proteomics Analysis Dynamic Calibration of Precursor Ion (A)common MS signal background Detect the m/z shift in common MS background signal (B) lock spray The precursor m/z shift was determined by a independent reference electrospray. (C)search result The identified peptide m/z error was used for the calibration num delta no calibration calibration by search result calibration by lock ms calibration by background Figure 1. Peptides m/z accuracy with three different calibration methods. 31

32 Data Smoothing and Peak Picking The (A)raw MS spectrum was first smoothed by (B)Savitzky Golay algorithm and follow by (C)centroiding peak height(80%) with selected. (A) (B) (C) Performance on MS Based Identification Conventional TPP Workflow V.S. UNIQUA TPP (A) (B) (A) MS Based Peptide Identification. (B) MS Based Protein Identification. 32

33 Should We Process All the Signal for Quantitave Analysis? Flowchart of the UniQua for protein Qualitative and Quantitative Proteomics Analysis 33

34 Spectra Processing for itraq Analysis Original MS Spectra: Reporter Ion Peptide Fragments Spectra Processing for itraq Analysis Spectrum Smoothing: Reporter Ion Peptide Fragments 34

35 Spectra Processing for itraq Analysis Resolution Cutoff X X Reporter Ion Peptide Fragments Spectra Processing for itraq Analysis Peak Cendroiding: Reporter Ion Peptide Fragments 35

36 Spectra Processing for itraq Analysis Deisotoping: Reporter Ion Peptide Fragments Spectra Processing for itraq Analysis Noise Removal: Reporter Ion Peptide Fragments 36

37 Dynamic Range and Reporter Ion Intensity Limit of Linearity LOL Spectra Processing for itraq Analysis 1 : : 1.03 Savitzky Golay Smoothing 3.0 Acerage R115_R116_R Average Peptide Intensity 114_115_116_117 37

38 Ratio Accuracy and Reporter Ion Intensity UniQua Performance A. Protein Identification for Different MS Types B. mzxml File Size before and after UniQua Processing 4 GB/File PLGS UniQua PLGS UniQua Protein Discoverer UniQua B. MS Level Quantitation C. MS/MS Level Quantitation (itraq) Anal. Chem. (2013) 38

39 Speed Up The Processing MutipleThreads Analyze mzxml Split mzxml Splitted mzxml Splitted mzxml Splitted mzxml Splitted mzxml Splitted mzxml Splitted mzxml Task Assignment Operating System Main Memory CPU 1 Core Core Core Core CPU 2 Core Core Core Core Assembling into mzxml The Parallel Computing for the Large Scale OMICS Data 105 (min) 53 (min) 28 (min) 19 (min) 16 (min) 3 (min) For a 3GB mzxml file 39

40 Speed Up The Processing MutipleThreads Analyze mzxml Split mzxml Splitted mzxml Splitted mzxml Splitted mzxml Splitted mzxml Splitted mzxml Splitted mzxml Task Assignment Operating System Main Memory CPU 1 Core Core Core Core CPU 2 Core Core Core Core Processors Per GPU Assembling into mzxml The Parallel Computing for the Large Scale OMICS Data 105 (min) 53 (min) 28 (min) 19 (min) 16 (min) 3 (min) 64 Processors 40

41 ucloud Platform ucloud Proteomics Pipeline Metabolomics Pipeline Result Analysis Thank You for Your Attention E Mail: yetran@gate.sinica.edu.tw 41