基于质谱的蛋白质药物定性定量分析技术及应用

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1 基于质谱的蛋白质药物定性定量分析技术及应用 蛋白质组学质谱数据分析 单抗全蛋白测序及鉴定 Bioinformatics Solutions Inc. Waterloo, ON, Canada 上海 中国

2 3 PEAKS Studio GUI Introduction

3 Overview of PEAKS Studio GUI Project View Tasks, running info, and properties 4

4 Project command icons 2. Analytical functions 3. Additional tools: i. Configuration ii. Preferences iii. Mass calculator 5

5 Protein Identification Using PEAKS Studio 6

6 Approaches for Data Analysis MS/MS Spectra 2 de novo Sequencing Peptides 1 Database Search Protein DB Inexact Protein DB Homology Search 3 Peptides Homologous Peptides 7

7 PEAKS Identification MS/MS Spectra de novo Sequencing Protein DB PEAKS DB Search Peptides PTMs Sequence Variants 8

8 Integrating de novo and Database Search 9

9 1. Peptide de novo Sequencing Try to find y 1 R: = K: = y 1 10

10 Peptide de novo Sequencing peptide prefix suffix B. Ma et al., Rapid Mass Commu. (2003) 17,

11 Local Confidence for de novo Sequencing 12

12 2. Database Search FDR Estimation PEAKS DB: Zhang et al, MCP (2012) 11: M

13 Protein Inference Parsimony and protein grouping PEAKS Top All 14 A. Nesvizhskii, R. Aebersold, MCP (2005) 4,

14 3. PTM Identification [Nature 2010] The inefficiency in modified peptide identification is an important factor for the low characterization rate of the tandem mass spectra. Software limitation is the main reason for low identification rate of modified peptides The problem of using a traditional database search tool: Limited number of specified PTMs May not know what PTMs to specify One naïve way is to specify all the PTMs in the Unimod PTM database Cause an exponential growth of the search space 15

15 PEAKS PTM Han, X. et al. J. Proteome Res. 10(7),

16 4. Homology Search Problem Database search Incomplete database Database errors De novo sequencing Ambiguity of de novo sequence Partially correct tags Solution Homology search SPIDER homology search Han, Y., et al. JBCB. 3,

17 SPIDER Homology Search Problem:de novo errors, database mutations (de novo) X: (homolog) Z: LSCFAK SLAAFK de novo errors (de novo) X: [LS]C[FA]K (real) Y: [SL]C[AF]K (homolog) Z: [SL]A[AF]K mutation Solution:minimize de novo errors and mutations Alhaider, A. et al. J. Mass Spectrom. 48,

18 Integrating de novo and Database Search 19

19 Peptide/Protein Quantification with PEAKS Q 63

20 Mass Spectrometry-Based Quantitative Proteomics Shao-En Ong & Matthias Mann, Nat Chem Biol 1, (2005)

21 LC-MS/MS MS/MS MS all ionized peptide features in the sample 65

22 Label-free Quantification 1) Spectral counting spectral number 2) MS intensity peak area under the extracted ion chromatograms (1) Spectral Counts A IPLENLQIIR A IPLENLQIIR B Ion Current A A IPLENLQIIR ELVEPLpTPSGEAPNQLLR IPLENLQIIR ELVEPLpTPSGEAPNQLLR ELVEPLpTPSGEAPNQLLR High Resolution SRM: B High Resolution B High Res B H GSTAENAEpYLR GSTAENAEpYLR ELVEPLpTPSGEAPNQLLR (2) Extracted Ion Chromatogram 66

23 1. Feature Detection Isotopes m/z Overlapping deconvolution 67

24 2. RT Alignment and Feature Matching Sample I Sample II 68

25 3. Abundance Ratio Calculation RT integration Isotope summation 69

26 4. Significance Assessment Replicates σ: variance quality = 1/log 2 (σ) significance = -10 logp * 70

27 5. Feature Identification LC-MS/MS MS2 71

28 Labelling Quantification - SILAC In cell culture, replace one (or two) amino acids in the culture medium with 13 C and/or 15 N equivalent. For tryptic digests, lysine (C6, N2) and/or arginine (C6, N4) are sensible choices. Long SE et al. MCP 1: (2002) 72

29 Labelling Quantification - itraq / TMT 114 Peptide 115 Peptide 116 Peptide 117 Peptide 73 Ross PL et al. MCP 3: (2004)

30 Ratio calculation of itraq/tmt Quantification: 1. For each PSM / MS2, Labelling Quantification Extract reporter ion intensity for each channel and calculate the total intensity Generate a list of quantitative PSMs (1% FDR / score and intensity filter) with Labels 2. For each quantitative protein group (# of channels, presence in reference channel and # of unique peptides), Protein intensity in each channel = sum of reporter ion intensities of all unique quantitative PSMs 3. Normalization (auto): Protein intensities summed across all quantified proteins are normalized across all channels. Therefore, minor mixing errors corrected, and reflects an equal total protein content per sample. 74

31 Labelling Quantification (cont d) 4. Calculate the protein ratio and look for proteins that were significantly altered in at least one sample (fold threshold) Associate each protein ratio a significance score with the analysis of variance (ANOVA) 5. For each quantitative peptide (# of channels and presence in reference channel), peptide intensity in each channel = sum of intensities of its quantitative PSMs with the same normalization factor. Calculate the peptide ratio 75

32 Monoclonal Antibody Protein Sequencing & Characterization Using PEAKS AB 129

33 Questions Proteomics Studies Which proteins are existing in the given sample? PEAKS Therapeutic Protein Studies What are the exact amino acid sequences of the protein in the given sample? 130

34 Challenges Proteomics experiment identifying proteins in a sample One enzyme, usually trypsin Peptides existence protein existence Too small peptide No MS2 scans No overlap 131

35 Challenges (cont d) Purified sample + Multiple enzyme digestion + Antibody protein databases Overlapping helps Hypervariable regions are always not recorded Chymotrypsin Trypsin CDR3 Asp N 132

36 de novo Sequencing Helps de novo sequencing tags help to fix gaps Ref: de novo: 133

37 Assemble de novo Sequencing Tags de novo peptide sequencing for each MS2 spectrum Find overlaps between de novo sequencing tags Assemble de novo sequencing tags according to overlapping information 134

38 Automated de novo Protein Sequencing Find protein sequences with LC-MS/MS of multiple enzyme digestions 135

39 Our Procedure From Sample to Sequence Antibody sample Result presentation Chain separation (Optional) Multi-enzyme digestion LC-MS/MS MS/MS data Sequence assembly* Peptide de novo sequencing * Tran, N.H. et al. Scientific Reports. 6: /08/

40 Case Study 6: Monoclonal Antibody Protein Sequencing 137

41 Sample Description & Preparation Sample Waters mab mass check standard Wet-lab analysis Reduction and alkylation Separate HC and LC by SDS-PAGE Multiple enzyme digestion LC-MS/MS 138

42 MS Data Analysis Using PEAKS AB 1 Load MS data 139

43 MS Data Analysis Using PEAKS AB 2 Set parameters 140

44 de novo Sequence Assembly Confident amino acids (w/ red tags below) high confident de novo sequencing tags supporting the assembled sequence Show evidences for de novo sequence assembly 141

45 Difference Between Assembled & Published Sequences Assembled Sequence assembled by PEAKS AB Published Published sequence for Waters mab mass check standard Assembled Published 142

46 Protein Sequence View Filters PTMs 143 Enzyme selection view

47 Peptide Mapping Traditional way to show peptide level confidence Amino acid level confidence display CDR area annotation Different colors for multi-enzyme induced PSMs Enzymes Asp N chymotrypsin Trypsin 144

48 Intact Mass Assisting de novo Sequencing Check sample purity Validate assembled sequences Da Experimental procedure: Reduction to separate heavy and light chains ESI-TOF (Agilent, Waters) Before deconvolution After deconvolution 145

49 Intact Mass Help Revealing Heterogeneity Identify different glycan forms Determine whether C-terminal lysine chopped Intensity Heavy chain of an mab 128 Da 203 Da 203 Da Mass (Da) 162 Da 146