Mass Spectrometry in Proteomics

Transcription

1 Mass Spectrometry in Proteomics Bin Ma Assistant Professor Canada Research Chair in Bioinformatics Department of Computer Science University of Western Ontario From the Genome to the Proteome Genomics has provided a vast amount of information. But not complete for the study of disease. gene transcript can be spliced in different ways most proteins are chemically changed through post-translational modifications. 30,000~40,000 human genes potentially encoding 40,000 different proteins, alternative splicing and PTM may increase this number to 2 million proteins or protein fragments. Protein expression level and PTMs differ in different tissues and cell cycles. Proteomics research allows the discovery of new protein markers for diagnostic purposes novel molecular targets for drug discovery Summary In this tutorial I will show you how scientists from different areas collaborate to solve a particular problem in proteomics protein identification. Proteomics the problem Mass Spectrometry the data Mass Fingerprinting and de novo Sequencing the mathematical model and algorithm. PEAKS etc. the software These are also a typical cycle of every successful bioinformatics research.

2 Part I. Proteomics Basics Background knowledge Mass Spectrometry Basics Instruments and data. Protein ID with mass fingerprinting using MALDI-TOF where Bioinformatics, computer algorithms and programs are useful. Part II. Tandem Mass Spectrometry (MS/MS) Instruments and data. Database search method Bioinformatics De novo sequencing methods More sophisticated Bioinformatics Software. More work than publishing a research paper. Part III Common types of (tandem) mass spectrometers. and how much they worth. LC-MS/MS protein/peptide separation prior MS is important. Quantitative MS ICAT.

3 Part I Proteomics Flow of Biological Information DNA mrna Proteins Modified Proteins Genome Transcriptome Proteome Proteome: entire protein complement expressed by a genome or by a cell or tissue type

4 Central Dogma of Molecular Biology Amino Acids There are 20 amino acids. All have the same basic structure but with different side chains: Examples: side chain group H Glycine, or Gly, or G Arginine, or Arg, or R Peptides and Proteins H Glycine, or Gly, or G GR Arginine, or Arg, or R N-terminal C-terminal peptide bonds

5 All the 20 Structures * Picture copied from Dr. R.J. Huskey s website: /~rjh9u/aminacid.html Back to Basics Chemical Composition of Living Matter 27 of 92 natural elements are essential. Elements in biomolecules (organic matter): H, C, N, O, P, S These elements represent approximately 92% of dry weight. Organic Matter Organized in "building blocks" amino acids polypeptides ( proteins) monosaccharides starch, glycogen nucleic acids DNA, RNA Mass (Weights) of Atoms and Molecules element nominal exact Percent average mass mass abundance mass C % % H % % O % % N % %

6 Mass or Molecular Weight of molecules Ethyl acetate C 4 H 8 O 2 4 C 12 4 x H 1 8 x O 16 2 x Nominal Mass: = 88 Monoisotopic Mass: Average Mass: = Mass of Amino Acids Residues Exact Mass of Amino Acid Residues in Proteins Gly G Ala A Gln Q Lys K Glu E Note: Leu (L) = Ile (I) = Mass of Peptides and Proteins Ala-Ser-Phe (ASF) tripeptide (MW )= More precisely: monoisotopic mass average mass

7 PTM Posttranslational Modification Phosphorylation is one of the common types of PTMs. lists 183 types of PTMs when this tutorial is prepared (June 2004). H phosphorylation serine Goals of Proteomics - Identification of each protein - Levels of protein expression: do not always correlate with mrna levels (microarrays or gene chips) - Post-translational modifications (PTMs): types and sites - Protein/protein Interactions: molecular machines - Protein localization (organellar proteomics, cell map) - Protein function in normal, stressed and diseases states Proteomics: Post-Genomics Era Much more complex than genomics: proteins are expressed at different levels, different times different forms Yeast 6,127 genes ~2,000 of unknown function C. elegans (worm) 19,000 genes Human genome: ~ 30-35,000 genes (~20,000(?)of unknown function) Many other microorganisms have been fully sequenced (~100) (used as models for more complex organisms)

8 Challenges of Analyzing Proteins Size: From ~ 50 to ~ 100,000 amino acids 5,000-1,000,000 Da Diversity in human cells: ~ 30,000 different sequences Relative abundance: broad dynamic range ~ 10-1,000,000 copies /per cell Different forms: Post-translational modifications 93 forms of tau proteins!! Proteins: Other Characteristics Sequence is important Ala- Ser- Phe Phe - Ser - Ala Convention: N-terminal (left) to C-terminal (right) Diversity: Many theoretical possibilities i.e. for a tripeptide 20 x 20 x 20 = 8000 possibilities, 50 mer = 20 50!! Evolution selected much less ie ~1,000,000 (?) different sequences Proteins Have Shape and Function Primary structure: sequence of amino acids Secondary structure: stretch of amino acids form Helices (ribbon) Sheets (arrows) turns and loops (line) Tertiary structure: 3 dimensional (3D) arrangement of helices, sheets, turns 3D structure is important for function Structural Proteomics Protein Folding problem: 3D structure from primary sequence

9 Basic Question How to identify the proteins? Using masses of proteins and protein fragments! For example: If we know m i, each amino acid in the following peptide can be identified. m 1 Well. This is the idea. But things are never so simple. m 2 m 3 m 4 Some models closer to reality Measure the intact protein mass. If we anticipate a protein, and find something with the same mass in the sample Digest the proteins at certain sites. Then measure the masses of the peptides (substrings). Different proteins likely have different mass fingerprints. Database search can help to identify the protein. Fragment many copies of a peptide randomly. Then measure the masses of all fragments. You get the masses of all substrings. To reconstruct the sequence.

10 Some questions to be answered How to measure those masses accurately? Up to 0.1 or even 0.01 Dalton! How to pull out the proteins we want and put them into the instrument? How to do these experiments in a reliable and high-throughput way? How to identify the protein after the data are collected into a computer? Free or commercial software available? Protein Separation - 2D Gel electrophoresis (most popular) - Affinity purification or immunoprecipitation and 1D gel electrophoresis - Organellar purification (cell map) - Multidimensional HPLC based methods with ion exchange or gel filtration or electrophoresis - Capillary electrophoresis - Microfluidics devices 2D Gel Electrophoresis First Dimension: Isoelectric Focusing with Immobilized ph Gradients Second Dimension: SDS-PAGE Visualization: Stain Coomassie Blue, Silver, SYPRO Dye Resolution: 1,000-2,000 proteins spots (up to 10,000) Not alls spots are different proteins (588 spots were from same protein) Many spots contain more than one protein (up to 40) Sensitivity: Silver stain gels Proteins with >10,000 copies / cell

11 Typical 2D Gel Mass Spectrometry How to measure masses accurately.

12 Three Components of an MS A typical mass spectrometer contains Ionizer Mass analyzer Detector Ion source charges the to-be-measured molecules. Charge can be negative but often positive. Two common types: MALDI and ESI. John B. Fenn & Koichi Tanaka 2002 Nobel Prize in Chemistry for Electrospray and MALDI Mass analyzer separates ions according to the mass to charge ratio (m/z) of the ions. Iontrap, TOF, Quadrupole, FTICR. Detector detects the ions. Ionization (1): MALDI Matrix Assisted Laser Desorption/Ionization Sample is co-crystallized with matrix (solid) Formation of singly charged ions Koichi Tanaka, Nobel Prize 2002 Mass Analyzer (1) TOF Time of Flight. + - Detector + Time of flight is proportional to sqrt(m/z)

13 Putting Them Together MALDI Time-of-flight MALDI TOF Drift region (D)

14 MALDI-TOF Linear Mass range = ,000 Sensitivity and accuracy decrease rapidly with size! MALDI-TOF Linear vs Reflectron Mode Reflectron gives much better accuracy for mass < 6,000 Measure Intact Proteins MALDI-MS of Myoglobin (measured on an early instrument.)

15 Measure Tryptic Peptides Intact protein mass does not have enough information for identification. More information must be collected. The protein is digested into short peptides, and masses of the peptides are measured. Cleavage with Trypsin (tryptic digestion) Trypsin cleaves at only at peptide bond R n-1 = Lys, Arg; Rn Pro Xxx-Xxx-Xxx-Xxx-Xxx-Xxx-Xxx-Xxx- R n-1 R n R n+1 Example: Trypsin Trypsin Arg - Gly - Phe - Lys - Ile - Ala - Glu - Trp - Met MW (Average mass): 1136 Treatment with trypsin gives 3 different fragments: 1. Arg (MW 174) 2. Gly - Phe Lys (MW = = 350) 3. Ile-Ala-Glu-Trp-Met (MW = = 648 Note: each internal peptide will end with Lys (K) or Arg (R) MALDI-TOF/R MS of Peptides from a Tryptic Digest % Peptides from trypsin self-digestion internal calibrants m/z Mass Fingerprint of a Pure Protein

16 Search a database for match MPSESSYKVHRPAKSGGS another protein in-silicon digestion in-silicon digestion Mascot interface Proteomics with MALDI-TOF/R 1. Cut spots from 2D Gel, destained and tryptic digest each spot ( Medium to high silver stained spot) 2. Extract peptides and purify by ZipTip 3. Mix with matrix and analyze by MALDI-TOF/R 4. Compare observed masses with masses in databases obtained from virtual tryptic digest of all proteins 5. Confidence for hits depends on coverage : minimum 5 masses

17 Patient with DCM 1. ATP Synthase B-chain 2. ATP Synthase 3. Creatine kinase 4. 3-oxoacyl-CoA thiolase 5. Isocitrate dehydrogenase 6. Serum albumin fragment 7. α-crystallin, B-chain 8. Cytochrome c oxidase 9. Fatty acid binding protein Typical Problems 1. No MS signals!! Insufficient sample (poor digestion, poor extraction) Contaminants that affect ionization: SDS, acrylamide, salts, detergents, PEG 2. Protein contamination Keratins, peptides from trypsin self-digestion, bacterial proteins, etc.. 3. False positive due to noise and mass ambiguity.

18 Most Peptides Do Not Have Unique Mass! Yeast 0.1 ppm 1 ppm 10 ppm C. Elegans 0.1 ppm 1 ppm 10 ppm Calculated percentage uniqueness for masses 500-4,000 Part II. Ionization (2) ESI Electrospray Ionization: Formation of Charged Droplets Formation of multiply charged ions

19 Mass Analyzer (2) Quadrupole Ions are lost Mass filter; complete spectrum is obtained by scanning whole range Mass range 10-4,000 Da Hybrid Quadrupole/Time-of-Flight (Q-TOF) MS Q1 Selection Q2 Collision Pusher Detector TOF with reflectron

20 Electrospray MS and MS/MS of Proteins John Fenn, Nobel Prize 2002 Sample Preparation tissue fraction gel GTDIMR PAK MPSER HPLC To MS/MS Add trypsin peptides Tandem Mass Spectrometer QTOF ions parent ions fragment ions detector MPSER + PAK + SG + Quadrupole mass analyzer PAK + P + PAK + P AK+ collision PAK + PA + K PAK + PA K+ TOF mass analyzer AK + PA + K + P + ESI peptide sequencing

21 How Does a Peptide Fragment? m(b 1 )=1+m(A 1 ) m(b 2 )=1+m(A 1 )+m(a 2 ) m(b 3 )=1+m(A 1 )+m(a 2 )+m(a 3 ) m(y 1 )=19+m(A 4 ) m(y 2 )=19+m(A 4 )+m(a 3 ) m(y 3 )=19+m(A 4 )+m(a 3 )+m(a 2 ) Matching Sequence with Spectrum peptide sequence: LGSSEVEQVQLVVDGVK tandem mass spectrometry: MS/MS spectrum de novo sequencing: database LGSSEVEQVQLVVDGVK

22 Database Search Methods Mascot matrix sciences General software Sequest John Yates et. al. Distributed by Thermo Finnigan. Works for Thermo s LTQ. Mascot De Novo Sequencing De Novo Sequencing (Dancik et al., JCB 6: ) Given a spectrum, a mass value M, compute a sequence P, s.t. m(p)=m, and the matching score is maximized. We consider the matching score of P is the sum of the scores of the matched peaks. We use intensity of a peak as its score to illustrate PEAKS algorithm.

23 Spectrum Graph Approach Convert the peak list to a graph. A peptide sequence corresponds to a path in the graph. Bartels (1990), Biomed. Environ. Mass Spectrom 19: Taylor and Johnson (1997). Rapid Comm. Mass Spec. 11: (Lutefisk) Dancik et al. (1999), JCB 6: Chen et al. (2001), JCB 8: Difficulties Spectrum graph approach has difficulties to handle errors: Missing of ions break a path. Too many peaks in a small error tolerance too many edges connecting to the same peak. (reduce efficiency) Error accumulation. A peak is used as both a y-ion and a b-ion. It is still possible to solve these problems under the spectrum graph schema E.g. The y-b overlap problem had been addressed by Dancik et al (1999) and Chen et al. (2001). But things are getting complicated. A reliable signal preprocessing is required. PEAKS approach Because of the time limit, in this tutorial we only introduce PEAKS approach in more details. It is more natural and easier to handle the errors and noises. Less dependent to the signal preprocessing. Solved the missing ions and y-b overlap problems naturally. Showed great success on real-life lab data. Has been licensed by tens of research labs in public and private sectors.

24 Warm up Counting Only Y-ions 19 The Score of a Suffix y 1 y 2 y 3 Let Q be a suffix of the peptide. It can determine some y-ions. score(q) are the sum of scores of those y-ions of Q. DP( m) = max score( Q) m ( Q) = m 19 Recursive Computation of DP(m) Q a Suppose Q is such that DP(m)=score(Q). score ( Q) = h( m + 19) + score( Q') score(q )=DP(m(Q )) DP( m) = h( m + 19) + DP( m m( a)) Do not know a? DP( m) = h( m + 19) + max DP( m m( a)) a Σ

25 Dynamic Programming 1. for m from 0 to M DP( m) h( m + 19) + maxa DP( m a) 2. backtracking = Σ Counting Both y and b Ions Good News y 1 y 2 y 3 b n-3 b n-2 bn-1

26 Bad News Ions Determined By a Pair P=LGEY Q=LLVR score(p,q) is the sum of matched peak intensities. A peak can only count once. Chummy Pairs Two strings P and Q are called chummy pairs, iff. either of the following two is true: (C1) 1+ m ( Pf ) < 19 + m( Q) 1+ m( P) P = LG, f Q = VR, P = LGE (C2) 19 m ( Q ) 1+ m( P) < 19 + m( Q) + p Q = VR, p P = LGE, Q = LVR

27 Recursive Computation of score(p,q) P=LGEY Q=LLVR Q = LVR p u=m(p), v=m(q) score( P, Q) = f ( u, v) + score( P, Q p ) Chummy pairs Lemma 1 Suppose P and Q are a chummy pair. u=m(p), v=m(q). If (C1) is true, score ( P,Q) = score( P f,q) + g( u, v) If (C2) is true, score ( P,Q) score( P,Q ) + f ( u, v) = p Chummy Pairs Lemma 2 Let(P,Q) be a chummy pair, a be a letter. (C1) (P,aQ) is a chummy pair but (Pa,Q) is not. (C2) (Pa,Q) is a chummy pair but (P,aQ) is not. Lemma 3 Let S be the optimal solution. Then there is a chummy pair (P,Q) and a letter a such that S=PaQ. Also, there is a chummy pair series such that ε, ε ) = ( P, Q ) p K p ( P, Q ) ( P, ) ( 1 1 n n = Q

28 Dynamic Programming Combining Lemma 1, 2, 3, we can compute DP( u, v) = max m ( P) = u, m( Q) = v ( P, Q) chummy score( P, Q) Suppose (P,Q) is the pair maximizing DP(u,v) under the condition m(p)+m(q)+m(a)=m. Then PaQ is the optimal peptide. Algorithm Sandwich DP(0,0) = 0; DP(u,v) = -infinity for (u,v)!=(0,0); for u from 1 to M/2 step d do for v from u-m(w) to u+m(w) step d do for a in Σ do if u<v then DP ( u + a, v) = max{ DP( u, v) + f ( u, v), DP( u + a, v) } else DP ( u, v + a) = max{ DP( u, v) + g( u, v), DP( u, v + a) } find u,v,a, s.t. u+v+m(a)=m and DP(u,v) maximized; backtracking; Time: M m(w) O( ) 2 d PEAKS The Software

29 Comparison LCQ data (Iontrap instrument): Generously provided by Dr. Richard Johnson. 144 spectra. Micromass Q-Tof data: Measured in UWO s Protein ID lab. 61 spectra Sciex Q-Star data: Provided by U. Victoria s Genome BC Proteomics Centre. 13 good/okay spectra. PEAKS v.s. Lutefisk completely correct sequences: 38/144 v.s. 15/144 correct amino acids: 1067/1702 v.s. 767/1702 v.s. partially correct sequences with 5 or more contiguous correct amino acids: 94/144 v.s. 64/144 PEAKS v.s. Micromass PLGS completely correct sequences: 13/61 v.s. 7/61 correct amino acids: 456/764 v.s. 232/764 partially correct sequences with 5 or more contiguous correct amino acids: 38/61 v.s. 24/61

30 PEAKS v.s. Sciex BioAnalyst completely correct sequences: 7/13 v.s. 1/13 correct amino acids: 115/150 v.s. 86/150 partially correct sequences with 5 or more contiguous correct amino acids: 12/61 v.s. 7/61 Users Other Techniques Used by PEAKS Preprocess the MS/MS spectra Peak centroid, deconvolution, noise reduction, and signal enhancement. It does a better job than spectrometer vendor s software. Recalibration compress/stretch the spectrum for calibration error Positional Confidence Estimate the confidence level of individual amino acids.

31 25-MAR gfmar25b 399 (9.653) Sm (SG, 2x4.00); Cm (397:403) A Deconvolution : TOF MS Survey ES+ 2.59e3 A: ± Da % Da Da m/z Sophisticated Ion Matching Score Score of one peak matching b ion log( h) 2 ( error ) tolerance e supp( b 17, b 18, a, c) PEAKS 2.x s Additional Feature Identify the proteins by matching the de novo (partial) sequences. Then further match the spectra with the peptides of the proteins.

32 Collaborators and References Sandwich algorithm: B. Ma, K. Zhang, C. Liang, CPM 03. (sandwich algorithm) PEAKS: B. Ma, K. Zhang, C. Hendrie, C. Liang, M. Li, A. Doherty-Kirby, G. Lajoie, Rapid Comm. Mass Spec. (software feature, score function, experiments) Acknowledgement: PEAKS development team. (Bioinformatics Solutions Inc.). Part III miscellaneous LC/MS/MS ESI MS/MS ionizes all the peptides at the same time but only fragments and measures one at a time. Sample is wasted and sensitivity is low. LC (Liquid Chromatography) has been used to separate the peptides in front of ESI. Different peptides were separated and sent to ESI at different time. Less sample is wasted and sensitivity is high.

33 High Performance Liquid Chromatography (HPLC) liquid (water etc.) after 20 minutes or so to ESI MS/MS Comparison of Some Types of Mass Spectrometers MALDI-TOF Q-TOF Ion Trap FTICR Sensitivity Highest High High High Accuracy High High Low Highest Sequencing Difficult Yes Yes Yes Selective ion No Yes Yes Yes Monitoring Throughput High Med Med Low Ease of Easiest Med Med Hardest operation Cost 300K 650K 300K 1.5M Newest: MALDI -TOF/TOF; MALI- Q-TOF; FTICR MS 12 Tesla MALDI-ion trap/quadrupole; ESI Quad/Trap/TOF Isotope coded affinity tag (ICAT)

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