Multiple flavors of mass analyzers
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Multiple flavors of mass analyzers
Single MS (peptide fingerprinting): Identifies m/z of peptide only Peptide id’d by comparison to database, of predicted m/z of trypsinized proteins
Tandem MS/MS (peptide sequencing): Pulls each peptide from the first MS Breaks up peptide bond Identifies each fragment based on m/z
Collision cell
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Now multiple types of collision cells:CID: collision induced dissociationETD: electron transfer dissociationHCD: high-energy collision dissociation
Mass Spec MS Spectrum
Ion source Mass analyzer Detector
Intro to Mass Spec (MS)Separate and identify peptide fragments by their Mass and Charge (m/z ratio)
Basic principles:1. Ionize (i.e. charge) peptide fragments2. Separate ions by mass/charge (m/z) ratio3. Detect ions of different m/z ratio4. Compare to database of predicted m/z fragments for each genome
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Mann Nat Reviews MBC. 5:699:7113
How does each spectrum translate to amino acid sequence?
1. De novo sequencing: very difficult and not widely used (but being developed)for large-scale datasets
2. Matching observed spectra to a database of theoretical spectra
3. Matching observed spectra to a spectral database of previously seen spectra
How does each spectrum translate to amino acid sequence?
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Nesvizhskii (2010) J. Proteomics, 73:2092-2123.
- spectral matching is supposedly more accurate but …- limited to the number of peptides whose spectra have been observed before
With either approach, observed spectra are processed to:group redundant spectra, remove bad spectra, recognized co-fragmentation, improve z estimates
Many good spectra will not match a known sequence due to:absence of a target in DB, PTM modifies spectrum, constrained DB search,incorrect m or z estimate.
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Result: peptide-to-spectral match (PSM)
A major problem in proteomics is bad PSM calls … therefore statistical measures are critical
Methods of estimating significance of PSMs:
p- (or E-) value: compare score S of best PSM against distribution ofall S for all spectra to all theoretical peptides
FDR correction methods:1.B&H FDR2.Estimate the null distribution of RANDOM PSMs:
- match all spectra to real (‘target’) DB and to fake (‘decoy) DB- often decoy DB is the same peptides in the library but reverse
sequence
one measure of FDR: 2*(# decoy hits) / (# decoy hits + # target hits)3. Use #2 above to calculate posterior probabilities for EACH PSM
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3. Use #2 above to calculate posterior probabilities for EACH PSM
- mixture model approach: take the distribution of ALL scores S- this is a mixture of ‘correct’ PSMs and ‘incorrect’ PSMs
- but we don’t know which are correct or incorrect
- scores from decoy comparison are included, which can providesome idea of the distribution of ‘incorrect’ scores
-EM or Bayesian approaches can then estimate the proportion of correct vs.incorrect PSM … based on each PSM score, a posterior probability is calculated
FDR can be done at the level of PSM identification … but often doneat the level of Protein identification
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Error in PSM identification can amplify FDR in Protein identification
Often focus on proteins identified by at least 2 different PSMs (or proteins with single PSMs of very high posterior probability)
Nesvizhskii (2010) J. Proteomics, 73:2092-2123.
Some methodscombine PSM FDRto get a protein FDR
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Some practical guidelines for analyzing proteomics results
1. Know that abundant proteins are much easier to identify
2. # of peptides per protein is an important consideration- proteins ID’d with >1 peptide are more reliable- proteins ID’d with 1 peptide observed repeatedly are more reliable- note than longer proteins are more likely to have false PSMs
3. Think carefully about the p-value/FDR and know how it was calculated
4. Know that proteomics is no where near saturating … many proteins will be missed
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Quantitative proteomics
1. Spectral counting
2. Isotope labeling (SILAC)
3. Isobaric tagging (iTRAQ & TMT)
4. SRM
Either absolute measurements or relatively comparisons
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Spectral countingcounting the number of peptides and counts for each protein
Challenges:- different peptides are more (or less) likely to be assayed- analysis of complex mixtures often not saturating – may miss some
peptides in some runs
newer high-mass accuracy machines alleviate these challenges
- quantitation comes in comparing separate mass-spec runs … thereforenormalization is critical and can be confounded by error
- requires careful statistics to account for differences in:quality of run, likelihood of observing each peptide, likelihoodof observing each protein (eg. based on length, solubility, etc)
Advantages / Challenges+ label-free quantitation; cells can be grown in any medium- requires careful statistics to quantify- subject to run-to-run variation / error 11
SILAC(Stable Isotope Labeling with Amino acids in Cell culture)
Cells are grown separately in heavy (13C) or light (12C) amino acids (often K or R),
lysates are mixed, then analyzed in the same mass-spec run
Mass shift of one neutron allows deconvolution, and quantification, of peaks in the same run.
Advantages / Challenges:+ not affected by run-to-run variation- need special media to incorporate heavy aa’s,- can only compare (and quantify) 2 samples directly- incomplete label incorporation can confound MS/MS identification 12
Isobaric TaggingiTRAQ or
Tandem Mass Tags, TMTs
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Each peptide mix covalently taggedwith one of 4, 6, or 8 chemicaltags of identical mass
Samples are then pooled and analyzedin the same MS run
Collision before MS2 breaks tags –
Tags can be distinguished in the small-mass range and quantified togive relative abundance acrossup to 8 samples.
Advantages / Challenges:+ can analyze up to 8 samples,
same run- still need to deal with normalization13
Selective Reaction Monitoring (SRM)
Targeted proteomics to quantify specific peptides with great accuracy
- Specialized instrument capable of very sensitively measuringthe transition of precursor peptide and one peptide fragment
- Typically dope in heavy-labeled synthetic peptides of precisely knownabundance to quantify
Advantages:- best precision measurements
Disadvantages:- need to identify ‘proteotypic’ peptides for doping controls- expensive to make many heavy peptides of precise abundance- limited number of proteins that can be analyzed
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Phospho-proteomics and Post-translational modifications (PTMs)
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phosphorylated (P’d) peptides are enriched, typically through chromatography- P’d peptides do not ionize as well as unP’d peptides- enrichment of P’d peptides ensures ionization and aids in mapping
IMAC: immobilized metal ion affinity chromatography- phospho groups bind charged metals- contamination by negatively-charged peptides
Titanium dioxide (TiO2) column: - binds phospho groups (mono-P’d better than multi-P’d)
SIMAC: Sequential Elution from IMAC:- IMAC followed by TiO2 column
Goal: identify which residues are phosphorylated (Ser, Thr, Tyr),mapped based on known m/z of phospho group