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

LT

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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