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Informatics for proteomic inventories Biomedical Informatics Vanderbilt University.

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1 Informatics for proteomic inventories Biomedical Informatics Vanderbilt University

2 Overview Explaining the whys and hows of proteomics Matching peptides from protein sequence databases to MS/MS spectra Filtering peptide-spectrum matches (PSMs) to an acceptable false discovery rate (FDR) Inferring proteins parsimoniously and scalably

3 Methods capture only part of story J_Alves: glycine tRNA J_Alves: glucose and cholesterol ElaineMeng: H-ras, PDB 121P Genomics and epigenetics describe state of “catalog.” Transcriptomics describes current “purchase orders.” Proteomics measures current inventory of cell capabilities. Metabolomics examine cell state most directly.

4 What does proteomics include? Protein Inventories Tissue Imaging 1D and 2D Gel Electrophoresis Protein Quantitation Post-Translational Modifications Gerald_G scales, Gsagri04: gel, AB SCIEX tissue image

5 Discovery Proteomics Peptide Mixture Liquid Chromatography Electrospray Ionization High-Resolution Mass Spectrometry Isolate Ions of Peptide Collide Ions to Dissociate Collect Fragments in Tandem MS Protein Mixture Two types of measurements for each peptide: intact m/z (mass/charge) and a list of fragment m/zs. Peptide Fractionation

6 Collision-induced dissociation (CID) “Tickle” energizes peptide, causing varied conformations and proton movement. A mobile proton associates with a carbonyl adjoining a peptide bond, drawing electrons. Electrons of the prior carbonyl attack, forming a ringed intermediate that quickly dissociates. Wysocki et al, Anal. Chem. (2000) 35: Paizs and Suhai, Rapid Comm. Mass Spectrom. (2002) 16:

7 TSIIGTIGPK N-terminal b4 ion C-terminal y6 ion Broken peptide bonds yield fragments

8 HF- -LEK-SELEK -ISELEK -FISELEK Neutral loss of water from peptide HFISELEK, +2 charge state

9 Same spectrum compared to FHEIKELS instead of HFISELEK FH- has same mass as HF- -EIKELS has same mass as -ISELEK Neutral loss of water from peptide

10 Disassembly and reassembly After AI Nesvizhskii, Mol Cell Proteomics (2005) 4:

11 Database search overview Eng et al (1994) J. Amer. Soc. Mass Spectrom. 5: Yates et al (1995) Anal. Chem. 67:

12 Emulating proteases in silico N Edwards and R Lippert. Lecture Notes In Computer Science (2002) 2452:

13 Dynamic PTMs grow search space Because multiple PTMs may be in each peptide, adding PTMs to a search creates an exponential cost. Here, three sites lead to eight PTM variants. CASA1_BOVIN

14 Peptide mass filter Sequences outside mass tolerance are not compared. Many sequences may share a common mass. Sequences of one mass may score differently. Sequences of different mass may score the same.

15 Fragment masses and charge segregation AA HOH H+H+ H+H+ AA HOH H+H+ H+H+ AA HOH H+H+ H+H+ H+H+ H+H

16 Sequest cross correlation Normalize observed spectrum. Generate model spectrum for each candidate. Convert observed and model spectrum to frequency domain by FFT. Cross-correlate, reporting ratio between zero- offset alignment and nearby alignments. J Eng et al. J. Proteome Res. (2008) 7: J Eng et al. J Amer. Soc. Mass. Spectrom. (1994) 5:

17 X!Tandem scoring Predict more accurate fragment intensities Count matched b ions and matched y ions Compute dot product of intensities Generate hyperscore = Build histogram of scores per spectrum Report expectation value Craig and Beavis. Rapid Comm. Mass Spectrom. (2003) 17: Fenyö and Beavis. Anal. Chem. (2003) 75:

18 Random match probabilities Imagine spectrum as jar of 100 black and 900 white marbles (peaks and voids). Sample 20 marbles for a predicted peaklist, drawing 15 black and 5 white. Compute probability of random match by hypergeometric distribution: T Fridman. J. Bioinfo. Computat. Bio. (2005) 3:

19 Disassembly and reassembly After AI Nesvizhskii, Mol Cell Proteomics (2005) 4:

20 The “longest list” problem Perceived value of early proteomics experiments was linked only to sensitivity. Systems to evaluate specificity lagged behind, and false positive rates were left unchecked. Two developments were needed: – Community consensus on reporting standards – New tools for evaluating identification error rates Carr et al. Mol. Cell. Proteomics (2004) 3: Taylor et al. Nature Biotech. (2007) 25:

21 Strategy I: Target/decoy estimates FDR Sequence database has equal numbers of target and decoy sequences. False IDs distribute evenly between target and decoy sequences. Apply a threshold, and: – False estimate = 2 x [decoy hit count]. – False Discovery Rate (FDR) = False estimate divided by number of passing IDs. Elias and Gygi. Nature Methods (2007) 4:

22 Decoys model false distribution Elias Nat. Methods (2007) 4:

23 Strategy II: Peptide Prophet Estimates correctness probability for individual identifications Combines multiple subscores from each Sequest identification through DFA Fits mixed model to observed matches with expectation maximization A Keller. Anal. Chem. (2002) 74:

24 Discriminant Function Analysis combines sub-scores from Sequest

25 Mixture Model analysis separates true and false distributions Expectation maximization adjusts two curves to fit observed data. Here, negatives are fit to a gamma distribution and positives to a normal distribution.

26 Disassembly and reassembly After AI Nesvizhskii, Mol Cell Proteomics (2005) 4:

27 Why are peptides shared among proteins? “Orthologs are direct evolutionary counterparts derived from a common ancestor through vertical descent; whenever we speak of the ‘the same gene in different species,’ we actually mean orthologs. In contrast, paralogs are genes within the same genome that have evolved by duplication.” Koonin. Genome Biology (2001) 2: comment

28 Protein isoforms A single gene may give rise to many transcripts that overlap for one or more exons. When isoforms are listed as separate proteins in the FASTA, a peptide may match a shared or distinctive part of a protein sequence. VEGF incorporates eight exons, where either 6 or 7, both, or neither may be incorporated.

29 Parsimony noun: “economy of explanation in conformity with Occam's razor” – Merriam Webster OnLine “Plurality ought never be posed without necessity.” – William of Occam

30 IDPicker 1.Assemble maximal protein list. 2.Combine proteins that point to the same peptides, and combine peptides that point to the same proteins. 3.Find “set cover” by greedy algorithm to pick minimal protein list to explain peptides. B Zhang et al. J. Proteome Res. (2007) 6: Z Ma et al. J. Proteome Res. (2010) 8:

31 Two proteins or seven? Sample mixes mouse and human proteins. Isoforms, paralogs, and orthologs complicate protein-peptide map. Untangling relationships is non-trivial. Data from Broad Institute, CPTAC

32 Greedy algorithm Data from Broad Institute, CPTAC

33 ProteinProphet 1.Combine peptide identification probabilities into protein identification probabilities. 2.Distribute probability for shared peptides across multiple proteins. 3.Compute protein probability by subtracting probability that all observed peptides are false from 1. – AI Nesvizhskii. Anal. Chem. (2003) 75:

34 Number of Sibling Peptides and Degenerate Peptides NSP places more confidence in peptides for proteins with abundant supporting evidence. Degenerate peptides match multiple potential proteins, each associated with a weight. Expectation maximization determines weights that minimize proteins count and maximize protein probability.

35 Parsimony reduces protein lists Maximal list Grouping indiscernibles Grouping + parsimony SwissProt HUMAN International Protein Index SwissProt Multispecies Zhang et al. J. Proteome Res. (2007) 6:

36 Protein FDR is not PSM FDR Minimum PSMs/Prot Confident PSMs Distinct Peptides Distinct Protein Groups Empirical Protein FDR PSM FDR fixed at 3% Two distinct peptides required per protein True PSMs group together on true proteins. False PSMs spread across the database. Data from Broad Institute, CPTAC

37 Takeaway messages Tandem mass spectrometry produces lists of fragment m/z values and precursor masses. Database search narrows the set of all possible peptides to plausible candidates. Controlling peptide and protein FDR is essential for credible, publishable inventories. Parsimony and scalable filtering are necessary to field modern data sets.


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