Informatics for proteomic inventories
This presentation is the property of its rightful owner.
Sponsored Links
1 / 37

Informatics for proteomic inventories PowerPoint PPT Presentation


  • 87 Views
  • Uploaded on
  • Presentation posted in: General

Informatics for proteomic inventories. [email protected] Biomedical Informatics Vanderbilt University. Overview. Explaining the whys and hows of proteomics Matching peptides from protein sequence databases to MS/MS spectra

Download Presentation

Informatics for proteomic inventories

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Informatics for proteomic inventories

Informatics for proteomic inventories

[email protected]

Biomedical Informatics

Vanderbilt University


Overview

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


Methods capture only part of story

Methods capture only part of story

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.

J_Alves: glycinetRNA

J_Alves: glucose and cholesterol

ElaineMeng: H-ras, PDB 121P


What does proteomics include

What does proteomics include?

Protein

Inventories

Protein

Quantitation

1D and 2D Gel

Electrophoresis

Tissue

Imaging

Post-Translational

Modifications

Gerald_G scales, Gsagri04: gel,

AB SCIEX tissue image


Discovery proteomics

Discovery Proteomics

Protein

Mixture

Peptide

Fractionation

Peptide

Mixture

Liquid

Chromatography

Electrospray

Ionization

High-Resolution

Mass Spectrometry

Isolate

Ions of Peptide

Collide Ions to

Dissociate

Collect Fragments

in Tandem MS

Two types of measurements for each peptide: intact m/z (mass/charge) and a list of fragment m/zs.


Collision induced dissociation cid

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: 1399-406.

Paizs and Suhai, Rapid Comm. Mass Spectrom. (2002) 16: 1699-1702.


Broken peptide bonds yield fragments

Broken peptide bonds yield fragments

TSIIGTIGPK

N-terminal

b4 ion

C-terminal

y6 ion


Hfiselek 2 charge state

HFISELEK, +2 charge state

Neutral loss of

water from peptide

-ISELEK

-FISELEK

-LEK

-SELEK

HF-


Same spectrum compared to fheikels instead of hfiselek

Same spectrum compared toFHEIKELS instead of HFISELEK

Neutral loss of

water from peptide

-EIKELS has same

mass as -ISELEK

FH- has same

mass as HF-


Disassembly and reassembly

Disassembly and reassembly

After AI Nesvizhskii, Mol Cell Proteomics (2005) 4: 1419-40.


Database search overview

Database search overview

Eng et al (1994) J. Amer. Soc. Mass Spectrom. 5: 976-989.

Yates et al (1995) Anal. Chem. 67: 1426-1436.


Emulating proteases in silico

Emulating proteases in silico

N Edwards and R Lippert. Lecture Notes In Computer Science (2002) 2452: 68-81.


Dynamic ptms grow search space

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


Peptide mass filter

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.


Fragment masses and charge segregation

Fragment masses andcharge segregation

H+

H+

+2

+3

AA

AA

AA

H

AA

AA

AA

OH

H+

H+

H+

AA

AA

AA

H

AA

AA

AA

OH

H+

H+

H+

AA

AA

AA

H

AA

AA

AA

OH


Sequest cross correlation

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: 4598-4602.

J Eng et al. J Amer. Soc. Mass. Spectrom. (1994) 5: 976-989.


X tandem scoring

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) 17:2310-2316.

Fenyö and Beavis. Anal. Chem. (2003) 75: 768-774.


Random match probabilities

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: 455-476.


Disassembly and reassembly1

Disassembly and reassembly

After AI Nesvizhskii, Mol Cell Proteomics (2005) 4: 1419-40.


The longest list problem

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: 531-533.

Taylor et al. Nature Biotech. (2007) 25: 887-893


Strategy i target decoy estimates fdr

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: 207-214


Decoys model false distribution

Decoys model false distribution

  • A match to targets is possibly true; a match to decoys is surely false.

  • As threshold slides to lower scores, more decoys are kept, escalating FDR.

  • Alternatively, may be used if decoys are excluded from final list.

Elias Nat. Methods (2007) 4: 207-214


Strategy ii peptide prophet

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: 5383-5392.


Discriminant function analysis combines sub scores from sequest

Discriminant Function Analysiscombines sub-scores from Sequest


Mixture model analysis separates true and false distributions

Mixture Model analysisseparates 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.


Disassembly and reassembly2

Disassembly and reassembly

After AI Nesvizhskii, Mol Cell Proteomics (2005) 4: 1419-40.


Why are peptides shared among proteins

Why are peptides sharedamong 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 1005.1-1005.2.


Protein isoforms

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.


Parsimony

Parsimony

  • noun: “economy of explanation in conformity with Occam's razor”

    • Merriam Webster OnLine

  • “Plurality ought never be posed without necessity.”

    • William of Occam


Idpicker

IDPicker

  • Assemble maximal protein list.

  • Combine proteins that point to the same peptides, and combine peptides that point to the same proteins.

  • Find “set cover” by greedy algorithm to pick minimal protein list to explain peptides.

B Zhang et al. J. Proteome Res. (2007) 6: 3549-3557.

Z Ma et al. J. Proteome Res. (2010) 8: 3872-3881.


Two proteins or seven

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


Greedy algorithm

Greedy algorithm

Data from Broad Institute, CPTAC


Proteinprophet

ProteinProphet

  • Combine peptide identification probabilities into protein identification probabilities.

  • Distribute probability for shared peptides across multiple proteins.

  • Compute protein probability by subtracting probability that all observed peptides are false from 1.

    • AI Nesvizhskii. Anal. Chem. (2003) 75: 4646-4658.


Number of sibling peptides and degenerate peptides

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.


Parsimony reduces protein lists

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: 3549-57.


Protein fdr is not psm fdr

Protein FDR is not PSM 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


Takeaway messages

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.


  • Login