Proteomics Informatics (BMSC-GA 4437)

Course Director

David Fenyö

Contact information

David@FenyoLab.org

http://fenyolab.org/pi2015/

http://fenyolab.org/pi2015/

Proteomics Informatics – Learning Objectives

Be able analyze proteomics data sets and understand the limitations of the results.

Proteomics Informatics – Syllabus

Lecture 1 Overview of proteomics (February 3, 2014 TRB 717 4pm)

Lecture 2 Overview of mass spectrometry (February 10, 2014 TRB 717 4pm)

Lecture 3 Signal processing I: analysis of mass spectra (February 17, 2014 TRB 718 4pm)

Lecture 4 Protein identification I: searching protein sequence collections and significance testing (February 24, 2014 TRB 718 4pm)

Lecture 5 Protein quantitation I: overview (March 3, 2014 TRB 717 4pm)

Lecture 6 Databases, data repositories and standardization (March 10, 2014 TRB 717 4pm)

Lecture 7 Protein identification II: de novo sequencing (March 17, 2014 TRB 717 4pm)

Lecture 8 Protein quantitation II: multiple meaction monitoring (March 24, 2014 TRB 717 4pm)

Lecture 9 Proteogenomics (March 31, 2014 TRB 619 4pm)

Lecture 10 Protein characterization I: post-translational modifications (April 7, 2014 TRB 717 4pm)

Lecture 11 Signal processing II: image analysis (April 21, 2014 TRB 717 4pm)

Lecture 12 Protein characterization II: protein interactions (April 28, 2014 TRB 619 4pm)

Lecture 13 Data analysis and visualization (May 5, 2014 TRB 717 4pm)

Lecture 14 Molecular signatures (May 12, 2014 TRB 717 4pm)

Lecture 15 Presentations of projects (May 19, 2014 TRB 717 4pm)

Overview of Proteomics (Week 1)

• Why proteomics?

• Bioinformatics

• Overview of the course

Motivating Example: Protein Regulation

Geiger et al., “Proteomic changes resulting from gene copy number variations in cancer cells”, PLoS Genet. 2010 Sep 2;6(9). pii: e1001090.

Motivating Example: Protein Complexes

Alber et al., Nature 2007

Motivating Example: Signaling

Choudhary & Mann, Nature Reviews Molecular Cell Biology 2010

Bioinformatics

Biological System

Samples

Measurements

Experimental Design

Raw Data

Information

Data Analysis

Mass Spectrometry Based Proteomics

Mass spectrometry

LysisFractionation

MS

Digestion

Identified and Quantified Proteins

Peak Finding Charge determination

De-isotopingIntegrating Peaks

Searching

Ion Source

Mass Analyzer

Detector

mass/charge

inte

nsi

ty

Overview of Mass spectrometry (Week 2)

Mass Analyzer 1

Frag-mentation

DetectorIon

SourceMass

Analyzer 2

b y

Overview of Mass spectrometry (Week 2)

Mass Analyzer 1

Frag-mentation

Detector

inte

ns

ity

mass/charge

Ion Source

Mass Analyzer 2

LC

inte

ns

ity

mass/chargeinte

ns

ity

mass/charge

inte

ns

ity

mass/chargeinte

ns

ity

mass/chargeinte

ns

ity

mass/charge

Time

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mass/chargeinte

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mass/chargeinte

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ity

mass/charge

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ns

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mass/chargeinte

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mass/chargeinte

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mass/charge

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ns

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mass/chargeinte

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mass/charge

Overview of Mass spectrometry (Week 2)

Signal processing I: Analysis of mass spectra (Week 3)

m/z

Inte

ns

ity

Protein identification I: searching protein sequence collections and significance

testing (Week 4)

MS/MS

LysisFractionation

MS/MS

Digestion

SequenceDB

All FragmentMasses

Pick Protein

Compare, Score, Test Significance

Re

pe

at fo

r all p

rote

ins

Pick PeptideLC-MS

Re

pe

at fo

ra

ll pe

ptid

es

Protein identification I: searching protein sequence collections and significance

testing (Week 4)

Protein quantitation I: Overview (Week 5)

Fractionation

Digestion

LC-MS

Lysis

MS

C ij

I ik

pij

Pr

pD

ijk

pPep

ik

pLC

ik

pMS

ik

pL

ij

ppppppCIMS

ik

LC

ik

Pep

ikj

D

ijkij

L

ijijkik

Pr

Sample iProtein jPeptide k

ppppppIC MS

ik

LC

ik

Pep

ik

D

ijkij

L

ijk

ikk

ij Pr

k

Protein quantitation I: Overview (Week 5)

Fractionation

Digestion

LC-MS

Lysis

MS MS

ppppppMS

ik

LC

ik

Pep

ik

D

ijkij

L

ijk

Pr

Assumption:

constant for all samples

IICC jjjj iiii mnmn//

Sample iProtein jPeptide k

Databases, data repositories and standardization (Week 6)

Most proteins show very reproducible peptide patterns

Databases, data repositories and standardization (Week 6)

Query Spectrum

Best match In GPMDB

Secondbest match In GPMDB

Databases, data repositories and standardization (Week 6)

Protein identification II: de novo sequencing (Week 7)

m/z

% R

ela

tive

Ab

un

da

nce

100

0250 500 750 1000

[M+2H]2+

762

260 389 504

633

875

292405 534

9071020663 778 1080

1022

Mass Differences

1-letter code

3-letter code

Chemical formula

Monoisotopic

Average

A Ala C3H5ON 71.0371 71.0788

R Arg C6H12ON4 156.101 156.188

N Asn C4H6O2N2 114.043 114.104

D Asp C4H5O3N 115.027 115.089

C Cys C3H5ONS 103.009 103.139

E Glu C5H7O3N 129.043 129.116

Q Gln C5H8O2N2 128.059 128.131

G Gly C2H3ON 57.0215 57.0519

H His C6H7ON3 137.059 137.141

I Ile C6H11ON 113.084 113.159

L Leu C6H11ON 113.084 113.159

K Lys C6H12ON2 128.095 128.174

M Met C5H9ONS 131.04 131.193

F Phe C9H9ON 147.068 147.177

P Pro C5H7ON 97.0528 97.1167

S Ser C3H5O2N 87.032 87.0782

T Thr C4H7O2N 101.048 101.105

W Trp C11H10ON2 186.079 186.213

Y Tyr C9H9O2N 163.063 163.176

V Val C5H9ON 99.0684 99.1326

Amino acid masses

Sequences consistent

with spectrum

Protein quantitation II: Targeted (Week 8)

Fractionation

Digestion

LC-MS

Lysis

MS

Shotgun proteomics Targeted MS

1. Records M/Z

2. Selects peptides based on abundance and fragments MS/MS

3. Protein database search for peptide identification

Data Dependent Acquisition (DDA) Uses predefined set of peptides

1. Select precursor ion

MS

2. Precursor fragmentation

MS/MS

3. Use Precursor-Fragment pairs for identification

Proteogenomics (Week 9)

Tumor Specific

Protein DB

Non-Tumor Sample Genome sequencing Identify germline variants

Reference Human Database (Ensembl)

Genome sequencingRNA-SeqTumor Sample

Identify alternative splicing, somatic variants and

novel expression

TCGAGAGCTGTCGAGAGCTGTCGAGAGCTGTCGAGAGCTGTCGAGAGCTGTCGATAGCTG

Exon 1 Exon 2 Exon 3

Exon 1

Variants

Alt. Splicing Novel Expression

Exon 1 Exon X Exon 2

Fusion Genes

Gene XExon 1

Gene XExon 2

Gene YExon 1

Gene YExon 2

Gene X Gene Y Kelly Ruggles

Protein characterization I: post-translational modifications (Week 10)

Peptide with two possible modification sites

MS/MS spectrum

m/z

Inte

nsi

ty

Matching

Which assignment doesthe data support?

1, 1 or 2, or 1 and 2?

Signal processing II: image analysis (Week 11)

Agullo-Pascual E, Reid DA, Keegan S, Sidhu M, Fenyö D, Rothenberg E, Delmar M, "Super-resolution fluorescence microscopy of the cardiac connexome reveals plakophilin-2 inside the connexin43 plaque", Cardiovasc Res. 2013

AB

A

CD

Digestion

Mass spectrometry

EF

Identification

Protein Characterization II: protein interactions (Week 12)

Data analysis and visualization (Week 13)

Molecular Signatures (Week 14)

Molecular Signatures (Week 14)

Presentations of projects (Week 15)

Select a published data set that has been made public and reanalyze it.

Highlighted data sets: http://www.thegpm.org/

10 min presentations

Proteomics Informatics (BMSC-GA 4437)

Course Director

David Fenyö

Contact information

David@FenyoLab.org

http://fenyolab.org/pi2015/