Proteomics Informatics (BMSC-GA 4437) Course Director David Feny - - PowerPoint PPT Presentation

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Jan 03, 2024 448 likes •768 views

Proteomics Informatics (BMSC-GA 4437) Course Director David Feny Contact information David@FenyoLab.org http://fenyolab.org/presentations/Proteomics_Informatics_2014/ http://fenyolab.org/presentations/Proteomics_Informatics_2014/ Proteomics

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Proteomics Informatics (BMSC-GA 4437)

Course Director David Fenyö Contact information David@FenyoLab.org http://fenyolab.org/presentations/Proteomics_Informatics_2014/

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http://fenyolab.org/presentations/Proteomics_Informatics_2014/

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Proteomics Informatics – Learning Objectives

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

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Proteomics Informatics – Syllabus

Week 1 Overview of proteomics (1/28/2014 at 4 pm in TRB 718) Week 2 Overview of mass spectrometry (2/4/2014 at 4 pm in TRB 718) Week 3 Analysis of mass spectra: signal processing, peak finding, and isotope clusters (2/11/2014 at 4 pm in TRB 119) Week 4 Protein identification I: searching protein sequence collections and significance testing (2/18/2014 at 4 pm in TRB 718) Week 5 Protein identification II: de novo sequencing (2/25/2014 at 4 pm in TRB 718) Week 6 Databases, data repositories and standardization (3/4/2014 at 4 pm in TRB 718) Week 7 Proteogenomics (3/11/2014 at 4 pm in TRB 718) Week 8 Protein quantitation I: Overview (3/18/2014 at 4 pm in TRB 718) Week 9 Protein quantitation II: Targeted (3/25/2014 at 4 pm in TRB 718) Week 10 Protein characterization I: post-translational modifications (4/1/2014 at 4 pm in TRB 718) Week 11 Protein characterization II: Protein interactions (4/10/2014 at 4 pm in TRB 718) Week 12 Molecular Signatures (4/17/2014 at 4 pm in TRB 718) Week 13 Presentations of projects (4/22/2014 at 4 pm in TRB 718)

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Proteomics Informatics – Overview of Proteomics (Week 1)

Why proteomics?
Bioinformatics
Overview of the course

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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.

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Motivating Example: Protein Complexes

Alber et al., Nature 2007

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Motivating Example: Signaling

Choudhary & Mann, Nature Reviews Molecular Cell Biology 2010

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Bioinformatics

Biological System Samples Measurements Experimental Design Raw Data Information Data Analysis

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Mass Spectrometry Based Proteomics Mass spectrometry Lysis Fractionation

MS

Digestion Identified and Quantified Proteins

Peak Finding Charge determination De-isotoping Integrating Peaks Searching

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Proteomics Informatics – Overview of Mass spectrometry (Week 2) Ion Source Mass Analyzer Detector

mass/charge intensity

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Mass Analyzer 1 Frag- mentation Detector Ion Source Mass Analyzer 2

b y

Proteomics Informatics – Overview of Mass spectrometry (Week 2)

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Proteomics Informatics – Overview of Mass spectrometry (Week 2)

Mass Analyzer 1 Frag- mentation Detector

intensity mass/charge

Ion Source Mass Analyzer 2 LC

intensity mass/charge intensity mass/charge intensity mass/charge intensity mass/charge intensity mass/charge

Time

intensity mass/charge intensity mass/charge intensity mass/charge intensity mass/charge intensity mass/charge intensity mass/charge intensity mass/charge intensity mass/charge intensity mass/charge

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Proteomics Informatics – Analysis of mass spectra: signal processing, peak finding, and isotope clusters (Week 3)

m/z Intensity

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Proteomics Informatics – Protein identification I: searching protein sequence collections and significance testing (Week 4)

MS/MS Lysis Fractionation

MS/MS

Digestion Sequence DB All Fragment Masses Pick Protein Compare, Score, Test Significance Repeat for all proteins Pick Peptide LC-MS Repeat for all peptides

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Proteomics Informatics – Protein identification I: searching protein sequence collections and significance testing (Week 4)

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Proteomics Informatics – Protein identification II: de novo sequencing (Week 5)

m/z % Relative Abundance 100 250 500 750 1000 [M+2H]2+ 762 260 389 504 633 875 292 405 534 907 1020 663 778 1080 1022

Mass Differences

1-letter code 3-letter code Chemical formula Monois

topic

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

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Proteomics Informatics – Databases, data repositories and standardization (Week 6)

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Most proteins show very reproducible peptide patterns

Proteomics Informatics – Databases, data repositories and standardization (Week 6)

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Query Spectrum Best match In GPMDB Second best match In GPMDB

Proteomics Informatics – Databases, data repositories and standardization (Week 6)

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Proteomics Informatics – Proteogenomics (Week 7)

Tumor Specific Protein DB Non-Tumor Sample Genome sequencing Identify germline variants Reference Human Database (Ensembl) Genome sequencing RNA-Seq Tumor Sample Identify alternative splicing, somatic variants and novel expression

TCGAGAGCTG TCGAGAGCTG TCGAGAGCTG TCGAGAGCTG TCGAGAGCTG TCGATAGCTG

Exon 1 Exon 2 Exon 3 Exon 1

Variants

Alt. Splicing

Novel Expression

Exon 1 Exon X Exon 2

Fusion Genes

Gene X Exon 1 Gene X Exon 2 Gene Y Exon 1 Gene Y Exon 2 Gene X Gene Y

Kelly Ruggles

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Proteomics Informatics – Protein quantitation I: Overview (Week 8)

Fractionation Digestion LC-MS Lysis

MS

C ij

I ik

p

ij Pr

p

D ijk p Pep ik

p

LC ik

p

MS ik

p

L ij

p p p p p p C I

MS ik LC ik Pep ik j D ijk ij L ij ij k ik

∑

      =

α

Sample i Protein j Peptide k

p p p p p p I C

MS ik LC ik Pep ik D ijk ij L ij k ik k ij Pr

α

=

α k

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Proteomics Informatics – Protein quantitation I: Overview (Week 8)

Fractionation Digestion LC-MS Lysis

MS MS

p p p p p p

MS ik LC ik Pep ik D ijk ij L ij k Pr

α

Assumption: constant for all samples

I I C C

j j j j

i i i i

m n m n

/ /

= Sample i Protein j Peptide k

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Proteomics Informatics – Protein quantitation II: Targeted (Week 9)

Fractionation Digestion LC-MS Lysis

MS

Shotgun proteomics Targeted MS

1. Records M/Z
2. Selects peptides based
n 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

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

Peptide with two possible modification sites MS/MS spectrum

m/z Intensity

Matching Which assignment does the data support? 1, 1 or 2, or 1 and 2?

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A B A C D

Digestion Mass spectrometry

E F

Identification Proteomics Informatics – Protein Characterization II: protein interactions (Week 11)

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Proteomics Informatics – Molecular Signatures (Week 12)

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Proteomics Informatics – Molecular Signatures (Week 12)

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Proteomics Informatics – Presentations of projects (Week 13)

Select a published data set that has been made public and reanalyze it. Highlighted data sets: http://www.thegpm.org/ 10 min presentations

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