BIOINF 4399B Computational Proteomics and...

BIOINF 4399B Computational Proteomics and

Metabolomics

Oliver Kohlbacher & Sven Nahnsen

WS 11/12 1. Introduction and Overview

Overview

• Administrative stuff (credits, requirements)

• Motivation/ quick review of relevant contents of

Bioinformatics 2

• Overview of the contents of this lecture

• Proteomics and Metabolomics

• Computational mass spectrometry

• http://abi.inf.uni-tuebingen.de/Teaching/ws-2011-12/

http://abi.inf.uni-tuebingen.de/Teaching/ws-2011-12/








Course Requirements

To pass this course you must:

• regularly and actively participate in the weekly problem sessions,

• pass the final exam and assignments

• You have to work on assignments alone (no groups!)

Course Credits & Grading

• Credits

• MSc Bioinfo: 4 LP, module “Wahlpflichtbereich Bioinformatik”

• MSc Info: 4 LP, area “Wahlpflichtbereich Informatik”

• Diplom: 2+2 SWS Prakt. Informatik (passing required)

• Grade

• 40% assignments

• 60% finals

• Finals: oral exam (30 minutes) covering the contents of the whole lecture and the assignments

• Finals will be scheduled at the end of the semester (in the week of February 7)

Assignments

• One assignment every week

• One week for each assignment, hand in: via e-mail

before the problem sessions on Tuesdays

• Work alone on assignments!

• Assignments will comprise theoretical tasks, as well as

programming tasks

• Schedule 1st assignment:

online: Oct. 17; printed: Oct. 18; due: Oct. 25

Recommended Software

OpenMS/ TOPP

A software library for mass spectrometry

www.openms.de

Contact

• Questions concerning the lecture/assignments

[email protected]

• Website

abi.inf.uni-tuebingen.de/Teaching/SS12/CPM

• Timo Sachsenberg (Sand 14, C322)

• Mathias Walzer (Sand 14, C 304)

• Sven Nahnsen (Sand 14, C322, please send e-mail first)

The central dogma of molecular biology

Origin of the “Central Dogma of Molecular Biology” (Francis Crick, 1956)

• First articulation by Francis Crick in 1956

• Published in Nature in 1970

The central dogma – classical view

• In general, the classic view reflects how biology is (biological data are) organized

• Genomics enabled a more complex view • Barry, P. 2007. Genome 2.0: Mountains of new data are challenging

old views. Science News 172(10):154 (week of Sept. 8).

• The RNA revolution: Biology's Big Bang. The Economist, Jun 14th 2007

• Gerstein et al., 2007. What is a gene, post-ENCODE? History and updated definition. Genome Research 17(6):669-81

• RNA editing can lead to protein sequences that are very different from the initial DNA (Li, M. et al. Science doi:10.1126/science.1207018 (2011))

• …

National Human Genome Research Institute (NHGRI)

Reminder (Bioinformatics 2)

Systems Biology

Oltvai-Barabasi, Science, 2002

Reminder (Bioinformatics 2)

Systems Biology

• Quantitative data on various levels of biological complexity build fundaments of systems biology

• Mathematical modeling has been based on gene expression

• Recent important technological improvements allow the analysis of protein and metabolite profiles to a great depth

• Important layers for understanding biology

• New experimental techniques offer tremendous challenges for computational analysis

Aims of systems biology

• Describe large-scale organization

• Quantitative modeling

• Describe cell as system of networks

• Fundamental research: time-resolved quantitative understanding of living systems

• Medicine: enable personalized medicine (e.g., improve treatment strategies for cancer patients)

• Biotechnology: improve production, degradation, construction of synthetic organisms, etc.

Exp. Methods – Transcriptomics

• Extract and amplify RNA

• Hybridization on microarray

• Identify and quantify by fluorescence signal

• Sequences can be mapped back to genome

Lindsay, Nature Rev. Drug Discovery, 2003, 2, 803

Microarray Data Analysis

• Key problems in microarray data analysis are • Data normalization

• Clustering

• Dimension reduction

• Diagnostics/classification

• Network inference

• Visualization of results

Janko Dietzsch , Nils Gehlenborg and Kay Nieselt. Mayday-a microarray

data analysis workbench. Bioinformatics 2006 22(8):1010-1012

Genome sequencing February 15, 2001 February 16, 2001

Genome sequencing

• 2001: initial publication

• 2003: 2nd draft “Human Genome”

• > 13 years of work and > 3*109 $

• 2010: 8 days 1*104 $

• Future: within 3 years Biotech company (Pacific Biosciences) expects similar amount of data in < 15 min for < 1*103 $

Status genomics/transcriptomics

• Dramatic drop in cost for genome sequencing

• Number of sequenced genomes grows continuously

• Genome is a very static snapshot of living system

• Biological adaption is rather slow; long-term information storage

• Proteins and their reaction products, metabolites are much closer to reality

• Genome and transcriptome databases are essential bases for proteomics and metabolomics research

Genomics vs. Proteomics

Genomics Proteomics Genomes rather static

~ 20 k genes

established technology

(capillary sequencer)

Proteomes are dynamic

(age, tissue, breakfast,

…)

up to 1000 k proteins

emerging technologies

(MS, HPLC/MS, protein chips)

Main fields of proteomics

protein expression

protein characterization

(identification + PTMs) protein interaction

protein localization

?

0.0

0.5

1.0

Applications of proteomics

?

• Drug target identification • Determine content of a

protein mixture

• Understanding regulation

of protein activity

• Gene annotation

• Therapeutic markers

• Drug target identification

• Functional annotation

(compartment and function)

• Drug target identification

protein expression

protein characterization

(identification + PTMs) protein interaction

protein localization

Exp. Methods – Proteomics

• Compare two proteomes (e.g. healthy/diseased)

• Separate using 2D-PAGE (w.r.t. molecular mass, pI)

• Excise protein spots from the gel

• Tryptic digest of the proteins

• Identify proteins using mass spectrometry and Database search

Lindsay, Nature Rev. Drug Discovery, 2003, 2, 803

Separation 1

separate peptides

by their retention

time on column

Ionization

electrospray,

transfers charge

to the peptides

Separation 2

MS separates by

mass-to-charge

ratio (m/z)

HPLC ESI TOF

HPLC-MS

RT

I Spectrum (scan)

Mass Spectrometry

mass

spectrometry

measure a peptide‘s

mass-to-charge ratio

m/z

Inte

nsi

ty

Peak area proportional to

peptide concentration

Proteomics: Database Search

• Identification of mass spectra is easily done through database search

• Search all peptides of matching mass from a database

• Construct a theoretical mass spectrum for these peptide candidates

• Score against the experimental spectrum

Sequence DB

? ? ?

Exp. Metabolomics

• Extract all metabolites/ small molecules, usually < 800 Da

• Separate homogenous collection of analytes (lipids, di- or tripeptides, phospholipids, sugars, etc.)

• Identify and quantify the analytes

Exptl. Metabolomics

Nicholson and Lindon. Nature 2008, 455, 1054-1056

Metabolic Networks

http://www.genome.jp/dbget-bin/www_bget?pathway+ecj00020

Metabolomics: Simulation

http://www.systems-biology.org/cd/simulation/data/sim1cp2.gif

This lecture

Quantitative mass spectrometry System-wide biological data on

proteome and metabolome level

Computational

mass spectrometry

• Basics of Proteomics/ Metabolomics

• Basics of chromatography and mass spectrometry

• Computational mass spectrometry

• Algorithms for peptide/ protein quantification and identification

• Algorithms for metabolite identification and quantification

• Applications, e.g., biomarkers and complete proteomes

• Open research questions, e.g., clinical translation

This lecture

Proteomics

• Studying the proteome Proteome:=

Proteomics

• Studying the proteome Proteome:= All proteins that are expressed in a given organism, tissue or cell at a given state and time

Proteomics

• Studying the proteome Proteome:= All proteins that are expressed in a given organism, tissue or cell at a given state and time

• Goal of studying proteomes: understand the function of all proteins in a biological system

• Large databases have been established, e.g., the Gene Ontology Consortium (www.geneontology.org) catalogues all proteins by their molecular function, biological process and cellular compartment

http://www.geneontology.org/

Protein

• A protein or polypeptide consists of a linear chain of amino acids that build 3-dimensional structures

• Amino acids are connected via peptide bonds

H2N C

H

R1

C NH C C NH C

O

R2

O H

R3

C NH C C

O H O

R4

OH

Peptide bonds

C-terminus N-terminus

Protein

• There are some problematic issues on defining a protein • Protein identity: unique amino acid sequence and single

source of origin?

• There may be different genes encoding the identical amino acid sequence

• Different organisms may encode identical proteins

• Splice variants: A gene can give rise to different mRNAs

• Polymorphisms: many genes occur in allelic variants encoding sequence variations

• Posttranslational modifications: PTMs are very hetero-geneous and significantly alter the function of the protein

Metabolomics

• Studying the metabolome Metabolome:=

Metabolomics

• Studying the metabolome Metabolome:= The metabolome refers to the complete set of small-molecule metabolites (such as metabolic intermediates, hormones and other signaling molecules, and secondary metabolites) to be found within a biological sample, such as a single organism. (http://en.wikipedia.org/wiki/Metabolome)

Technologies

Modern Proteomics and Metabolomics studies are based on

Liquid chromatography (LC)

-

Mass spectrometry (MS)

(Liquid) chromatography

• Mobile phase liquid, stationary phase is usually solid

• Analytes are held back on a column

• “Mobile phase” is pumped over the column

• Analytes continously separate and elute from the column according to specific properties (e.g. hydrophobicity)

• Other chromatography (e.g. gas chromatography) techniques

will also be mentioned

HPLC (High Performance Liquid Chromatography)

pump

column

(stationary phase)

detector

mobile phase

retention time (RT)

detector

pump

eluent

analyte mixture

column

injection valve

HPLC (High Performance Liquid Chromatography)

Mass spectrometry

• Mass spectrometry (MS) is an analytical technique to measure the mass (or more precisely: mass-to-charge ratio, m/z) of an analyte

• MS has a long history in physics and chemistry and today the key technology in proteomics and metabolomics

• “soft ionization” methods enable its application in the bio(-analytical) sciences

• For OMICS analyses MS is usually coupled to a second separation technique (e.g. LC for proteomics and LC/GC for metabolomics)

• There are various types of mass spectrometers (see 3rd lecture)

Mass spectrometry

Modified from Aebersold and Mann, Nature, 2003

Ionization techniques

Mass analyzers Mass detector

Reflector

time-of-flight

(TOF)

time-of-flight

time-of-flight

(TOF-TOF)

Triple

Quadrupole

Quadrupole –

time–of-flightc

Ion trap

Fourier

transform –

Ioncyclotron

resonance

Orbitrap

Electron multiplier

Matrix Assisted

Laser Desorption/

Ionisation (MALDI) Electrospray

ionization (ESI)

Challenges in computational MS

• Huge data sets (up to TBs per experiment)

• Ambiguity in protein identification

• Uncertainty in proteome size

• Ambiguity of masses for small molecules • Peak picking/ Feature Finding

• Map alignment/ Quantification

• Peptide/ Protein Identification

• Metabolite identification

• Statistical analysis

• Enrichment analysis

• Analysis of time course data

• Data integration

upstream

downstream

Peak Picking

raw data sticks

• Identify peaks

• Integrate peaks to sticks

Quantification

• Determine volume of each feature in a map

Quantification

m/z: Isotopic pattern

RT: elution profile

feature model

• Quantification as a 3D signal detection problem

Map alignment

• Correct for retention time offset and distortions in label-free experiments

Peptide Identification

LC-MS/MS experiment Fragment m/z values

Sequence db

Theoretical fragment m/z

values from suitable peptides

Compare

Q9NSC5|HOME3_HUMAN Homer protein homolog 3 -

Homo sapiens (Human)

MSTAREQPIFSTRAHVFQIDPATKRNWIPAGKHALTVSYFY

DATRNVYRIISIGGAKAIINSTVTPNMTFTKTSQKFGQWDS

RANTVYGLGFASEQHLTQFAEKFQEVKEAARLAREKSQD

GGELTSPALGLASHQVPPSPLVSANGPGEEKLFRSQSADA

PGPTERERLKKMLSEGSVGEVQWEAEFFALQDSNNKLAG

ALREANAAAAQWRQQLEAQRAEAERLRQRVAELEAQAAS

EVTPTGEKEGLGQGQSLEQLEALVQTKDQEIQTLKSQTGG

PREALEAAEREETQQKVQDLETRNAELEHQLRAMERSLEE

ARAERERARAEVGRAAQLLDVSLFELSELREGLARLAEAAP

569.24

572.33

580.30

581.46

582.63

606.32

610.24

616.14

569.24

572.33

580.30

581.46

582.63

606.32

610.24

616.14

569.24

574.83

580.70

580.92

579.99

603.92

611.14

616.74

570.84

571.72

580.40

591.18

579.35

607.25

611.42

614.45

569.24

572.33

580.30

581.46

582.63

606.32

610.24

616.14

569.24

572.33

580.30

581.46

582.63

606.32

610.24

616.14

569.24

572.33

580.30

581.46

582.63

606.32

610.24

616.14

569.24

572.33

580.30

581.46

582.63

606.32

610.24

616.14

569.24

572.33

580.30

581.46

582.63

606.32

610.24

616.14

569.24

572.33

580.30

581.46

582.63

606.32

610.24

616.14

1 QRESTATDILQK 18.77

2 EIEEDSLEGLKK 14.78

3 GIEDDLMDLIKK 12.63

Score hits

Theoretical spectra

m/z

[%]

m/z

[%]

m/z

[%]

m/z

[%]

Experimental spectra

m/z

RT

Protein inference

Nesvizhskii, Molecular and Cellular Proteomics, 2005

Metabolite identification

Kind and Fiehn. BMC Bioiformatics 2006, 7:235

Preliminary Schedule Date Topic

Oct 11 Today’s overview

Oct 18 Proteomics and Metabolomics

Oct 25 Physics and chemistry of LC-MS

Nov 1 ALLERHEILIGEN

Nov 8 Lab Excursion: Proteome Center Tübingen

Nov 15 Basic statistics for computational MS

Nov 22 Protein/ Metabolite quantification I

Nov 29 Protein/ Metabolite quantification II

Dec 6 Protein/ Metabolite quantification III

Dec 13 Peptide ID I

Dec 20 Peptide ID II

Dec 27 / Jan 3 CHRISTMAS BREAK

Jan 10 Protein ID

Jan 17 Posttranslational Modifications

Jan 24 Metabolite ID

Jan 31 Summary/ repetition

Week of Feb 7 Exams

Textbooks

Eidhammer, Flikka, Martens, Mikalsen: Computational methods for mass spectrometry proteomics, Wiley, 2007

Good introduction:

• Biochemical basics

• Mass spectrometry

• Algorithms for protein identification/ quantification

Important papers

Nature Reviews, Molecular Cell Biology, 2004

Important papers

Nature, 2003

Important papers

Nature Methods, 2007

Nesvizhskii, Molecular and Cellular Proteomics, 2005

Important papers

Materials

• Script will be made available as a printout at the beginning of

each class

• Additional materials (papers, literature) available on the

course web site or can be requested via e-mail:

[email protected]

course web site:


• Textbooks are available in the library in the section dedicated

to this lecture (Handapparat)

BIOINF 4399B Computational Proteomics and...

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