institut für mathematik & informatik, freie universität...

VL Algorithmische BioInformatik (19710)

WS2013/2014

Woche 14 - Mittwoch

Tim Conrad

AG Medical Bioinformatics

Institut für Mathematik & Informatik, Freie Universität Berlin

Vorlesungsthemen

Part 1: Background Basics (4)

1. The Nucleic Acid World

2. Protein Structure

3. Dealing with Databases

Part 2: Sequence Alignments (3)

4. Producing and Analyzing Sequence Alignments

5. Pairwise Sequence Alignment and Database Searching

6. Patterns, Profiles, and Multiple Alignments

Part 3: Evolutionary Processes (3)

7. Recovering Evolutionary History

8. Building Phylogenetic Trees

Part 4: Genome Characteristics (4)

9. Revealing Genome Features

10. Gene Detection and Genome Annotation

Part 5: Secondary Structures (4) 11. Obtaining Secondary Structure from Sequence 12. Predicting Secondary Structures Part 6: Tertiary Structures (4) 13. Modeling Protein Structure 14. Analyzing Structure-Function Relationships Part 7: Cells and Organisms (6) 15. Proteome and Gene Expression Analysis 16. Clustering Methods and Statistics 17. Systems Biology

Tim Conrad, VL Algorithmische Bioinformatik, WS2013/2014 2

Buch: 15.2

Vorlesungsthemen

Heute: Proteomics I • Einleitung Methodik: 2D Gel

Electrophoresis und Mass Spec

Montag: Proteomics II • Einleitung Algorithmik: Wie

analysiert man Mass Spec Daten?


Colebatch et al (2002) Functional Genomics: tools of the trade. New Phytol 153: 27-36.

mRNA coding for Protein A

Protein A

DNA

Main idea: • Proteins are the „work horses“ in a cell but hard to measure • Last time: use number of mRNAs as proxy instead • This time: try to measure proteins anyway

Same Genome, different Proteome

Why Proteomics?

Contrarily to the static Genome, the Proteome is highly dynamic!


A Single Gene Can Produce Many Proteins

Peck (2005) Plant Physiol 138: 591

Principle:

One gene ≠ one transcript ≠ one protein

ONE Genome but MANY Proteomes!


Tim Conrad, VL Algorithmische Bioinformatik, WS2013/2014

mRNA level = Protein level?

Translation is a regulated process, mRNA is translated to protein at different rates and sometimes not at all

7

Genome vs. proteome


• Human Genome = 20 – 30.000 genes

• Human Proteome = 300.000 to 1.200.000 protein variants

• Genome – static; proteome - dynamic;

• „… there is only a 0.4 correlation between global mRNA and protein expression..”

• PTMs: • Phosphorylation, Acetylation, Methylation, Hydroxy amino

acids, Acylation, Myristic acid, Palmitic acid, Prenylation, Farnesol, Geranylgeranol, Nitrosylation, Oxidation, Other oxidation: loss of SH, Dityrosine formation, Isoaspartate, Glycation variable, Glycoxidation variable, Lipid peroxide adduction variable, … (more than 300 known)

Proteome - Definition

“If the genome is a list of the

instruments in an orchestra, the

proteome is the orchestra playing a

symphony.” R. Simpson

Proteins and Proteomics: A Laboratory Manual (2003)


The goals of proteomics

• Proteomics aims to simultaneously

characterize all proteins in biological

samples

• Identify / sequence the proteins and

determine their relative abundances

• Characterize their posttranslational

modifications

• Determine the three dimensional

structure

• Identify the interactions between

proteins

• Follow all the above during development,

in response to hormonal stimuli, in

health and disease, etc.


Science 291 (2001) 1221.

The Scope of Proteomics

• Protein identity (mutant protein)

• Protein quantity (up or down)

• Protein post-translational modifications (up or down)

• Protein structure

• Protein-protein interaction

• Protein localization

The change in any protein property may cause functional

abnormality and might be relevant to pathogenesis.

Tools

• 2D Gel Electrophorese

• Protein (antibody) array

• Mass Spectrometry


Proteins Are The Key to Understanding Disease

~30,000 Genes

RNA DNA

Transcription

Genome Transcriptome

> 1,000,000 Proteins

Proteins Modified Proteins

Translation Post-Translation Modification

Proteome

Biological Function

x 5 to 50 functional

links per protein


Lecture overview

• What we’ve talked about so far – DNA provides the information template

– RNA activates genetic “programs”

• Overview – How do we detect changes in protein amounts & types?

– How do we know what proteins are present?

Healthy versus Patient

Potential disease marker


• Proteomics

• The 2-D gel technology

• Extracting quantitative information

– Image analysis of 2-D gels

• Comparison with microarrays

Overview


2D Gel Electrophoresis

• Simultaneous

separation and

detection of ~2.000

proteins on a 20x25

cm gel

• Up to 10.000

proteins can be seen

using optimized

protocols


WT EXP

Differential proteomics


Why 2D GE?

• Oldest method for large scale

protein separation (since

1975) and still most popular

• Permits simultaneous

detection, display,

purification, identification,

quantification

• Robust, reproducible, simple

and cost effective

• Modestly scalable &

somewhat parallelizable

• Provides data about pI, MW,

protein quantity


Buch 15.12 (p.613)

Electrophoresis & Proteomics

.


• Proteomics





Overview


Proteomics

DNA

mRNA

Production Modification Degradation

Localisation

Interaction

ACTIVITY

P

TDP

Co-factors

2-D gels


2-D gel electrophoresis: Protein separation and quantification

”protein soup”

spot volume protein quantity

mo

lecu

lar

siz

e

molecular charge

acidic alkaline

sm

all

larg

e


WT EXP



A typical 2-D gel experiment

statistical analysis

conclusions

protein extracts

biological experiment control treatment

2-D gel images

2-D gel electrophoresis

quantified data

image analysis

25211511

225221215211

125121115111

mmmm zzzz

zzzz

zzzz

matrix with

spot volume

data

rows: proteins

(many)

columns: gels

(few)

experimental design

Example:


The image analysis task

• The task 1. In each gel image: Find and quantify the

protein spots

2. In the group of gel images: Match protein

spots in different images that correspond to

the same protein

• Issues – automation

– time


Pseudo-color superposition

0M NaCl 1M NaCl


OM NaCl 1M NaCl



(red: 0M NaCl, blue: 1M NaCl)



The standard solution – workflow

In each gel image 1. Background subtraction

2. Spot detection

3. Spot quantification

In the group of gel images 4. Spot pattern matching


1. Background subtraction

Before After

- =


2. Spot detection / image segmentation


3. Spot quantification

spot volume protein quantity


4. Spot pattern matching


The typical 2-D gel experiment

statistical analysis

conclusions

protein extracts

biological experiment control treatment

2-D gel images

2-D gel electrophoresis

quantified data

image analysis

25211511

225221215211

125121115111

mmmm zzzz

zzzz

zzzz

matrix with

spot volume

data

rows: proteins

(many)

columns: gels

(few)

experimental design

Example:


Limitations

• Technological

– hydrofobic proteins

don’t dissolve

– limited pI/size

coverage

– limited

labeling/staining

• Image analytical

– Limited global

matching efficiency

of automatic

algorithms

– Need for time

consuming manual

guidance

– ”The image analysis

bottle-neck”


Limited global matching efficiency

Voss and Haberl (2000)


Incomplete spot detection: Faint spots

Detected

Not detected


Incomplete spot detection: Close spots


Variability

normal 1M NaCl

normal 1M NaCl

bio

log

ical re

plic

ation

s

growth condition


Variance versus mean dependence

• A dot in the plot:

– the measurement of one

protein

• The quadratic dependence

indicates a multiplicative

error structure

(2x5 gel set; normal growth condition)

slope=2 variance mean2


2D Gel Reproducibility


• Proteomics





Overview


Comparison with microarrays

2-D gels Microarrays

Labeling one channel one or two-color

Background subtr. yes yes

Spot detection HARD easy

Spot quantitation can be difficult quite easy

Spot matching HARD known

Identification MS or reference atlas known

) recently also two-color


Advantages and Disadvantages of 2D GE

• Provides a hard-copy

record of separation

• Allows facile quantitation

• Separation of up to 9000

different proteins

• Highly reproducible

• Gives info on Mw, pI and

post-trans modifications

• Inexpensive

• Limited pI range (4-8)

• Proteins >150 kD not

seen in 2D gels

• Difficult to see

membrane proteins

(>30% of all proteins)

• Only detects high

abundance proteins

(top 30% typically)

• Time consuming


Steps in 2D GE & Peptide ID

• Sample preparation

• Isoelectric focusing (first dimension)

• SDS-PAGE (second dimension)

• Visualization of proteins spots

• Identification of protein spots

• Annotation & spot evaluation


WT EXP



2D GE - Idea

Buch 15.14 (p.616) Tim Conrad, VL Algorithmische Bioinformatik, WS2013/2014

47

Multicolor Staining with Sypro fluorescent stains



49

Gel comparison


50

Expression levels „healthy“ vs. „diseased“


51

WT EXP



Identifying the proteins

Trx

p53

G6PDH

Gel punch


Summary


55

Mass Spectrometry

• Analytical method to measure the

molecular or atomic weight of samples


MS Principles

• Find a way to “charge” an atom or molecule

(ionization)

• Place charged atom or molecule in a magnetic

field or subject it to an electric field and measure

its speed or radius of curvature relative to its

mass-to-charge ratio (mass analyzer)

• Detect ions using

microchannel plate or

photomultiplier tube


Mass Spec Principles

Ionizer

Sample

+

_

Mass Analyzer Detector


Typical Mass Spectrometer


Matrix-Assisted Laser Desorption Ionization

337 nm UV laser

MALDI

cyano-hydroxy

cinnamic acid


MALDI Ionization

+ +

+

+

-

- -

+ +

+

+

-

- - - + +

Analyte

Matrix

Laser

+

+ +

• Absorption of UV radiation

by chromophoric matrix and

ionization of matrix

• Dissociation of matrix,

phase change to super-

compressed gas, charge

transfer to analyte molecule

• Expansion of matrix at

supersonic velocity, analyte

trapped in expanding matrix

plume

(explosion/”popping”)

+

+

+


MALDI Spectra (Mass Fingerprint)

Tumor


Summary


63

Tim Conrad

AG Medical Bioinformatics

www.medicalbioinformatics.de

Mehr Informationen im Internet unter

medicalbioinformatics.de/teaching

Vielen Dank!

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