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“Proteomics & Bioinformatics”. MBI, Master's Degree Program in Helsinki, Finland. Lecture 5. 11 May, 2007. Sophia Kossida , BRF, Academy of Athens, Greece Esa Pit känen , Univeristy of Helsinki, Finland Juho Rousu , University of Helsinki, Finland. Mining proteomes.

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proteomics bioinformatics
“Proteomics & Bioinformatics”

MBI, Master's Degree Program in Helsinki, Finland

Lecture 5

11 May, 2007

Sophia Kossida, BRF, Academy of Athens, Greece

Esa Pitkänen, Univeristy of Helsinki, Finland

Juho Rousu, University of Helsinki, Finland

mining proteomes
Mining proteomes

To identify as many components of the proteome as possible

Mapping of proteomes of various organisms and tissues

Comparison of protein expression levels for the detection of disease biomarkers

slide3

How to select proteome?

A proteome is defined by the state of the organism, tissue, or cell that produces it.

Because these states are constantly changing, so are the proteomes.

Example of proteomes:

different kind of cells; liver, …

extracellular fluids;blood plasma, urine, CSF…

applications
Applications

Systems biology- understand cell-pathways, network, and complex interacting.

Biological processes- characterize sub-proteomes such as protein complexes, cellular machines, organelles

Biomarkers - discovery of disease (serological, urine, other biological fluids) - diagnostics, treat patients, monitor therapies

Drug targets- evaluate toxicity & other biological or pharmaceutical parameters associated with drug treatment

protein profiling
Protein Profiling

Measure the expression of a set of proteins in two samples and compare them - Comparative proteomics

  • 2D gel electrophoresis
  • Difference gel electrophoresis (DIGE)
  • LC-MS/MS using coded affinity tagging
  • (ICAT, iTrac, SILAC..)
  • ProteinChip Array (SELDI analysis)
  • Antibody arrays
laser capture micro dissection lmc
Laser-Capture Micro dissection, LMC

Technique for selectively sampling certain cells within a tissue

Biopsy

Tissue sample

Transfer film

Tumor

Glass slide

Laser beam activates film

Cells

Selected cells are transferred

Genomic/proteomic analysis

Modified from “National Cancer Institute”, US National Institutes of Health:

http://www.cancer.gov/cancertopics/understandingcancer/moleculardiagnostics/Slide29

2d gels dige

Coomassie blue stained gels

Silver stained

2D gels, DIGE

High resolving power

Absolute / relative quantity

Easily archived for further comparison

Detects some PTMs and alternatives splices

Low troughput

Poor detection of large, acidic, basic and membrane proteins

Only high abundance proteins

slide8
DIGE

Proteins are labeled prior to running the first dimension with up to three different fluorescent cyanide dyes

Mix labeled extracts

Internal standard

Allows use of an internal standard in each gel-to-gel variation, reduces the number of gels to be run

Adds 500 Da to the protein labeled

Additional post-electrophoretic staining needed

human brain proteins

Control

phosphoglycerate mutase

phosphoglycerate mutase

Alzheimer’s Disease

phosphoglycerate mutase

phosphoglycerate mutase

Human brain proteins

Differences in Expression Level in Thalamus

lc ms ms using coded affinity tagging
LC-MS/MS using coded affinity tagging

Moderate throughput, but can be automated

Detects some low abundance proteins

Most isotope label experiments limited to two versions –heavy and light isotope, i.e. binary comparisons only

Poor detection of alternative splices and PTMs

labeling
Labeling

Chemical, ICAT, ITRAQ

Chemical modifications to amino acids generally after digestion

Most labels differ by 3-10Da in mass (not complete / interferences)

Compares only 2-8 samples

SILAC Stable isotopes incorporated during cell growth

Must be able to grow cells

Compares 2 or 3 samples

Lys (+8 Da) and Arg (+10 Da)

Ion Current

No labeling of any kind, See everything in the sample not just what gets labeled

Normalization issues, (2 separate runs are compared) Standards needed

Robust and many samples and experimental conditions can be compared

isotope coded affinity tag icat

Quantification

heavy

LC-MS-MS

m/z

light

A

B

Identification

Isotope Coded Affinity Tag (ICAT)

Two protein samples, are labeled with normal and heavy versions of the same isotope-coded affinity tag (ICAT) reagent, respectively. The reagent binds to cysteine residues and carries a biotin-tag.

Samples are mixed, digested and ICAT-labeled peptides are recovered via the biotin tag of the ICAT reagents by -affinity chromatography.

Drawback: Cysteine containing peptides only

slide14
ICAT
  • Label protein samples with heavy and light reagent
  • Reagent contains affinity tag and heavy or light isotopes

Chemically reactive group: forms a covalent bond to the protein or peptide

Isotope-labeled linker: heavy or light, depending on which isotope is used

Affinity tag: enables the protein or peptide bearing an ICAT to be isolated by affinity chromatography in a single step

Modified from http://skop.genetics.wisc.edu/AhnaMassSpecMethodsTheory.ppt#260,11,Mass Spectrometry

example of an icat reagent

Reactive group: Thiol-reactive group will bind to Cys

Biotin Affinity tag: Binds tightly to streptavidin-agarose resin

O

Linker: Heavy version will have deuteriums at *

Light version will have hydrogens at *

NH

NH

H

H

N

O

O

*

N

*

I

O

*

*

O

S

O

Example of an ICAT Reagent

Modified from http://skop.genetics.wisc.edu/AhnaMassSpecMethodsTheory.ppt#260,11,Mass Spectrometry

stable isotope labeling
Stable-isotope labeling

Aebersold and Mann, Nature, 2004

isobaric tag reagent
Isobaric tag reagent

Isobaric tags for relative and absolute quantification

Allows us to compare the relative abundance of proteins from four different samples in a single mass spectrometry experiment

Isobaric Tag (Total mass =145 Da)

Peptide reactive group

Reporter

mass=114 to 117

Gives strong signature ion in MS/MS

Good b- and y-series

Maintains charge state and ion masses

Signature ion masses lie in quiet low mass region

Balance

mass 31 to 28

Balances the mass change of reporter to maintain a total mass of 145

Neutral loss in MS/MS

Amine specific

itraq

114

b

115

P

D

E

T

I

P

E

116

y

117

NHS + peptide

NHS + peptide

NHS + peptide

NHS + peptide

31

114

30

115

29

116

28

117

iTRAQ

Uses up to 4 tag reagents that bind covalently to the N-terminus of the peptide and any Lysine side chains at the amine group (global tagging).

Each sample set is digested separately and then mixed with the specific iTRAQ tag

Samples mixed

MS

Reporter – Balance - Peptide intact

4 samples identical m/z

MS/MS

Peptide fragments –equal

Reporter ions different

Modified from “Quantitative Proteomics Using

Isotope Tagging of Peptides” by Kathryn Lilley

s table isotope labelin g in cell culture

heavy

light

m/z

cell culture (in vivo)

amino acid metabolism

Steen & Mann, Nature, 2004

Stable isotope labeling in cell culture

SILAC

1. Cell culture with normal Arginine

2. Cell culture plus “heavy” Arginine.

LC-MS/MS

Combine, digest, (purification)

Quantify levels from peak ratio

slide21

Ratio ~4:1

4Da @ +2 ion = 8 Da (Lys)

SILAC Example

From presentation by: Nicholas E. Sherman, Ph.D.

http://www.healthsystem.virginia.edu/internet/biomolec/Keck_Dec12_2006.ppt#387,15,Slide 15

seldi
SELDI

Surface Enhanced Laser Desorption Ionization

Ionized proteins are detected and their mass accurately determined by Time-of-Flight Mass Spectrometry

High throughput

Small amounts of sample

More reproducible than 2DE, but lower resolving power

Applied for the analysis of crude samples

Process is not standardized

the seldi chip

Chemical Surfaces

(Hydrophobic)

(Anionic)

(Cationic)

(Metal Ion)

(Normal Phase)

Biological Surfaces

(PS10 or PS20)

(Antibody - Antigen)

(Receptor - Ligand)

(DNA - Protein)

The SELDI-chip
antibody arrays
Antibody arrays

Not discovery based

Must have 1 or 2 specific high affinity antibodies

Very high throughput

Can be highly quantitative - relative and absolute

Can design reagents to detect PTMs, splice forms

antibody array

Forward phase

Reverse phase

Sandwich assay

Direct assay

Detection with 2nd Antibody

Detection with Labeled Analyte

Detection with Labeled Antibody

Analyte

Antibody immobilized on glass substrate

Analytes immobilized on glass substrate

Antibody array

Modified from slide; FullMoonBiosystemsInc. (http://www.fullmoonbio.com/Doc/Overview.pdf)

protein protein interactions
Protein Protein Interactions

From single proteins to systems biology

protein protein interactions27
Protein-Protein Interactions

Proteins “work together” forming multi complexes to carry out the specific functions

identification of interactions
Identification of interactions
  • Experimental
    • x-ray crystallography
    • NMR spectroscopy
    • Mass spectrometry
          • (Tandem affinity purification)
    • Immunoprecipitation
    • Yeast two-hybrid
    • Microarrays
  • Computational
    • Genomic data
    • Phylogenetic profiling
    • Gene context
    • Gene fusion
    • Symmetric evolution
    • Structural data
    • Sequence profile
    • 3D structural distance matrix
    • Surface patches
    • Binding interactions
x ray crystallography
X-ray crystallography

Crystals hard to obtain

Good for large proteins

Bioinformatics center, University of Copenhagen

Modified from presentation

;http://www.biosys.dk/courses/Previous_courses/Introductory_Bioinformatics/protein_structure.pdf

nuclear magnetic resonance
Nuclear Magnetic Resonance

Multidimensional NMR

NMR Spectroscopy

For proteins in solution

Better for small proteins than large ones

identification by mass spectrometry

Protein complex

Identification by mass spectrometry

SDS-PAGE

MALDI-TOF

Immunoprecipitate

anti-

Peptide mixture

LC-MS-MS

“shotgun” identification

immunoprecipitation

Protein complex

anti-

anti-

anti-

anti-

Immunoprecipitation

Immunoprecipitation of a protein of interest, analyzed by 1D-SDS-PAGE

Electrophoretically transferred to membrane, the membrane is probed with antibodies suspected as partners of the target protein

SDS-PAGE

Immunoprecipitation

Western blot

undetected

Only detects what one sets out to look for.

Obtaining a suitable antibody is important.

The antibody might immuno-precipitate the protein successfully, but not when other interacting proteins are present.

yeast two hybrid system
Yeast Two-Hybrid System

A transcription factor is split into 2 domains and two hybrid proteins are designed.

One protein of interest (bait) is typically fused to a DNA-binding domain.

The proteins being screened for interactions with the bait (preys) are fused to a transcription-activating domain.

An interaction between the bait and a prey will bring these 2 domains close together which in turn results in the transcription of a reporter gene.

The reporter can be:

essential, in which case the colony dies if no interaction reversely, the reporter gene can be attached to a green fluorescent protein

Prey protein

Bait protein

mRNA

Activation

Domain

Binding

Domain

Promoter Region

Reporter Gene

The rate of false positive is high (estimated > 45%)

microarray co expression
Microarray co-expression

Microarray: study the expression of genes as a a function of time, or

following treatment with a drug, …

Co-expression of genes are usually a sign that the two proteins interact.

Gene A

Gene B

Expression level

Time or treatment

identification of co expressed genes
Identification of Co-expressed Genes

To determine which genes have similar/correlated expression patterns – to derive their functional relationships

  • Data clustering
    • We can represent each gene as a vector (5, 15, 10, 7, 5, 3)
    • So a set of expression data can be represented as a collection of data points in K-dimensional space
    • Genes with similar expression patterns form data clusters
slide36

C

A

In silico Prediction of PPI

Phylogenetic Profile

The phylogenetic profile of a protein is a string that encodes the presence or absence of the protein in every sequenced genome

Conserved presence orabsence of a protein pair suggests functional coupling.

  • Phylogenetic profile (against N genomes):
  • For each gene X in a target genome: if gene X has a homolog in genome #i, the ith bit of X’s phylogenetic profile is “1” otherwise it is “0”
in silico prediction of ppi

Org 1

Protein A

Org 2

Protein B

Org 3

Protein C

Org 4

B

A

Org 1

Org 2

In silico Prediction of PPI
    • Gene Context
  • Conserved gene neighbourhood suggests position- function coupling
    • Gene Fusion (Rosetta stone)
  • Seemly unrelated proteins are sometimes found fused in another organism

Though gene-fusion has low prediction coverage, its false-positive rate is low

in silico prediction of ppi38
In silico Prediction of PPI

Symmetric Evolution

Interaction positions on different proteins should co-evolve so as to maintain the interface.

Look for correlation between sequence changes at one position and those at another position in a multiple sequence alignment.

Docking

determination of protein complex structure from individual protein structures

structure and interaction databases
Structure- and interaction databases

STRING(EMBL)

BOND(Unleashed Informatics)

DIP(UCLA)

iHOP

string
STRING

http://string.embl.de

slide41
BOND

Biomolecular Object Network Databank

http://bond.unleashedinformatics.com

database of interacting proteins
Database of Interacting Proteins

The DIP database catalogs experimentally determined interactions between proteins. It combines information from a variety of sources to create a single, consistent set of protein-protein interactions.

http://dip.doe-mbi.ucla.edu/

slide43
ihop

http://www.ihop-net.org/UniPub/iHOP/

slide45

Laser

Flight tube

+

+

+

+

Identification of diagnostic proteomic patterns

Bladder Cancer

Benign

Fingerprinting of bladder cancer

Combination of protein extract

MALDI-TOF/TOF

LC

blood/urine

Application of bioinformatics tools

(feature extraction, classification algorithms)

Disease classification

strategy for biomarker discovery
Strategy for Biomarker Discovery

Genomic analysis

mRNA level

Diseasevs. Normal

Proteomic analysis

(2D gels / MS)

Discovery

Candidate gene

Validation

in situhybridizationImmunohistochemistry

Application

Large # samples

Small # candidates

Clinical Application

Diagnostic

Prognostic

Therapeutic

proteins as biomarkers
Proteins as biomarkers

The protein composition may be associated with disease processes in the organism and thus have potential utility as diagnostic markers.

Proteins are closer to the actual disease process, in most cases, than parent genes

Proteins are ultimate regulators of cellular function

Most cancer markers are proteins

The vast majority of drug targets are proteins

Individual biomarkers are not sufficient for accurate disease detection

Panel of biomarkers should be established

benefits of molecular diagnostics
Benefits of Molecular Diagnostics

proteins

MS

Patient’s blood sample

Ovarian pattern

  • Create new cancer screening tools
  • Inform design of new treatments
  • Monitor treatment effectiveness
  • Predict patient’s response to treatment
from known samples to serum proteins

no cancer

proteins

proteins

cancer

From known samples to serum proteins

Patterns as screening tool

MS

Protein patterns

Early diagnosis

of disease

Early warning

of toxicity

MS

proteomics in nutrition of food
Proteomics in nutrition of food

Development of fingerprinting techniques to identify changes in modified organisms at different integration levels (2D gels, MALDI) MALDI-MS).

identification of unintended side effects
Identification of unintended side effects

A proteome analysis of livers from mice traeted with WY14.643

Isolation of protein spots

Peptide mapping

MALDI-TOF analysis

Amino acid sequence

Data base

16 proteins

Protein identified

Proteins from animals after treatment

Liver proteins from control

http://i-council-biomed-biotech.org/Contacts%20to%20Add_files/Haoudi%20Oman%20Feb%202005.pdf

biomarker discovery
Biomarker Discovery
  • Markers can be easily found by comparing protein maps.
  • SELDI is faster and more reproducible than 2D PAGE.
  • Has been used to discover protein biomarkers of diseases such as ovarian cancer, breast cancer, prostate and bladder cancers.

Modified from Ciphergen Web Site)

gene ontology
Gene Ontology
  • A knowledge representation about the word or some part of it.
  • An ontology is used as a description of the concepts and relationships that exist for a community of agents.
  • Ontology generally describes:
    • Individuals: the basic or “ground level” objects
    • Classes: sets, collections, or types of objects
    • Attributes: properties, features, characteristics, or parameters that objects can have and share
    • Relations: ways that objects can be related to one another

from: wikipedia

goals
Goals

Develop a set of controlled, structured vocabularies – gene ontology (GO) to describe aspects of molecular biology

Describe gene products using vocabulary terms (annotation)

Provide a public resource, allowing access to the GO, annotations and software tools developed for use with the GO data

www.geneontology.org

slide56

The Three Ontologies

Molecular Function — describes activities, or tasks, performed by individual or by assembledcomplexes of gene products.

DNA binding, transcription factor

Biological Process — a series of events accomplished by one or more ordered assemblies of molecular functions.

NOT a “pathway”!

mitosis, signal transduction, metabolism

Cellular Component — location or complex , a component of a cell, that also is part of some larger object

nucleus, ribosome, origin recognition complex

slide57

Relationships between terms

Directed acyclic graph: each child may have one or more parents

Every path from a node back to the root must be biologically accurate(the true path rule)

Relationship types:

is_a; class-subclass relationship, meaning that a is a type of b

Exemple: nuclear chromosome is_a chromosome.

part_of :physical part of (component) subprocess of (process)

part_ofcpart_ of d,meaning that whenever c is present, it is a part of d, but c doesn’t always have to be present.

Example: nuleus part_of cell ; meaning that nuclei are always part of a cell, but not all cells have nuclei.

relationships between terms
Relationships between terms

Example:

the biological process term hexose biosynthesis has two parents, hexose metabolism and monosaccaride biosynthesis. This is because biosynthesis is a subtype of metabolism, and a hexose is a subtype of monosaccharide.

When any gene involved in hexose biosynthesis is annotated to this term, it is automatically annotated to both hexose metabolsim and monosaccharide biosynthesis, because every GO term must obey the “true path rule”, if the child term deescribes the gene product, then all its parent terms must also apply to that gene product..

evidence codes
Evidence codes

IC: Inferred by Curator

IDA: Inferred from Direct Assay

IEA: Inferred from Electronic Annotation

IEP: Inferred from Expression Pattern

IGC: Inferred from Genomic Context

IGI: Inferred from Genetic Interaction

IMP: Inferred from Mutant Phenotype

IPI: Inferred from Physical Interaction

ISS: Inferred from Sequence or Structural Similarity

NAS: Non-traceable Author Statement

ND: No biological Data available

RCA: Inferred from Reviewed Computational Analysis

TAS: Traceable Author Statement

NR: Not Recorded

go tools
GO tools
  • search for gene products and view the terms with which they are associated;
  • search or browse the ontology for GO terms of interest and see term details and gene product annotations.
  • AmiGO also provides a BLAST search engine, which searches the sequences of genes and gene products that have been annotated to a GO term and submitted to the GO Consortium.