mn-b-wp ii (binf 2) bioinformatische datenbanken

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-B-WP II (BInf 2) Bioinformatische Datenban Kay Hofmann – Protein Evolution Group http://www.genetik.uni-koeln.de/groups/H Woche 4: Interaktionsdatenbanken

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MN-B-WP II (BInf 2) Bioinformatische Datenbanken. Woche 4: Interaktionsdatenbanken. Kay Hofmann – Protein Evolution Group http://www.genetik.uni-koeln.de/groups/Hofmann. Classification of physical protein interactions. By mechanism b inding ( non-covalent ) b inding (covalent ) - PowerPoint PPT Presentation

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Page 1: MN-B-WP II (BInf 2)  Bioinformatische Datenbanken

MN-B-WP II (BInf 2) Bioinformatische Datenbanken

Kay Hofmann – Protein Evolution Grouphttp://www.genetik.uni-koeln.de/groups/Hofmann

Woche 4: Interaktionsdatenbanken

Page 2: MN-B-WP II (BInf 2)  Bioinformatische Datenbanken

Classification of physical protein interactionsBy mechanism· binding (non-covalent)· binding (covalent)· Modification (e.g. phosphorylation)· Cleavage, degradation· Folding (e.g. chaperones)

By effect· undirected· Activating A → B or B → A· Inhibiting A ─┤B or B ─┤ A

By time frame· transient· permanent· covalent)

By stoichiometry· binary· multimeric

Page 3: MN-B-WP II (BInf 2)  Bioinformatische Datenbanken

Binary and complex interactions

Several binary interactions can lead to multi-protein complexes

A B A B C

DE

A B

C

DE

A B C A B

C

A B

C

A B

C

2x binary ternary

Page 4: MN-B-WP II (BInf 2)  Bioinformatische Datenbanken

Protein complexes with variable Stoichiometry

A BC

DA B

C

D+

Kd

A BC A B

C

D

A BC

E

A BC

F

Equilibrium with sub-complexe

Complex with 'part time' subunits

Page 5: MN-B-WP II (BInf 2)  Bioinformatische Datenbanken

Example: Proteasome

Page 6: MN-B-WP II (BInf 2)  Bioinformatische Datenbanken

Example: SCF-complexes

ub

R1

F

S1

R2

F

S2

R3

F

S3

skp1

cullin

rbx1

E2F

R SCF is a ubiquitin ligase complex with variable Adaptor-Subunits

Page 7: MN-B-WP II (BInf 2)  Bioinformatische Datenbanken

Methods for establishing protein interaction

Yeast Two-Hybrid Co-IP, Pulldown

Phage Display FRET

Page 8: MN-B-WP II (BInf 2)  Bioinformatische Datenbanken

Yeast two-hybrid system

Only if protein X binds to protein Y,the activation domain (AD) will bebrought into proximity of the DNA-binding domaoin (DBD) and starttranscription of the reporter gene.

Typically, Y2H will be performed in yeast cells that require the reporter gene for viability. In a proteome-wide screen, the surviving cells can be picked and the identity of the Y protein is determined

Page 9: MN-B-WP II (BInf 2)  Bioinformatische Datenbanken

Pro:• Conditions similar to in vivo-Situation• Unbiased (proteome-wide) screens possible

Con:• Both false-positive and false-negative results are common• Interaction has to take place in the nucleus• Proteins can show Y2H interaction that will never meet in real

life

Limits of the Y2H method

Validation with additional method mandatory

Page 10: MN-B-WP II (BInf 2)  Bioinformatische Datenbanken

Co-Immunoprecipitation

Cell expressing Protein X

Cell expressing Protein Y

Cell lysis

Mat

rix

Cell lysisM

atrix

Matrix

Washing steps

Interaction of protein X und Y ?

Detection of protein Y

Protein X with tag A

Protein Y mit Tag BAntibody againsttag AAntibody againsttag B with Reporter

Page 11: MN-B-WP II (BInf 2)  Bioinformatische Datenbanken

Pull-down with MS evaluation

Protein X with tag A

Cell lysis

Mat

rix

Add proteinmixture Washing

Elution

Analysis

Page 12: MN-B-WP II (BInf 2)  Bioinformatische Datenbanken

Pro:• Test proteins can be extracted from the 'right' cells with PTMs.• Allows purification and detection of multi-protein complexesCon:• Both false-positive and false-negative results are common• Proteins have to be extracted in soluble form• Finding the proper washing and elution conditions can be

tricky• Indirect interaction via bridging factors from the cell lysate

cannot be excluded

Limits of Co-IP and Pull-down

Validation with additional method mandatory

Page 13: MN-B-WP II (BInf 2)  Bioinformatische Datenbanken

Interaction databases

A large number of protein interaction databases is available, most contain largely overlapping data. Examples : BioGRID, INTACT, MINT

A B C

DE

Most databases store interactions in binary form, even when derived from complex purifications. There are different strategies for treating complexes.

Real Complex

AB C

D E

Measured data (A as bait)

A

B C

D E

'hub model' predicts4 binary interactions

A

B C

D E

'network model' predicts 10 binary interactions

Page 14: MN-B-WP II (BInf 2)  Bioinformatische Datenbanken

'Guilt by Association'A useful method to guess the function of an unknown protein X is to look at functional data of genes/proteins associated with X

V

C

U

A

W

B

A

physical interactors

?

co-regulatedgenes

genetic interactors

Integrate functional data from different

sources