Protein-protein Interactions PowerPoint Presentation, PPT - DocSlides

Protein-protein Interactions PowerPoint Presentation, PPT - DocSlides

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June 12, 2018. Why PPI?. Protein-protein interactions determine outcome of most cellular processes. Proteins which are close homologues often interact in the same way. Protein-protein interactions place evolutionary constraints on protein sequence and structural divergence. ID: 718641

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Slide1

Protein-protein Interactions

June 12, 2018

Slide2

Why PPI?

Protein-protein interactions determine outcome of most cellular processes

Proteins which are close homologues often interact in the same way

Protein-protein interactions place evolutionary constraints on protein sequence and structural divergence

Pre-cursor to networks

Slide3

PPI classification

Strength of interaction

Permanent or transient

Specificity

Location within polypeptide chain

Similarity of partners

Homo- or hetero-oligomers

Direct (binary) or a complex

Confidence score

Slide4

Determining PPIs

Small-scale methods

Co-immunoprecipitation

Affinity chromatography

Pull-down assays

In vitro

binding assays

FRET,

Biacore

, AFM

Structural (co-crystals)

Slide5

PPIs by high-throughput methods

Yeast two hybrid systems

Affinity tag purification followed by mass spectrometry

Microarrays/gene co-expression

Implied functional PPIs

Synthetic lethality

Genetic interactions, implied functional PPIs

Slide6

Yeast two hybrid system

Gal4 protein comprises DNA

binding

and

activating

domains

Binding

domain interacts with

promoter

Measure reporter enzyme activity (e.g.

blue colonies

)

Activating

domain interacts with

polymerase

Slide7

Yeast two hybrid system

Gal4 protein: two domains do not need to be transcribed in a single protein

If they come into close enough proximity to interact, they will activate the RNA polymerase

Binding

domain interacts with

promoter

Measure reporter enzyme activity

(e.g.

blue colonies

)

Activating

domain interacts with

polymerase

A

B

Two other protein domains

(

A

&

B

)

inter

act

Slide8

Yeast two hybrid system

A

B

This is achieved using gene fusion

Plasmids carrying different constructs can be expressed in yeast

Binding domain

as a translational fusion with the gene encoding

another protein

in one plasmid.

Activating domain

as a translational fusion with the gene encoding a

different protein

in a second plasmid.

If the two proteins interact, then GAL4 is expressed and

blue colonies

form

Slide9

Yeast two hybrid

Advantages

Fairly simple, rapid and inexpensive

Requires no protein purification

No previous knowledge of proteins needed

Scalable to high-throughput

Is not limited to yeast proteins

Limitations

Works best with cytosolic proteins

Tendency to produce false positives

Slide10

Mass spectrometry

Need to purify protein or protein complexes

Use a affinity-tag system

Need efficient method of recovering fusion protein in low concentration

Slide11

TAP (tandem affinity purification)

Spacer

CBP

TEV site

Protein

A

Spacer

CBP

TEV site

Protein

A

Homologous recombination

Chromosome

PCR product

Fusion protein

Protein

Calmodulin

binding peptide

Slide12

TAP process

"

Taptag

simple" by

Chandres

- Own work.

Licensed under CC BY-SA 3.0 via Wikimedia Commons

Slide13

TAP

Advantages

No prior knowledge of complex composition

Two-step purification increases specificity of pull-down

Limitations

Transient interactions may not survive 2 rounds of washing

Tag may prevent interactions

Tag may affect expression levels

Works less efficiently in mammalian

cells

Slide14

Other tags

HA, Flag and His

Anti-tag antibodies can interfere with MS analysis

Streptavidin binding peptide (SBP)

High affinity for streptavidin beads

10-fold increase in efficiency of purification compared to conventional TAP tag

Successfully used to identify components of complexes in the

Wnt

/

b

-catenin pathway

Slide15

Slide16

Nature Cell Biology 4:348-357 (2006)

The KLHL12-Cullin-3 ubiquitin ligase negatively regulates

Wnt

-

b

-catenin pathway by targeting

Dishevelled

for degradation

Used Dsh-2 and Dsh-3 as bait proteins

Slide17

Binding partners of Bruton’s tyrosine kinase

Protein Science 20:140-149 (2011)

Role in lymphocyte development & B-cell maturation

Slide18

Slide19

Slide20

How to identify contaminants?

Ideally, you would have negative controls in the form of a tagged non-related protein or mock purifications

Technical limitations (and time and money) may preclude this step

Small scale experiments do not sample enough to identify all possible contaminants

Contaminant Repository for Affinity Purification -- the

CRAPome

Negative controls are largely BAIT-independent

Aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol

https://reprint-apms-org/

Slide21

DIP -- Database of Interacting Proteins (UCLA)>81,000 interactions with >28,000 proteins

CCSB Interactome Database (Harvard)

Human,

virhostome

,

A. thaliana

,

C. elegans

,

S. cerevisiae

Databases of experimental PPIs

Slide22

Databases of known and inferred PPIs

IntAct

– EBI molecular interaction database

Curated data from multiple sources

>84,000 interactions with >100,000 proteins

Complex Portal

Macromolecular complexes from model organisms

BioGRID

Open source repository for physical, genetic and chemical interactions model organisms

Provides data to other databases

STRING:

S

earch

T

ool for the Retrieval of I

nteracting GenesIntegrates information from existing PPI data sourcesProvides confidence scoring of the interactionsPeriodically runs interaction prediction algorithms on newly sequenced genomes

Slide23

EBI IntAct

Submit single or lists of proteins

Provides method and reference for interactions

List format, can download easily

Slide24

TLR4 PPI at

IntAct

If do not restrict search to gene name, will get >2000 interactions

Slide25

TLR4 protein interactors

Slide26

BioGRID

Slide27

STRING database

S

earch

T

ool for the

R

etrieval of

I

nteracting

G

enes

Integrates information from existing PPI data sources

Provides confidence scoring of the interactions

Periodically runs interaction prediction algorithms on newly sequenced genomesv.10 covers >2000 organisms

http://string-db.org/

Slide28

Networks in STRING database

Starting protein

Nice graphical view

Interactive, can expand

Not so easy to download lists of data

Slide29

Networks can be expanded

3 indirect interactions

Slide30

Information about the proteins

Slide31

Accessing Interaction data

From a

UniprotKB

(reviewed record):

Slide32

Inferring protein-protein interactions

Most of the high-throughput PPI work is done in model organisms

Can you transfer that annotation a homologous gene in a different organism?

Slide33

Defining homologs

Orthologue of a protein is usually defined as the best-matching homolog in another species

Candidates with significant BLASTP E-value (<10

-20

)

Having ≥80% of residues in both sequences included in BLASTP alignment

Having one candidate as the best-matching homologue of the other candidate in corresponding organism

Slide34

Interologs

If two proteins, A and B, interact in one organism and their orthologs, A’ and B’, interact in another species, then the pair of interactions A—B and A’—B’ are called interologs

Align the homologs (A & A’, B & B’) to each other.

Determine the percent identity and the E-value of both alignments

Then calculate the Joint identity and the Joint

Evalue

Joint identity

Joint E-value

Slide35

Transfer of annotation

Compared interaction datasets between yeast, worm and fly

Assessed chance that two proteins interact with each other based on their joint sequence identities

Performed similar analysis based on joint E-values

All protein pairs with

J

I

≥ 80%

with a known interacting pair will interact with each other

More than half of protein pairs with

J

E

 E

-70 could be experimentally verified.

Yu, H. et. al. (2004) Genome

Res. 14: 1107-1118PMID: 15173116

Slide36

Examples of Protein-Protein Interologs

In

C. elegans

, mpk-1 was experimentally shown to interact with 26 other proteins (by yeast 2-hybrid)

In

S. cerevisiae,

Ste5 is the homolog of the

C.

elegans

Mpk-1 protein

Based on the similarity between the interaction partners of mpk-1 and their closest homologs in

S. cerevisiae

, the interolog approach predicted 5 of the 6 subunits of the Ste5 complex in S. cerevisiae

Slide37

This paper has been cited >100 times

Why the interest in predicting protein-protein interactions?

Determining protein-protein interactions is challenging and the high-throughput (genome-wide) methods are still difficult and expensive to conduct

Identifying candidate interaction partners for a targeted pull-down assay is a more viable strategy for most labs

Slide38

Today in computer lab

Explore your gene lists using STRING

Finding PPIs in your sublist using

BioGrid

Continue Exercise 5 on protein analyses by exploring possible PPI for your selected genes

Change in schedule:

Exercise 5 and 6 will both be due on Friday, June 22

Slide39

Slide40


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