Protein-protein PowerPoint Presentation, PPT - DocSlides

Protein-protein PowerPoint Presentation, PPT - DocSlides

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Interactions. June 18, 2015. 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: 320281

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Slide1

Protein-protein Interactions

June 18, 2015

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

Protein microarrays

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 proteinIf 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

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

Slide16

Binding partners of Bruton’s tyrosine kinase

Protein Science 20:140-149 (2011)

Role in lymphocyte development & B-cell maturation

Slide17

Slide18

Slide19

MINT – Molecular Interaction Database>240,000 interactions with 35,000 proteinsCovers multiple specesDIP -- Database of Interacting Proteins (UCLA)>79,000 interactions with >27,000 proteinsCCSB – Proteomics base interactomes (Harvard)Human, viruses, C. elegans, S. cerevisiaeSome unpublished dataIntAct – EBI molecular interaction databaseCurated data from multiple sources

Databases of protein-protein interactions

Slide20

Integrated Databases of PPIs

MiMI: Michigan Molecular InteractionsData merged from several PPI databases; source provenance maintainedLinks to literature sources for the PPILinked to Entrez Gene, InterPro, Gene ontologyIncludes pathway data Various methods of viewing the dataNOT CURATEDData only as good as source data

http://

mimi.ncibi.org

Slide21

MiMI database

Slide22

MiMI search results

Slide23

MiMI Gene Detail

Gene Ontology

Pathways

Interactions

Slide24

KEGG pathway

Each protein name is a link to another page

Arrows & lines provide information about the type of interaction

Slide25

Other viewing options

MeSH

terms that involve this gene

PPI with this gene in

Cytoscape

Adaptive

PubMed search

Slide26

On average, two databases curating the same publication agree on 42% of their interactions. Discrepancies between sets of proteins annotated from the same publication are less pronounced, with an average agreement of 62%, but the overall trend is similar

Better agreement on non-vertebrate model organisms data sets than for vertebratesIsoform complexity is a major issue

Literature curation of protein interactions: measuring agreement across databases.

Turinsky

A.L. et. al. Database, Vol. 2010, Article ID baq026

Slide27

iRefWeb

Web interface to integrated database of protein-protein interactions Better review of the records after pulling in the data from the various source databasesCan search by gene name or various IDs, including batch searches.Does not have the pathway and other information, but has a better measure of confidence of PPI

http://

wodaklab.org

/

iRefWeb

/

Slide28

iRef Web search

The search will try to match automatically, both name and species.

Slide29

MI score: (Mint-inspired) score is a measure of confidence in molecular interactions for interactions between A and B:

Total

number of unique PubMed publications that support the interactions

Cumulative

sum of weighted evidence from

all

The cumulative sum of weighted evidence from all

interologs

, i.e. interactions containing homologous pairs A' and B'.

Slide30

Interaction detail

Slide31

STRING database

Search Tool for the Retrieval of Interacting GenesIntegrates information from existing PPI data sourcesProvides confidence scoring of the interactionsPeriodically runs interaction prediction algorithms on newly sequenced genomesv.10 covers >2000 organisms

http://string-

db.org

/

Slide32

Networks in STRING database

Starting protein

Slide33

Networks can be expanded

3 indirect interactions

Slide34

Information about the proteins

Slide35

Transferring PPI annotation

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

Can you transfer that annotation

a homologous gene in a different organism?

Slide36

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

Slide37

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 interologsAlign the homologs (A & A’, B & B’) to each other.Determine the percent identity and the E-value of both alignmentsThen calculate the Joint identity and the Joint Evalue

Joint identity

Joint E-value

Slide38

Transfer of annotation

Compared interaction datasets between yeast, worm and flyAssessed chance that two proteins interact with each other based on their joint sequence identitiesPerformed similar analysis based on joint E-valuesAll protein pairs with JI ≥ 80% with a known interacting pair will interact with each otherMore than half of protein pairs with JE  E-70 could be experimentally verified.

Yu

, H. et. al. (2004)

Genome

Res

.

14: 1107-

1118

PMID: 15173116

Slide39

Examples of Protein-Protein Interologs

In

C. elegans

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

Ste5 is the homolog of

Mpk-1 in

S. cerevisiae

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

Slide40

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

Slide41

BIPS: BIANA Interolog Prediction Server

Based on concept of

interolog

Pre-defined alignments

Can submit list of proteins to get predicted interaction partners

Can filter predicted list to increase confidence

Slide42

Today in computer lab

Tutorial on finding PPIs

in your gene list using

MiMI

or

iRefWeb

Exploring a subset of PPIs using the STRING database

Prediction of interactions homologs using the BIPS

server

Exercise 4 on protein domain analysis

Slide43

Slide44

Slide45


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