Protein-protein Interactions and Pathways

1. Protein-protein Interactionsand Pathways June 26, 2014

2. 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

3. 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

4. Determining PPIs • Small-scale methods • Co-immunoprecipitation • Affinity chromatography • Pull-down assays • In vitro binding assays • FRET, Biacore, AFM, EM • Structural (co-crystals)

5. PPIs by high-throughput • Yeast two hybrid systems • Affinity tag purification followed by mass spectrometry • Protein microarrays • Microarrays/gene co-expression (functional PPIs) • Synthetic lethality (genetic/functional)

6. Yeast two hybrid • Gal4 protein is a transcriptional activator with 2 separate functions: DNA binding domain and an activating domain • The GAL4 protein does NOT need to be expressed as a single protein to function

7. Yeast two hybrid system Gal4 protein comprises DNA bindingand activating domains Binding domain interacts with promoter Activating domain interacts with polymerase Measure reporter enzyme activity (e.g. blue colonies)

8. 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 Two other protein domains (A & B) interact Activating domain interacts with polymerase Binding domain interacts with promoter A B Measure reporter enzyme activity (e.g. blue colonies)

9. A B Yeast two hybrid system • This is achieved using gene fusion • Plasmids carrying different constructs can be expressed in yeast Binding domainas a translational fusion with the gene encoding another protein in one plasmid. Activating domainas a translational fusion with the gene encoding adifferent protein in a second plasmid. If the two proteins interact, then GAL4 is expressed and blue colonies form

10. 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

11. Mass spectrometry • Need to purify protein or protein complexes • Use a affinity-tag system • Need efficient recovery method of fusion protein in low concentration

12. TAP (tandem affinity purification) Spacer PCR product TEV site ProteinA CBP Homologous recombination Chromosome Fusion protein Spacer TEV site ProteinA Protein CBP

13. PCR of TAP cassette ORFs processed: 1,739 Positive homologous recombinations Transformation of cells (homologous recombination) 1,548 Selection of positive clones Expressing clones: 1,167 Large scale cultivation TAP purifications: 589 Cell lyses and Tandem affinity purification SDS-PAGE Identified complexes: 232 MALDI-TOF protein identification

14. TAP • Advantages • No prior knowledge of complex composition • Relatively simple; high yield • 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

15. 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

16. Used Dsh-2 and Dsh-3 as bait proteins The KLHL12-Cullin-3 ubiquitin ligase negatively regulates Wnt-b-catenin pathway by targeting Dishevelled for degradation Nature Cell Biology 4:348-357 (2006)

17. Protein Science 20:140-149 (2011)

20. Databases of protein-protein interactions • MINT – Molecular Interaction Database • >240,000 interactions with 35,000 proteins • DIP -- Database of Interacting Proteins (UCLA) • >77,000 interactions with >26,000 proteins • CCSB – Proteomics base interactomes (Harvard) • Human, viruses, C. elegans, S. cerevisiae • Some unpublished data • IntAct – EBI molecular interaction database • Curated data from multiple sources

21. 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 setsthan for vertebrates • Isoform 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

22. Integrated Databases of PPIs • MiMI: Michigan Molecular Interactions • Data merged from several PPI databases; source provenance maintained • Links to literature sources for the PPI • Linked to Entrez Gene, InterPro, Gene ontology • Includes pathway data • Various methods of viewing the data • NOT CURATED • Data only as good as source data http://mimi.ncibi.org

23. MiMI database

24. MiMI search results

25. MiMI Gene Detail Gene Ontology Interactions Pathways

26. KEGG pathway Each protein name is a link to another page Arrows & lines provide information about the type of interaction

27. Other viewing options MeSH terms that involve this gene PPI with this gene in Cytoscape Adaptive PubMed search

28. iRefWeb • Web interface to integrated database of protein-protein interactions • Better review of the records after pulling in the data from the various source databases • Can search by gene name or various IDs, including batch searches. • Does not have the pathway and other information that MiMI has, but it has better documentation of the quality of the PPI http://wodaklab.org/iRefWeb/

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

30. MI score: (Mint-inspired) score is a measure of confidence in molecular interactions curated from the literature. • They do not curate, but rely on the curation done by the MINT team

32. STRING database • Search Tool for the Retrieval of Interacting Genes • Integrates information from existing PPI data sources • Provides confidence scoring of the interactions • Periodically runs interaction prediction algorithms on newly sequenced genomes • v.9 covers >1100 organisms http://string-db.org/

33. Networks in STRING database Starting protein

34. Networks can be expanded 3 indirect interactions

35. Information about the proteins

36. 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?

37. 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

38. 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 Joint identity Joint E-value Geometric mean or nth root of the product of n values

39. Interologs • Use blast2seq to 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

40. 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

41. Transfer of annotation • Using interaction annotation from C. elegans, D. melanogaster, S. cerevisiae and Helicobacter pylori • All protein pairs with JI ≥ 80% with a known interacting pair will interact with each other • More than half of protein pairs with JE E-70 could be experimentally verified. Genome Res. 2004 14: 1107-1118 PMID: 15173116

42. 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

43. 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

44. Today in computer lab • Summary of PPI in your gene list using MiMI • Exploring a subset of PPI using the STRING database • Identification of homologs in some species TBD and transfer of PPI interaction