PPI network construction and false positive detection

1. PPI network construction and false positive detection Jin Chen CSE891-002 2012 Fall

2. Layout • Protein-protein interaction (PPI) networks • PPI network construction • PPI network false-positive detection

3. Background • Study of interactions between proteins is fundamental to the understanding of biological systems • PPIs have been studied through a number of high-throughput experiments • PPIs have also been predicted through an array of computational methods that leverage the vast amount of sequence data generated • Comparative genomics at sequence level has indicated that species differences are due more to the difference in the interactions between the component proteins, rather than the individual genes themselves * * Valencia A, Pazos F: Computational methods for the prediction of protein interactions. CurrOpinStructBiol 2002, 12:368-373.

4. PPI at different levels 3D structure Protein folding Protein docking Domain Nidhi et al. DSiMB 2009

5. PPI at different levels • Node – protein • Every node represents an unique protein • Edge – protein interaction • Physical interaction • Functional interaction HawoongJeong

6. PPI Identification • Concept of PPI ranges from direct physical interactions inferred from experimental methods (yeast two-hybrid) to functional linkages predicted on the basis of computational analysis (based on protein sequences and structures ) • Given the difficulties in experimentally identifying PPIs, a wide range of computational methods have been used to identify functional PPIs

7. Domain Fusion Hypothesis: if domains A and Bexist fused in a single polypeptide AB in another organism, then A and B are functionally linked Marcotte EM et al. Detecting Protein Function and Protein-Protein Interactions from Genome Sequences. Science, 285(5428) 751-753 1999

8. Domain Fusion • Inclusion of eukaryotic sequences increased the robustness of domain fusion predictions * • Eukaryotes, with a larger volume, cannot afford to accommodate separate proteins Aand B, as the required concentrations of Aand Bwould be prohibitively high, to achieve the same equilibrium concentration of AB. • Limitation: low coverage *Veitia RA: Rosetta Stone proteins: "chance and necessity"? Genome Biol 2002,3(2):interactions1001.1-1001.3.

9. Conserved Neighborhood Hypothesis: If the genes that encode two proteins are neighbors on the chromosome in several genomes, the corresponding proteins are likely to be functionally linked Dandekar T et al. Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochemical Sci 1998 , 23(9):324-328

10. Conserved Neighborhood • The method has been reported to identify high-quality functional relationships • The method suffers from low coverage, due to the dual requirement of identifying orthologues in another genome and then finding those orthologues that are adjacent on the chromosome Marcotte EM: Computational genetics: finding protein function by nonhomology methods. CurrOpinStructBiol 2000 , 10:359-365

11. Phylogenetic Profiles • Hypothesis: functionally linked proteins would co-occur in genomes • Phylogenetic profile of a protein can be represented as a 'bit string', encoding the presence or absence of the protein in each of the genomes considered

12. Co-evolution • Hypothesis: Co-evolution requires the existence of mutual selective pressure on two or more species • in silico Two-hybrid (i2h) method has been proposed based on the study of correlated mutations in multiple sequence alignments Protein family A Protein family B Pazos F et al: In silico Two-Hybrid System for the Selection of Physically Interacting Protein Pairs. Proteins 2002, 47:219-227

13. Software: Protein Link Explorer (PLEX) Date, S.V. and E.M. Marcotte, Protein function prediction using the Protein Link EXplorer (PLEX). Bioinformatics, 2005. 21(10): p. 2558-2559.

14. Biological Problem  Algorithm  Knowledge • Biological hypothesis • Mathematical representation • Algorithm design • Biological verification

15. High-throughput PPI Detection • Booming of biotechnology • Yeast-two hybrid / split ubiquitin system • Mass spectrometry • Protein microarrays • etc. • Limitations of computational prediction • Low coverage • Locally optimized (pair-wise) • Super-high negative PPI rates

16. Yeast Two-Hybrid • Two hybrid proteins are generated with transcription factor domains • Both fusions are expressed in a yeast cell that carries a reporter gene whose expression is under the control of binding sites for the DNA-binding domain Activation Domain Prey Protein Bait Protein Binding Domain Reporter Gene

17. Yeast Two-Hybrid • Interaction of bait and prey proteins localizes the activation domain to the reporter gene, thus activating transcription • Since the reporter gene typically codes for a survival factor, yeast colonies will grow only when an interaction occurs Activation Domain Prey Protein Reporter mRNA Bait Protein Reporter mRNA Reporter mRNA Reporter mRNA Binding Domain Reporter mRNA Reporter Gene

18. Mating based Split-ubiquitin System Lalonde S et al. Plant J 2008

19. Yeast Cell Growth Rate Biomass The trends for yeast cell growth over time

20. PPI Databases • STRING – PPIs derived from high-throughput experimental data, mined of databases and literature, analyses of co-expressed genes and also from computational predictions • HPRD - Human Protein Reference Database. It integrates information relevant to the function of human proteins in health and disease • DIP - Experimentally derived PPIs with assessments. DIP is generally considered as a valuable benchmark or verify the performance of any new method for prediction of PPIs • Many others: MIPS, YGD, BIND, TAIR…

21. False-Positive Detection in PPI Networks • Background: PPI networks generated with high-throughput methods contain a sizeable number of false-positives and their reproducibility is not satisfactory* • Central to the understanding of PPI is the definition of “interaction” itself • Binding energy / Interaction / Complex • We need to define what we mean by interaction * von Mering Comparative assessment of large-scale data sets of protein-protein interactions. Nature ;417(6887):399-403 2002

22. Useful Data for False-Positive Detection • Functional and localization data (Gene Ontology) • Indirect high-throughput data (gene and protein expression) • Sequence related data ( protein domain (domain fusion), interologs) • Structure data (protein 3D structure) • Network topological features (connectivity, network motif)

23. Different Hypothesis for Different Data Other hypotheses include: synthetic lethality, interlogs, linear motif, etc.

24. Gold Standard for PPI Networks • For algorithm evaluation and comparison • To train a model as positive training data • Manually annotated databases such as DIP • Interactions from low-throughput experiments • True negative set is equally important • Co-localized? No?

25. Estimate PPI Network Reliability • Overall index of reliability of a PPI network

26. Estimate PPI Network Reliability “capture-recapture” model - reaching back to the raw counts of observed bait–prey clones of yeast-two hybrid experiments Huang et al. Where Have All the Interactions Gone? Estimating the Coverage of Two-Hybrid Protein Interaction Maps. PLoS Computational Biology 2007

27. PPI Filtering • GOAL: To identify reliable protein complexes from two existing mass spectrometry (MS) data • Analyze the data with a purification enrichment (PE) scoring system • Using gold standard PPIs, the consolidated dataset is of greater accuracy than the original sets and is comparable to PPIs defined using more conventional small-scale methods Collins et al. Toward a comprehensive atlas of the physical interactome of Saccharomycescerevisiae. Mol Cell Proteomics. 2007 Mar;6(3):439-50

28. PPI Filtering • e=0  no evidence for or against the validity of a particular interaction was collected • Two types of observations: bait-prey observations and prey-prey observations • i and j are two proteins (bait & prey). k indicates a distinct purification. Mij measures indirect evidence due to co-occurrence of proteins i and j as preys in the same purifications

29. PPI Filtering where r representing the probability that a true association will be preserved and detected in a purification experiment and pijk representing the probability that a bait-prey pair will be observed for nonspecific reasons where nikprey is the number of preys identified in purification k with bait i, nibait is the number of times protein i was used as bait, and fj is an estimate of the nonspecific frequency of occurrence of prey j in the dataset

30. PPI Filtering

31. PPI Filtering

32. PPI Filtering • PPI topological analysis • First student presentation is about a topological measure called “FS-weight”, which was compared with other topological measures • Suitable for large PPI networks rather than preliminary networks

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