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

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Presentation on theme: "PPI network construction and false positive detection Jin Chen CSE891-002 2012 Fall 1."— Presentation transcript:

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

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

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 * 3 * Valencia A, Pazos F: Computational methods for the prediction of protein interactions. Curr Opin Struct Biol 2002, 12:368-373.

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

5 Hawoong Jeong 5 PPI at different levels Node – protein Every node represents an unique protein Edge – protein interaction Physical interaction Functional interaction

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 6

7 Domain Fusion Hypothesis: if domains A and B exist fused in a single polypeptide AB in another organism, then A and B are functionally linked 7 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 A and B, as the required concentrations of A and B would be prohibitively high, to achieve the same equilibrium concentration of AB. Limitation: low coverage 8 *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 9 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 10 Marcotte EM: Computational genetics: finding protein function by nonhomology methods. Curr Opin Struct Biol 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 11

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 12 Pazos F et al: In silico Two-Hybrid System for the Selection of Physically Interacting Protein Pairs. Proteins 2002, 47:219-227 Protein family A Protein family B

13 Software: Protein Link Explorer (PLEX) 13 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 1.Biological hypothesis 2.Mathematical representation 3.Algorithm design 4.Biological verification 14

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 15

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 Reporter Gene Bait Protein Binding Domain Prey Protein Activation Domain

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 Reporter Gene Bait Protein Binding Domain Prey Protein Activation Domain

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

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

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… 20

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 21 * 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) 22

23 Different Hypothesis for Different Data DataExample of Hypothesis Gene OntologyTwo proteins which share a similar annotation are more likely to interact than proteins with different or null annotations Gene ExpressionTwo proteins which have similar genes express patterns are more likely to interact Domain InteractionIf two domains are often found in PPIs, two proteins containing such domains are more likely to interact PPI network topological analysis PPI topologies fit spoke or matrix models are more likely to be true 23 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? 24

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

26 Estimate PPI Network Reliability “capture-recapture” model - reaching back to the raw counts of observed bait–prey clones of yeast-two hybrid experiments 26 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 27 Collins et al. Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae. 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. M ij measures indirect evidence due to co-occurrence of proteins i and j as preys in the same purifications 28

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

30 PPI Filtering 30

31 PPI Filtering 31

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 32

33 33 "Most good programmers do programming not because they expect to get paid or get adulation by the public, but because it is fun to program." - Linus Torvalds

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