1. Create and assess protein networks through molecular characteristics of individual proteins Yanay Ofran et al. ISMB ’06 Presenter: Danhua Guo 12/07/2006

2. Roadmap Motivation Motivation Introduction Introduction Methods Methods Results and Discussion Results and Discussion Conclusion Conclusion

3. Motivation Study of biological systems relies on network topology. Study of biological systems relies on network topology. Integrating protein information into the network enhance the analysis of biological systems. Integrating protein information into the network enhance the analysis of biological systems.

4. Introduction Protein-Protein Interaction (PPI) Network Protein-Protein Interaction (PPI) Network –Help identify process or functions –Major problem Generation problem Generation problem –Experimental errors: should not be in the network –“In vitro”: should be include in the network Data representation problem Data representation problem –Essential connection between PPI and protein

5. Introduction An ideal framework An ideal framework –Macro level: network topology –Micro level: characteristics of each protein Localization Localization Functional annotation Functional annotation

6. Introduction Protein interaction Network Assessment Tool (PiNAT) Protein interaction Network Assessment Tool (PiNAT)

7. Methods Large-scale Assessment of PPIs Large-scale Assessment of PPIs –Based on localization –Based on GO annotation (if applicable) Automatic generation of networks Automatic generation of networks –Get submitted list of proteins from user –Search DIP and IntAct Display of networks in the cellular context Display of networks in the cellular context Alzheimer’s disease related pathway Alzheimer’s disease related pathway

8. Methods Localization criteria Localization criteria –LOCtree: classify eukaryotic proteins (60%) Threshold: confidence score >=4 Threshold: confidence score >=4 –PHDhtm: predict transmembrane helices (7%) Threshold: average score among 20 reliable predictions >8.5 Threshold: average score among 20 reliable predictions >8.5 –Experiment on 4800 interactions (2191 proteins) High-confidence prediction: 2312 (1482 proteins) High-confidence prediction: 2312 (1482 proteins) Total protein pairs: 1,097,421 Total protein pairs: 1,097,421 Binomial approximation to the cumulative hypergeometric probability distribution to get a p-value for over and under representation Binomial approximation to the cumulative hypergeometric probability distribution to get a p-value for over and under representation

9. Methods GO criteria GO criteria –The functionality annotation of a protein –Distance between 2 GO terms measure the similarity m,n: respective numbers of annotations in i and j m,n: respective numbers of annotations in i and j simGo: GO similarity defined by Lord et al. simGo: GO similarity defined by Lord et al. Ck, Cp: respective individual annotation in protein i and j Ck, Cp: respective individual annotation in protein i and j Cjmax: Ck’s most similar term in j Cjmax: Ck’s most similar term in j Cimax: Cp’s most similar term in i Cimax: Cp’s most similar term in i

10. Methods Display of networks in the cellular context Display of networks in the cellular context –Based on LOCtree and PHDhtm predictions –Generate Graph Markup Language (GML) –Localization overide rule: High PHDhtm > High LOCtree > Low PHDhtm > Low LOCtree High PHDhtm > High LOCtree > Low PHDhtm > Low LOCtree

11. Results Interactions across subcellular compartments Interactions across subcellular compartments –Intra-compartment interactions: high score –Distant compartment: low score –Nearby compartment: likely

12. Results Likely and unlikely interactions across GO Likely and unlikely interactions across GO –Likely: >3.25 –Unlikely: <1.3 –Neutral: else

13. Result Alzheimer in the perspective of PiNAT Alzheimer in the perspective of PiNAT –Reflects the unclarity regarding Amyloid beta A4 protein (APP) ’s localization –APP interacts extensively with almost every compartment of the cell

14. Result APP’s role in Alzheimer APP’s role in Alzheimer –APP-related PPI deemed “unlikely” –Conflicts between 2 scoring systems

15. Conclusion Molecular knowledge and network structure can enhance our understanding of biological processes. Molecular knowledge and network structure can enhance our understanding of biological processes. PiNAT is efficient and meaningful. PiNAT is efficient and meaningful.