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P ro I nt Finder to Search Protein Interactions

P ro I nt Finder to Search Protein Interactions. Shwe S. Lin Mentor: Matteo Pellegrini, UCLA. Outlines. Project overview Purpose Background My part Algorithms Tool used Examples Acknowledgments References. Project Overview.

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P ro I nt Finder to Search Protein Interactions

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  1. ProInt Finder to Search Protein Interactions Shwe S. Lin Mentor: Matteo Pellegrini, UCLA

  2. Outlines • Project overview • Purpose • Background • My part • Algorithms • Tool used • Examples • Acknowledgments • References

  3. Project Overview • To develop a protein-protein interaction database and an interactive web-based interface to search for protein interactions from the database

  4. Purpose of the Project • To study protein-protein interactions • Example: p53-MDM2 interactions • To predict protein functions which may be inferred from analyzing protein interactions • Example: protein A interacts with 5 cell cycle proteins and we therefore infer that it is a cell cycle protein

  5. Importance of Studying Protein Interactions Example: • Essential for cell communications which result in activation or inactivation of biological responses • Protein MDM2 inactivates p53’s function as a tumor suppressor • EPO interacts with the EPO receptor to trigger growth of erythrocytes

  6. Traditional Methods for Studying Protein Functions • Sequence alignment techniques • Protein’s 3-D structure

  7. Limitations of Traditional Methods • Yield functional information only on experimentally characterized homologous proteins • Detect protein’s biochemical function only; not biological process

  8. Approach to Address Limitations • Develop protein interaction databases • Example: DIP, MINT, BIND etc. • Implement methods for function prediction • Example: guilt-by-association

  9. My Part • Develop web application (ProInt Finder) to utilize the database for studying protein interactions • Django: Python Web framework • Database: Automated collections of data by text mining of web

  10. Algorithm (Input) • Accept query: gene name or gene id of interest • Example - Gene name: p53 Gene ID: 7157

  11. Algorithm (Search) actors acted by • Search for gene_ids from the database gene table

  12. Algorithm (Output) • Return: gene_1_id interacts with gene_2_id • Result: List of protein pairs that are experimentally identified to interact with each other pro_ A >> activates pro_B pro_E phosphorylates >> pro_A >> acetylates pro_C pro_H binds to >> >> methylates pro_D pro_I activates >> >> interacts with pro_E pro_K regulates >>

  13. Tool Used: Django • Python web framework to build Web applications • Provides Model-View-Controller (MVC) approach to programming • Database layer: Models or data models • Controller layer: View or control logic • View layer: Templates or user interface

  14. Database Layer: Models • Each model is a Python class • Contains fields and behaviors of the stored data • Each model maps to a single database table A Model in Django • from django.db import models • class Gene(models.Model): • gene_id = models.IntegerField (primary_key=True) • long_name = models.TextField( ) • symbol = models.TextField( ) • tax_id = models.ForeignKey( Species ) • class Meta: • db_table = 'protInt_gene' • ordering = ['symbol'] Gene Gene ID Gene Name Taxonomy ID Fields/ class attributes

  15. View Layer: Templates • A text file that contains variables • and tags ProInt Finder • Variables: Get replaced with values • when the template is evaluated • Tags: Control the logic of the template Templates for each of the web pages for ProInt Finder results.html index.html search.html

  16. Controller (View) search.html results.html Control Layer • Takes user input from search.html template • Defines how to process the data • Returns results to results.html template User input Protein interactions

  17. This is a link which leads to the search page. These are links to other protein interaction databases. Home Page

  18. Search page

  19. User Input Description of the protein Result page Query protein interacts with different protein Proteins interacts with query protein

  20. More on Result

  21. Click on the link Web Links

  22. Acknowledgements • Dr. Matteo Pellegrini • Shawn Cokus • Joseph Kim and Cory Tobin • Dr. Sandra Sharp and Dr. Wendie Johnston • SoCalBSI • NIH, NSF, and LAOC

  23. References • http://www.djangoproject.com/

  24. Questions?

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