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ProInt Finder to Search Protein Interactions Shwe S. Lin Mentor: Matteo Pellegrini, UCLA
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Outlines Project overview Purpose Background My part Algorithms Tool used Examples Acknowledgments References
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Project Overview To develop a protein-protein interaction database and an interactive web-based interface to search for protein interactions from the database
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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
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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
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Traditional Methods for Studying Protein Functions Sequence alignment techniques Protein’s 3-D structure
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Limitations of Traditional Methods Yield functional information only on experimentally characterized homologous proteins Detect protein’s biochemical function only; not biological process
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Approach to Address Limitations Develop protein interaction databases Example: DIP, MINT, BIND etc. Implement methods for function prediction Example: guilt-by-association
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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
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Algorithm (Input) Accept query: gene name or gene id of interest Example - Gene name: p53 Gene ID: 7157
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Algorithm (Search) Search for gene_ids from the database gene table actors acted by
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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 >>
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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
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Each model is a Python class Contains fields and behaviors of the stored data Each model maps to a single database table Database Layer: Models Gene A Model in Django Gene ID Gene Name Taxonomy ID 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'] Fields/ class attributes
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View Layer: Templates ProInt Finder A text file that contains variables and tags 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 index.html results.html search.html
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Control Layer Takes user input from search.html template Defines how to process the data Returns results to results.html template search.html User input Controller (View) results.html Protein interactions
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Home Page This is a link which leads to the search page. These are links to other protein interaction databases.
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Search page
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Result page User Input Description of the protein Query protein interacts with different protein Proteins interacts with query protein
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More on Result
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Web Links Click on the link
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Acknowledgements Dr. Matteo Pellegrini Shawn Cokus Joseph Kim and Cory Tobin Dr. Sandra Sharp and Dr. Wendie Johnston SoCalBSI NIH, NSF, and LAOC
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References http://www.djangoproject.com/
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Questions?Questions?
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