ProInt Finder to Search Protein Interactions
Shwe S. LinMentor: Matteo Pellegrini, UCLA
Outlines
Project overviewPurposeBackgroundMy partAlgorithmsTool usedExamplesAcknowledgmentsReferences
Project Overview
To develop a protein-protein interaction database and an interactive web-based interface to search for protein interactions from the database
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
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
Traditional Methods for Studying Protein Functions
• Sequence alignment techniques
• Protein’s 3-D structure
Limitations of Traditional Methods
Yield functional information only on experimentally characterized homologous proteins
Detect protein’s biochemical function only;
not biological process
Approach to Address Limitations
Develop protein interaction databases • Example: DIP, MINT, BIND etc.
Implement methods for function prediction • Example: guilt-by-association
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
Algorithm (Input)
Accept query: gene name or gene id of interest • Example - Gene name: p53
Gene ID: 7157
Algorithm (Search)QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture. Search for gene_ids from the database gene
table
actors acted by
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 >>
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
• 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 modelsclass 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
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.htmlsearch.html
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
Home Page
This is a link which leads to the search page.
These are links to other protein interaction databases.
Search page
Result page
User Input
Description of the protein
Query protein interacts with different protein
Proteins interacts with query protein
More on Result
Web Links
Click on the link
Acknowledgements
Dr. Matteo Pellegrini Shawn Cokus Joseph Kim and Cory Tobin Dr. Sandra Sharp and Dr. Wendie
Johnston SoCalBSI NIH, NSF, and LAOC
References
• http://www.djangoproject.com/
Questions?
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