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

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

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

Documents

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