Building a semi-supervised machine learning model to predict biomolecular condensates

Princess Margaret Cancer Centre belongs to the University Health Network (UHN), Canada’s leading biomedical research organization. The Centre focuses on cancer research across various fields, including genomics, informatics, signaling, health services, and biophysics.
Dr. Kumar’s lab is currently investigating the consequences of genomic alterations in intrinsically disordered regions (IDR). IDRs are present in proteins that undergo liquid-liquid phase separation (LLPS) and form biomolecular condensates [1]. IDRs lack a fixed structure yet play vital roles in cellular function [2]-[4]. Genetic alterations can disrupt biomolecular condensate activity, leading to neurodevelopmental disorders and cancer [5]. Despite their biological significance, IDRs are often overlooked in drug discovery. Current experimental methods to identify IDRs lack throughput. Therefore, this project aims to address these challenges by developing an advanced in-silico approach to predict LLPS proteins.
As a research assistant, the intern will take on this project and contribute to cutting-edge computational research. If successful, this in-silico approach could significantly reduce reliance on costly experimental procedures while accelerating breakthroughs in precision medicine. By enhancing predictive efficiency, this computational technique could open new avenues for drug discovery, enabling the targeted intervention in disordered proteins. Furthermore, identifying key LLPS proteins could provide deeper insights into cancer biology.

Faculty Supervisor:

Alan Moses;Karthik Kuber

Student:

Partner:

University Health Network

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology

University:

University of Toronto

Program: