Project 467045

Machine-Learning Aided Discovery of targeted ER-AF2 PROTACs

467045

Machine-Learning Aided Discovery of targeted ER-AF2 PROTACs

$17,500
Project Information
Study Type: Unclear
Research Theme: N/A
Institution & Funding
Principal Investigator(s): Mslati, Hazem
Institution: University of British Columbia
CIHR Institute: N/A
Program: Master's Award: Canada Graduate Scholarships
Peer Review Committee: Special Cases - Awards Programs
Competition Year: 2021
Term: 1 yr 0 mth
Abstract Summary

Proteolysis targeting chimera (PROTAC) aims at selectively degrading biological targets of interest (TOI) intracellularly using host cell's ubiquitin ligases thereby achieving lasting ablation of activity. PROTACs carry special interest in this project since the objective is to target conventionally 'undruggable' targets - estrogen receptor (ER) - through unconventional allosteric sites - co-activator binding domain (AF2) of ER - which is a relatively solvent-exposed pocket. PROTACs consist of three chemical moieties: 1) a binder for the to-be-degraded TOI. 2) a binder to the host cell's E3 ubiquitin ligases. 3) a linker that connects parts 1) and 2). Finding the best combination of those three elements to form a stable ternary complex constitutes the challenge in the design of new PROTACs. First, formation of the ternary complex is thermodynamically driven by cooperativity of favorable interactions between the TOI and the E3 ligase. Second, the choice of the E3 ligase is predicated on the abundance of the E3 ligase concentration in target tissues. Third, the linker needs to be minimal in length in order to limit off target effects and ideally participate in promoting the ternary complex. In order to predict choice of E3 ligase and optimal linker type and length, we will employ a machine learning model that will train on all available empirical active/inactive PROTAC data points (more than 1200 PROTACs were tested to date) and incorporate protein surface-surface encodings from protein-protein dockings in order to derive TOI specific predictions. Additionally, tissue-specific expression profiles will be curated. The application of this project may generalize beyond PROTACs to lysosomal targeting chimeras, molecular glues, and other novel non-inhibition mediated schemes.

No special research characteristics identified

This project does not include any of the advanced research characteristics tracked in our database.

Keywords
Af2 Breast Cancer Coactivator Domain Estrogen Receptor Machine Learning Modelling Protacs Predicting Active Protacs Protac Protein Degradation Protein-Protein Docking