Detargeting Protein-Protein Interactions For Cellular Design Applications, Using 3D Structure-Based Deep Learning Models

Rational protein design has had a tremendous impact on pharmaceutical, agriculture, and chemical industries over the past 30 years, by focusing exclusively on individual proteins and their intrinsic activities. The next generation of protein design tasks will seek to modify function inside living cells, competing and interacting directly with pre-existing cellular machinery. Modifying systems in living cells will open a new wave of biotechnology applications, such as living drug implants and diagnostic tools. However, effectively introducing new or engineered proteins into a living cell system requires attentive coordination to ensure that designed protein surfaces do not inadvertently interact with other host cell proteins and disrupt otherwise vital activities. Biological pathways are also very sensitive to perturbation, which can often result in cell death or functional failure. This partnership aims to build a predictive computational technology to ensure that designed protein surfaces do not display recognition features of proteins in the host cell that will minimize these unwanted interactions.

Faculty Supervisor:

Michael Garton

Student:

Haleh Shahbazi

Partner:

Cyclica

Discipline:

Engineering - biomedical

Sector:

Professional, scientific and technical services

University:

University of Toronto