Research Projects

The collaboratory is actively pursing a variety of research projects rooted in biomedical informatics, signal processing, bioinformatics, and computer vision. Below are some of the ongoing research projects:

Prediction of protein-protein interactions

Members involved

Francois Charih, Kevin Dick, Aishwarya Purohit, Eric Arezza

Project description

cuBIC participated actively in the development of the Protein-Protein Interaction Prediction Engine (PIPE) which was the first algorithm capable of generating the comprehensive human interactome. Today, PIPE is currently in its 4th iteration, and we are continuously trying to improve its performance, and trying to apply it to elucidate new interactomes such as that of human-SARS-CoV-2 for example.

In silico peptide design

Members involved

Francois Charih

Collaborators

Project description

Peptide inhibitor display outstanding specificity for their target, and as such, show great potential as therapeutics through disruption of protein-protein interactions and/or modulation of enzyme activity. Unfortunately, designing peptide inhibitors in the wet lab is labour and resource-intensive. Recent developments in in silico peptide design (e.g. InSiPS) have made it possible to generate active peptide therapeutics using only the primary sequence of the targeted protein as an input. Unfortunately, such methods are slow and have suboptimal convergence.

Consistent with our core belief that collaboration across discipline is vital in the modern era, we initiated a partnership with the Biggar lab (biology) to develop a new method that integrates wet lab experiments (eg. peptide arrays) with computational methods in an iterative fashion to improve the state of the art in in silico peptide design.