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Prediction of novel methyllysine events

Given the prevalence of dysfunctional lysine methylation events in human health and disease, the development of widespread identification technologies has been receiving considerable attention. For this project, we development of a support vector machine-based model for predicting lysine methylation sites among human proteins. This project is in collaboration with the lab of Dr. Jim Green. The model is based on fully-alignment-free features which encode sequence-based information surrounding lysine residues. Click on the MethylSight logo on the homepage to visit our web server developed in collaboration with the Green lab at Carleton University. 



Annotation of the methyllysine interactome

Building on previous knowledge and predictions of the protein-protein interaction network, we have been working to annotate the protein interactions that are dependant on a lysine methylation event. These interactions are typically between methyl-binding domains (MBDs) and a lysine methylation site. We have been using peptide arrays and supercomputing resources to identify and validate methyllysine-dependant interactions on a proteome scale. 

Check out Francois Charih's Ph.D. project on peptide inhibitor design

Francois's research interests are focused on the implementation and development of computational resources to develop peptide inhibitors. This includes the combination of machine learning with large proteomic databases to help guide wet-lab validation experiments. He is a co-supervised student with Dr. James Green.

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