In a nutshell

I am currently a PhD student in Electrical and Computer Engineering specializing in computational biochemistry. I am particularly interested in applications of artificial intelligence (AI)/machine learning (ML) to study and modify lysine methylation. I have also developed an interest and expertise in high performance computing throughout the past years. My thesis advisors are Profs. James R. Green (Systems and Computer Engineering) and Kyle K. Biggar (Biochemistry).

Beyond my thesis work, I am also interested in how AI/ML can be applied to further our understanding of the pathophysiology and clinical evolution of amyotrophic lateral sclerosis (ALS), an understudied illness of the motoneurons that has eluded us for way too long.

Thesis work

As part of my thesis work, I apply AI/ML tools to the study of lysine methylation. My research can be subdivided into three research themes:

  1. Proteome-wide identification of the lysine methylation sites
    Our cells express over 20,000 proteins, a large portion of which are substrates in a set of chemical reactions called post-translational modifications (phosphorylation, acetylation, methylation, etc.) These reactions can modulate the function, stability and localization within the cell. I aim to develop new machine learning approaches to answer questions such as: What proteins are methylated? How does this relate to other modifications? What are the implications of this modification?
  2. Identifying the features that underpin the specificity of lysine methyltransferases for their substrate
    Lysine methyltransferase enzymes (KMTs) which carry out the reaction are specific; i.e. they each only modify a specific subset of proteins. By combining in vitro and in silico methods, I seek to understand the features that associate a KMT to its substrates. Once this is achieved, we hope to be able to identify new substrates for a given KMT. This will have broad implications for drug development.
  3. In silico design of lysine methyltransferase inhibitors
    I am developing a novel algorithm for peptide inhibitor design. We are hopeful that interleaving the algorithm with peptide array experiments will significantly accelerate the discovery of active peptides capable of specifically modulating the activity of KMTs (and KDMs). Overactive KMTs/KDMs are involved in a number of cancers, and these peptides could supplement existing therapies and improve their efficacy.

Previous work

  1. Structural biology
    Under the supervision of Prof. Jean-François Couture, I completed my undergraduate thesis on the structural characterisation of the Fur (Ferric Uptake Regulator) protein in Campylobacter jejuni a pathogenic agent responsible for numerous cases of gastroenteritis. I crystallized the protein and built a draft model of the structure, which was further refined by Prof. Couture. This research was published in the scientific journal Scientific Reports.
  2. Biomedical informatics
    I have previously worked in health informatics. My master's thesis work focused on the ML-assisted classification of audiograms to facilitate their interpretation by non-experts. My thesis is available here.