A Machine Learning-Guided Platform to Engineer Functional Analogues of Therapeutic Peptides

Comprised of a small chain of amino acids, therapeutic peptides are a unique class of drugs that have played a significant role in medicine since the discovery of insulin. Despite their importance in Pharma, the discovery of new peptides and the improvement of existing therapeutics remains a highly specialized domain and has not seen the cost barrier fall yet. Additionally, the pharmaceutical industry is heavily patent-protected allowing only a few major corporations to monopolize the market.
Novel platforms for designing patent-free mimetics of high-value peptide-based therapeutics can potentially mitigate this problem. Combining the advantages of synthetic biology-enabled cell-free systems with microfluidics, I propose to create a machine learning-guided platform for designing functional analogs of high-value peptide therapeutics that share less than 50% sequence identity with the original therapeutic.

Faculty Supervisor:

Keith Pardee

Student:

Partner:

Okinawa Institute of Science and Technology

Discipline:

Life Sciences

Sector:

Education

University:

University of Toronto

Program: