Computational Prediction of Thioamide Stabilization of Peptides

Sumant writing code on a computer.



Associate Professor of Chemistry

Project Summary

Peptides are appealing scaffolds for many therapeutics due to their inherent relevance in key biochemical processes, their low toxicity, and bioavailability. However, the promise of peptide therapeutics has been limited by their metabolic instability. The Petersson group has pioneered a novel single atom substitution from an oxygen to sulfur in the peptide backbone, which has stabilizing effect. To truly push this thioamide substitution into commercial viability, the effects of this substitution must be better understood. In silico techniques, specifically the Rosetta Modelling Suite, provides us the ability to predict the best position of thioamide substitution and understand why this is the appropriate position.

During the summer, I learnt a host of computational tools. With Rosetta, I learnt how to prepare structure to be used, learned how to do docking simulations, and lastly how to use supervised machine learning techniques. Moreover, I learnt generally how to code much better.  This gave me a framework in pursuing projects of these types

Research in the Petersson Group provided me a uniques experience that a course could have not given me. It taught me how to be self-reliant, since in research, most answers are not obvious. I figured out how to solve many of my problem through digging through the literature. In class, we learn to value knowledge. In this research environment, I learnt how to value knowledge and application of it.

Many thanks to my mentors: Sam Giannakoulias, Jack Ferrie, and James Petersson.