Polypeptide Helicity and Ensemble Prediction Tool

Description

Helix-coil models have a long tradition in structural biology, and predicting the behavior of nascently helical polypeptides has many potential applications. Duke faculty have built a Bayesian parameterized model capable of predicting the folding into alpha-helix by any polypeptide based on its amino acid sequence. This model can be used to predict important properties of intrinsically disordered proteins, which have a growing list of important biological functions. It can also be used to design semi-rigid linkers in multivalent recognition proteins that are being developed as "biological" therapeutics targeting diseases such as HIV, COVID and cancer.

A team of students will work with the Office of Information Technology and faculty from Biochemistry, Statical Science, Computer Science and Biomedical Engineering to developing a web application and API to make the model available to scientists around the world.

Watch the team's final presentation on YouTube.

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Team

Members

• Amy Zhang
• Eric Chen
• Hannah Hortman
• Jonathan Ortega
• Marco Castillo
• Samia Evans

Leaders

• Mark McCahill
• Jeremy Bandini

Project Facilitators

• Jaelyn Cuellar
• Mariam Gvenetadze

Sponsors

• Apple