Testing a New Method for Macrocyclic Peptide Binder Design
Project Description
In November 2024, the Baker lab, recipient of the 2024 Nobel Prize in Chemistry, published their latest method, RFDiffusion, for designing macrocyclic peptide binders. This project aims to replicate some of their in silico experiments and design cyclic peptide binders targeting the HIV gp120 receptor. The designed binders will be compared with those reported in the literature.
This project requires a basic understanding of biology, and the ability to install and run deep learning packages.
References:
1. Rettie, Stephen, et al. "Accurate de novo design of high-affinity protein binding macrocycles using deep learning." bioRxiv (2024): 2024-11.
2. Pancera, M., Lai, Y. T., Bylund, T., Druz, A., Narpala, S., O'Dell, S., ... & Kwong, P. D. (2017). Crystal structures of trimeric HIV envelope with entry inhibitors BMS-378806 and BMS-626529. Nature Chemical Biology, 13(10), 1115-1122.
3. Meredith, L. W., Wilson, G. K., Fletcher, N. F., & McKeating, J. A. (2012). Hepatitis C virus entry: beyond receptors. Reviews in Medical Virology, 22(3), 182-193.
This project requires a basic understanding of biology, and the ability to install and run deep learning packages.
References:
1. Rettie, Stephen, et al. "Accurate de novo design of high-affinity protein binding macrocycles using deep learning." bioRxiv (2024): 2024-11.
2. Pancera, M., Lai, Y. T., Bylund, T., Druz, A., Narpala, S., O'Dell, S., ... & Kwong, P. D. (2017). Crystal structures of trimeric HIV envelope with entry inhibitors BMS-378806 and BMS-626529. Nature Chemical Biology, 13(10), 1115-1122.
3. Meredith, L. W., Wilson, G. K., Fletcher, N. F., & McKeating, J. A. (2012). Hepatitis C virus entry: beyond receptors. Reviews in Medical Virology, 22(3), 182-193.
Supervisor
ZHANG Nevin Lianwen
Quota
1
Course type
UROP4100
Applicant's Roles
This project aims to replicate some of their in silico experiments and design cyclic peptide binders targeting the HIV gp120 receptor.
Applicant's Learning Objectives
Understand the newest development in AI for drug discovery
Gain experience of installing and run deep learning packages for drug discovery
Gain experience of installing and run deep learning packages for drug discovery
Complexity of the project
Challenging