AlphaFold [1,2] is an artificial intelligence program developed by DeepMind that predicts the 3D structures of proteins from their amino acid sequences. Although it was not trained to handle cyclic peptides, AlphaFold has been shown to be useful in cyclic peptide structure prediction and design [3,4]. In this project, students are provided with an opportunity to explore this aspect of AlphaFold. More specifically, the students are expected to repeat some experiments in [3,4] to get acquainted with the technology. Afterwards, the students are expected to come up with cyclic peptide binders to two receptor proteins (HIV gp120 and HCV E2), and compare the designs with those reported in the literature [5,6].

1. Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., ... & Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583-589.
2. Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2022). ColabFold: making protein folding accessible to all. Nature methods, 19(6), 679-682.
3. Rettie, S. A., Campbell, K. V., Bera, A. K., Kang, A., Kozlov, S., De La Cruz, J., ... & Bhardwaj, G. (2023). Cyclic peptide structure prediction and design using AlphaFold. bioRxiv.
4. Kosugi, T., & Ohue, M. (2023). Design of Cyclic Peptides Targeting Protein–Protein Interactions Using AlphaFold. International Journal of Molecular Sciences, 24(17), 13257.
5. 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.
6. 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.