Cyclic Peptides

The YSL Group seeks to model structure prediction for cyclic peptides using molecular dynamics simulations and enhanced sampling methods.

Many cellular signal transduction events are regulated by protein‒protein interactions. Inappropriate protein‒protein recognitions in signal transduction processes are involved in many diseases. The ability to selectively modulate these interactions would provide a means to control cellular signaling for basic research and for therapeutic purposes. Cyclic peptides are promising modulators of protein‒protein interactions: they can bind large protein surfaces with high affinity and specificity and they have enhanced biostability and bioavailability compared to their linear counterparts.
Despite several successful demonstrations of the clinical application of cyclic peptides as immunosuppressants, antibiotics, and antifungals, most of these cyclic peptides are actually natural products and their derivatives. Synthetic cyclic peptides remain severely underexplored. This under-exploitation is largely due to the current impossibility of predicting their three-dimensional structures de novo. The current methodology for designing and optimizing cyclic peptides requires comprehensive synthesis and characterization of numerous variants to empirically search for molecules with improved properties that retain the necessary three-dimensional conformation. The requirement for searching conformational space via chemical synthesis presents a major hurdle to the development of these promising molecules. The ability to rapidly and accurately predict cyclic peptide structures computationally would dramatically reduce synthetic burden, expedite peptide design and lower overall costs.
Our group is developing computational methods to quickly and accurately predict three-dimensional structures for cyclic peptides. We expect this new tool to have many applications in basic science and therapeutic development, substantially advancing the exploitation of cyclic peptides as modulators of protein‒protein interactions.

Related YSL Group Papers

T. Hui, M. L. Descoteaux, J. Miao, Y.-S. Lin“Training neural network models using molecular dynamics simulation results to efficiently predict cyclic hexapeptide structural ensembles,” J. Chem. Theory Comput. 19, 4757–4769 (2023).
Equal contributions.

J. Miao, M. Descoteaux, Y.-S. Lin“Structure prediction of cyclic peptides by molecular dynamics + machine learning,” Chem. Sci. 12, 14927–14936 (2021).

J. Damjanovic, J. Miao, H. Huang, Y.-S. Lin, “Elucidating solution structures of cyclic peptides using molecular dynamics simulations,” Chem. Rev. 121, 2292–2324 (2021).
Equal contributions.

H. Huang, J. Damjanovic, J. Miao, Y.-S. Lin, “Cyclic peptides: Backbone rigidification and capability of mimicking motifs at protein–protein interfaces,” Phys. Chem. Chem. Phys. 23, 607–616 (2021).
Equal contributions.

A. E. Cummings, J. Miao, D. P. Slough, S. M. McHugh, J. A. Kritzer,* Y.-S. Lin,* “Beta-branched amino acids stabilize specific conformations of cyclic hexapeptides,” Biophys. J. 116, 433–444 (2019).
Equal contributions. *Co-corresponding authors.

D. P. Slough, S. M. McHugh, A. E. Cummings, P. Dai, B. L. Pentelute, J. A. Kritzer, Y.-S. Lin“Designing well-structured cyclic pentapeptides based on sequence—structure relationships,” J. Phys. Chem. B 122, 3908–3919 (2018).
Equal contributions.

Y. Li, N. P. Lavey, J. A. Coker, J. E. Knobbe, D. C. Truong, H. Yu, Y.-S. Lin, S. L. Nimmo, A. S. Duerfeldt, “Consequences of depsipeptide substitution on the ClpP activation activity of antibacterial acyldepsipeptides,” ACS Med. Chem. Lett. 8, 1171–1176 (2017).

L. Peraro, Z. Zou, K. M. Makwana, A. E. Cummings, H. L. Ball, H. Yu, Y.-S. Lin, B. Levine, J. Kritzer, “Diversity-oriented stapling yields intrinsically cell-penetrant inducers of autophagy,” J. Am. Chem. Soc. 139, 7792–7802 (2017).

D. P. Slough, H. Yu, S. M. McHugh, Y.-S. Lin“Toward accurately modeling N-methylated cyclic peptides,” Phys. Chem. Chem. Phys. 19, 5377–5388 (2017).

S. M. McHugh, H. Yu, D. P. Slough, Y.-S. Lin, “Mapping the sequence—structure relationships of simple cyclic hexapeptides,” Phys. Chem. Chem. Phys. 19, 3315–3324 (2017).
Equal contributions.

S. M. McHugh, J. R. Rogers, S. A. Solomon, H. Yu, Y.-S. Lin“Computational methods to design cyclic peptides,” Curr. Opin. Chem. Biol. 34, 95–102 (2016).

S. M. McHugh, J. R. Rogers, H. Yu, Y.-S. Lin“Insights into how cyclic peptides switch conformations,” J. Chem. Theory Comput. 12, 2480–2488 (2016).
Equal contributions.

H. Yu and Y.-S. Lin“Toward structure prediction of cyclic peptides,” Phys. Chem. Chem. Phys. 17, 4210–4219 (2015).

J. S. Quartararo, M. R. Eshelman, L. Peraro, H. Yu, J. D. Baleja, Y.-S. Lin, J. A. Kritzer, “A bicyclic peptide scaffold promotes phosphotyrosine mimicry and cellular uptake,” Bioorg. Med. Chem. 22, 6387–6391 (2014).

Y. Zou, A. M. Spokoyny, C. Zhang, M. D. Simon, H. Yu, Y.-S. Lin, B. L. Pentelute, “Convergent diversity-oriented side-chain macrocyclization scan for unprotected polypeptides,” Org. Biomol. Chem. 12, 566–573 (2014).

A. M. Spokoyny, Y. Zou, J. J. Ling, H. Yu, Y.-S. Lin, B. L. Pentelute, “A perfluoroaryl-cysteine SNAr chemistry approach to unprotected peptide stapling,” J. Am. Chem. Soc. 135, 5946–5949 (2013).