A novel computational method for head-to-tail peptide cyclization: application to urotensin II

Backbone head-to-tail cyclization is one effective strategy to stabilize the conformation of bioactive peptides, preventing enzymatic degradation and improving their bioavailibility. However, very little is known about the requirements to rationally design linkers for the cyclization of linear peptides. Recently, we have shown that large scale data-mining of protein structures can lead to the precise identification of protein loop conformations. Here, we transpose this approach to head-to-tail peptide cyclization. We first show that given a linker sequence and the conformation of the linear peptide, it is possible to accurately predict the cyclized peptide conformation improving by over 1 [A] over pre-existing protocols. Secondly, and more importantly, we show that it is possible to elaborate on the information inferred from protein structures to propose effective candidate linker sequences constrained by length and amino acid composition. As experimental validation, we first apply our approach to design linkers for the head-to-tail cyclizations of a peptide derived from Nrf2. The designed cyclized peptide shows a 26-fold increase in binding affinity. We then consider urotensin II, a cyclic peptide already stabilized by a disulfide bond, that exerts a broad array of biological activities. The designed head-to-tail cyclized peptide, the first synthesized bicyclic 14-residue long urotensin II analogue shows an excellent retention of in vitro activity. Overall, we propose the first framework for the rational peptide head-to-tail cyclization and reveal its potential for cyclic peptide-based drug design.