Laurent Trembleau

A Blind Test of Computational Technique for Predicting the Likelihood of Peptide Sequences to Cyclize

An in silico computational technique for predicting peptide sequences that can be cyclized by cyanobactin macrocyclases, e.g., PatGmac, is reported. We demonstrate that the propensity for PatGmac-mediated cyclization correlates strongly with the free energy of the so-called pre-cyclization conformation (PCC), which is a fold where the cyclizing sequence C and N termini are in close proximity. This conclusion is driven by comparison of the predictions of boxed molecular dynamics (BXD) with experimental data, which have achieved an accuracy of 84%. A true blind test rather than training of the model is reported here as the in silico tool was developed before any experimental data was given, and no parameters of computations were adjusted to fit the data. The success of the blind test provides fundamental understanding of the molecular mechanism of cyclization by cyanobactin macrocyclases, suggesting that formation of PCC is the rate-determining step. PCC formation might also play a part in other processes of cyclic peptides production and on the practical side the suggested tool might become useful for finding cyclizable peptide sequences in general.

CHAPTER 15:Cyclic Peptides – A Look to the Future

This chapter sums up the different approaches to harness the promising therapeutic potential of cyclic peptides in drug discovery and proposes a number of areas which need improvement in order to make cyclic peptides live up to their full potential as drug candidates. It is clear that improved methods to rapidly and efficiently synthesize cyclic peptides and modify them are essential to explore this region of chemical space. A better understanding of what governs the physicochemical characteristics of this compound class is essential to allow the better prediction of the properties of designed cyclic peptides. With this goes the ability to accurately and reliably predict the solution and binding conformations of cyclic peptides, as well as theoretical approaches for determining which peptides are likely to cyclize easily, and which are not. How such compounds bind to their target proteins is just beginning to be understood and improvements are necessary to allow the design of cyclic peptides that bind specifically to extended binding sites. Finally, the ability to utilize biosynthetic machineries from diverse pathways to create hybrid molecules with desirable characteristics is proposed as a major target for future investigation.