Study on the cyclization tendency of backbone cyclic tetrapeptides

Abstract

The cyclization kinetics of five backbone-cyclic tetrapeptides was investigated both experimentally and computationally. The aim was to both accurately measure the cyclization rates in solution and develop a method that efficiently estimates the relative cyclization tendencies computationally. Progression of the cyclization reaction was monitored directly, yielding the kinetics of changes in the amounts of the linear precursor and the products. These measurements were used to calculate the reaction rates; the results were consistent with a first-order reaction kinetics. In order to predict the cyclization rates computationally, the conformation space of the linear precursors was mapped and used to construct an approximate partition function. We assumed that the cyclization tendency was correlated with the relative probability of being found in a cyclization-prone conformation of the backbone, this probability was estimated from the partition function. The results supported this assumption and demonstrated that, within reasonable accuracy, we are able to predict the relative cyclization tendencies of the peptides measured.

Modeling of peptides containing D-amino acids: implications on cyclization

“…4C. The ''curved'' conformation, has been recognized as pre-requisite to be fulfilled in order to undergo cyclization [25]. Thus, molecules that are able to reach ''curved'' conformations have overcome the first conformational requirement for cyclization.…”

“…Methodologies to predict the tendencies of peptides to undergo cyclization have been reported. [24,25] Based on molecular mechanics calculations and experimental evidence involving cyclotetrapeptides, Cavelier-Frontin et al [24] concluded that the determining factor for ring closure is the transition-state energy, wherein those peptides that are able to have transition-states in a low strain conformation have more chances to undergo cyclization. In a subsequent study Besser et al [24,25] developed a methodology based on the structural comparison between the conformations of the linear precursor and the conformation in the transition-state.…”

“…[24,25] Based on molecular mechanics calculations and experimental evidence involving cyclotetrapeptides, Cavelier-Frontin et al [24] concluded that the determining factor for ring closure is the transition-state energy, wherein those peptides that are able to have transition-states in a low strain conformation have more chances to undergo cyclization. In a subsequent study Besser et al [24,25] developed a methodology based on the structural comparison between the conformations of the linear precursor and the conformation in the transition-state. Their first assumption was that the cyclization tendency was correlated with the relative probability of being found in a cyclization-prone conformation of the backbone, and concluded that the conformational space of the linear analogs should be considered in the calculation of the cyclization tendencies.…”

Development of Conformationally Restricted Analogues of Bradykinin and Somatostatin Using Constrained Amino Acids and Different Types of Cyclization

“…Up to now, several attempts have been made to develop different types of backbone modification, including investigations on the use of different protecting group combinations, resins and linkers, but also optimised conditions for the peptide assembly and cyclization on the solid phase, the cyclization tendency, side reactions and chemical characterisation of backbone cyclic peptides have been described [30,31]. Furthermore, the method of backbone cyclization has been proven useful for systematic SAR studies of bioactive scaffolds of the peptide hormones substance P, bradykinin and somatostatin within the recently developed "Cycloscan" technology [32,33].…”

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

“…For all 25 sequences, their probabilities of PCC formation were calculated, and it was shown that that correlation between the calculated probability and cyclization indeed exists. Although some computational studies − of peptide cyclization have been reported, to the best of the authors’ knowledge, no predictive model has been rigorously tested against an experimental data set.…”