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

Jonathan Booth, Christina-Nicoleta Alexandru-Crivac, Kirstie A. Rickaby, Ada F. Nneoyiegbe, Ugochukwu Umeobika, Andrew R. McEwan, Laurent Trembleau, Marcel Jaspars, Wael E. Houssen, Dmitrii V. Shalashilin

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Abstract

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.

Original languageEnglish
Pages (from-to)2310-2315
Number of pages6
JournalThe Journal of Physical Chemistry Letters
Volume8
Issue number10
Early online date5 May 2017
DOIs
Publication statusPublished - 18 May 2017

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Booth, J., Alexandru-Crivac, C-N., Rickaby, K. A., Nneoyiegbe, A. F., Umeobika, U., McEwan, A. R., ... Shalashilin, D. V. (2017). A Blind Test of Computational Technique for Predicting the Likelihood of Peptide Sequences to Cyclize. The Journal of Physical Chemistry Letters, 8(10), 2310-2315. https://doi.org/10.1021/acs.jpclett.7b00848