A Blind Test of Computational Technique for Predicting the Likelihood of Peptide Sequences to Cyclize.
A Blind Test of Computational Technique for Predicting the Likelihood of Peptide Sequences to Cyclize.
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DOI：
10.1021/acs.jpclett.7b00848
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发表时间：
2017-05-18
期刊：
影响因子：
--
通讯作者：
Shalashilin DV
中科院分区：
文献类型：
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作者：
Booth J;Alexandru-Crivac CN;Rickaby KA;Nneoyiegbe AF;Umeobika U;McEwan AR;Trembleau L;Jaspars M;Houssen WE;Shalashilin DV
一种用于预测可被氰基菌素大环化酶环化的肽序列的计算机计算技术，例如，据报道，PatGmac。我们证明了PatGmac介导的环化的倾向与所谓的预环化构象（PCC）的自由能密切相关，这是一个折叠，其中环化序列C和N末端非常接近。这一结论是通过比较盒装分子动力学（BXD）的预测与实验数据，其中已达到84%的准确性。这里报告了一个真正的盲测而不是模型的训练，因为在给出任何实验数据之前开发了计算机工具，并且没有调整计算参数以拟合数据。盲法试验的成功提供了对氰基菌素大环化酶环化的分子机制的基本理解，表明PCC的形成是速率决定步骤。PCC的形成也可能在环肽生产的其他过程中发挥作用，并且在实践方面，所建议的工具通常可能对发现可环化肽序列有用。
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.