Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, 1-3-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8526, Japan.
Research Center for the Mathematics on Chromatin Live Dynamics (RcMcD), Graduate School of Integrated Sciences for Life, Hiroshima University, 1-3-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8526, Japan.
Biomolecules. 2019 Sep 30;9(10):549. doi: 10.3390/biom9100549.
Simple protein elastic networks which neglect amino-acid information often yield reasonable predictions of conformational dynamics and are broadly used. Recently, model variants which incorporate sequence-specific and distance-dependent interactions of residue pairs have been constructed and demonstrated to improve agreement with experimental data. We have applied the new variants in a systematic study of protein fluctuation properties and compared their predictions with those of conventional anisotropic network models. We find that the quality of predictions is frequently linked to poor estimations in highly flexible protein regions. An analysis of a large set of protein structures shows that fluctuations of very weakly connected network residues are intrinsically prone to be significantly overestimated by all models. This problem persists in the new models and is not resolved by taking into account sequence information. The effect becomes even enhanced in the model variant which takes into account very soft long-ranged residue interactions. Beyond these shortcomings, we find that model predictions are largely insensitive to the integration of chemical information, at least regarding the fluctuation properties of individual residues. One can furthermore conclude that the inherent drawbacks may present a serious hindrance when improvement of elastic network models are attempted.
简单的蛋白质弹性网络忽略了氨基酸信息,往往可以对构象动力学做出合理的预测,因此被广泛应用。最近,构建了包含残基对序列特异性和距离依赖性相互作用的模型变体,并证明其可以提高与实验数据的一致性。我们将新变体应用于蛋白质波动特性的系统研究,并将其预测结果与传统各向异性网络模型的预测结果进行了比较。我们发现,预测质量通常与高度灵活的蛋白质区域的估计不佳有关。对一大组蛋白质结构的分析表明,所有模型都非常容易过高估计网络中非常弱连接残基的波动。这个问题在新模型中仍然存在,并且通过考虑序列信息也无法解决。在考虑非常柔软的长程残基相互作用的模型变体中,该问题甚至会加剧。除了这些缺点之外,我们发现模型预测对于化学信息的整合基本上不敏感,至少对于单个残基的波动特性是如此。人们还可以得出结论,当试图改进弹性网络模型时,内在的缺陷可能会严重阻碍改进。