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Analyzing Fluctuation Properties in Protein Elastic Networks with Sequence-Specific and Distance-Dependent Interactions

机译:用序列特异性和距离依赖性相互作用分析蛋白质弹性网络的波动性能

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摘要

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.
机译:简单的蛋白质弹性网络,忽略氨基酸信息通常会产生合理性动力学的合理预测,并且广泛使用。最近,已经构建了包含残留对的序列特异性和距离依赖性相互作用的模型变体,并证明了与实验数据的协议。我们在蛋白质波动性能的系统研究中应用了新的变体,并将其预测与传统各向异性网络模型的预测进行了比较。我们发现预测质量经常与高度柔性蛋白质区域的差的估计相关联。对大量蛋白质结构的分析表明,非常弱连接的网络残留物的波动本质上易于通过所有模型显着高估。此问题在新模型中仍然存在,并且无法通过考虑序列信息来解决。在模型变体中,效果变得增强,这考虑了非常柔软的长距离残留物相互作用。除了这些缺点之外,我们发现模型预测在很大程度上对化学信息的整合而言,至少关于单个残留物的波动性能。此外,人们可以得出结论,当尝试改善弹性网络模型时,固有的缺点可能存在严重的障碍。

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