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首页> 外文期刊>Biophysical Journal >Improving the Accuracy of Protein Thermostability Predictions for Single Point Mutations
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Improving the Accuracy of Protein Thermostability Predictions for Single Point Mutations

机译:提高单点突变的蛋白质热稳定性预测的准确性

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摘要

Accurately predicting the protein thermostability changes upon single point mutations in silico is a challenge that has implications for understanding diseases as well as industrial applications of protein engineering. Free energy perturbation (FEP) has been applied to predict the effect of single point mutations on protein stability for over 40 years and emerged as a potentially reliable prediction method with reasonable throughput. However, applications of FEP in protein stability calculations in industrial settings have been hindered by a number of limitations, including the inability to model mutations to and from prolines in which the bonded topology of the backbone is modified and the complexity in modeling charge-changing mutations. In this study, we have extended the FEP+ protocol to enable the accurate modeling of the effects on protein stability from proline mutations and from charge-changing mutations. We also evaluated the influence of the unfolded model in the stability calculations using increasingly longer peptides with native sequence and conformations. With the abovementioned improvements, the accuracy of FEP predictions of protein stability over a data set of 87 mutations on five different proteins has drastically improved compared with previous studies, with a mean unsigned error of 0.86 kcal/mol and root mean square error of 1.11 kcal/mol, comparable with the accuracy of previously published state-of-the-art small-molecule relative binding affinity calculations, which have been shown to be capable of driving discovery projects.
机译:精确预测硅胶蛋白质的蛋白质热稳定性变化是一种对了解疾病以及蛋白质工程的工业应用具有影响的挑战。已经应用自由能量扰动(FEP)以预测单点突变对蛋白质稳定性超过40年的影响,并作为具有合理吞吐量的潜在可靠的预测方法。然而,FEP在工业环境中的蛋白质稳定性计算的应用已经受到许多限制的阻碍,包括无法模拟突变的突变,其中骨干的粘合拓扑结构和模拟变化变化突变的复杂性。在这项研究中,我们已经扩展了FEP +方案,以便能够精确建模对脯氨酸突变和电荷变化的蛋白质稳定性的影响。我们还评估了使用具有天然序列和构象的越来越长的肽的稳定性计算中展开模型对稳定性计算的影响。随着上述改进,与先前的研究相比,在五种不同蛋白质上的87个突变的数据集上对87个突变的数据集的预测的准确性,其平均无符号误差为0.86kcal / mol,1.11 kcal的均方根误差/ mol,与先前公布的最先进的小分子相对结合亲和计算的准确性相当,已被证明能够驾驶发现项目。

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