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Stereochemical Criteria for Prediction of the Effects of Proline Mutations on Protein Stability

机译:预测脯氨酸突变对蛋白质稳定性影响的立体化学标准

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摘要

When incorporated into a polypeptide chain, proline (Pro) differs from all other naturally occurring amino acid residues in two important respects. The φ dihedral angle of Pro is constrained to values close to −65° and Pro lacks an amide hydrogen. Consequently, mutations which result in introduction of Pro can significantly affect protein stability. In the present work, we describe a procedure to accurately predict the effect of Pro introduction on protein thermodynamic stability. Seventy-seven of the 97 non-Pro amino acid residues in the model protein, CcdB, were individually mutated to Pro, and the in vivo activity of each mutant was characterized. A decision tree to classify the mutation as perturbing or nonperturbing was created by correlating stereochemical properties of mutants to activity data. The stereochemical properties including main chain dihedral angle φ and main chain amide H-bonds (hydrogen bonds) were determined from 3D models of the mutant proteins built using MODELLER. We assessed the performance of the decision tree on a large dataset of 163 single-site Pro mutations of T4 lysozyme, 74 nsSNPs, and 52 other Pro substitutions from the literature. The overall accuracy of this algorithm was found to be 81% in the case of CcdB, 77% in the case of lysozyme, 76% in the case of nsSNPs, and 71% in the case of other Pro substitution data. The accuracy of Pro scanning mutagenesis for secondary structure assignment was also assessed and found to be at best 69%. Our prediction procedure will be useful in annotating uncharacterized nsSNPs of disease-associated proteins and for protein engineering and design.
机译:当掺入多肽链中时,脯氨酸(Pro)在两个重要方面与所有其他天然氨基酸残基不同。 Pro的φ二面角被限制为接近-65°的值,Pro缺少酰胺氢。因此,导致引入Pro的突变会显着影响蛋白质的稳定性。在目前的工作中,我们描述了一种可以准确预测Pro导入对蛋白质热力学稳定性的影响的过程。模型蛋白CcdB中的97个非Pro氨基酸残基中的77个分别突变为Pro,并鉴定了每种突变体的体内活性。通过将突变体的立体化学性质与活性数据相关联,创建了将突变分类为干扰或非干扰的决策树。立体化学性质包括主链二面角φ和主链酰胺H键(氢键)是由使用MODELLER建立的突变蛋白的3D模型确定的。我们从文献中评估了163个T4溶菌酶,74 nsSNPs和52个其他Pro替代单突变Pro突变的大型数据集上决策树的性能。发现该算法的整体准确性在CcdB情况下为81%,在溶菌酶情况下为77%,在nsSNP情况下为76%,在其他Pro替代数据情况下为71%。还评估了Pro扫描诱变用于二级结构分配的准确性,发现其最高为69%。我们的预测程序将有助于注释疾病相关蛋白的非特征性nsSNP,以及用于蛋白工程和设计。

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