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首页> 外文期刊>Journal of Molecular Biology >Evaluation of structural and evolutionary contributions to deleterious mutation prediction.
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Evaluation of structural and evolutionary contributions to deleterious mutation prediction.

机译：评估结构和进化对有害突变预测的贡献。

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Methods for automated prediction of deleterious protein mutations have utilized both structural and evolutionary information but the relative contribution of these two factors remains unclear. To address this, we have used a variety of structural and evolutionary features to create simple deleterious mutation models that have been tested on both experimental mutagenesis and human allele data. We find that the most accurate predictions are obtained using a solvent-accessibility term, the C(beta) density, and a score derived from homologous sequences, SIFT. A classification tree using these two features has a cross-validated prediction error of 20.5% on an experimental mutagenesis test set when the prior probability for deleterious and neutral cases is equal, whereas this prediction error is 28.8% and 22.2% using either the C(beta) density or SIFT alone. The improvement imparted by structure increases when fewer homologs are available: when restricted to three homologs the prediction error improves from 26.9% using SIFT alone to 22.4% using SIFT and the C(beta) density, or 24.8% using SIFT and a noisy C(beta) density term approximating the inaccuracy of ab initio structures modeled by the Rosetta method. We conclude that methods for deleterious mutation prediction should include structural information when fewer than five to ten homologs are available, and that ab initio predicted structures may soon be useful in such cases when high-resolution structures are unavailable.
机译：自动预测有害蛋白质突变的方法已经利用了结构和进化信息，但是这两个因素的相对贡献仍然不清楚。为了解决这个问题，我们使用了各种结构和进化特征来创建简单的有害突变模型，该模型已经在实验诱变和人类等位基因数据上进行了测试。我们发现，使用溶剂可及性术语，Cβ密度和源自同源序列SIFT的得分可获得最准确的预测。当有害和中性病例的先验概率相等时，使用这两种功能的分类树在诱变实验集上的交叉验证预测误差为20.5％，而使用C（ beta）密度或仅SIFT。当可用的同系物较少时，由结构赋予的改进会增加：当限制为三个同系物时，预测误差从仅使用SIFT的26.9％改进为使用SIFT和Cβ密度的22.4％，或者使用SIFT和嘈杂的C（24.8％）。 β）密度项，近似通过Rosetta方法建模的从头算结构的不准确性。我们得出结论，当少于五到十个同系物可用时，有害突变预测的方法应包括结构信息，并且在没有高分辨率结构的情况下，从头算起的预测结构可能很快会有用。

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