• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Bioinformatics >Protein stability: a single recorded mutation aids in predicting the effects of other mutations in the same amino acid site
【24h】

Protein stability: a single recorded mutation aids in predicting the effects of other mutations in the same amino acid site

机译：蛋白质稳定性：单个记录的突变有助于预测同一氨基酸位点上其他突变的影响

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Motivation: Accurate prediction of protein stability is important for understanding the molecular underpinnings of diseases and for the design of new proteins. We introduce a novel approach for the prediction of changes in protein stability that arisefrom a single-site amino acid substitution; the approach uses available data on mutations occurring in the same position and in other positions. Our algorithm, named Pro-Maya (Protein Mutant stAbilitY Analyzer), combines a collaborative filtering baseline model, Random Forests regression and a diverse set of features. Pro-Maya predicts the stability free energy difference of mutant versus wild type, denoted as DELTA DELTA G.Results: We evaluated our algorithm extensively using cross-validation on two previously utilized datasets of single amino acid mutations and a (third) validation set. The results indicate that using known DELTA DELTA G values of mutations at the query position improves the accuracy of DELTA DELTA G predictions for other mutations in that position. The accuracy of our predictions in such cases significantly surpasses that of similar methods, achieving, e.g. a Pearson's correlation coefficient of 0.79 and a root mean square error of 0.96 on the validation set. Because Pro-Maya uses a diverse set of features, including predictions using two other methods, it also performs slightly better than other methods in the absence of additional experimental data on the query positions.
机译：动机：准确预测蛋白质的稳定性对于理解疾病的分子基础和设计新蛋白质非常重要。我们引入了一种新的方法来预测由单位氨基酸取代引起的蛋白质稳定性的变化;该方法使用有关在同一位置和其他位置发生的突变的可用数据。我们的算法名为Pro-Maya（蛋白质突变稳定性分析器），结合了协作式过滤基准模型，Random Forests回归和多种功能。 Pro-Maya预测了突变型与野生型的稳定自由能差，表示为DELTA DELTAG。结果：我们在两个先前使用的单个氨基酸突变数据集和一个（第三个）验证集上使用交叉验证对我们的算法进行了广泛评估。结果表明，在查询位置使用已知的DELTA DELTA G突变值可提高该位置其他突变的DELTA DELTA G预测准确性。在这种情况下，我们的预测准确性大大超过了类似方法，从而达到了验证集上的Pearson相关系数为0.79，均方根误差为0.96。由于Pro-Maya使用了多种功能，包括使用其他两种方法的预测，因此在查询位置上没有其他实验数据的情况下，Pro-Maya的性能也比其他方法稍好。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号