...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>PLoS Computational Biology >Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles
【24h】

Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles

机译：通过基于结构的界面概况预测突变对蛋白质与蛋白质结合相互作用的影响

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies.
机译：蛋白质-蛋白质复合物的形成对于蛋白质在细胞中执行其生理功能至关重要。阻止正确形成正确复合物的突变可能对相关的细胞过程造成严重后果。由于当大规模进行蛋白质-蛋白质结合亲和力的实验确定仍然很困难，因此非常需要用于预测突变对结合亲和力的后果的计算方法。我们显示基于从PDB中类似的蛋白质-蛋白质相互作用收集的界面结构概况的评分功能是突变后蛋白质结合亲和力变化的有力预测因子。作为一个独立的功能，突变体和野生型蛋白质的界面谱得分之间的差异具有与最佳全原子电势相当的精度，尽管一旦构建了谱，速度就会快两个数量级。由于其在收集类似结合相互作用的进化图谱和计算速度方面的独特敏感性，因此界面图谱得分具有补充优势，可以与基于物理的电势相结合，从而提高复合评分方法的准确性。通过将序列衍生的和残基级的粗粒电势与界面结构轮廓得分相结合，通过随机森林训练构建了一个复合模型，该模型在预测的和观察到的结合自由能变化之间产生了> 0.8的皮尔逊相关系数突变后。在大多数情况下，此准确性可与之媲美或在大多数情况下优于其最佳方法，但不需要突变体结构的高分辨率全原子模型。结合界面分析方法应在人类疾病突变识别和蛋白质界面设计研究中找到有用的应用。

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