Albrecht Birgit, Grant Guy H, Sisu Cristina, Richards W Graham
Department of Chemistry, University of Oxford, Central Chemistry Laboratory, South Parks Road, Oxford OX13QH, UK.
Biophys Chem. 2008 Nov;138(1-2):11-22. doi: 10.1016/j.bpc.2008.08.004. Epub 2008 Aug 29.
Data reduction techniques are now a vital part of numerical analysis and principal component analysis is often used to identify important molecular features from a set of descriptors. We now take a different approach and apply data reduction techniques directly to protein structure. With this we can reduce the three-dimensional structural data into two-dimensions while preserving the correct relationships. With two-dimensional representations, structural comparisons between proteins are accelerated significantly. This means that protein-protein similarity comparisons are now feasible on a large scale. We show how the approach can help to predict the function of kinase structures according to the Hanks' classification based on their structural similarity to different kinase classes.
数据约简技术如今是数值分析的重要组成部分，主成分分析常被用于从一组描述符中识别重要的分子特征。我们现在采用一种不同的方法，将数据约简技术直接应用于蛋白质结构。通过这种方法，我们可以在保留正确关系的同时，将三维结构数据简化为二维数据。有了二维表示，蛋白质之间的结构比较得以显著加速。这意味着蛋白质-蛋白质相似性比较现在在大规模上是可行的。我们展示了该方法如何根据汉克斯分类法，通过激酶结构与不同激酶类别的结构相似性来帮助预测其功能。
