基于序列预测功能位点和蛋白质与配体相互作用
Sequence-based prediction of function site and protein-ligand interaction by a functionally annotated domain profile database
摘要：
蛋白质功能位点（PFSs）和蛋白质与配体相互作用（PLIs）的识别对于理解蛋白质功能和所涉及的生化反应至关重要。后基因组时代，大量未知蛋白质的快速积累，迫切需要在剩余水平上预测PFSs和PLIs。目前，许多基于知识的预测方法已经发展得很好，但PLI的准确预测方法仍然缺乏。在本研究中，我们提出了一种基于查询蛋白质序列的PLI和PFS预测的新方法。该方法的关键在于一个功能和相互作用注释的蛋白质结构域概况数据库，称为fiDPD，这是建立在蛋白质结构分类（SCOP）数据库，使用隐马尔可夫模型程序。该方法应用于最近的结构预测临界评估（CASP 10/11）中的13种靶蛋白。我们的计算给出了一个马修斯相关系数（MCC）值为0.66的预测PFS，和80%的召回预测PLIs。我们的方法揭示了PLIs在蛋白质的进化过程中是保守的，并且它们可以从fiDPD可靠地预测。fiDPD可用作现有生物信息学工具的补充，用于蛋白质功能注释。
英文摘要：
Identifying protein functional sites (PFSs) and protein-ligand interactions (PLIs) are critically important in understanding the protein function and the involved biochemical reactions. As large amount of unknown proteins are quickly accumulated in this post-genome era, an urgent task arises to predict PFSs and PLIs at residual level. Nowadays many knowledge-based methods have been well developed for prediction of PFSs, however, accurate methods for PLI prediction are still lacking. In this study, we have presented a new method for prediction of PLIs and PFSs based on sequence of the inquiry protein. The key of the method hinges on a function- and interaction-annotated protein domain profile database, called fiDPD, which was built from the Structural Classification of Proteins (SCOP) database, using a hidden Markov model program. The method was applied to 13 target proteins from the recent Critical Assessment of Structure Prediction (CASP10/11). Our calculations gave a Matthews correlation coefficient (MCC) value of 0.66 for prediction of PFSs, and an 80% recall in prediction of PLIs. Our method reveals that PLIs are conserved during the evolution of proteins, and they can be reliably predicted from fiDPD. fiDPD can be used as a complement to existent bioinformatics tools for protein function annotation.
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