Liu Zhi-Ping, Wu Ling-Yun, Wang Yong, Chen Luonan, Zhang Xiang-Sun
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China.
BMC Bioinformatics. 2007 Dec 11;8:475. doi: 10.1186/1471-2105-8-475.
Annotation of protein functions is an important task in the post-genomic era. Most early approaches for this task exploit only the sequence or global structure information. However, protein surfaces are believed to be crucial to protein functions because they are the main interfaces to facilitate biological interactions. Recently, several databases related to structural surfaces, such as pockets and cavities, have been constructed with a comprehensive library of identified surface structures. For example, CASTp provides identification and measurements of surface accessible pockets as well as interior inaccessible cavities.
A novel method was proposed to predict the Gene Ontology (GO) functions of proteins from the pocket similarity network, which is constructed according to the structure similarities of pockets. The statistics of the networks were presented to explore the relationship between the similar pockets and GO functions of proteins. Cross-validation experiments were conducted to evaluate the performance of the proposed method. Results and codes are available at: http://zhangroup.aporc.org/bioinfo/PSN/.
The computational results demonstrate that the proposed method based on the pocket similarity network is effective and efficient for predicting GO functions of proteins in terms of both computational complexity and prediction accuracy. The proposed method revealed strong relationship between small surface patterns (or pockets) and GO functions, which can be further used to identify active sites or functional motifs. The high quality performance of the prediction method together with the statistics also indicates that pockets play essential roles in biological interactions or the GO functions. Moreover, in addition to pockets, the proposed network framework can also be used for adopting other protein spatial surface patterns to predict the protein functions.
蛋白质功能注释是后基因组时代的一项重要任务。早期执行此任务的大多数方法仅利用序列或全局结构信息。然而，蛋白质表面被认为对蛋白质功能至关重要，因为它们是促进生物相互作用的主要界面。最近，已经构建了几个与结构表面相关的数据库，例如口袋和空腔，并带有一个已识别表面结构的综合库。例如，CASTp提供表面可及口袋以及内部不可及空腔的识别和测量。
提出了一种从口袋相似性网络预测蛋白质基因本体（GO）功能的新方法，该网络是根据口袋的结构相似性构建的。展示了网络的统计数据，以探索相似口袋与蛋白质GO功能之间的关系。进行了交叉验证实验以评估所提出方法的性能。结果和代码可在以下网址获取：http://zhangroup.aporc.org/bioinfo/PSN/。
计算结果表明，所提出的基于口袋相似性网络的方法在计算复杂性和预测准确性方面对于预测蛋白质的GO功能都是有效且高效的。所提出的方法揭示了小表面模式（或口袋）与GO功能之间的紧密关系，这可进一步用于识别活性位点或功能基序。预测方法的高质量性能以及统计数据还表明口袋在生物相互作用或GO功能中起着至关重要的作用。此外，除了口袋之外，所提出的网络框架还可用于采用其他蛋白质空间表面模式来预测蛋白质功能。
