Institute of Systems Biology, Shanghai University, Shanghai, China.
PLoS One. 2011 Jan 19;6(1):e14556. doi: 10.1371/journal.pone.0014556.
With the huge amount of uncharacterized protein sequences generated in the post-genomic age, it is highly desirable to develop effective computational methods for quickly and accurately predicting their functions. The information thus obtained would be very useful for both basic research and drug development in a timely manner.
METHODOLOGY/PRINCIPAL FINDINGS: Although many efforts have been made in this regard, most of them were based on either sequence similarity or protein-protein interaction (PPI) information. However, the former often fails to work if a query protein has no or very little sequence similarity to any function-known proteins, while the latter had similar problem if the relevant PPI information is not available. In view of this, a new approach is proposed by hybridizing the PPI information and the biochemical/physicochemical features of protein sequences. The overall first-order success rates by the new predictor for the functions of mouse proteins on training set and test set were 69.1% and 70.2%, respectively, and the success rate covered by the results of the top-4 order from a total of 24 orders was 65.2%.
CONCLUSIONS/SIGNIFICANCE: The results indicate that the new approach is quite promising that may open a new avenue or direction for addressing the difficult and complicated problem.
在后基因组时代，产生了大量未被描述的蛋白质序列，因此非常需要开发有效的计算方法，以便快速准确地预测它们的功能。及时获得这些信息对基础研究和药物开发都非常有用。
方法/主要发现：尽管在这方面已经做了很多努力，但大多数方法都是基于序列相似性或蛋白质-蛋白质相互作用（PPI）信息。然而，如果查询蛋白与任何功能已知的蛋白没有或只有很少的序列相似性，前者往往无法工作，而如果没有相关的 PPI 信息，后者也会遇到类似的问题。鉴于此，我们提出了一种新的方法，通过杂交 PPI 信息和蛋白质序列的生化/物理化学特征。对于训练集和测试集上的小鼠蛋白功能，新预测器的总体一阶成功率分别为 69.1%和 70.2%，而在总共 24 个阶的前 4 个阶的结果所涵盖的成功率为 65.2%。
结论/意义：结果表明，该新方法非常有前途，可能为解决这一困难而复杂的问题开辟了新的途径或方向。
