Lei Seak Fei, Huan Jun
School of Electrical Engineering and Computer Science, University of Kansas, Lawrence, Kansas 66045, USA.
Int J Data Min Bioinform. 2010;4(4):452-70. doi: 10.1504/ijdmb.2010.034200.
The exact relationship between protein active centres and protein functions is unclear even after decades of intensive study. To improve functional prediction ability based on the local structures, we proposed three different methods. 1. We used Markov Random Field (MRF) to describe protein active region. 2. We developed filtering method that considers the local environment around the active sites. 3. We created multiple structure motifs by extending the motif to neighbouring residues. Our experiment results with enzyme families < 40% sequence identity demonstrated that our methods reduced random matches and could improve up to 70% of the functional annotation ability (using area under curve).
即使经过数十年的深入研究,蛋白质活性中心与蛋白质功能之间的确切关系仍不明确。为了提高基于局部结构的功能预测能力,我们提出了三种不同的方法。1. 我们使用马尔可夫随机场(MRF)来描述蛋白质活性区域。2. 我们开发了考虑活性位点周围局部环境的过滤方法。3. 我们通过将基序扩展到相邻残基来创建多个结构基序。我们对序列同一性小于40%的酶家族进行的实验结果表明,我们的方法减少了随机匹配,并且可以将功能注释能力提高多达70%(使用曲线下面积)。