基于小波分析的BP神经网络膜蛋白跨膜螺旋区段预测  

Prediction of Transmembrane Helical Segments in Membrane Proteins Based on Back Propagation Neural Network of Wavelet Analysis

作　　者：于彬[1] 

机构地区：[1]青岛科技大学数理学院,山东青岛266061

出　　处：《青岛科技大学学报（自然科学版）》2010年第6期583-587,597,共6页Journal of Qingdao University of Science and Technology:Natural Science Edition

基　　金：国家自然科学基金项目(30871341);科技部重大科技专项基金项目(2008ZX10002-017)

摘　　要：基因组计划所产生的大量蛋白质序列迫切需要从理论上预测跨膜螺旋区段。提出了基于小波多分辨分析的BP神经网络膜蛋白跨膜螺旋区段的预测新方法,并把此方法称之为WnnTM。从MPtopo数据库中随机抽取80条三维结构已知的膜蛋白质序列构建数据集,把它们映射成疏水值序列,通过小波分解和重构得到小波系数,并结合BP神经网络构造小波BP神经网络预测模型,对膜蛋白跨膜螺旋区段的位置和数目进行预测。实例验证,WnnTM预测方法比单独用BP神经网络对膜蛋白跨膜螺旋区段进行预测更有效。The increasing protein sequences from the genome project require theoretical methods to predict transmembrane helical segments（TMHs）.In this paper,WnnTM,a new method of BP neural network based on wavelet multiresolution analysis（MRA） is initially developed to predict the number and location of TMHs in membrane proteins.Eighty proteins with known 3D-structure chosen randomly from Mptopo database are mapped onto sequences of hydrophobicity values.Combining BP neural network with wavelet coefficients,which are through decomposing and reconstructing wavelet,the WnnTM method was constructed to predict the location and number of TMHs in membrane proteins.The obtained results indicate that the presented method is more effective than BP neural networks.

关 键 词：小波分析 BP神经网络 膜蛋白 跨膜螺旋区段 

分 类 号：O174.22[理学—数学] Q811.4[理学—基础数学]