摘要
将代码为 1gca蛋白质的氨基酸序列映射为疏水值序列 ,在合适的尺度下 ,通过连续小波变换法分别对其α螺旋 ,α螺旋和 β折叠之间的连接多肽 (即部分规则和无规则二级结构 )进行预测 ,准确率分别为 76.5 %和 85 .7% .从PDBsum数据库中随机抽取 10 0个蛋白质作为测试对象 ,其中全α螺旋、全 β折叠、α/β以及α +β蛋白质各 2 5个 .在 10 0个蛋白质中共有 1618个连接多肽和 747个α螺旋 .本法预测到的连接多肽共有 15 3 6个 ,其中 13 0 8个与实际结构一致 ,平均预测准确率为 85 .2 % ;预测到的α螺旋有 770个 ,其中 5 81个与实际结构一致 ,平均预测准确率为 75 .5 % .结果表明 :该法可较好地预测蛋白质的α螺旋、连接多肽 。
alpha-Helices and short peptides connecting alpha-helices and beta-strands can be predicted by using continuous wavelet transform (CWT) under the appropriate dilation after the amino acids of 1 gca protein are transformed into sequences of hydrophobic values per residue, the prediction accuracy is 76.5% and 85.7%, respectively. We randomly choose 100 proteins, which consist of 25 all-alpha-helices, 25 beta, 25 alpha + beta and 25 alpha/beta proteins from PDBsum database as the test objects, there, are 1618 connecting peptides and 747 alpha-helices. It was found that 1536 connecting peptides can be predicted by CWT and 1308 among them are consistent with the actual structure, the average predicted accuracy is 85.2%. Comparing with the 747 alpha-helices contained in the 100 proteins, 770 of alpha-helices can be predicted by this method and 581 of them are accurate, the average predicted accuracy is 75.5%. The result indicates that CWT is an efficient tool to predict the secondary structures of proteins, and ha a tremendous development foreground.
出处
《化学学报》
SCIE
CAS
CSCD
北大核心
2003年第5期748-754,共7页
Acta Chimica Sinica
基金
国家自然科学基金 (No .2 99750 33)
广东省自然科学基金 (No.980 340 )
关键词
连续小波变换
预测
蛋白质
二级结构
多肽
疏水性
continuous wavelet transform
alpha-helices
connecting peptides
hydrophobicity
