CAJ | 학술논문

提出一种新的能依据蛋白质序列自动地识别被查询蛋白质的四级结构类型的方法.首先采用伪特定位点记分矩阵方法(PsePSSM)提取蛋白质序列的特征.采用这种方法提取出的特征能尽可能多地反映蛋白质序列的原始信息如顺序和进化等信息.但随之产生的问题是特征维数很高,使得预测系统复杂化.因此,引入线性维数约简算法最大方差映射方法(MVP),它可以从高维的特征空间中提取出低维的关键特征.最后,在约简后的特征上再应用分类算法预测未知蛋白质的四级结构.试验结果表明,采用降维方法不但使得预测系统得到简化,同时还提高了分类性能.
제출일충신적능의거단백질서렬자동지식별피사순단백질적사급결구류형적방법.수선채용위특정위점기분구진방법(PsePSSM)제취단백질서렬적특정.채용저충방법제취출적특정능진가능다지반영단백질서렬적원시신식여순서화진화등신식.단수지산생적문제시특정유수흔고,사득예측계통복잡화.인차,인입선성유수약간산법최대방차영사방법(MVP),타가이종고유적특정공간중제취출저유적관건특정.최후,재약간후적특정상재응용분류산법예측미지단백질적사급결구.시험결과표명,채용강유방법불단사득예측계통득도간화,동시환제고료분류성능.
@@@@An automated method to identify the quaternary structure of queried protein is proposed. Firstly, a PsePSSM (Pseudo Position-Specific Score Matrix) is adopted to extract the features of proteins. The features extracted by PsePSSM can mostly reflect the original information of protein sequence such as the evolution information and sequence-correlated information. But it may cause the“high dimension disaster” problem and make the prediction system complex. To overcome such a problem, a linear dimensionality reduction algorithm MVP (Maximum Variance Projections) is introduced to extract the key features from the high-dimensional PsePSSM space. Finally, based on the reduced features, classifier is used to identify the protein quaternary structure. Experiment results prove that the prediction system is simplified and classification performances are improved by adopting dimension reduction methods.