CAJ | 학술논문

目的准确预测蛋白质结构类,为研究其空间结构及生物功能打下基础.方法应用隐马尔可夫模型(HMM)预测蛋白质结构类,分别构建3-状态HMM和8-状态HMM.数据来源于Chou和Zhou构建的蛋白质数据集,分别包含有204条蛋白质序列和498条蛋白质序列,通过留一法预测其准确率.结果所构建的3-状态HMM和8-状态HMM对全α类的预测准确率最高,尤其是3-状态HMM的预测准确率达到95％以上.与Chou数据集相比,Zhou数据集对于全β类和α/β类的预测准确率也有所提高,同时,总体预测率也提高了2％左右；但α+β类的预测准确率有所下降.结论将整条蛋白质序列作为预测模型的输入信息所构建的HMM模型能有效地预测蛋白质的结构类.
목적 준학예측단백질결구류,위연구기공간결구급생물공능타하기출.방법 응용은마이가부모형(HMM)예측단백질결구류,분별구건3-상태HMM화8-상태HMM.수거래원우Chou화Zhou구건적단백질수거집,분별포함유204조단백질서렬화498조단백질서렬,통과류일법예측기준학솔.결과 소구건적3-상태HMM화8-상태HMM대전α류적예측준학솔최고,우기시3-상태HMM적예측준학솔체도95％이상.여Chou수거집상비,Zhou수거집대우전β류화α/β류적예측준학솔야유소제고,동시,총체예측솔야제고료2％좌우；단α+β류적예측준학솔유소하강.결론 장정조단백질서렬작위예측모형적수입신식소구건적HMM모형능유효지예측단백질적결구류.
Objective Predicting protein structural class is the basis for predicting protein spatial structure,so it is important to improve the prediction accuracy of protein structural class.Methods We proposed 3-state and 8-state Hidden Markov model (HMM),and applied these HMMs to the prediction of protein structural class,respectively.We evaluated their accuracy on two different datasets through the rigorous jackknife cross-validation test.Results Prediction ability of 8-state HMM and 3-state HMM to all α class were excellent,the prediction accuracy of 3-state HMM even reached above 95％.Compared with Chou data set,the prediction accuracy of Zhou data set for all β class and α/β class of was improved,while overall prediction accuracy increased by 2％.Conclusion HMM is an effective method to predict protein structural class.