CAJ | 학술논문

目的预测蛋白质二级结构是预测其空间结构的基础,提高蛋白质二级结构的预测率非常重要.方法在本研究中,结合氨基酸的疏水性与含有进化信息的位置特异性得分矩阵(PSSM),构建BP神经网络.本文的数据来源于蛋白质数据集合CB513,在此集合中去除氨基酸个数小于30及含有X、B的序列,共492条蛋白序列作为数据集.通过4-交互验证预测准确率.在本研究中,将蛋白质二级结构预测的结果与仅用PSSM作为输入的神经网络预测相比较.结果采用疏水性与进化信息相结合作为输入所构建的神经网络对α螺旋的预测准确率有了较大的提高,达到近79%,敏感性及特异性分别达到79%及91%.同时对二级结构总体预测准确率达到75.96%.结论此种方法构建的BP网络能提高蛋白质二级结构,尤其是α螺旋的预测准确率.
목적 예측단백질이급결구시예측기공간결구적기출,제고단백질이급결구적예측솔비상중요.방법재본연구중,결합안기산적소수성여함유진화신식적위치특이성득분구진(PSSM),구건BP신경망락.본문적수거래원우단백질수거집합CB513,재차집합중거제안기산개수소우30급함유X、B적서렬,공492조단백서렬작위수거집.통과4-교호험증예측준학솔.재본연구중,장단백질이급결구예측적결과여부용PSSM작위수입적신경망락예측상비교.결과 채용소수성여진화신식상결합작위수입소구건적신경망락대α라선적예측준학솔유료교대적제고,체도근79%,민감성급특이성분별체도79%급91%.동시대이급결구총체예측준학솔체도75.96%.결론 차충방법구건적BP망락능제고단백질이급결구,우기시α라선적예측준학솔.
Objective Since predicting protein secondary structure is the basis of predicting protein spacial structure, it is important to improve the prediction accuracy of secondary structure. Methods A two-stage BP neural network was constructed based on the method of combining hydrophobicity of amino acid residues with PSSM which contains evolution information. CB513 dataset was employed in our study. After excluding the protein chains containing X,B and those with sequence length shorter than 30 amino acids, 492 protein chains in the dataset were used. The results of protein secondary structure prediction of our study were compared with those from the networks using only PSSN as their inputs. Accuracy of the network was tested by 4-fold cross-validation. Results In our study, α-helix was predicted with an averaged accuracy of nearly 79%, sensitivity of 79% and specificity of 91%. Total prediction accuracy of secondary structure reached 75.96%, which was higher than that of only using PSSM as input. Conclusion The new method developed can better predict protein secondary structure, especially α-helix with a higher accuracy.