摘要
随着人类基因组计划的顺利进展,越来越多的蛋白质序列被测定出来;而通过实验确定其结构与功能的蛋白质序列则相对较少,且两者之间的差距有迅速扩大的趋势。由于通过实验确定蛋白质的结构和功能费时、费力、费财,且实验中可能还会遇到一些目前无法解决的困难,因此探索利用理论及计算方法来研究蛋白质结构和功能具有重要意义。本文从蛋白质的一级序列出发,研究了蛋白质的结构、功能分类预测,其主要贡献如下:
1.提出一种新的组合分类思想,即将氨基酸组成成分、自相关函数二种特征提取法与支持向量机恰当组合,首次对蛋白质同源二聚体和非同源二聚体进行分类研究,并与国际上现有的Garian方法进行了对比。在10CV检验下,本文方法的分类总精度比Garian方法最大可提高17.1个百分点。
2.提出二种新的特征提取法,并引入以前已有的二种特征提取法,与支持向量机和不同的分类策略,进行恰当的组合构成分类系统,首次对蛋白质同源二聚体、同源三聚体、同源四聚体和同源六聚体进行分类研究。结果表明整合了氨基酸残基序列顺序信息的三种特征提取法,其分类能力均好于氨基酸组成成分特征提取法,尤以我们提出的加权自相关函数特征提取法的分类效果最好,其分类总精度可比氨基酸组成成分特征提取法最大可提高6.39个百分点,比Chou的特征提取法提高2.41个百分点;采用“一对一”策略的分类能力明显优于“一对多”策略,其分类总精度最大可提高17.69个百分点。
3.一种新的组合分类方法,即将自相关函数特征提取法和支持向量机、以及本文提出的“改进的唯一的一对多”分类策略恰当组合,应用于蛋白质折叠子分类研究。结果显示:对于独立测试样本,自相关函数特征提取法的分类总精度比氨基酸组成成分特征提取法,大约可提高7个百分点;“改进的唯一的一对多”分类策略优于“一对多”策略,其独立测试和5CV检验的分类总精度,比“一对多”策略最大可分别提高约18和12个百分点。
4.引入加权思想,以一种新的特征提取法—加权自相关函数,表示蛋白质序列,并采用“一对多”、“一对一”分类策略对膜蛋白和亚细胞定位进行了分类和预测研究,结果有明显改善:
1).对于膜蛋白分类,在采用支持向量机算法及“一对多”分类策略下,加权自相关函数特征提取法的分类总精度为87.98%,比氨基酸组成成分特征提取
With the success of human genome project, the protein sequences entering into the data banks are rapidly increasing. The structures and functions of these proteins may be determined by means of experiments, but it is very time-consuming and almost impossible. Thus the scientists have being sought after the theoretical or computational methods for predicting the structures and functions of proteins. Several methods of classifying or predicting protein structures and functions based on the protein primary sequences are investigated in this dissertation. The main contributions are summarized as follows:1. A new idea of composite classification is raised, that is the support vector machine (SVM) algorithm is combined felicitously with two feature extraction methods of amino acid composition and the auto-correlation functions based on the amino acid index, to classify the homodimers and non-homodimers from the protein primary sequences. Compared with previous Garian's investigation, the total classifying accuracy of our method is 17.1 percentage points higher than that of Garian's method in 10CV test.2. Two new feature extraction methods are put forward by this dissertation, and two previous feature extraction methods are also introduced. Then these four feature extraction methods are combined felicitously with SVM and two classifying strategies to investigate the classification of homodimers, homotrimers, homotetramers and homohexamers from the protein primary sequences. The simulation results show that the performances of three feature extraction methods by incorporating the information of sequence order are higher than that of the conventional amino acid composition method. Among them, our weighted auto-correlation function method is the best one. Its total accuracy is 6.39 and 2.41 percentage points higher than that of amino acid composition and Chou's feature extraction methods respectively. The classification performance of using 'one-versus-one' strategy is superior to the 'one-versus-rest' strategy, and the total accuracy is 17.69 percentage points higher than that of 'one-versus-rest' strategy.3. A new method of composite classification, it is that the feature extraction method of auto-correlation function is combined felicitously with SVM and the strategy of 'improved unique one-versus-rest', to classify 27 class folds. The results show that the total classification accuracy of auto-correlation function method is about 7 percentage points higher than that of amino acid composition in independent test. The results of using 'improved unique one-versus-rest' strategy are superior to 'one-versus-rest' strategy, and the total accuracies of independent test and 5CV test are about 18, 12 percentage points higher than that of using 'one-versus-rest' strategy respectively.4. The weighted idea is introduced in this dissertation to form a new feature extraction method, that is, the weighted auto-correlation function method, to represent the protein sequences. And two classification strategies ('one-versus-rest' and 'one-versus-one') are also used to classify
the membrane proteins, and to predict the protein subcellular locations. The results are significantly improved:1) For membrane protein, the total accuracy of our new feature extraction method is 87.98% in jackknife test, which is 3.38 percentage points higher than that of amino acid composition with the same 'one-versus-rest' strategy and SVM; the total accuracy of one-versus-one' strategy may be up to 94.88% in jackknife test, which is 6.9 percentage points higher than that of "one-versus-rest" strategy.2) For protein subcellular location, the total predictive accuracies of prokaryotic subcellular location and eukaryotic subcellular location are 92.38% and 95.22% respectively in jackknife test, and the total predictive accuracy of eukaryotic subcellular location is far higher than that of Hua's result 79.4%. The total predictive accuracy of eukaryotic protein with 'one-versus-one' strategy is 12.19 percentage points higher than that of 'one-versus-rest' strategy in jackknife test. The total predictive accuracy of eukaryotic protein with the new feature extraction method is 2.96 percentage points higher than that of amino acid composition feature extraction method in jackknife test.5. In the end, the kernel functions and their parameters are simply discussed.
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