【作者】 樊国梁；

【导师】 李前忠；

【作者基本信息】 内蒙古大学 ， 生物物理学， 2013， 博士

【摘要】 随着人类基因组计划的顺利进行,数据库中出现了大量的未知功能的蛋白质序列,分析这些未知功能的蛋白质成为当今的首要任务。目前,亚细胞定位作为分析蛋白质功能的手段达到了一定水平,人们开始关注亚细胞器定位研究,由于实验分析耗时、成本高,因此利用计算的方法来预测蛋白质亚细胞器定位成为当前研究的热点。本文系统的从蛋白质亚线粒体定位数据集的构建、特征参数的提取及优化、预测算法的建立以及算法的推广性等方面对蛋白质亚线粒体定位预测问题进行了研究,主要研究成果如下：1、蛋白质亚线粒体定位研究的数据集建立时间较早,包含的序列较少,我们构建了一个最新的蛋白质亚线粒体定位数据集,扩大了序列数。采用ID-SVM算法进行预测,取得较好的预测结果,同时对Du建立的数据集进行预测,在Jackknife检验下的总体预测成功率达到94.95%,比AC算法和DWT算法的总体预测成功率提高了5.3%和1.6%。2、在构建蛋白质化学位移数据集的基础上,通过分析蛋白质中20个氨基酸的二级结构与四种骨架原子的化学位移关系,发现每一种氨基酸的四种骨架原子的化学位移与二级结构有关联,呈现有规律的变化。通过化学位移的自相关算法来构建代表蛋白质的特征参数,应用在蛋白质亚线粒体定位中,达到目前最好预测结果。3、提出氨基酸黏性(stickiness)特征参数,利用该特征参数结合化学位移等信息参数对Du建立的数据集进行预测,Jackknife检验下的总体预测结果为96.21%,比我们之前的研究结果提高了1.26%,定位于matrix的蛋白质全部预测正确,对outer membrane的蛋白质预测成功率有所改善,Sn达到85.37%,提高了4.87%。4、建立了分歧杆菌蛋白质亚细胞定位数据集,并且用此数据集对我们提出的特征参数提取方式和预测算法进行推广性检测,得到较好的结果,Jackknife检验结果为94.00%,比Lin的方法高出2.8%,比Rashid的算法提高了11.3%,验证了我们算法有较强的推广性,可以应用到其他亚细胞定位问题上。5、建立了化学位移参数算法acACS服务网站(http://wlxy.imu.edu.cn/college/biostation/fuwu/acACS/index.asp)和亚线粒体数据集网站(http://wlxy.imu.edu.cn/college/biostation/fuwu/mito/index.asp),为生物信息学和蛋白质亚线粒体定位预测研究提供服务。更多还原

【Abstract】 With the success of human genome project, abundance of unknown functional proteins appear in the database and the most important task in today is to analyzing these unknown functional proteins. Researchers begin to interest in subcellular organelle location for analyzing the functions of protein when the subcellular location as analyzing methods has achieved certain standard. Due to the experiment approach is time-consuming and expensive, thus the computational methods for predicting the protein subcellular organelle location has become the current research focuses.In this dissertation, we systematically studied the protein submitochondria location from the aspects of the dataset construction, the extraction and optimization of feature vectors, establishing the prediction algorithm and the generalization of algorithm. The main research findings are as follows:1. Because there are a few sequences in the current dataset of submitochondria location constructed earlier, we constructed a new dataset, which has more sequences. We achieved better prediction results using ID-SVM algorithm with our dataset, and obtained overall prediction accuracy of94.95%in Jackknife validation for the dataset of Du. The result was improved by5.3%and1.6%than AC and DWT algorithms respectively.2. On the bases of constructing the protein chemical shift dataset, we found that the four kinds of chemical shift of every amino acid relate with secondary structure and vary regularly after we had analyzed the relationship between secondary structure and the chemical shifts of backbone atoms for20amino acids. We achieved best results in predicting submitochondria location in present by constructing the protein feature vector using the auto covariance of chemical shifts.3. We proposed the amino acid stickiness feature vector, and achieved overall prediction accuracy of96.21%with Jackknife validation for the dataset of Du by using the stickiness, chemical shifts and other feature vectors. The result was improved1.26%than we studied before, and the protein located in matrix was predicted correctly. The prediction result of outer membrane was also improved by Sn of85.37%, which was about4.87%more than the best literature.4. We constructed the mycobacterial proteins dataset, and checked the generalization of extracting feature vector methods and prediction algorithm. The result was achieved94.00%by Jackknife validation and was2.8%and11.3higher than Lin’s and Rashid’s result respectively. The results show that our methods have strong generalization and can be used in other problems of subcellular locations.5. We established the chemical shifts algorithm-acACS web server (http://wlxy.imu.edu.cn/college/biostation/fuwu/acACS/index.asp) and submitochondria dataset web server (http://wlxy.imu.edu.cn/college/biostation/fuwu/mito/index.asp), which provide services for bioinformatics and protein submitochondria localization更多还原