基于特征挖掘的离子通道功能类型预测与跨膜域识别

Ion channels are a diverse group of proteins that poke through.the lipid membrane of cells and form the channel pores. They can selectively control movement of ions into or out of cells, which is the key extremely important for maintaining the activity of life. The abnormity of structure and function of ion channels can result in multi-disease. Some aspect of structure and function of ion channel have been studied and understood, however, because it is difficult to determine the structure of membrane proteins, there is still lack of exhaustive knowledge for their mechanisms. It is necessarily to study ion channels systematically by using computational approaches. In this project, based on the protein informatics appraoches, we will build a set of theoretical models for studying ion channels by use of feature mining techniques. Firstly, we will develop shannon entropy-based method to study the long and short correlation of amino acids, which can be regarded as standard of feature extraction. Secondly, we will develop various statistical methods and manifold learning methods to optimize feature sets. Furthermore, we will construct novel information-based distance and combine it with machine learning approach for predicting the function types of ion channels. Last, we will develop and mend structural recurrence graph and combine it with discriminate analysis for predicting transmembrane regions of ion channels. The results and tools developed by this project are of great significance for revealing the physiological functions of ion channels and rationally selecting drug targets.

离子通道是一种跨膜成孔蛋白，能够选择性控制离子进出细胞，对维持正常生命过程至关重要，其结构与功能的异常会引起多种疾病。尽管人们已对离子通道的结构和功能有一定了解，但由于解析膜蛋白结构非常困难，因此，缺乏对离子通道分子机制的完整详细的描述。借助计算的方法系统研究离子通道势在必行。本项目是申请者在蛋白质信息学研究方法的基础上，建立一套基于特征提取的离子通道预测的理论模型，构建高精度的离子通道功能类型识别和跨膜域预测工具。首先，利用信息熵解决序列中氨基酸间长短程的关联问题，并用于特征提取；其次，利用统计检验和流形学习方法，进行特征优化、筛选；进而，提出信息距离测度，结合机器学习方法对离子通道功能类型进行预测；最后，利用改进的结构重现图方法,结合判别分析方法对离子通道跨膜域进行预测。本课题的研究对揭示离子通道的生理功能、合理选择药物靶标都有重要的科学意义。

离子通道是一种跨膜成孔蛋白，能够选择性控制离子进出细胞，对维持正常生命过程至关重要，其结构与功能的异常会引起多种疾病。确定离子通道的类型对揭示离子通道的生理功能、合理选择药物靶标意义重大。本项目的研究目标是建立一套基于特征提取的离子通道预测的理论模型，构建高精度的离子通道功能类型识别。经过几年的努力，项目取得的主要成果有：（1）构建了钾离子通道蛋白亚家族数据库，在该数据库的基础上，开发了基于统计特征筛选的分类器，获得良好预测效果；（2）构建了作用于离子通道的芋螺毒素数据库，利用特征筛选方法对作用于钾、钠、钙离子通道的芋螺毒素进行识别，获得了很好预测效果。.由于科学前沿问题的不断涌现，我们在相关领域还进行了研究，取得的成果包括：（1）利用特征筛选方法对分枝杆菌的膜蛋白类型进行了研究，使用机器学习方法获得了高精度的预测模型；（2）开发了基于二项式分布的特征筛选技术，应用于多种蛋白质分类问题；（3）统计特征中，序列的长程关联至关重要，我们开发了一种基于核苷酸对的结构性质参数的序列描述算法，并在DNA功能位点分析中取得成功。.基于这些工作，我们在Nucleic Acids Research (IF: 8.808)，Bioinformatics (IF: 5.323)，Frontiers in Microbiology (IF: 3.9)，International Journal of Molecular Sciences (IF: 2.464)和Journal of Proteomics（IF：4.088）等国际期刊发表SCI 收录论文23篇，参加国际会议2 次，国内会议5次，1 篇论文获得青年论文竞赛三等奖，培养毕业硕士研究生4人，在读研究生7人。

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