基于新型机器学习方法的核酸-结合氨基酸位点的分析与预测

项目名称： 基于新型机器学习方法的核酸-结合氨基酸位点的分析与预测

项目编号： No.61203289

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 吴建盛

作者单位： 南京邮电大学

项目金额： 25万元

中文摘要： 蛋白质-核酸相互作用是分子生物学研究的中心问题之一，是许多生命活动的重要组成部分。尤其是，small RNA在RNA干扰过程中通过与蛋白质特异性结合调控着细胞内许多重要的生命活动和疾病发生过程，更是当今生命科学的研究热点。而识别核酸-结合氨基酸位点是认识蛋白质-核酸相互作用机制的重要途径。本项目拟将数学统计方法和特征选取方法结合起来，从结构上分析蛋白质与small RNA特异性相互作用机制；发展基于序列的DNA/RNA-结合位点预测新方法，和引入半监督学习思想发展特异性的small RNA-结合位点预测方法。在发展结合位点预测方法时，引入结合区域的物化特性及结构偏好性信息，利用特征选取方法筛选特征，解决样本类不平衡和代价敏感问题，建立在线预测平台。项目完成后，将为系统研究蛋白质-核酸相互作用提供新方法，并将推进蛋白质其它功能位点的预测研究和机器学习技术的发展。

中文关键词： 核酸-结合位点；small RNA-结合位点；蛋白质功能；半监督学习；特征选取方法

英文摘要： The interaction between proteins and nucleic acids is one of the central issues in molecular biology researches and an important part of many life activities. Especially, the specific recognition of small RNAs by proteins in the process of RNA interference is in charge of many important life activities and disease processes, and is the focus of today's life science researches. The identification of nucleic acid - binding sites in proteins is an important way of understanding the mechanism of protein - nucleic acid interaction, and has important significance for understanding the related biological processes, the related diseases and their treatment, and protein functions and drug researches. In this project, the mechanism of specific recognition of small RNAs by proteins will be analyzed from structural data by combining mathematical and statistical methods with feature selection methods, and new methods will be developed for predicting DNA/RNA-binding sites from the protein sequences, and novel classifiers will be designed to recognize small RNA-binding sites based on semi-supervised machine learning methods. Moreover, the ideal methods for predicting binding sites in proteins will be reached by introducing the propensity information about physico-chemical properties and structures in the binding domains, and b

英文关键词： nucleic acid -binding sites；small RNA- binding sites；protein function prediction；semi-supervised machine learning methods；feature selection methods