整合机器学习方法和多源异构特征筛选分析单氨基酸多态性对蛋白质的功能影响

项目名称： 整合机器学习方法和多源异构特征筛选分析单氨基酸多态性对蛋白质的功能影响

项目编号： No.61303169

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 王明君

作者单位： 中国科学院天津工业生物技术研究所

项目金额： 22万元

中文摘要： 单氨基酸多态性（single amino acid polymorphisms, SAPs）与人类遗传疾病密切相关，如何从蛋白质序列和三维结构信息出发研究单氨基酸多态性对蛋白质的功能影响以及由此引起的致病机理，一直都是生物信息学领域的研究热点和难点问题。本项目以与孟德尔遗传疾病突变和致癌性突变相关的单氨基酸多态性作为主要研究对象，拟采用高效、准确的机器学习算法和特征选择方法，针对单氨基酸突变的临近序列和结构信息进行创新性、系统性的智能计算和生物信息学研究，找出导致孟德尔遗传疾病突变和致癌性突变的有判别力的重要序列、结构特征以及规律性信息，在此基础上构建合理、高准确率的单氨基酸多态性致病性或致癌性的推断模型，为深入探索孟德尔遗传及癌症等复杂疾病提供重要信息和理论依据。这一项目对深入理解和阐明蛋白质的结构-功能关系以及由氨基酸突变导致的分子水平上的致病机理有着重要的意义。

中文关键词： 单氨基酸多态性；疾病机制；蛋白质后修饰；机器学习；功能预测

英文摘要： Single amino acid polymorphisms (SAPs) are commonly associated with inherited disease susceptibility in humans. Study of the functional impact of SAPs on protein function from protein sequence and structure information as well as the corresponding diseases mechanisms has become a hot and challenging topic in bioinformatics. Using disease-associated and cancer-associated SAPs as the research objective, in this project, we aim to develop innovative bioinformatic approaches and user-friendly tools to carry out innovative and systematic inteligent computing and bioinformatic analyses of the contributive features and underlying rules that arise from multiple different levels, including sequence, secondary structure, tertiary structure as well as residue-contact network features proximal to the SAP sites, which are relevant for discrminative prediction of the functional impact of Mendelian disease-associated, cancer-associated SAPs. We will develop effective and high-performing inference models using machine learning and feature selection techniques to quantify the contribution of sequence and structural features that collectively determine the functional impacts of SAPs. This study will significantly improve our understanding of the complex sequence-strucutre-function relationship of proteins and provide fundamental

英文关键词： Single amino acid polymorphisms；disease mechanism；protein post-translational modification；machine learning；function prediction