Single amino acid polymorphisms(SAPs),also known as non-synonymous single nucleotide polymorphisms(nsSNPs),are responsible for most of human genetic diseases.Discriminate the deleterious SAPs from neutral ones can help identify the disease genes and understand the mechanism of diseases.In this work,a method of deleterious SAP prediction at system level was established.Unlike most existing methods,our method not only considers the sequence and structure information,but also the network information.The integration of network information can improve the performance of deleterious SAP prediction.To make our method available to the public,we developed SySAP(a System-level predictor of deleterious Single Amino acid Polymorphisms),an easy-to-use and high accurate web server.SySAP is freely available at http://www.biosino.org/SySAP/and http://lifecenter.sgst.cn/SySAP/.
目的建立一个综合了生物信息学和化学信息学的网络信息平台M&Function,致力于新药筛选和研究。方法通过数据挖掘,整合小分子药物的名称、结构、功能、分类等信息,建立一个小分子药物信息资源库。在此基础上,借助基于最大公共子结构(maximum common substructure,MCS)和Fingerprint的结构比对软件,通过统计分析,建立了功能预测系统,对活性小分子进行生物学功能预测。结果 M&Function平台拥有丰富的数据信息、内嵌的图形显示和数据统计插件人性化的网站设计,具有直观、高效、简便易用、测试数据结果可靠等优点。结论 M&Function平台不仅是一个小分子药物信息资源库,也是一个小分子功能预测平台,可以为高通量先导化合物的筛选提供信息准备和数据支持。网站可以通过http://lifecenter.sgst.cn/mcs/home.do进行访问。
