COMPUTATIONAL CHARACTERIZATION OF HOMING ENDONUCLEASE BINDING SPECIFICITY

归巢核酸内切酶结合特异性的计算表征

• 批准号: 8365906
• 负责人: William Noble
• 金额: $ 0.97万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8365906
• 关键词: Binding; Binding Sites; Biological Neural Networks; Biology; Cleaved cell; Computing Methodologies; DNA Binding; DNA Sequence; Deoxyribonuclease I; Enzymes; Event; Funding; Fungal Genome; Genetic Recombination; Genomics; Grant; Homing; Medical; National Center for Research Resources; Pattern; Principal Investigator; Process; Proteins; Research; Research Infrastructure; Resources; Site; Source; Specificity; United States National Institutes of Health; cost; endonuclease; gene therapy; human DNA; insertion/deletion mutation

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Homing endonucleases are DNA-cleaving enzymes that generate double strand breaks at specific genomic invasion sites. These proteins are attractive for many biotech and medical applications, including gene therapy, because they have the potential to activate site-specific recombination events that result in the insertion, deletion, mutation or correction of DNA sequences. In this project, we develop computational methods to identify regions in human DNA that are bound and cleaved by homing endonucleases with extremely high accuracy. Specifically, we developed a neural network classifier that can discriminate with high accuracy between sites that are bound by the enzyme and sites that are both bound and cleaved. In this context, higher order feature representations have the potential to capture the statistical patterns that contribute to the cleavage process.

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 归巢核酸内切酶是DNA切割酶， 在特定的基因组侵入位点发生链断裂。这些蛋白质 对许多生物技术和医学应用具有吸引力， 治疗，因为它们有可能激活位点特异性 导致插入、缺失、突变的重组事件 或DNA序列的校正。在这个项目中，我们开发 计算方法来识别人类DNA中结合的区域， 并被归巢核酸内切酶以极高的准确性切割。 具体来说，我们开发了一个神经网络分类器， 以高准确度区分由 酶和结合和切割的位点。在这一背景下， 高阶特征表示具有捕获 有助于分裂过程的统计模式。