TOWARD DEVELOPING NEW ANTIVIRALS AGAINST AVIAN INFLUENZA MEMBRANE GLYCOPROTEINS

致力于开发针对禽流感膜糖蛋白的新型抗病毒药物

• 批准号: 8362795
• 负责人: JAMES ANDREW MCCAMMON
• 金额: $ 3万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-05-01 至 2012-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8362795
• 关键词: Accounting; Active Sites; Antiviral Agents; Avian Influenza; Binding; Biological Assay; Biomedical Computing; Communities; Complex; Computer Assisted; Data; Development; Docking; Drug Design; Funding; Goals; Grant; Influenza; Lead; Ligands; Membrane Glycoproteins; National Center for Research Resources; Neuraminidase; Play; Principal Investigator; Proteins; Research; Research Infrastructure; Resources; Role; Scheme; Screening procedure; Source; Structure; Techniques; Testing; United States National Institutes of Health; base; computerized tools; cost; design; drug discovery; flexibility; improved; receptor; virtual

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. The influenza membrane glycoproteins, particularly neuraminidase, which is a major antiviral target, are remarkably flexible proteins. Thus it is highly likely that development of new antivirals will depend on our ability to account for and design around this flexibility. This presents a key methodological challenge to the drug discovery community and is one that we are actively pursuing. The overall goals of this collaborative project are to utilize our new computational tool to optimize compounds discovered in virtual screens and then obtain key experimental data in order to improve the current docking approaches for large and highly flexible ligands and receptors. The Wilson lab (TSRI) is a pioneer in determining structures of influenza proteins and is presently testing our first set of newly discovered compounds, which were discovered using the recently developed relaxed complex scheme (RCS) ensemble-based virtual screening technique (presented in (Cheng et al., 2008)), in neuraminidase inhibition assays. Crystallographic data on the precise binding modes of the most promising compounds will also be obtained. These compounds and their binding modes will subsequently be used in the refinement of the project computer-aided drug design (CADD) technique. Based on these results, the McCammon lab will then optimize the compounds using a new AutoDock-based approach to compound optimization that again takes receptor flexibility into account. After optimization, our synthetic collaborators in the Sharpless lab (TSRI) will synthesize the most promising compounds we predict and the Wilson lab will determine their binding modes. Multiple rounds of optimization are envisioned, with the ultimate goal being the development of new compounds that take advantage of the flexibility in the N1 active site region, as compared to other subtypes. The experimental data that we generate will play a critical role in the verification and refinement of the theoretical approach and will aid in the development of new lead compounds that could be developed into antiviral drugs.

该子项目是利用资源的众多研究子项目之一 由 NIH/NCRR 资助的中心拨款提供。子项目的主要支持 并且子项目的主要研究者可能是由其他来源提供的， 包括其他 NIH 来源。 子项目可能列出的总成本 代表子项目使用的中心基础设施的估计数量， NCRR 赠款不直接向子项目或子项目工作人员提供资金。 流感膜糖蛋白，特别是神经氨酸酶，它是一种主要的 抗病毒靶标，显着 柔性蛋白质。因此，新的抗病毒药物的开发很可能取决于 我们有能力解释和 围绕这种灵活性进行设计。 这对该药物提出了关键的方法学挑战 发现社区，是其中之一 我们正在积极追求。该合作项目的总体目标是利用 我们的新计算工具 优化虚拟筛选中发现的化合物，然后获得关键实验数据 为了改善目前的 用于大型且高度灵活的配体和受体的对接方法。 威尔逊实验室 (TSRI) 是确定流感蛋白结构的先驱， 目前正在测试我们的第一组 新发现的化合物，是使用最近开发的 宽松复杂方案 (RCS) 基于集合的虚拟筛选技术（在（Cheng et al., 2008）中提出）， 神经氨酸酶抑制测定。 最有前途的化合物的精确结合模式的晶体学数据 也将获得。这些 化合物及其结合模式随后将用于改进 计算机辅助药物项目 设计（CADD）技术。根据这些结果，麦卡蒙实验室将优化 该化合物使用新的 基于 AutoDock 的化合物优化方法再次提高了受体的灵活性 考虑到。优化后， 我们在 Sharpless 实验室 (TSRI) 的合成合作者将合成最多 我们预测的有前途的化合物和 威尔逊实验室将确定它们的结合模式。多轮优化 有预想，有最终目标 正在开发利用灵活性的新化合物 N1活性位点区域，如 与其他亚型相比。 我们生成的实验数据将发挥关键作用 在验证和验证中的作用 理论方法的完善将有助于新先导材料的开发 可开发的化合物 转化为抗病毒药物。