Integrated Cheminformatics Resource for Orphan Neurodegenerative Diseases

孤儿神经退行性疾病的综合化学信息学资源

基本信息

批准号：
7559157
负责人：
Weifan Zheng
金额：
$ 9.84万
依托单位：
NORTH CAROLINA CENTRAL UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-01-01 至 2012-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7559157
关键词：
Academia Address Affect Algorithms Applications Grants Appointment Area Benchmarking Binding Sites Bioinformatics Biological Biotechnology Brain Chemical Structure Chemicals Chicago Collaborations Communities Complement Computer Simulation Computer Vision Systems Computing Methodologies Data Data Set Databases Descriptor Development Disease Drug Design Drug Industry Ensure Family Foundations Funding Future Genomics Goals Grant Histone Deacetylase Huntington Disease Illinois Individual Industry Informatics Internet Letters Libraries Ligands Marketing Medicine Methods Mission Modeling Molecular Molecular Models Names Neurodegenerative Disorders North Carolina Orphan Pain Patients Pattern Performance Pharmaceutical Preparations Pharmacologic Substance Phase Productivity Protein Binding Protein Family Proteins Protocols documentation Publications Quantitative Structure-Activity Relationship Research Research Infrastructure Research Personnel Research Support Research Training Resource Informatics Resources Role Science Scientist Screening procedure Shapes Solid Speed Spinal Muscular Atrophy Staging Structure Structure-Activity Relationship Techniques Technology Testing Translational Research United States National Institutes of Health Universities Validation Vision Work base career cheminformatics computer infrastructure computer science computerized tools computing resources conformer design drug candidate drug discovery innovation method development model development molecular modeling novel pharmacophore phosphodiesterase IV phosphodiesterase V phosphoric diester hydrolase predictive modeling research study success three dimensional structure tool virtual web-accessible

项目摘要

DESCRIPTION (provided by applicant): In this project, we seek to continue our development of new innovative computational tools, apply them to model relevant protein targets of orphan neurodegenerative diseases, and build a publicly accessible web resource to host these tools and models. These goals will be achieved via two Specific Aims. Aim-1 is to develop and validate two new structure-based computational tools. The first tool (Shape4) is a fast, structure-based virtual screening method designed to search large multi-conformer molecular databases for potential ligands for a protein target. It is based on this chief hypothesis: a ligand molecule's topographical shape and pharmacophore features should be complementary to those of its protein binding site. Novel computational geometry and shape modeling algorithms will be employed to fulfill the shape / pharmacophore matching tasks. The second tool (SB-PPK) generates structure-based descriptors for organic molecules. The descriptors so generated depend not only on the structure of an organic molecule, but also on the binding site features of the target protein. Thus, these new descriptors overcome the drawbacks of traditional molecular descriptors that depend only on the structures of organic molecules, regardless of what the target protein is. The new descriptors will be employed in conjunction with QSAR (quantitative structure activity relationship) modeling workflow to develop predictive models for selected protein targets (Aim-2a). Specifically, the two new methods developed in Aim-1 will be applied to build predictive models for targets from phosphodiesterase (PDE) and histone deacetylase (HDAC) families: PDE- 4, PDE-5, HDAC-7 and HDAC-8. We will then deploy the validated models via a web portal (Aim-2b) to benefit the research community of orphan neurodegenerative diseases. We aim to develop and apply innovative computational tools to study the protein targets of orphan neurodegenerative diseases, and to establish an open-access informatics resource to support the drug discovery efforts in these disease areas. We will ultimately contribute to alleviating the pain of orphan neurodegenerative disease patients.

描述（由申请人提供）：在此项目中，我们试图继续开发新的创新计算工具，将其应用于孤儿神经退行性疾病的相关蛋白质目标，并建立公开访问的Web资源以托管这些工具和模型。这些目标将通过两个具体目标实现。 AIM-1是开发和验证两个新的基于结构的计算工具。第一个工具（Shape4）是一种基于结构的快速虚拟筛选方法，旨在搜索大型多构造物分子数据库以寻找蛋白质靶标的潜在配体。它基于这一主要假设：配体分子的地形形状和药效团特征应与其蛋白质结合位点的特征互补。新型的计算几何形状和形状建模算法将采用来完成形状 /药效团匹配任务。第二个工具（SB-PPK）生成了有机分子的基于结构的描述符。如此生成的描述符不仅取决于有机分子的结构，还取决于靶蛋白的结合位点特征。因此，这些新的描述符克服了仅取决于有机分子结构的传统分子描述符的缺点，而不论靶蛋白是什么。新的描述符将与QSAR（定量结构活性关系）建模工作流程一起使用，以开发选定蛋白质靶标（AIM-2A）的预测模型。具体而言，AIM-1中开发的两种新方法将用于构建磷酸二酯酶（PDE）和组蛋白脱乙酰基酶（HDAC）家族的靶标的预测模型：PDE-4，PDE-5，HDAC-7，HDAC-7和HDAC-8。然后，我们将通过Web门户（AIM-2B）部署经过验证的模型，以使孤儿神经退行性疾病的研究社区受益。我们旨在开发和应用创新的计算工具来研究孤儿神经退行性疾病的蛋白质靶标，并建立开放式信息信息学资源，以支持这些疾病领域的药物发现工作。我们最终将有助于减轻孤儿神经退行性疾病患者的疼痛。