HIGH PERFORMANCE COMPUTING FOR DRUG DISCOVERY

用于药物发现的高性能计算

• 批准号: 7723329
• 负责人: Weifan Zheng
• 金额: $ 0.05万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7723329
• 关键词: Accounting; Address; Algorithms; Award; Binding Sites; Biological; Biological Process; Biotechnology; Chemical Structure; Chemicals; Collection; Complement; Computer Retrieval of Information on Scientific Projects Database; Computer Vision Systems; Computing Methodologies; Databases; Development; Docking; Education; Elements; Funding; Genomics; Goals; Grant; HIV; High Performance Computing; Historically Black Colleges and Universities; Image; Institution; Investigation; Lead; Libraries; Ligands; Link; Measures; Membrane Proteins; Methods; Molecular; Molecular Structure; North Carolina; Nuclear Hormone Receptors; Pathway interactions; Preclinical Drug Evaluation; Process; Proteins; RNA-Directed DNA Polymerase; Rate; Research; Research Personnel; Resources; Retrieval; Screening procedure; Set protein; Shapes; Side; Source; Structure; Techniques; Trees; United States National Institutes of Health; Universities; Validation; Work; abstracting; base; chemical resource; cheminformatics; computing resources; drug discovery; fitness; improved; inhibitor/antagonist; innovation; interest; neglect; novel; pharmacophore; phosphoric diester hydrolase; programs; protein structure; restraint; scaffold; shape analysis; size; small molecule; structural genomics; tool; virtual

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. ABSTRACT The BRITE Center has been established by North Carolina as the premier Center of Excellence in biotechnology and drug discovery within NC Central University, an HBCU (Historically Black College/University) to advance the education and research in this promising field. As a component of this Center, we are engaged in the research and application of cheminformatics and computational drug discovery tools. Specifically, we aim to conduct the following work using the computing resources of DAC-TeraGrid if we are granted the award. 1. Virtual Computational Drug Screening Background: Current pharmacophore based computational virtual screening methods often ignore the intricate details of the binding site shapes and focus only on the key pharmacophore elements. Thus, they often miss critically important information during the virtual screening process, resulting in many false positives. For example, large molecules with multiple side chains attached to a central scaffold may be selected as false positives, simply because their core structures have the required pharmacophore elements. This situation may be alleviated if binding site excluded volumes are considered. However, the efficiency of such a process is dramatically reduced due to the frequent checks for clashes with the excluded volumes. Thus, there is a great need to increase the efficiency of such method for database searching while taking into account the volume restraints that help reduce the false positive rate. The Computational Method: To address the pitfalls and shortcomings of current pharmacophore methods, we have engaged in the study of a novel structure-based shape pharmacophore method for virtual screening. It takes advantage of a computational geometry algorithm (Delauney tessellation / alpha-shape analysis) to detect the binding site atoms and generate a negative image of the binding site, which complements the binding site shape. This negative image is represented by a set of spheres of different sizes. There are multiple ways to represent the overall shape of this set of spheres. Currently, we use the OEChem Shape library functions to represent it due to the well-known efficiency of the shape matching algorithm. Other computer vision method is also being explored to help improve the accuracy and efficiency of the shape matching process. The innovative aspect of our method comes from the fact that a rigorous computational geometry algorithm has been used to detect the binding site atoms, and a deterministic process to generate the matching image, as well as the representation of this image with OEChem Shape functions. Additional development will include adding more advanced computer vision techniques for shape matching and recognition. The Computing Plan: The above method will be applied to several selected targets: PDE (phosphodiesterases), HIV reverse transcriptase, nuclear hormone receptors and a few other targets. Validation of the method will be performed based on the information on known inhibitor/ligands in the WOMBAT database, which contains over 50,000 molecular structures. The computing intensive *conformational analysis* of these 50,000 molecules as well as *shape matching* with each of the above targets will be conducted in this proposal. The retrieval rate of known active compounds will be compared with ligand-based shape matching (ROCS) as well as FRED docking program (both are computationally intensive as well). 2. Biologically Relevant Molecular Diversity Measure Background: In diversity analysis of compound libraries, most methods look at only the self-dissimilarity among the compound structures, neglecting known information about the biological space revealed by structural genomics projects. This may lead to a hugely diverse set of compounds which may not have any biological effect on most targets. We use the structure-based shape pharmacophore method (see section 1) to evaluate the relevance of a given compounds by comparing its shape with a PANEL of shapes derived from a selected set of protein structures. The RATIONALE behind this is that the shapes of functional pockets on protein surface or binding sites are often the determinant for a molecules biological functions. By using a PANEL of shapes derived from biologically relevant protein pockets, we can evaluate whether a molecule might be biologically active. Such a method is extremely useful especially in the context of the NIH Roadmap initiative where finding chemical probes for biological pathways is the main task. The Computing Plan: a HitMap where the fitness of each molecule in a collection with each of the PANEL shapes will be evaluated. Such a HitMap across a collection of protein structures (>100) will essentially build tentative links between protein structures and small molecule collections. For example, the HitMap for the PubChem molecules will be a useful resource for Chemical Genomics investigations where researchers will have a holistic view of what a molecule might do to other proteins in addition to the target of interest. This grant would greatly enhance our computational effort to obtain such a chemical genomics tree (CGTree) that links chemical structures to their potential biological targets, and ultimately help advance the goal of NIH Roadmap on Chemical Genomics Research.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 摘要Brite中心已由北卡罗来纳州建立为NC Central University（历史悠久的黑人学院/大学）的北卡罗来纳大学生物技术和药物发现卓越中心，以推进这一有前途的领域的教育和研究。作为该中心的一个组成部分，我们从事化学信息和计算药物发现工具的研究和应用。具体而言，如果我们获得奖励，我们旨在使用DAC-Teragrid的计算资源进行以下工作。 1。虚拟计算药物筛查背景：当前基于药效的计算虚拟筛选方法通常忽略结合位点形状的复杂细节，而仅关注关键的药效团元素。因此，他们通常会在虚拟筛选过程中错过至关重要的信息，从而导致许多误报。例如，在中央支架上附着多个侧链的大分子可能被选为假阳性，仅仅是因为它们的核心结构具有所需的药理元件。如果考虑结合地点排除量，则可以缓解这种情况。但是，由于经常检查与排除体积的冲突，这种过程的效率大大降低了。因此，在考虑到有助于降低误报率的体积限制的同时，非常需要提高此类方法的效率。计算方法：为了解决当前药物团方法的陷阱和缺点，我们研究了一种基于结构的新型形状药效团方法，用于虚拟筛查。它利用了计算几何算法（Delauney Tessellation / alpha形分析）来检测结合位点原子并产生结合位点的负图像，从而补充了结合位点形状。这种负图像由一组不同大小的球体表示。有多种方法可以代表这组球体的整体形状。目前，由于形状匹配算法的众所周知的效率，我们使用OECHEM形状库函数来表示它。还探索了其他计算机视觉方法，以帮助提高形状匹配过程的准确性和效率。我们方法的创新方面来自以下事实：严格的计算几何算法已用于检测结合位点原子，以及生成匹配图像的确定性过程，以及具有Oechem Shape函数的该图像的表示。额外的开发将包括添加更先进的计算机视觉技术，以进行形状匹配和识别。计算计划：上述方法将应用于几个选定的靶标：PDE（磷酸二酯酶），HIV逆转录酶，核激素受体和其他一些靶标。该方法的验证将根据有关wombat数据库中已知抑制剂/配体的信息进行的，该数据库包含超过50,000个分子结构。这些50,000个分子以及 *形状匹配 *的计算密集型 *构象分析将与上述每个目标进行。将已知活性化合物的检索速率与基于配体的形状匹配（ROC）以及FRED对接程序进行比较（均在计算密集型上）。 2。生物学相关的分子多样性度量背景：在复合库的多样性分析中，大多数方法仅着眼于复合结构之间的自我差异性，忽略了有关结构基因组学项目所揭示的生物学空间的已知信息。这可能会导致多种多样的化合物，这些化合物可能对大多数靶标有任何生物学作用。我们使用基于结构的形状药效团方法（请参阅第1节）来评估给定化合物的相关性，通过将其形状与源自选定的蛋白质结构集的形状面板进行比较。其背后的理由是，蛋白质表面或结合位点上功能性袋的形状通常是分子生物学功能的决定因素。通过使用源自生物学相关蛋白质口袋的一组形状，我们可以评估分子是否可能具有生物活性。这种方法非常有用，尤其是在NIH路线图计划的背景下，为生物途径寻找化学探针是主要任务。计算计划：将评估一个集合中每个分子在每个面板形状中的适应性。在蛋白质结构集合（> 100）中，这种命中率基本上将在蛋白质结构和小分子集合之间建立暂定联系。例如，Pubchem分子的HITMAP将是化学基因组学研究的有用资源，研究人员将对分子除了感兴趣的靶点外对其他蛋白质的作用有整体视野。该赠款将大大加强我们的计算努力，以获取将化学结构与其潜在生物学靶标联系起来的化学基因组树（CGTREE），并最终有助于促进NIH路线图上的化学基因组学研究的目标。