HIGH PERFORMANCE COMPUTING FOR DRUG DISCOVERY

用于药物发现的高性能计算

• 批准号: 7956190
• 负责人: Weifan Zheng
• 金额: $ 0.08万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7956190
• 关键词: Accounting; Address; Algorithms; Award; Binding Sites; Biological; Biological Process; Biomedical Research; 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; 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; 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.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 摘要BRITE中心是北卡罗来纳州在北卡罗来纳州中央大学（HBCU）内建立的生物技术和药物发现卓越中心，旨在推进这一有前途的领域的教育和研究。作为该中心的组成部分，我们从事化学信息学和计算药物发现工具的研究和应用。具体而言，如果我们获得该奖项，我们的目标是使用DAC-TeraGrid的计算资源进行以下工作。1.虚拟计算药物筛选背景：目前基于药效团的计算虚拟筛选方法往往忽略了结合位点形状的复杂细节，只关注关键药效团元素。因此，他们经常在虚拟筛选过程中错过至关重要的信息，导致许多误报。例如，具有多个连接到中心支架的侧链的大分子可以被选择为假阳性，仅仅是因为它们的核心结构具有所需的药效团元件。如果考虑结合位点排除的体积，这种情况可能会得到缓解。然而，由于频繁检查与排除卷的冲突，这样的过程的效率显著降低。因此，非常需要在考虑有助于降低假阳性率的体积限制的同时提高这种用于数据库搜索的方法的效率。计算方法：为了解决现有药效团方法的缺陷和不足，我们研究了一种新的基于结构的形状药效团虚拟筛选方法。它利用计算几何算法（Delauney镶嵌/α形分析）检测结合位点原子并生成结合位点的负像，这补充了结合位点的形状。这个负像由一组不同大小的球体表示。有多种方法可以表示这组球体的整体形状。目前，我们使用OEChem形状库函数来表示它，由于形状匹配算法的众所周知的效率。其他计算机视觉方法也在探索中，以帮助提高形状匹配过程的准确性和效率。我们的方法的创新方面来自于这样一个事实，即严格的计算几何算法已被用于检测结合位点原子，和一个确定性的过程来生成匹配的图像，以及该图像的表示与OEChem形状函数。额外的开发将包括增加更先进的计算机视觉技术，用于形状匹配和识别。计算计划：上述方法将应用于几个选定的目标：PDE（磷酸二酯酶），HIV逆转录酶，核激素受体和一些其他目标。将根据WOMBAT数据库中已知抑制剂/配体的信息进行方法验证，该数据库包含超过50，000种分子结构。本提案将对这50，000个分子进行计算密集型 * 构象分析 * 以及与上述每个目标的 * 形状匹配 *。已知活性化合物的检索率将与基于配体的形状匹配（ROCS）以及FRED对接程序（两者也是计算密集型的）进行比较。2.背景：在化合物文库的多样性分析中，大多数方法只考虑化合物结构之间的自相异性，忽略了结构基因组学项目揭示的生物空间的已知信息。这可能导致一组非常多样化的化合物，这些化合物可能对大多数靶点没有任何生物学作用。我们使用基于结构的形状药效团方法（见第1节），通过将给定化合物的形状与来自一组选定蛋白质结构的PANEL形状进行比较来评估其相关性。这背后的理由是，蛋白质表面或结合位点上的功能口袋的形状通常是分子生物学功能的决定因素。通过使用源自生物相关蛋白质口袋的PANEL形状，我们可以评估分子是否具有生物活性。这种方法非常有用，特别是在NIH路线图计划的背景下，其中寻找生物途径的化学探针是主要任务。计算计划：一个HitMap，其中将评估集合中每个分子与每个PANEL形状的适应性。这样一个跨越蛋白质结构集合（>100）的HitMap将基本上在蛋白质结构和小分子集合之间建立起试探性的联系。例如，PubChem分子的HitMap将成为化学基因组学研究的有用资源，研究人员将全面了解分子除了感兴趣的靶标外还可能对其他蛋白质起什么作用。这笔赠款将大大提高我们的计算工作，以获得这样一个化学基因组学树（CGTree），将化学结构与其潜在的生物靶点联系起来，并最终帮助推进NIH化学基因组学研究路线图的目标。