Development and Testing of New Computational Methods for Ligand Discovery and Mechanism

配体发现和机制的新计算方法的开发和测试

• 批准号: 10406014
• 负责人: Brian K Shoichet
• 金额: $ 79.83万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10406014
• 关键词: 2019-nCoV; Affect; Biological; Biological Models; Cells; Chemicals; Complement; Computing Methodologies; Consumption; Development; Docking; Drug Delivery Systems; Experimental Models; Funding; G-Protein-Coupled Receptors; Goals; Grant; Head; Human; Individual; Libraries; Ligands; Logic; Methods; Modeling; Modernization; Molecular Conformation; Outcome; Partner in relationship; Pattern; Pharmacology; Productivity; Reader; Role; Scientist; Signal Transduction; Site; Structure; Testing; Viral Proteins; Work; Yeasts; base; drug discovery; flexibility; interest; novel therapeutics; receptor; tool; yeast genetics

Abstract Our long-term goal is to develop structure-based and chemoinformatic methods in ligand discovery, testing these in experimental model systems and applying them to targets of biological interest. To give the reader a sense of our direction, we sketch several questions within four broad foci: A. Development of new docking methods and their testing in model systems like simplified cavity sites, where individual terms may be disentangled. Several upcoming projects leverage the ultra-large libraries we introduced in the last period, including: i. are bigger libraries always better, or at some library size do we saturate? ii. As the library grows, are we still bounded by bio-like molecules, or do we diverge away from these? How does this affect docking hit quality? iii. Can we treat libraries of tens-of-billions to trillions of molecules with better methods and look-ahead pattern matching? We are also exploring iv. flexible receptor docking in the model cavities, and v. treatment of ligand conformational and desolvation strain. B. Turning the structure-based enterprise on its head, we return to the logic of classical pharmacology with modern chemoinformatics, seeking to predict targets from their ligands. Leveraging work in the last period, we i. Investigate if widely consumed molecules, such as neutraceuticals, have specific targets and ii. Use network pharmacology to find molecules to modulate the human targets subverted by SARS-Cov-2 viral proteins. C. Application of these methods to biologically interesting targets, often GPCRs. In the upcoming period, we i. explore bespoke ultra-large libraries for amingergic GPCRs; ii. Extend domain of applicability to transporters; iii. Seek chemical probes for the yeast mating-factor GPCR Ste2 to complement the power of yeast genetics. D. The role of colloidal aggregation and phospholipidosis in early drug discovery. Projects in the upcoming period investigate i. Exploiting colloidal aggregates for drug delivery; ii. Investigating the mechanism of colloidal aggregation and its impact in early discovery; iii. Investigating a new phenomenon that may have wide impact on cell-based ligand discovery, phospholipidosis. This MIRA was previously funded by five grants, and its breadth reflects that. While ambitious, its pragmatism is supported by the productivity of the last period and by extensive preliminary results.

摘要 我们的长期目标是发展基于结构和化学信息学的配体发现、测试和分析方法。 这些在实验模型系统中，并将它们应用于生物学感兴趣的目标。给读者一个 为了确定我们的方向，我们在四个主要焦点内概述了几个问题： A.开发新的对接方法，并在简化的空腔部位等模型系统中进行测试， 可以解开各个项。几个即将到来的项目利用了我们 在上一个时期，包括：i。是越大的图书馆越好，还是在一定的图书馆规模上， 饱和？二.随着文库的增长，我们是否仍然受到生物分子的限制，或者我们是否偏离了 这些吗这如何影响对接命中质量？三.我们能不能把数以百亿到万亿计的 分子与更好的方法和前瞻模式匹配？我们也在探索IV。柔性感受器 在模型腔中对接，以及V.配体构象和去溶剂化应变的处理。 B。把结构为基础的企业在其头上，我们回到经典药理学的逻辑与 现代化学信息学，寻求从其配体预测靶点。利用上一阶段的工作，我们 I.调查广泛消费的分子（例如营养药物）是否具有特定的靶点，以及ii.使用网络 药理学寻找分子来调节被SARS-Cov-2病毒蛋白破坏的人类靶点。 C.将这些方法应用于生物学上感兴趣的靶标，通常是GPCR。在接下来的一段时间里，我。 探索用于amingergic GPCR的定制超大文库; ii.将适用范围扩大到运输者; 三.寻找酵母交配因子GPCR Ste 2的化学探针，以补充酵母遗传学的力量。 D.胶体聚集和磷脂沉积在早期药物发现中的作用。即将推出的项目 调查期间，利用胶体聚集体用于药物递送; ii.调查的机制 胶体聚集及其在早期发现中的影响; iii.调查一种新现象 广泛影响基于细胞的配体发现，磷脂病。 该MIRA以前由五项赠款资助，其广度反映了这一点。虽然雄心勃勃，但其实用主义 上一个时期的生产力和广泛的初步结果支持了这一点。