Theory and Modeling of Noncovalent Binding

非共价结合的理论和建模

• 批准号: 7941512
• 负责人: MICHAEL K. GILSON
• 金额: $ 4.27万
• 依托单位: UNIVERSITY OF MD BIOTECHNOLOGY INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2009-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7941512
• 关键词: Address; Adsorption; Affinity; Agreement; Algorithms; Binding; Binding Proteins; Biological; Biological Availability; Biology; Catalysis; Chemicals; Cleaved cell; Clinic; Cognition; Computer software; Computing Methodologies; Decontamination; Disease; Dissociation; Encapsulated; Entropy; Enzymes; Generations; Goals; Hybrids; Immunity; Laboratories; Ligands; Medicine; Metabolism; Methodology; Methods; Modeling; Molecular Weight; Pharmaceutical Preparations; Pharmacologic Substance; Play; Process; Property; Proteins; Resources; Role; Series; Signal Transduction; Solutions; Speed; Supercomputing; System; Testing; Time; Toxin; Validation; aqueous; design; drug discovery; graphical user interface; improved; insight; interest; macromolecule; medical schools; molecular recognition; receptor; research study; theories; three dimensional structure

Many medications are small organic molecules that bind an enzyme or receptor that is involved in a disease process. Chemical hosts represent another class of compounds of biomedical interest; these are molecules which are much smaller than proteins but which still possess a cleft enabling them to bine1 a targeted ligand. Chemical hosts are used to improve the bioavailability and stability of medications, and have potential new uses as Pharmaceuticals in their own right; for examples as scavengers of toxins or even as artificial enzymes. Currently, there is no reliable computational method for designing targeted ligands or chemical hosts, so the discovery of such compounds relies heavily upon costly and time- consuming experimental trial and error. This proposal aims to speed the discovery of targeted compounds for medicalapplications by developing improved theoretical and computational methods, including ah automated approach to the design of chemical hosts, and an accurate new method of computing ligand-protein binding affinities. The approaches taken here focus on predominant states models, which have provided promising results in recent applications to host-guest systems in the Pi's laboratory. These methods are highly parallelizable, and this project will benefit from supercomputing expertise and resources available in the collaborating laboratory at the Mayo Clinic School of Medicine.

许多药物是结合与A的酶或受体结合的小有机分子 疾病过程。化学宿主代表了另一类生物医学兴趣的化合物；这些都是 分子比蛋白质小得多，但仍具有裂缝，使它们能够bine1 a 靶向配体。化学宿主用于改善药物的生物利用度和稳定性，以及 本身具有潜在的新用途作为药品；作为示例作为毒素或 即使是人工酶。当前，没有可靠的计算方法来设计目标 配体或化学宿主，因此这种化合物的发现在很大程度上依赖于昂贵和时间 - 消耗实验试验和错误。 该建议旨在通过开发来加快针对医疗应用的目标化合物 改进了理论和计算方法，包括AH自动化方法的设计 化学宿主，也是计算配体 - 蛋白结合亲和力的准确新方法。这 此处采用的方法重点是主要国家模型，这些模型在 最近在PI实验室中对宿主 - 吉斯特系统的应用。这些方法是高度可行的， 该项目将受益于超级计算的专业知识和合作中的资源 梅奥诊所医学院的实验室。