Theory and Modeling of Noncovalent Binding

非共价结合的理论和建模

• 批准号: 7280373
• 负责人: MICHAEL K. GILSON
• 金额: $ 27.37万
• 依托单位: UNIVERSITY OF MD BIOTECHNOLOGY INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7280373
• 关键词: Address; Adsorption; Affinity; Agreement; Algorithms; Binding; Binding Proteins; Biological; Biological Availability; Biology; Catalysis; Chemicals; Class; Cleaved cell; Clinic; Cognition; Computer software; Computing Methodologies; Decontamination; Disease; Dissociation; Encapsulated; Entropy; Enzymes; Generations; Goals; Hybrids; Immunity; Laboratories; Ligands; Medical; Medicine; Metabolism; Methodology; Methods; Modeling; Molecular Weight; Pharmaceutical Preparations; Pharmacologic Substance; Process; Property; Proteins; Range; Resources; Role playing therapy; 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

DESCRIPTION (provided by applicant): 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 bind 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 medical applications 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.

描述（由申请人提供）：许多药物是小的有机分子，与参与疾病过程的酶或受体结合。化学宿主代表了另一类具有生物医学意义的化合物;这些分子比蛋白质小得多，但仍然具有使它们能够结合靶向配体的裂缝。化学宿主用于提高药物的生物利用度和稳定性，并且本身具有作为药物的潜在新用途;例如作为毒素的清除剂，甚至作为人工酶。目前，还没有可靠的计算方法来设计靶向配体或化学宿主，因此这些化合物的发现在很大程度上依赖于昂贵且耗时的实验试错。该提案旨在通过开发改进的理论和计算方法，包括化学宿主设计的自动化方法，以及计算配体-蛋白质结合亲和力的准确新方法，来加速发现用于医学应用的靶向化合物。这里采取的方法集中在占主导地位的国家模型，这提供了有前途的结果，在最近的应用程序中的主-客体系统在PI的实验室。这些方法是高度并行化的，该项目将受益于马约临床医学院合作实验室的超级计算专业知识和资源。