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.

许多药物都是小的有机分子，它们与一种酶或受体结合， 疾病过程。化学主体代表另一类具有生物医学意义的化合物;这些是 比蛋白质小得多，但仍具有使其能够结合的裂缝的分子。 靶向配体。化学宿主用于提高药物的生物利用度和稳定性， 本身具有作为药物的潜在新用途;例如作为毒素的清除剂或 甚至是人造酶。目前，还没有可靠的计算方法来设计目标， 配体或化学主体，因此发现此类化合物在很大程度上依赖于昂贵和时间- 消耗实验性的尝试和错误 该提案旨在通过开发药物来加速发现用于医疗应用的靶向化合物。 改进的理论和计算方法，包括自动化的设计方法， 化学宿主，以及计算配体-蛋白质结合亲和力的精确新方法。的 这里所采取的方法集中在主导状态模型上，这些模型在以下方面提供了有希望的结果： 最近在Pi的实验室中应用于主客体系统。这些方法是高度并行的， 这个项目将受益于超级计算的专业知识和资源， 马约临床医学院的实验室。