Theory and Modeling of Noncovalent Binding

描述(由申请人提供)：许多药物是小的有机分子，它们结合了参与疾病过程的酶或受体。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，但仍然具有裂隙，使它们能够结合目标配体。化学主体被用来提高药物的生物利用度和稳定性，并有潜在的新用途作为药物本身；例如，作为毒素的清除剂，甚至作为人造酶。目前，还没有可靠的计算方法来设计靶向配体或化学主体，因此这类化合物的发现在很大程度上依赖于昂贵且耗时的实验试验和错误。这项提议旨在通过开发改进的理论和计算方法来加速发现用于医疗应用的靶向化合物，包括自动设计化学主体的方法，以及计算配体-蛋白质结合亲和力的准确的新方法。这里所采用的方法集中在优势态模型上，这些模型在PI实验室最近对主-客体系统的应用中提供了有希望的结果。这些方法是高度可并行化的，该项目将受益于梅奥临床医学院合作实验室提供的超级计算专业知识和资源。

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.