Computational tools to aid the design of glycomimetic agents

帮助设计糖模拟剂的计算工具

基本信息

批准号：
10245292
负责人：
ROBERT J WOODS
金额：
$ 37.43万
依托单位：
UNIVERSITY OF GEORGIA
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10245292
关键词：
Address Adhesions Affinity Antibiotics Antiviral resistance Bacteria Bacterial Adhesins Binding Binding Sites Biological Carbohydrates Cations Cell Membrane Permeability Cell surface Cells Chemical Structure Chemicals Complex Computer Models Crystallization DNA Sequence Alteration Data Development Diarrhea Disease Docking Down-Regulation Drug Targeting Endocytosis Ensure Enzymes Evaluation Familiarity Food Poisoning Glycoside Hydrolases Hemagglutinin Human Hydrogen Bonding Hydrophobicity In Situ Infection Informatics Intention Intestines Lectin Libraries Ligands Mainstreaming Mediating Membrane Fusion Methods Modeling Modification Molecular Conformation Mucous Membrane Multiple Bacterial Drug Resistance Nomenclature Oligosaccharides Pathogenicity Pharmaceutical Chemistry Pharmaceutical Preparations Polysaccharides Positioning Attribute Property Protein-Carbohydrate Interaction Proteins Reporting Role Salmonella Scheme Scientist Seminal Sialic Acids Site Specificity Structure Technology Testing Therapeutic Therapeutic Agents Therapeutic Intervention Up-Regulation Validation Variant Viral War Water analog base carbohydrate analog carbohydrate binding protein carbohydrate structure chemical property computerized tools design experience experimental group glycosyltransferase hydroxyl group improved influenzavirus inhibitor/antagonist lead optimization nanomolar novel therapeutics pathogen protein complex scaffold screening simulation small molecule inhibitor structural biology targeted treatment tool van der Waals force virtual screening web based interface

项目摘要

Project Summary Specific interactions between carbohydrates (also known as glycans) and proteins underlie the initiation or progression of many diseases. Carbohydrate-binding proteins (human, bacterial or viral lectins and adhesins) and carbohydrate-processing enzymes (glycosyltransferases and glycosidases) are therefore important targets for therapeutic intervention, however the creation of drug-like molecules that can competitively inhibit carbohydrate-binding sites is uniquely challenging. The optimization of a glycomimetic inhibitor involves the synthesis and screening of chemical analogs in an attempt to increase the inhibitory potential and biological activity. Given that carbohydrate synthesis is notoriously laborious, the task of evaluating innumerable analogs with incrementally increasing affinities introduces a particularly significant bottleneck for glycomimetic development. Despite the challenges, the benefit of employing the native carbohydrate as a scaffold is that it intrinsically confers the desired specificity. The fundamental challenge in the creation of a glycomimetic is that of divining which modifications will lead to enhanced affinity without compromising specificity. Computational approaches that are specifically designed to screen analogs of carbohydrates could be invaluable aids to both increasing the objectivity of the synthetic choices and to prioritizing the synthetic effort required for glycomimetic development. Virtual screening is commonplace in mainstream medicinal chemistry and has led to the discovery of non-glycomimetic small molecule inhibitors with nanomolar affinities (12,29). However, it has yet to be widely applied in glycomimetic design. We believe that this is due to several factors, including the complexity of carbohydrate structure and nomenclature, which creates a significant barrier for non-glycoscientists, and, for glycoscientists, a lack of familiarity with sophisticated modeling methods. In the present application, we propose to develop, validate, and implement an alternative strategy to ligand docking that leverages the benefits of computational modeling and structural biology. Specifically, we will develop an online computational approach that uses carbohydrate-protein co-crystal (or NMR) structures as the basis for lead optimization by modifying the bound oligosaccharide in situ. We have assembled a group of experimental glycobiologists and chemists who have agreed to provide data and independently validate the predictive accuracy of the tools we are developing. These scientists have over 200 years of combined experience in glycomimetic synthesis and evaluation. Successful completion of the aims will lead to a validated computational tool to aid in the discovery and optimization of therapeutic agents that target carbohydrate-protein interactions that are particularly relevant in the ongoing battle against multidrug resistant bacteria.

项目摘要碳水化合物（也称为聚糖）和蛋白质之间的特定相互作用是启动或许多疾病的进展。碳水化合物结合蛋白（人，细菌或病毒凝集素和粘附素）因此对于治疗性干预，但是创建可以竞争性抑制的药物样分子碳水化合物结合位点具有独特的挑战。糖抑制剂的优化涉及化学类似物的合成和筛选，以提高抑制潜力和生物学活动。鉴于碳水化合物的合成众所周知，它是艰苦的，因此评估无数类似物随着逐步增加的亲和力，引入了特别重要的胶囊瓶颈发展。尽管面临挑战，但使用本地碳水化合物作为脚手架的好处是本质上同意所需的特异性。糖创建的基本挑战是分离哪些修改将导致增强的亲和力，而不会损害特异性。专门设计用于筛选碳水化合物的类似物的计算方法可能是无价的辅助工具既可以提高合成选择的客观性，又要确定合成工作的优先级糖化开发所需的。虚拟筛查在主流药物中很普遍并导致发现了具有纳摩尔亲和力的非透明度小分子抑制剂（12,29）。但是，它尚未广泛应用于糖化设计。我们认为这是由于几个因素，包括碳水化合物结构和命名法的复杂性，这为非血糖主义者，对于糖基督主义者来说，缺乏对复杂建模方法的熟悉程度。在本应用中，我们建议开发，验证和实施配体的替代策略停靠，利用计算建模和结构生物学的好处。具体来说，我们会的开发一种在线计算方法，该方法使用碳水化合物 - 蛋白质共晶（或NMR）结构铅优化的基础，通过修改原位界限的寡糖。我们组装了一组同意提供数据并独立验证的实验性糖生物学家和化学家我们正在开发的工具的预测准确性。这些科学家已有200多年的合并具有糖化合成和评估的经验。成功完成目标将导致经过验证的计算工具，以帮助发现和针对靶向碳水化合物 - 蛋白质相互作用的治疗剂的优化，这些相互作用特别相关与多药抗性细菌的持续战斗。