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.

项目概要碳水化合物（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（人类、细菌或病毒凝集素和粘附素）因此，碳水化合物加工酶（糖基转移酶和糖苷酶）是重要的目标用于治疗干预，然而，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性。糖模拟抑制剂的优化涉及化学类似物的合成和筛选，试图提高抑制潜力和生物活性活动。鉴于碳水化合物的合成非常费力，评估无数类似物的任务随着亲和力的逐渐增加，给模拟糖带来了一个特别重要的瓶颈发展。尽管面临挑战，使用天然碳水化合物作为支架的好处是本质上赋予了所需的特异性。创造糖模拟物的根本挑战是预测哪些修饰将导致亲和力增强而不损害特异性。专门设计用于筛选碳水化合物类似物的计算方法可以是对于提高综合选择的客观性和确定综合工作的优先顺序具有宝贵的帮助糖模拟物开发所需的。虚拟筛选在主流药物化学中很常见并导致了具有纳摩尔亲和力的非糖模拟小分子抑制剂的发现 (12,29)。然而，它尚未在糖模拟设计中得到广泛应用。我们认为这是由以下几个因素造成的：包括碳水化合物结构和命名的复杂性，这为对于非糖科学家来说，对于糖科学家来说，缺乏对复杂建模方法的熟悉。在本申请中，我们建议开发、验证和实施配体的替代策略利用计算建模和结构生物学优势的对接。具体来说，我们将开发一种在线计算方法，使用碳水化合物-蛋白质共晶（或 NMR）结构作为通过原位修饰结合的寡糖来优化先导化合物的基础。我们聚集了一群实验糖生物学家和化学家同意提供数据并独立验证我们正在开发的工具的预测准确性。这些科学家加起来有 200 多年的研究经验具有糖模拟物合成和评价方面的经验。成功完成这些目标将产生一种经过验证的计算工具，以帮助发现和针对碳水化合物-蛋白质相互作用的治疗剂的优化，这些相互作用在以下方面特别相关：与多重耐药细菌的持续斗争。