Development of Computer Aided Design of Helical Arylamide Foldamer Receptors for Selective Carbohydrate Recognition

用于选择性碳水化合物识别的螺旋芳酰胺折叠体受体的计算机辅助设计的发展

• 批准号: 9232818
• 负责人: Zhiwei Liu
• 金额: $ 42.75万
• 依托单位: UNIVERSITY OF THE SCIENCES PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-23 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9232818
• 关键词: Affinity; Binding; Biochemical; Biochemical Pathway; Biological Markers; Breast; Caliber; Carbohydrates; Cells; Chemicals; Code; Colon; Complex; Computer Assisted; Computer Simulation; Computer-Aided Design; Detection; Development; Diagnostic; Disease Pathway; Evolution; Generations; Goals; HIV; Health; Hydrogen Bonding; Hydrophobic Interactions; Immune response; Inflammation; Isomerism; Lead; Library Catalogs; Ligand Binding; Lung; Malignant Neoplasms; Methods; Modification; Molecular Conformation; Neoplasm Metastasis; Outcome; Ovarian; Pathologic Processes; Pathway interactions; Play; Population; Positioning Attribute; Process; Production; Prostate; Protocols documentation; Role; Sampling; Scheme; Shapes; Speed; Structure; Structure-Activity Relationship; Testing; Therapeutic Agents; Therapeutic Human Experimentation; Training; Vertebral column; Virus Diseases; Work; anomer; base; candidate selection; capsule; carbohydrate receptor; combinatorial; design; functional group; microbial; molecular dynamics; molecular recognition; molecular shape; monomer; novel; pathogen; protein folding; prototype; receptor; scaffold; stem; sugar

ABSTRACT Carbohydrates are ubiquitous and critical in many biochemical and disease pathways. They regulate many pathological processes, such as cancer metastasis, microbial and viral infections, and inflammation, and are biomarkers for pathogens (e.g. HIV), and a range of cancers (e.g. breast, ovarian, prostate, lung and colon). Therefore, carbohydrates hold a great promise for health-related diagnostic, detection, therapeutic and research applications. Yet, they are among the most difficult targets for molecular recognition, due to their immense variety, and subtle, but consequential structural differences. These include anomers, ring-closure isomers, many stereocenters, branching points, and chemical modifications, resulting in remarkably high similarities between species. Thus, a major obstacle for further development of effective receptors is selectivity. Despite an intense experimental effort, designing sugar receptors with high affinity and selectivity still poses a serious challenge. We propose to develop an approach for computationally aided design of foldamer receptors with high selectivity, based on specifically tailored non-covalent interactions and tight shape fit. The approach combines rational, combinatorial and iterative optimization design components. It is to be general, applicable to a variety of carbohydrate targets. Furthermore, it will rapidly generate optimal lead sequences for synthesis, and thus result in a transformative increase in efficiency and speed of producing carbohydrate receptors. The goal of the proposal is to establish a computer-aided strategy for designing receptors that selectively recognize carbohydrate targets based on the position and orientation of sugar's OH groups, their hydrophobic patches, and molecular shapes. Chemical entities particularly amenable for such a receptor design are foldamers, oligomers that fold into stable secondary structures. Their attractiveness for the proposed combined rational and combinatorial design stems from their modularity, stability, general structure predictability and ease of synthesis. We focus on arylamide foldamer based receptors as they have been recently shown to have unprecedented selectivity towards tested sugars. In the past several years, we have been developing computational approaches for predicting foldamer structure, dynamics and ligand binding modes, and are thus in a unique position to advance the proposed idea using arylamide foldamers as the chemical basis for receptor design. An additional value of the proposed work is that it will be adaptable to other modular supramolecular scaffolds and targets.

抽象的 碳水化合物在许多生化和疾病途径中普遍存在且至关重要。他们监管许多 病理过程，例如癌症转移、微生物和病毒感染以及炎症， 病原体（例如 HIV）和一系列癌症（例如乳腺癌、卵巢癌、前列腺癌、肺癌和结肠癌）的生物标志物。 因此，碳水化合物在健康相关的诊断、检测、治疗和治疗方面具有广阔的前景。 研究应用。然而，它们是分子识别最困难的目标之一，因为它们 巨大的多样性和微妙但重要的结构差异。这些包括端基异构体、闭环 异构体、许多立体中心、分支点和化学修饰，导致非常高的 物种之间的相似性。因此，进一步开发有效受体的主要障碍是 选择性。 尽管进行了大量的实验工作，设计具有高亲和力和选择性的糖受体仍然是一个难题 严峻的挑战。我们建议开发一种折叠受体的计算辅助设计方法 具有高选择性，基于专门定制的非共价相互作用和紧密的形状配合。方法 结合了理性、组合和迭代优化设计组件。它是通用的，适用于 多种碳水化合物目标。此外，它将快速生成用于合成的最佳先导序列， 从而导致产生碳水化合物受体的效率和速度发生革命性的提高。 该提案的目标是建立一种计算机辅助策略来设计受体，选择性地 根据糖的 OH 基团的位置和方向、其疏水性来识别碳水化合物目标 斑块和分子形状。特别适合这种受体设计的化学实体是 折叠体，折叠成稳定二级结构的低聚物。他们对拟议合并的吸引力 合理的组合设计源于其模块化、稳定性、总体结构的可预测性和 易于合成。我们专注于基于芳基酰胺折叠体的受体，因为它们最近被证明具有 对测试糖的前所未有的选择性。在过去的几年里，我们一直在发展 用于预测折叠体结构、动力学和配体结合模式的计算方法，因此 处于独特的位置来推进使用芳基酰胺折叠体作为化学基础的提议想法 受体设计。拟议工作的另一个价值是它将适用于其他模块化 超分子支架和靶标。