Modeling the mucosal glycopeptide mesh for improved disease understanding and mucin-inspired biomaterial design

对粘膜糖肽网进行建模，以提高对疾病的理解和粘蛋白启发的生物材料设计

• 批准号: 10715230
• 负责人: Srirupa Chakraborty
• 金额: $ 39.9万
• 依托单位: NORTHEASTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10715230
• 关键词: Biocompatible Materials; Biological Process; Biophysics; Case Study; Charge; Combinatorics; Comprehension; Computer Models; Cystic Fibrosis; Data; Disease; Equilibrium; Gel; Glycopeptides; Glycoproteins; Grain; Machine Learning; Malignant Neoplasms; Mediating; Methods; Modeling; Molecular; Molecular Machines; Molecular Structure; Mucins; Mucositis; Mucous Membrane; Pattern; Peptides; Play; Polymers; Polysaccharides; Property; Protein Glycosylation; Resources; Role; Structure; System; Techniques; Therapeutic; computerized tools; conformational conversion; design; experimental study; glycosylation; improved; in silico; intermolecular interaction; mimetics; multimodality; nanomaterials; novel; physical property; predictive modeling; sugar; tool

ABSTRACT Mucins and other densely glycosylated proteins play critical roles in a number of biological processes, disease conditions, and therapeutics. The functioning of these sugar-coated molecular machines depends on their structure, dynamics, and conformational transitions. Experimental techniques for capturing such structural dynamics, however, can be extremely challenging and resource intensive. We seek to improve upon some of the existing glycan modeling computational tools as well as design new in silico techniques, as robust alternatives to experimental studies. These tools will be used to build interconnected mucin glycoprotein gel systems with native glycosylation patterns, and obtain understanding of functional underpinnings at the molecular level. Effects of perturbations in terms of pH variance, varying glycosylation patterns, and charge distribution changes will be investigated. This will enable detailed comprehension of the physical properties of mucins that drive their function, as well as the molecular elucidation of disease conditions of cystic fibrosis, mucosal inflammation, and mucin-mediated cancers. A multi-modal approach will be employed to study these mucin networks in different scales – (i) first-principles based atomistic modeling to capture the equilibrium structure-dynamics; (ii) biophysics-based coarse-grained methods to describe bulk properties and transitions, and (iii) data-driven machine learning approaches to predict topology and intermolecular interactions. Inspired from mucosal gels, we will use these tools to design novel mucin-like nanomaterials constructed from glycan-peptide heteropolymer networks to target different biomedical applications. We aim to optimize a machine learning (ML)-driven combinatorics method for glycan arrangement in these polymers that will provide enhanced control over material properties – a molecular LEGO of glycans geared towards customizable mucin-mimetic biomaterials.

抽象的 粘蛋白和其他纯糖基化蛋白在许多生物学过程，疾病中起关键作用 疾病和治疗。这些糖涂层的分子机的功能取决于它们 结构，动力学和构象过渡。捕获这种结构的实验技术 但是，动态可能是极其挑战和资源密集型的。我们试图改善一些 现有的聚糖建模计算工具以及在硅技术中设计的新设计，作为强大的替代方案 进行实验研究。这些工具将用于构建与 天然糖基化模式，并了解分子水平的功能基础。效果 在pH方差，不同的糖基化模式和电荷分布变化方面的扰动将是 调查。这将详细理解粘液的物理特性 功能，以及囊性纤维化，粘膜注射和疾病条件的分子阐明 粘蛋白介导的癌症。将采用一种多模式方法来研究这些粘蛋白网络 尺度 - （i）基于第一原理的原子建模，以捕获平衡结构动力学； （ii） 基于生物物理学的粗粒方法来描述大量特性和过渡，以及（iii）数据驱动 机器学习方法可预测拓扑结构和分子间相互作用。灵感来自粘膜凝胶， 我们将使用这些工具设计由糖肽杂聚物制成的新型粘蛋白样纳米材料 网络针对不同的生物医学应用。我们旨在优化机器学习（ML）驱动 在这些聚合物中用于聚糖布置的组合方法，可以增强对材料的控制 特性 - 一种用于可定制粘蛋白模拟生物材料的聚糖分子乐高。