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 粘蛋白和其他密集的糖基化蛋白在许多生物学过程中起关键作用，疾病 条件和治疗方法。 结构，动力学和同置过渡。 但是，动态可能是极具挑战性的，我们试图改进一些 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 实验研究。 天然糖基化模式，并了解分子水平的功能基础。 在短语，变化的糖基化模式和电荷分布变化方面的扰动将是 调查了。 功能以及分子精明。 粘蛋白介导的癌症将采用多模式的方法来研究这些粘蛋白网络 鳞片 - （i）基于原子的原子建模，以捕获平衡结构 - （ii）； 基于生物物理学基础的粗粒方法来降级和过渡，以及（iii）数据驱动 通过粘膜凝胶启发的机器学习方法来预测拓扑和分子间相互作用。 我们将使用工具来设计由糖肽杂聚物制成的类似纳米材料 针对不同生物医学应用的网络。 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 属性 - 聚糖的分子长聚糖，适合于可定制的粘蛋白 - 渗透生物材料。