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

摘要 粘蛋白和其他高密度糖基化蛋白在许多生物过程、疾病 条件和治疗方法。这些糖衣分子机器的功能取决于它们的 结构、动力学和构象转变。捕捉这种结构的实验技术 然而，动态可能是极具挑战性和资源密集型的。我们寻求改进一些 现有的葡聚糖模拟计算工具以及设计新的硅胶技术，作为可靠的替代方案 去做实验研究。这些工具将用于建立相互连接的粘蛋白糖蛋白凝胶系统 天然糖基化模式，并在分子水平上了解功能基础。效应 PH变化、糖基化模式变化和电荷分布变化方面的扰动将是 调查过了。这将使我们能够详细地理解驱动它们的粘蛋白的物理性质 功能，以及囊性纤维化、粘膜炎症和 粘蛋白介导的癌症。多模式方法将被用来研究这些粘蛋白网络在不同的 尺度-(I)基于第一原理的原子建模，以捕捉平衡结构-动力学；(Ii) 基于生物物理学的描述整体属性和转变的粗粒度方法，以及(Iii)数据驱动 预测拓扑和分子间相互作用的机器学习方法。灵感来自于粘膜凝胶， 我们将使用这些工具来设计由多聚糖-多肽杂化聚合物构建的新型粘蛋白类纳米材料 针对不同生物医学应用的网络。我们的目标是优化机器学习(ML)驱动的 这些聚合物中的葡聚糖排列的组合方法将提供对材料的增强控制 特性-一种分子乐高多糖，面向可定制的粘蛋白仿生生物材料。

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.