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

摘要 粘蛋白和其他密集糖基化的蛋白质在许多生物过程、疾病和免疫反应中起关键作用。 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。用于捕获这种结构的实验技术 然而，动态可能极具挑战性，而且需要大量资源。我们寻求改善一些 现有的聚糖建模计算工具以及设计新的计算机技术，作为可靠的替代方案 到实验研究。这些工具将用于构建互连的粘蛋白糖蛋白凝胶系统， 天然糖基化模式，并获得在分子水平上的功能基础的理解。影响 在pH变化，不同的糖基化模式和电荷分布变化方面的扰动将是 研究了这将使我们能够详细了解粘蛋白的物理特性，这些物理特性驱动它们的功能。 功能，以及囊性纤维化，粘膜炎症， 粘蛋白介导的癌症。一个多模态的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，以捕获平衡结构动力学;（ii） 基于生物制药学的粗粒度方法来描述批量属性和转换，以及（iii）数据驱动 机器学习方法来预测拓扑结构和分子间相互作用。灵感来自于粘膜凝胶， 我们将利用这些工具来设计由聚糖-肽杂聚物构建的新型粘蛋白样纳米材料 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 用于这些聚合物中聚糖排列的组合学方法， 特性-聚糖的分子乐高，面向可定制的粘蛋白模拟生物材料。

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.