State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China.
School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China.
Biomacromolecules. 2024 May 13;25(5):3001-3010. doi: 10.1021/acs.biomac.4c00134. Epub 2024 Apr 10.
Glycosylation is a valuable tool for modulating protein solubility; however, the lack of reliable research strategies has impeded efficient progress in understanding and applying this modification. This study aimed to bridge this gap by investigating the solubility of a model glycoprotein molecule, the carbohydrate-binding module (CBM), through a two-stage process. In the first stage, an approach involving chemical synthesis, comparative analysis, and molecular dynamics simulations of a library of glycoforms was employed to elucidate the effect of different glycosylation patterns on solubility and the key factors responsible for the effect. In the second stage, a predictive mathematical formula, innovatively harnessing machine learning algorithms, was derived to relate solubility to the identified key factors and accurately predict the solubility of the newly designed glycoforms. Demonstrating feasibility and effectiveness, this two-stage approach offers a valuable strategy for advancing glycosylation research, especially for the discovery of glycoforms with increased solubility.
糖基化是调节蛋白质溶解度的一种有价值的工具；然而，可靠的研究策略的缺乏阻碍了人们对这种修饰的理解和应用的有效进展。本研究旨在通过对模型糖蛋白分子（碳水化合物结合模块，CBM）的两阶段过程来弥补这一差距。在第一阶段，采用化学合成、文库的比较分析和分子动力学模拟等方法，研究不同糖基化模式对溶解度的影响以及影响溶解度的关键因素。在第二阶段，创新性地利用机器学习算法推导出一个预测性的数学公式，将溶解度与确定的关键因素联系起来，并准确预测新设计的糖型的溶解度。该两阶段方法证明了其可行性和有效性，为推进糖基化研究提供了一种有价值的策略，特别是对提高溶解度的糖型的发现。
