Enhancing Protein Solubility via Glycosylation: From Chemical Synthesis to Machine Learning Predictions

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.PMID:38598264 | DOI:10.1021/acs.biomac.4c00134

Source: Biomacromolecules - April 10, 2024 Category: Biochemistry Authors: Bo Ma Hedi Chen Jinyuan Gong Wenqiang Liu Xiuli Wei Yajing Zhang Xin Li Meng Li Yani Wang Shiying Shang Boxue Tian Yaohao Li Ruihan Wang Zhongping Tan Source Type: research