Glycosort: A Computational Solution to Post-process Quantitative Large-Scale Intact Glycopeptide Analyses.

Abstract

Protein glycosylation is a post-translational modification involving the addition of carbohydrates to proteins and plays a crucial role in protein folding and various biological processes such as cell recognition, differentiation, and immune response. The vast array of natural sugars available allows the generation of plenty of unique glycan structures in proteins, adding complexity to the regulation and biological functions of glycans. The diversity is further increased by enzymatic site preferences and stereochemical conjugation, leading to an immense amount of different glycan structures. Understanding glycosylation heterogeneity is vital for unraveling the impact of glycans on different biological functions. Evaluating site occupancies and structural heterogeneity aids in comprehending glycan-related alterations in biological processes. Several software tools are available for large-scale glycoproteomics studies; however, integrating identification and quantitative data to assess heterogeneity complexity often requires extensive manual data processing. To address this challenge, we present a python script that automates the integration of Byonic and MaxQuant outputs for glycoproteomic data analysis. The script enables the calculation of site occupancy percentages by glycans and facilitates the comparison of glycan structures and site occupancies between two groups. This automated tool offers researchers a means to organize and interpret their high-throughput quantitative glycoproteomic data effectively.