GlycoMME, a Markov modeling platform for studying N-glycosylation biosynthesis from glycomics data

Chenguang Liang; Austin W T Chiang; Nathan E Lewis

doi:10.1016/j.xpro.2023.102244

GlycoMME, a Markov modeling platform for studying N-glycosylation biosynthesis from glycomics data

STAR Protoc. 2023 Apr 21;4(2):102244. doi: 10.1016/j.xpro.2023.102244. Online ahead of print.

Authors

Chenguang Liang¹, Austin W T Chiang², Nathan E Lewis³

Affiliations

¹ Department of Pediatrics, University of California, San Diego, La Jolla, San Diego, CA 92130, USA; Department of Bioengineering, University of California, San Diego, La Jolla, San Diego, CA 92130, USA.
² Department of Pediatrics, University of California, San Diego, La Jolla, San Diego, CA 92130, USA. Electronic address: austin.chiang@gmail.com.
³ Department of Pediatrics, University of California, San Diego, La Jolla, San Diego, CA 92130, USA; Department of Bioengineering, University of California, San Diego, La Jolla, San Diego, CA 92130, USA. Electronic address: nlewisres@ucsd.edu.

Abstract

Variations in N-glycosylation, which is crucial to glycoprotein functions, impact many diseases and the safety and efficacy of biotherapeutic drugs. Here, we present a protocol for using GlycoMME (Glycosylation Markov Model Evaluator) to study N-glycosylation biosynthesis from glycomics data. We describe steps for annotating glycomics data and quantifying perturbations to N-glycan biosynthesis with interpretable models. We then detail procedures to predict the impact of mutations in disease or potential glycoengineering strategies in drug development. For complete details on the use and execution of this protocol, please refer to Liang et al. (2020).¹.

Keywords: Bioinformatics; Biotechnology and bioengineering; Systems biology.

Grants and funding

R35 GM119850/GM/NIGMS NIH HHS/United States