ArticleMetabolic engineering communications2020
Harnessing the potential of artificial neural networks for predicting protein glycosylation.
Article in Metabolic engineering communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 82 citations in OpenAlex.
- A Review on Applications of Artificial Intelligence (AI) in Monoclonal Antibody (mAb) Manufacturing.Molecules (Basel, Switzerland) · 2026Review
- Engineering mammalian protein secretion: Toward the convergence of high-throughput biology and computational methods.Cell systems · 2025Review
- GlycoMaple: recent updates and applications in visualization and analysis of glycosylation pathways.Analytical and bioanalytical chemistry · 2025Review
- Wheels turning: CHO cell modeling moves into a digital biomanufacturing era: Subtitle: CHO Metabolic Modeling.Computational and structural biotechnology journal · 2025Review
- In silico simulation of glycosylation and related pathways.Analytical and bioanalytical chemistry · 2024Review
- A perspective-driven and technical evaluation of machine learning in bioreactor scale-up: A case-study for potential model developments.Engineering in life sciences · 2024Article
- Integration Approaches to Model Bioreactor Hydrodynamics and Cellular Kinetics for Advancing Bioprocess Optimisation.Bioengineering (Basel, Switzerland) · 2024Review
- Decoding glycosylation potential from protein structure across human glycoproteins with a multi-view recurrent neural network.bioRxiv : the preprint server for biology · 2024Article
- Soft-sensor model development for CHO growth/production, intracellular metabolite, and glycan predictions.Frontiers in molecular biosciences · 2024Article
- From observational to actionable: rethinking omics in biologics production.Trends in biotechnology · 2023Review
- Deglycosylation Differentially Regulates Weaned Porcine Gut Alkaline Phosphatase Isoform Functionality along the Longitudinal Axis.Pathogens (Basel, Switzerland) · 2023Article
- Artificial intelligence in the analysis of glycosylation data.Biotechnology advances · 2022Review
- Glycoinformatics in the Artificial Intelligence Era.Chemical reviews · 2022Review
- Deep Learning in Therapeutic Antibody Development.Methods in molecular biology (Clifton, N.J.) · 2022Article
- An analytical study on the identification of N-linked glycosylation sites using machine learning model.PeerJ. Computer science · 2022Article
- Global mapping of glycosylation pathways in human-derived cells.Developmental cell · 2021Article
- Big-Data Glycomics: Tools to Connect Glycan Biosynthesis to Extracellular Communication.Trends in biochemical sciences · 2021Review
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing.mAbsReview
- A recurrent neural network for soft sensor development using CHO stable pools in fed-batch process for SARS-CoV-2 spike protein production as a vaccine antigen.Biotechnology progressArticle
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Kinetic models offer incomparable insight on cellular mechanisms controlling protein glycosylation. However, their ability to reproduce site-specific glycoform distributions depends on accurate estimation of a large number of protein-specific kinetic parameters and prior knowledge of enzyme and transport protein levels in the Golgi membrane. Herein we propose an artificial neural network (ANN) for protein glycosylation and apply this to four recombinant glycoproteins produced in Chinese hamster ovary (CHO) cells, two monoclonal antibodies and two fusion proteins. We demonstrate that the ANN model accurately predicts site-specific glycoform distributions of up to eighteen glycan species with an average absolute error of 1.1%, correctly reproducing the effect of metabolic perturbations as part of a hybrid, kinetic/ANN, glycosylation model (HyGlycoM), as well as the impact of manganese supplementation and glycosyltransferase knock out experiments as a stand-alone machine learning algorithm. These results showcase the potential of machine learning and hybrid approaches for rapidly developing performance-driven models of protein glycosylation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.