Evidence map›Paper›PMID 42248452›Full record

ArticleThe Journal of biological chemistry2026

LeGenD: High-throughput N-glycan profiling using explainable AI and lectin profiling.

Haining Li, Angelo G Peralta, Sanne Schoffelen, Anders Holmgaard Hansen, Johnny Arnsdorf, Song-Min Schinn, Jonathan Skidmore, Biswa Choudhury, Frances Rocamora, Mousumi Paulchakrabarti and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of biological chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Haining LiDepartment of Bioengineering, University of California, San Diego, La Jolla, California, USA.
Angelo G PeraltaDepartment of Pediatrics, University of California, San Diego, La Jolla, California, USA.
Sanne SchoffelenNational Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark.
Anders Holmgaard HansenNational Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark.
Johnny ArnsdorfNational Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark.
Song-Min SchinnDepartment of Bioengineering, University of California, San Diego, La Jolla, California, USA.
Jonathan SkidmoreDepartment of Bioengineering, University of California, San Diego, La Jolla, California, USA; Department of Microbiology and Molecular Biology, Brigham Young University, Provo, Utah, USA.
Biswa ChoudhuryGlycobiology Research and Training Center, University of California, San Diego, La Jolla, California, USA.
Frances RocamoraDepartment of Pediatrics, University of California, San Diego, La Jolla, California, USA.
Mousumi PaulchakrabartiGlycobiology Research and Training Center, University of California, San Diego, La Jolla, California, USA.
Bjorn G VoldborgNational Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark.
Austin W T ChiangDepartment of Pediatrics, University of California, San Diego, La Jolla, California, USA; Immunology Center of Georgia, Augusta University, Augusta, Georgia, USA. Electronic address: auchiang@augusta.edu.
Nathan E LewisDepartment of Bioengineering, University of California, San Diego, La Jolla, California, USA; Department of Pediatrics, University of California, San Diego, La Jolla, California, USA; Center for Molecular Medicine, Complex Carbohydrate Research Center, and Department of Biochemistry and Molecular Biology, University of Georgia, Athens, Georgia, USA. Electronic address: natelewis@uga.edu.

Funding

Unraveling the mammalian secretory pathway through systems biology and algorithm developmentR35GM119850 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LEWIS, NATHAN ENOCH · 2016 to 2025
$4.3M
NIGMS NIH HHS R35 GM119850
6 · The paper itself

Abstract

Glycosylation affects many vital functions in organisms. Thus, their measurement is critical from basic science to biotechnology, including biopharmaceutical development and clinical diagnostics. However, the throughput and cost of conventional glycan analysis can be challenging. Lectins offer an alternative approach for analyzing glycans, but they only provide glycan epitopes and not full glycan structure information. To overcome these limitations, we developed Lectin to Glycoprofile ENhanced with Data-driven (LeGenD), a lectin and AI-based approach, to predict dominant N-glycan structures and determine their relative abundance on purified proteins based on lectin-binding patterns. We trained the LeGenD model on 309 glycoprofiles from 10 recombinant proteins, produced in 30 glycoengineered CHO cell lines. Independent test data showed that the dominant glycosylation patterns in a given protein can be effectively determined. Further analysis using SHapley Additive exPlanations helped to identify critical lectins for glycoprofile predictions. Thus, our LeGenD approach presents an alternative platform for analyzing protein glycosylation and could complement the existing toolkits used to study glycosylation.

Indexed as

Artificial IntelligenceHigh-Throughput Screening AssaysLectinsPolysaccharidesAnimalsCHO CellsCricetulusGlycomicsGlycosylationLectinsPolysaccharidesAIbiotechnologyglycobiologyglycomicsmachine learning

Identifiers

PMID42248452
PMCPMC13330679

What OpenQuestion holds

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Registered trials

None linked

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.