Evidence map›Paper›PMID 35084820›Full record

ArticleACS chemical biology2022

A Useful Guide to Lectin Binding: Machine-Learning Directed Annotation of 57 Unique Lectin Specificities.

Daniel Bojar, Lawrence Meche, Guanmin Meng, William Eng, David F Smith, Richard D Cummings, Lara K Mahal

Open access · hybridAbstract read
In one paragraph

Article in ACS chemical biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 209 papers.

0numbers the graph read from it
0cells of the map it votes in
209citing papers in PubMed
28.9field-weighted citation impact, top 1% of its field
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

209 citing papers in PubMed, 353 citations in OpenAlex.

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  13. Airway macrophage specific glycocalyx expression and remodeling following viral infection.Matrix biology : journal of the International Society for Matrix Biology · 2026
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149 more citing papers are in PubMed but not listed here.

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

7 authors at 5 institutions in 3 countries.

Daniel BojarDepartment of Chemistry and Molecular Biology and Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden 405 30.
Lawrence MecheBiomedical Chemistry Institute, Department of Chemistry, New York University, 100 Washington Square East, Room 1001, New York, New York 10003, United States.
Guanmin MengDepartment of Chemistry, University of Alberta, Edmonton, Canada, T6G 2G2.
William EngBiomedical Chemistry Institute, Department of Chemistry, New York University, 100 Washington Square East, Room 1001, New York, New York 10003, United States.
David F SmithDepartment of Biochemistry, Glycomics Center, School of Medicine, Emory University, Atlanta, Georgia 30322, United States.
Richard D CummingsDepartment of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 02115, United States.ORCID 0000-0002-8918-5034
Lara K MahalBiomedical Chemistry Institute, Department of Chemistry, New York University, 100 Washington Square East, Room 1001, New York, New York 10003, United States.ORCID 0000-0003-4791-8524
New York University · USUniversity of Alberta · CABeth Israel Deaconess Medical Center · USEmory University · USUniversity of Gothenburg · SE

Funding

Structural Basis of Immune Recognition of Glycosylated AntigensU54GM062116 · NIGMS · SCRIPPS RESEARCH INSTITUTE, THE · PI PAULSON, JAMES C · 2001 to 2010
$80.3M
COVID Supplement - COMPONENT A OF THE COLLABORATIVE INFLUENZA VACCINE INNOVATION CENTERS (CIVICS) PROGRAM TO DESIGN AND EVALUATE INNOVATIVE INFLUENZA VACCINE APPROACHES,75N93019C00052 · NIAID · UNIVERSITY OF GEORGIA · PI ROSS, TED · 2019 to 2025
$74.6M
Overall Priority Score P41GM103694 · NIGMS · EMORY UNIVERSITY · PI CUMMINGS, RICHARD D · 2013 to 2018
$6.9M
Protein-Glycan Interaction Resource at the National Center for Functional Glycomics (NCFG)R24GM137763 · NIGMS · BETH ISRAEL DEACONESS MEDICAL CENTER · PI RICHARD D CUMMINGS · 2020 to 2026
$5.8M
NIAID NIH HHS 75N93019C00052NIGMS NIH HHS P41 GM103694NIGMS NIH HHS R24 GM137763NIGMS NIH HHS U54 GM062116
6 · The paper itself

Abstract

Glycans are critical to every facet of biology and medicine, from viral infections to embryogenesis. Tools to study glycans are rapidly evolving; however, the majority of our knowledge is deeply dependent on binding by glycan binding proteins (e.g., lectins). The specificities of lectins, which are often naturally isolated proteins, have not been well-defined, making it difficult to leverage their full potential for glycan analysis. Herein, we use a combination of machine learning algorithms and expert annotation to define lectin specificity for this important probe set. Our analysis uses comprehensive glycan microarray analysis of commercially available lectins we obtained using version 5.0 of the Consortium for Functional Glycomics glycan microarray (CFGv5). This data set was made public in 2011. We report the creation of this data set and its use in large-scale evaluation of lectin-glycan binding behaviors. Our motif analysis was performed by integrating 68 manually defined glycan features with systematic probing of computational rules for significant binding motifs using mono- and disaccharides and linkages. Combining machine learning with manual annotation, we create a detailed interpretation of glycan-binding specificity for 57 unique lectins, categorized by their major binding motifs: mannose, complex-type

Indexed as

FucoseLectinsMachine LearningMicroarray AnalysisPolysaccharidesFucoseLectinsPolysaccharides

Identifiers

PMID35084820
PMCPMC9679999
OpenAlexW4220918884

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.