Evidence map›Paper›PMID 41722567›Full record

ArticleCell systems2026

Identifying microbial protease allergens through protein language model-guided homology.

Kumar Thurimella, Elena Wu, Chenhao Li, Daniel B Graham, Róisín M Owens, Damian R Plichta, Caroline L Sokol, Ramnik J Xavier, Sergio Bacallado

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell systems, 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

9 authors.

Kumar ThurimellaBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, UK; School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Elena WuCenter for Immunology and Inflammatory Diseases, Division of Rheumatology, Allergy and Immunology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
Chenhao LiBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
Daniel B GrahamBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
Róisín M OwensDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, UK.
Damian R PlichtaBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA. Electronic address: damian@broadinstitute.org.
Caroline L SokolCenter for Immunology and Inflammatory Diseases, Division of Rheumatology, Allergy and Immunology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA. Electronic address: clsokol@mgh.harvard.edu.
Ramnik J XavierBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA. Electronic address: xavier@molbio.mgh.harvard.edu.
Sergio BacalladoDepartment of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge CB3 0WB, UK. Electronic address: sb2116@cam.ac.uk.

Funding

Pilot & Feasibility ProgramP30DK043351 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · 1991 to 2025
$10.1M
Neuroimmune Control of Allergic ImmunityR01AI151163 · MASSACHUSETTS GENERAL HOSPITAL · 2025 to 2025
$596k
NIAID NIH HHS R01 AI151163NIDDK NIH HHS P30 DK043351
6 · The paper itself

Abstract

Emerging research links the gut, skin, and oral microbiomes to allergies, with serine proteases (SPs) identified as potential allergens. This study leverages deep learning and pre-trained protein language models (pLMs) to uncover allergenic SPs in metagenomic data. First, we develop a model to identify the catalytic serine residue in serine hydrolases, demonstrating how pLMs capture structural information. Next, we create a deep learning framework to detect candidate SP allergens across gene catalogs, using the conserved catalytic triad to identify homologs in gut and oral sites despite low sequence identity. Our model predicts a putative SP allergen resembling V8 protease, a known trigger for protease-activated receptor 1. It also identifies a cysteine protease similar to Der f 1 from dust mites. Immunization with these proteases induced allergic responses, validating their allergenic potential experimentally. This approach uncovers candidate allergens beyond traditional methods, offering new targets for allergy research. A record of this paper's transparent peer review process is included in the supplemental information.

Indexed as

AllergensSerine ProteasesAnimalsHumansHypersensitivityMetagenomicsMicrobiotaAllergensSerine Proteasescatalytic triadcysteine protease allergensgut microbiomemetagenomicsoral microbiomeprotein language modelsserine protease allergens

Identifiers

PMID41722567
PMCPMC13015258

What Socratic holds

Textmetadata
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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 Socratic graph.