Evidence map›Paper›PMID 41509325›Full record

ArticlebioRxiv : the preprint server for biology2025

Profiling tyrosine kinase substrate recognition using bacterial peptide display and deep sequencing.

Minhee Lee, Neel H Shah

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

2 authors.

Minhee LeeColumbia University, Department of Chemistry, New York, NY 10027.ORCID 0000-0002-7141-7351
Neel H ShahColumbia University, Department of Chemistry, New York, NY 10027.ORCID 0000-0002-1186-0626

Funding

Probing tyrosine phosphatase structure and functionR35GM138014 · NIGMS · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI SHAH, NEEL H · 2020 to 2024
$2.3M
NIGMS NIH HHS R35 GM138014
6 · The paper itself

Abstract

Tyrosine kinases control a wide range of cell signaling pathways that are central to human physiology, and they are dysregulated in a variety of human diseases, most notably cancers. Our understanding of tyrosine kinase biology hinges upon a clear delineation of their protein substrates. Thus, much effort has been invested into defining the substrate specificities of tyrosine kinases, which is partly driven by recognition of the amino acid sequences surrounding the phospho-acceptor tyrosine residues. Numerous methods have been developed to profile tyrosine kinase sequence recognition, and these approaches have collectively demonstrated that different tyrosine kinases have distinct substrate sequence preferences. Here, we describe one such method that combines bacterial peptide display and deep sequencing to study tyrosine kinase substrate preferences. Our approach enables rapid measurement of relative phosphorylation efficiencies for thousands of peptides simultaneously. The genetically-encoded nature of the peptide libraries used with this method allows for facile and cheap construction of libraries tailored to answer a variety questions. Notably, our approach is compatible with genetic code expansion via Amber codon suppression, which allows for the construction and screening of libraries containing non-canonical amino acids. Importantly, results from this assay correlate strongly with quantitative measurements of enzyme kinetics, they corroborate previously reported tyrosine kinase substrate preferences, and they can reveal new insights into tyrosine kinase substrate specificity.

Indexed as

bacterial displaydeep sequencingKinasessubstrate recognition

Identifiers

PMID41509325
PMCPMC12776416

What OpenQuestion holds

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LicenceCC BY
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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.