Evidence map›Paper›PMID 41959461›Full record

ArticlebioRxiv : the preprint server for biology2026

High-throughput biochemical phenotyping of SHP2 variants reveals the molecular basis of diseases and allosteric drug inhibition.

Albert A Lee, Daniel A Mokhtari, Emily D Egan, Stephen C Blacklow, Daniel Herschlag, Polly M Fordyce

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

6 authors.

Albert A LeeDepartment of Genetics, Stanford University, Stanford, CA 94305.ORCID 0000-0002-7238-1274
Daniel A MokhtariDepartment of Biochemistry, Stanford University, Stanford, CA 94305.ORCID 0000-0003-4206-9471
Emily D EganDepartment of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Cambridge, MA 02115.
Stephen C BlacklowDepartment of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Cambridge, MA 02115.ORCID 0000-0002-6904-1981
Daniel HerschlagDepartment of Biochemistry, Stanford University, Stanford, CA 94305.ORCID 0000-0002-4685-1973
Polly M FordyceDepartment of Genetics, Stanford University, Stanford, CA 94305.ORCID 0000-0002-9505-0638

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Interpreting clinical and functional consequences of genetic variants remains challenging due to limited quantitative biochemical data at scale. We applied high-throughput microfluidic enzyme kinetics to profile 190 clinical variants of SHP2, a protein tyrosine phosphatase linked to developmental disorders and cancers. Through >300,000 reaction progress curves, we derived kinetic and thermodynamic parameters quantifying variant effects on catalysis, autoinhibition, stability, phosphopeptide binding, and drug responses. This multidimensional dataset reveals that dysregulated autoinhibition, rather than altered stability or catalysis, predominantly determines SHP2-associated pathogenesis. Thermodynamic modeling reveals that clinical-stage allosteric inhibitors preferentially stabilize a previously underappreciated, partially active conformation over the fully inactive state, leading to variant-dependent drug responses. Our high-throughput biochemical framework establishes a general approach to decipher the biochemical logic connecting protein variants to clinical outcomes.

Identifiers

PMID41959461
PMCPMC13060101

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.