Evidence map›Paper›PMID 39763864›Full record

ArticlebioRxiv : the preprint server for biology2024

Enhanced identification of small molecules binding to hnRNPA1 via cryptic pockets mapping coupled with X-Ray fragment screening.

Louise Dunnett, Sayan Das, Vincenzo Venditti, Filippo Prischi

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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

5 · Who and what money

Authors and funding

4 authors.

Louise DunnettDiamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot OX11 0QX, UK.ORCID 0000-0001-6710-6959
Sayan DasDepartment of Chemistry, Iowa State University, Ames, Iowa 50011, United States.
Vincenzo VendittiDepartment of Chemistry, Iowa State University, Ames, Iowa 50011, United States.ORCID 0000-0001-8734-0400
Filippo PrischiRandall Centre for Cell and Molecular Biophysics, King's College London, London, SE1 1UL, UK.ORCID 0000-0003-2107-938X

Funding

Atomic-level characterization of self-regulatory mechanisms in large multidomain enzymesR35GM133488 · NIGMS · IOWA STATE UNIVERSITY · PI Vincenzo Venditti · 2019 to 2026
$2.9M
NIGMS NIH HHS R35 GM133488
6 · The paper itself

Abstract

The human heterogeneous nuclear ribonucleoprotein (hnRNP) A1 is a prototypical RNA-binding protein essential in regulating a wide range of post-transcriptional events in cells. As a multifunctional protein with a key role in RNA metabolism, deregulation of its functions has been linked to neurodegenerative diseases, tumour aggressiveness and chemoresistance, which has fuelled efforts to develop novel therapeutics that modulates its RNA binding activities. Here, using a combination of Molecular Dynamics (MD) simulations and graph neural network pockets predictions, we showed that hnRNPA1 N-terminal RNA binding domain (UP1) contains several cryptic pockets capable of binding small molecules. To identify chemical entities for development of potent drug candidates and experimentally validate identified druggable hotspots, we carried out a large fragment screening on UP1 protein crystals. Our screen identified 36 hits which extensively samples UP1 functional regions involved in RNA recognition and binding, as well as mapping hotspots onto novel protein interaction surfaces. We observed a wide range of ligand-induced conformational variation, by stabilisation of dynamic protein regions. Our high-resolution structures, the first of an hnRNP in complex with a fragment or small molecule, provides rapid routes for the rational development of a range of different inhibitors and chemical tools for studying molecular mechanisms of hnRNPA1 mediated splicing regulation.

Identifiers

PMID39763864
PMCPMC11702612

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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.