Evidence map›Paper›PMID 42221021›Full record

ArticleMagnetic resonance letters2026

Fragment-based discovery of TopBP1 inhibitors integrated with AI-driven molecular docking.

Wenlong Chen, Song Zhang, Zihuan Li, Lei Wang, Jiahai Zhang, Jia Gao, Ke Ruan

Abstract read
In one paragraph

Article in Magnetic resonance letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. The perspective of artificial intelligence for NMR.Magnetic resonance letters · 2027
    Review
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.

Wenlong ChenThe First Affiliated Hospital & School of Life Sciences, Ministry of Education Key Laboratory for Membrane-less Organelles & Cellular Dynamics, Hefei National Research Center for Interdisciplinary Sciences at the Microscale, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China.
Song ZhangSchool of Network & Communication Engineering, Chengdu Technological University, Chengdu, 611730, China.
Zihuan LiThe First Affiliated Hospital & School of Life Sciences, Ministry of Education Key Laboratory for Membrane-less Organelles & Cellular Dynamics, Hefei National Research Center for Interdisciplinary Sciences at the Microscale, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China.
Lei WangThe First Affiliated Hospital & School of Life Sciences, Ministry of Education Key Laboratory for Membrane-less Organelles & Cellular Dynamics, Hefei National Research Center for Interdisciplinary Sciences at the Microscale, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China.
Jiahai ZhangThe First Affiliated Hospital & School of Life Sciences, Ministry of Education Key Laboratory for Membrane-less Organelles & Cellular Dynamics, Hefei National Research Center for Interdisciplinary Sciences at the Microscale, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China.
Jia GaoThe First Affiliated Hospital & School of Life Sciences, Ministry of Education Key Laboratory for Membrane-less Organelles & Cellular Dynamics, Hefei National Research Center for Interdisciplinary Sciences at the Microscale, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China.
Ke RuanThe First Affiliated Hospital & School of Life Sciences, Ministry of Education Key Laboratory for Membrane-less Organelles & Cellular Dynamics, Hefei National Research Center for Interdisciplinary Sciences at the Microscale, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA topoisomerase IIβ-binding protein 1 (TopBP1) converges on multiple oncogenic pathways, e.g., Rb, p53, and PI3K/Akt, which are hallmarks of most cancer progression. However, the lack of structural studies makes the underlying mechanism of action of TopBP1 inhibitors promiscuous. Herein, we employ a fragment-based screening approach to identify four small-molecule ligands against the TopBP1 BRCT7-8 domain: adamantane acetic acid (ADA), zaltoprofen, diclofenac sodium, and quinine. The dissociation constants of these compounds are determined by nuclear magnetic resonance (NMR) chemical shift perturbations, which suggests the former three hits bind to the same site, while quinine binds to a distinct site. The binding models of these four hits are further predicted by the deep learning model Chai-1. The complex crystal structure of the TopBP1 BRCT7-8 domain demonstrates ADA as an orthosteric inhibitor of TopBP1, as further validated by its binding to TopBP1 in competition with the native substrate BACH1. Our NMR screening nominates orthosteric inhibitors, as well as quinine, a potential allosteric inhibitor. It shall also facilitate following structure-based design of more potent allosteric and orthosteric inhibitors of TopBP1.

Indexed as

BRCT domainComplex structureDNA topoisomerase IIβ binding protein 1Fragment-based screening

Identifiers

PMID42221021
PMCPMC13216829

What OpenQuestion holds

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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 OpenQuestion graph.