Evidence map›Paper›PMID 36580587›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2023

Differential dynamics and direct interaction of bound ligands with lipids in multidrug transporter ABCG2.

Ali Rasouli, Qin Yu, Sepehr Dehghani-Ghahnaviyeh, Po-Chao Wen, Julia Kowal, Kaspar P Locher, Emad Tajkhorshid

Open access · greenAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
4.1field-weighted citation impact, top 4% of its field
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

24 citing papers in PubMed, 39 citations in OpenAlex.

  1. Review
  2. Review
  3. The fourth-generation EGFR tyrosine kinase inhibitor BLU-945 resensitizes ABCG2-overexpressing multidrug-resistant non-small cell lung cancer cells to cytotoxic anticancer drugs.European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences · 2025
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  5. Review
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  7. Review
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  13. Review
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  16. Topological Learning Approach to Characterizing Biological Membranes.Journal of chemical information and modeling · 2024
    Article
  17. Review
  18. Article
  19. Article
  20. Topological Learning Approach to Characterizing Biological Membranes.bioRxiv : the preprint server for biology · 2023
    Article
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 at 2 institutions in 2 countries.

Ali RasouliTheoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, University of Illinois, Urbana, IL 61801.
Qin YuInstitute of Molecular Biology and Biophysics, Department of Biology, ETH Zürich, 8093, Zürich, Switzerland.ORCID 0000-0001-9265-632X
Sepehr Dehghani-GhahnaviyehTheoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, University of Illinois, Urbana, IL 61801.ORCID 0000-0001-6113-3438
Po-Chao WenTheoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, University of Illinois, Urbana, IL 61801.ORCID 0000-0002-6049-6904
Julia KowalInstitute of Molecular Biology and Biophysics, Department of Biology, ETH Zürich, 8093, Zürich, Switzerland.ORCID 0000-0003-4694-1896
Kaspar P LocherInstitute of Molecular Biology and Biophysics, Department of Biology, ETH Zürich, 8093, Zürich, Switzerland.ORCID 0000-0002-8207-2889
Emad TajkhorshidTheoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, University of Illinois, Urbana, IL 61801.ORCID 0000-0001-8434-1010
University of Illinois Urbana-Champaign · USETH Zurich · CH

Funding

WHOLE CELL SIMULATIONP41GM104601 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI TAJKHORSHID, EMAD · 2012 to 2021
$19.0M
Resource for Macromolecular Modeling and VisualizationR24GM145965 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI Emad Tajkhorshid · 2022 to 2026
$6.2M
Toolkit for High-Resolution Structure and Dynamics of Functional LipidsR01GM123455 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI MORRISSEY, JAMES H., RIENSTRA, CHAD M · 2016 to 2020
$5.0M
NIGMS NIH HHS P41 GM104601NIGMS NIH HHS R01 GM123455NIGMS NIH HHS R24 GM145965
6 · The paper itself

Abstract

ABCG2 is an ATP-binding cassette (ABC) transporter that extrudes a wide range of xenobiotics and drugs from the cell and contributes to multidrug resistance in cancer cells. Following our recent structural characterization of topotecan-bound ABCG2, here, we present cryo-EM structures of ABCG2 under turnover conditions in complex with a special modulator and slow substrate, tariquidar, in nanodiscs. The structures reveal that similar to topotecan, tariquidar induces two distinct ABCG2 conformations under turnover conditions (turnover-1 and turnover-2). μs-scale molecular dynamics simulations of drug-bound and apo ABCG2 in native-like lipid bilayers, in both topotecan- and tariquidar-bound states, characterize the ligand size as a major determinant of its binding stability. The simulations highlight direct lipid-drug interactions for the smaller topotecan, which exhibits a highly dynamic binding mode. In contrast, the larger tariquidar occupies most of the available volume in the binding pocket, thus leaving little space for lipids to enter the cavity and interact with it. Similarly, when simulating ABCG2 in the apo inward-open state, we also observe spontaneous penetration of phospholipids into the binding cavity. The captured phospholipid diffusion pathway into ABCG2 offers a putative general path to recruit any hydrophobic/amphiphilic substrates directly from the membrane. Our simulations also reveal that ABCG2 rejects cholesterol as a substrate, which is omnipresent in plasma membranes that contain ABCG2. At the same time, cholesterol is found to prohibit the penetration of phospholipids into ABCG2. These molecular findings have direct functional ramifications on ABCG2's function as a transporter.

Indexed as

Drug Resistance, MultipleTopotecanATP-Binding Cassette TransportersCholesterolDrug Resistance, NeoplasmLigandsPhospholipidsATP-Binding Cassette TransportersCholesterolLigandsPhospholipidsTopotecanABC transporterscryo-EMlipidsmembrane proteinsmolecular dynamics

Identifiers

PMID36580587
PMCPMC9910490
OpenAlexW4313332500

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.