Review in The journal of physical chemistry. B, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
0numbers the graph read from it
0cells of the map it votes in
4citing 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.
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
8 authors.
Shashank PantTheoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States.ORCID 0000-0002-8222-3616
Sepehr Dehghani-GhahnaviyehTheoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States.ORCID 0000-0001-6113-3438
Noah TrebeschTheoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States.ORCID 0000-0001-5536-4862
Ali RasouliTheoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States.
Tianle ChenTheoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States.ORCID 0000-0001-6581-8012
Karan KapoorTheoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States.
Po-Chao WenTheoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States.ORCID 0000-0002-6049-6904
Emad TajkhorshidTheoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, Department of Biochemistry, and Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States.ORCID 0000-0001-8434-1010
Funding
WHOLE CELL SIMULATIONP41GM104601 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI SCHULTEN, KLAUS · 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
Molecular mechanism of Na+ -coupled HCO3- transporters: transport of CO3= and CO2R01DK128315 · NIDDK · CASE WESTERN RESERVE UNIVERSITY · PI BORON, WALTER F, TAJKHORSHID, EMAD · 2021 to 2024
$2.7M
Dynamics and mechanism of sodium-dependent carboxylate transportersR01DK135088 · NIDDK · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Ruben L Gonzalez, Emad Tajkhorshid · 2023 to 2026
$2.6M
Structural dynamics of sphingosine-1-phosphate transporters as key therapeutic targets for immune system modulation and cancerR01GM145783 · NIGMS · SAINT LOUIS UNIVERSITY · PI Reza Dastvan · 2023 to 2026
$1.6M
Dynamic changes in PIP2 binding sites and their impact on axonal targeting and function of epilepsy-associated KCNQ/Kv7 channelsR01NS126584 · NINDS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI Hee Jung Chung · 2023 to 2026
Membrane transporters are integral membrane proteins that act as gatekeepers of the cell, controlling fundamental processes such as recruitment of nutrients and expulsion of waste material. At a basic level, transporters operate using the "alternating access model," in which transported substances are accessible from only one side of the membrane at a time. This model usually involves large-scale structural changes in the transporter, which often cannot be captured using unbiased, conventional molecular simulation techniques. In this article, we provide an overview of some of the major simulation techniques that have been applied to characterize the structural dynamics and energetics involved in the transition of membrane transporters between their functional states. After briefly introducing each technique, we discuss some of their advantages and limitations and provide some recent examples of their application to membrane transporters.
Indexed as
Membrane Transport ProteinsMolecular Dynamics SimulationProtein ConformationThermodynamicsMembrane Transport Proteins
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.
Dissecting Large-Scale Structural Transitions in Membrane Transporters Using Advanced Simulation Technologies. · full record | OpenQuestion