Evidence map›Paper›PMID 42156670›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Characterization of Membrane Binding and Protein-Lipid Interactions at the Atomic Level with an Accelerated HMMM Model.

Hale S Hasdemir, Yupeng Li, Payam Kelich, Po-Chao Wen, Emad Tajkhorshid

Abstract read
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

5 authors.

Hale S HasdemirTheoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, 405 N Mathews Avenue, Urbana, 61801, IL, USA.
Yupeng LiTheoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, 405 N Mathews Avenue, Urbana, 61801, IL, USA.
Payam KelichTheoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, 405 N Mathews Avenue, Urbana, 61801, IL, USA.
Po-Chao WenTheoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, 405 N Mathews Avenue, Urbana, 61801, IL, USA.
Emad TajkhorshidTheoretical and Computational Biophysics Group, NIH Resource for Macromolecular Modeling and Visualization, Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, 405 N Mathews Avenue, Urbana, 61801, IL, USA. emad@illinois.edu.

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
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
Architecture, dynamics and regulation of erythrocyte ankyrin-1 complexesR01HL168178 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Oliver Biggs Clarke · 2023 to 2026
$2.4M
NHLBI NIH HHS R01 HL168178NIDDK NIH HHS R01 DK128315NIGMS NIH HHS P41 GM104601NIGMS NIH HHS R24 GM145965
6 · The paper itself

Abstract

Computational approaches, particularly molecular dynamics (MD) simulations, offer powerful tools for studying the membrane binding of peripheral membrane proteins (PMPs) and their interactions with lipids. However, the simulation of membranes at an atomic level is constrained by timescale limitations, further compounded by the slow diffusion of lipids in the membrane. The highly mobile membrane mimetic (HMMM) model was developed as one of the alternative membrane representations to overcome some of these limitations, particularly for investigating membrane binding of PMPs. By using short-tailed lipids, the HMMM model significantly enhances the lateral diffusion of lipids, thereby substantially accelerating membrane-associated phenomena. In this chapter, we outline best practices for setting up protein-HMMM systems and provide examples of relevant analyses. Ideally, one begins by thoroughly researching and understanding the protein of interest, including its cellular localization, the type of membrane it interacts with, and any biochemical evidence for structural changes upon membrane binding. Additional considerations include the quality of the available structures, as well as relevant posttranslational modifications, and involvement of ligands or cofactors. After constructing and equilibrating a representative full-length (FL) membrane with the desired lipid composition, it can be converted into an HMMM representation by using an in-house script provided in the SI materials or resources such as CHARMM-GUI. The PMP is then positioned on top of the HMMM membrane at a long enough distance from it and in different orientations to avoid biasing the membrane-binding process across multiple replicas with shuffled lipid arrangements. Following a careful equilibration protocol, one can capture spontaneous membrane-binding events for PMPs during production MD simulations. To quantify the membrane-binding events, one would first identify stable membrane-bound segments of the HMMM trajectories, which can be done by monitoring protein-membrane contacts. Analyzing these membrane-bound frames can then provide insights into membrane interaction hotspots, potential membrane-anchoring residues, the protein's selectivity for particular lipid types, and distinct membrane-bound poses that may be relevant to function or activation. Finally, it is crucial to verify the results from HMMM simulations by analyzing the findings after converting the protein-membrane system back to FL membranes, which can be done using an in-house script provided in the SI materials, and conducting additional simulations.

Indexed as

Cell MembraneLipid BilayersMembrane LipidsMembrane ProteinsMolecular Dynamics SimulationProtein BindingLipid BilayersMembrane LipidsMembrane ProteinsIntegral membrane proteinsLipid–protein interactionsMembranesMolecular dynamics simulationPeripheral membrane proteins

Identifiers

PMID42156670
PMCPMC13273228

What OpenQuestion holds

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Registered trials

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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.