ArticleBio-protocol2025
Identifying and Characterizing Lipid-Binding Cavities in Lipid Transfer Proteins With CG-MD Simulations.
Article in Bio-protocol, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- In vitro methods for measuring lipid binding, transfer and competitive displacement reactions by peripheral membrane proteins.Methods in enzymology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding how lipids interact with lipid transfer proteins (LTPs) is essential for uncovering their molecular mechanisms. Yet, many available LTP structures, particularly those thought to function as membrane bridges, lack detailed information on where their native lipid ligands are located. Computational strategies, such as docking or AI-methods, offer a valuable alternative to overcome this gap, but their effectiveness is often restricted by the inherent flexibility of lipid molecules and the lack of large training sets with structures of proteins bound to lipids. To tackle this issue, we introduce a reproducible computational pipeline that uses unbiased coarse-grained molecular dynamics (CG-MD) simulations on a free and open-source software (GROMACS) with the Martini 3 force-field. Starting from a configuration of a lipid in bulk solvent, we run CG-MD simulations and observe spontaneous binding of the lipid to the protein. We show that this protocol reliably identifies lipid-binding pockets in LTPs and, unlike docking methods, suggests potential entry routes for lipid molecules with no a priori knowledge other than the protein's structure. We demonstrate the utility of this approach in investigating bridge LTPs whose internal lipid-binding positions remain unresolved. Altogether, our study provides a cost-effective, efficient, and accurate framework for mapping binding sites and entry pathways in diverse LTPs. Key features • Demonstrates the reliability of unbiased coarse-grain molecular dynamics (CG-MD) simulations with the Martini 3 force-field in identifying lipid-binding sites in lipid transfer proteins (LTPs). • The protocol is straightforward to replicate, relying solely on freely available open-source software. • Furthermore, it is computationally efficient, with most simulations completing within a few hours on a standalone GPU-accelerated workstation. • As input, the user only needs to include the structure of the protein and select the lipid type to test.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.