Cholesterol Hot Spot Automated Mapping Protocol (CHAMP): Identifying Cholesterol-Binding Hot Spots in Membrane Proteins.
- 1Department of Biomolecular Sciences, Università degli Studi di Urbino Carlo Bo, Via Ca' Le Suore, 2/4, Urbino61029, Italy.
- 2School of Pharmacy, University of Birmingham, Edgbaston, BirminghamB15 2TT, U.K.
Abstract
Biological membranes are dynamic and heterogeneous environments that actively influence protein structure and function. Cholesterol, in particular, has been widely reported to modulate membrane proteins through binding to specific interaction sites. However, the identification of these sites remains challenging due to the intrinsic complexity and dynamics of lipid bilayers, especially when multiple systems and membrane environments are considered. Here, we present CHAMP (Cholesterol hot spot automated mapping protocol), an automated Python-based workflow that combines coarse-grained molecular dynamics simulations with a dual analysis based on contact persistence and spatial density to identify cholesterol-binding hot spots at protein-membrane interfaces. The protocol requires limited user intervention and is designed to enable consistent and reproducible analyses across different targets. We validated CHAMP on the serotonin transporter and the cannabinoid receptor 1 (CB1), where it recovers known cholesterol-binding regions reported in experimental and computational studies. In the CB1 system, the method also identifies a potential additional interaction site. To assess the generality of the approach, we applied the protocol to the CB1 negative allosteric modulator Org27569, showing that it can capture relevant interaction hot spots beyond sterol molecules. Overall, CHAMP provides a practical framework for mapping interaction hot spots at protein-membrane interfaces and may support large-scale and comparative studies of membrane proteins. Limitations of the current implementation and possible extensions are discussed.
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BibTeX
@article{sotillonunez2026cholesterol,
title = {{Cholesterol Hot Spot Automated Mapping Protocol (CHAMP): Identifying Cholesterol-Binding Hot Spots in Membrane Proteins.}},
author = {David Sotillo-Núñez and Matteo Nardi Cesarini and Gian Marco Elisi and Mattia Bernetti and Giovanni Bottegoni},
journal = {Journal of chemical information and modeling},
year = {2026},
month = oct,
doi = {10.1021/acs.jcim.6c01419},
url = {https://europepmc.org/article/MED/42912926},
note = {CC BY},
}
RIS
TY - JOUR
TI - Cholesterol Hot Spot Automated Mapping Protocol (CHAMP): Identifying Cholesterol-Binding Hot Spots in Membrane Proteins.
AU - David Sotillo-Núñez
AU - Matteo Nardi Cesarini
AU - Gian Marco Elisi
AU - Mattia Bernetti
AU - Giovanni Bottegoni
PY - 2026
DA - 2026/10/01
JO - Journal of chemical information and modeling
DO - 10.1021/acs.jcim.6c01419
UR - https://europepmc.org/article/MED/42912926
AB - Biological membranes are dynamic and heterogeneous environments that actively influence protein structure and function. Cholesterol, in particular, has been widely reported to modulate membrane proteins through binding to specific interaction sites. However, the identification of these sites remains challenging due to the intrinsic complexity and dynamics of lipid bilayers, especially when multiple systems and membrane environments are considered. Here, we present CHAMP (Cholesterol hot spot automated mapping protocol), an automated Python-based workflow that combines coarse-grained molecular dynamics simulations with a dual analysis based on contact persistence and spatial density to identify cholesterol-binding hot spots at protein-membrane interfaces. The protocol requires limited user intervention and is designed to enable consistent and reproducible analyses across different targets. We validated CHAMP on the serotonin transporter and the cannabinoid receptor 1 (CB1), where it recovers known cholesterol-binding regions reported in experimental and computational studies. In the CB1 system, the method also identifies a potential additional interaction site. To assess the generality of the approach, we applied the protocol to the CB1 negative allosteric modulator Org27569, showing that it can capture relevant interaction hot spots beyond sterol molecules. Overall, CHAMP provides a practical framework for mapping interaction hot spots at protein-membrane interfaces and may support large-scale and comparative studies of membrane proteins. Limitations of the current implementation and possible extensions are discussed.
SN - 1549-9596
SN - 1549-960X
ER -