@article{14734f7451444526967506270298b078,
title = "In silico identification of cholesterol binding motifs in the chemokine receptor CCR3",
abstract = "CC motif chemokine receptor 3 (CCR3) is a Class A G protein-coupled receptor (GPCR) mainly responsible for the cellular trafficking of eosinophils. As such, it plays key roles in inflammatory conditions, such as asthma and arthritis, and the metastasis of many deadly forms of cancer. However, little is known about how CCR3 functionally interacts with its bilayer environment. Here, we investigate cholesterol binding sites in silico through Coarse-Grained Molecular Dynamics (MD) and Pylipid analysis using an extensively validated homology model based on the crystal structure of CCR5. These simulations identified several cholesterol binding sites containing Cholesterol Recog-nition/Interaction Amino Acid Consensus motif (CRAC) and its inversion CARC motifs in CCR3. One such site, a CARC site in TM1, in conjunction with aliphatic residues in TM7, emerged as a candidate for future investigation based on the cholesterol residency time within the binding pocket. This site forms the core of a cholesterol binding site previously observed in computational studies of CCR2 and CCR5. Most importantly, these cholesterol binding sites are conserved in other chemokine receptors and may provide clues to cholesterol regulation mechanisms in this subfamily of Class A GPCRs.",
keywords = "CARC motif, CCR3, CRAC motif, Chemokine receptor, Cholesterol, GPCR, Molecular Dynamics, Pylipid",
author = "{van Aalst}, Evan and Jotham Koneri and Wylie, {Benjamin J.}",
note = "Funding Information: This research was funded by the National Institute of Health, grant number 1R35GM124979 (Maximizing Investigators? Research Award (MIRA) R35).We would like to thank Isaac Eason and Collin Borcik (Texas Tech University) for suggestions in experimental design and analysis and for donation of python data presentation scripts. The computational resources in this study were in part provided by the Texas Tech High Performance Computing Center. This study also made use of NMRbox: National Center for Biomolecular NMR Data Processing and Analysis, a Biomedical Technology Research Resource (BTRR), which is supported by NIH grant P41GM111135 (NIGMS). We acknowledge use of the GPCRdb (https://www.gpcrdb.org; accessed on 1 October 2020). Funding Information: Acknowledgments: We would like to thank Isaac Eason and Collin Borcik (Texas Tech University) for suggestions in experimental design and analysis and for donation of python data presentation scripts. The computational resources in this study were in part provided by the Texas Tech High Performance Computing Center. This study also made use of NMRbox: National Center for Biomolecular NMR Data Processing and Analysis, a Biomedical Technology Research Resource (BTRR), which is supported by NIH grant P41GM111135 (NIGMS). We acknowledge use of the GPCRdb (https://www.gpcrdb.org; accessed on 1 October 2020). Funding Information: Funding: This research was funded by the National Institute of Health, grant number 1R35GM124979 (Maximizing Investigators{\textquoteright} Research Award (MIRA) R35). Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2021",
month = aug,
doi = "10.3390/membranes11080570",
language = "English",
volume = "11",
journal = "Membranes",
issn = "2077-0375",
number = "8",
}