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Molecular Modeling of ABHD5 Structure and Ligand Recognition
Alpha/beta hydrolase domain-containing 5 (ABHD5), also termed CGI-58, is the key upstream activator of adipose triglyceride lipase (ATGL), which plays an essential role in lipid metabolism and energy storage. Mutations in ABHD5 disrupt lipolysis and are known to cause the Chanarin-Dorfman syndrome....
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9274090/ https://www.ncbi.nlm.nih.gov/pubmed/35836935 http://dx.doi.org/10.3389/fmolb.2022.935375 |
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author | Shahoei, Rezvan Pangeni, Susheel Sanders, Matthew A. Zhang, Huamei Mladenovic-Lucas, Ljiljana Roush, William R. Halvorsen, Geoff Kelly, Christopher V. Granneman, James G. Huang, Yu-ming M. |
author_facet | Shahoei, Rezvan Pangeni, Susheel Sanders, Matthew A. Zhang, Huamei Mladenovic-Lucas, Ljiljana Roush, William R. Halvorsen, Geoff Kelly, Christopher V. Granneman, James G. Huang, Yu-ming M. |
author_sort | Shahoei, Rezvan |
collection | PubMed |
description | Alpha/beta hydrolase domain-containing 5 (ABHD5), also termed CGI-58, is the key upstream activator of adipose triglyceride lipase (ATGL), which plays an essential role in lipid metabolism and energy storage. Mutations in ABHD5 disrupt lipolysis and are known to cause the Chanarin-Dorfman syndrome. Despite its importance, the structure of ABHD5 remains unknown. In this work, we combine computational and experimental methods to build a 3D structure of ABHD5. Multiple comparative and machine learning-based homology modeling methods are used to obtain possible models of ABHD5. The results from Gaussian accelerated molecular dynamics and experimental data of the apo models and their mutants are used to select the most likely model. Moreover, ensemble docking is performed on representative conformations of ABHD5 to reveal the binding mechanism of ABHD5 and a series of synthetic ligands. Our study suggests that the ABHD5 models created by deep learning-based methods are the best candidate structures for the ABHD5 protein. The mutations of E41, R116, and G328 disturb the hydrogen bonding network with nearby residues and suppress membrane targeting or ATGL activation. The simulations also reveal that the hydrophobic interactions are responsible for binding sulfonyl piperazine ligands to ABHD5. Our work provides fundamental insight into the structure of ABHD5 and its ligand-binding mode, which can be further applied to develop ABHD5 as a therapeutic target for metabolic disease and cancer. |
format | Online Article Text |
id | pubmed-9274090 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92740902022-07-13 Molecular Modeling of ABHD5 Structure and Ligand Recognition Shahoei, Rezvan Pangeni, Susheel Sanders, Matthew A. Zhang, Huamei Mladenovic-Lucas, Ljiljana Roush, William R. Halvorsen, Geoff Kelly, Christopher V. Granneman, James G. Huang, Yu-ming M. Front Mol Biosci Molecular Biosciences Alpha/beta hydrolase domain-containing 5 (ABHD5), also termed CGI-58, is the key upstream activator of adipose triglyceride lipase (ATGL), which plays an essential role in lipid metabolism and energy storage. Mutations in ABHD5 disrupt lipolysis and are known to cause the Chanarin-Dorfman syndrome. Despite its importance, the structure of ABHD5 remains unknown. In this work, we combine computational and experimental methods to build a 3D structure of ABHD5. Multiple comparative and machine learning-based homology modeling methods are used to obtain possible models of ABHD5. The results from Gaussian accelerated molecular dynamics and experimental data of the apo models and their mutants are used to select the most likely model. Moreover, ensemble docking is performed on representative conformations of ABHD5 to reveal the binding mechanism of ABHD5 and a series of synthetic ligands. Our study suggests that the ABHD5 models created by deep learning-based methods are the best candidate structures for the ABHD5 protein. The mutations of E41, R116, and G328 disturb the hydrogen bonding network with nearby residues and suppress membrane targeting or ATGL activation. The simulations also reveal that the hydrophobic interactions are responsible for binding sulfonyl piperazine ligands to ABHD5. Our work provides fundamental insight into the structure of ABHD5 and its ligand-binding mode, which can be further applied to develop ABHD5 as a therapeutic target for metabolic disease and cancer. Frontiers Media S.A. 2022-06-28 /pmc/articles/PMC9274090/ /pubmed/35836935 http://dx.doi.org/10.3389/fmolb.2022.935375 Text en Copyright © 2022 Shahoei, Pangeni, Sanders, Zhang, Mladenovic-Lucas, Roush, Halvorsen, Kelly, Granneman and Huang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Shahoei, Rezvan Pangeni, Susheel Sanders, Matthew A. Zhang, Huamei Mladenovic-Lucas, Ljiljana Roush, William R. Halvorsen, Geoff Kelly, Christopher V. Granneman, James G. Huang, Yu-ming M. Molecular Modeling of ABHD5 Structure and Ligand Recognition |
title | Molecular Modeling of ABHD5 Structure and Ligand Recognition |
title_full | Molecular Modeling of ABHD5 Structure and Ligand Recognition |
title_fullStr | Molecular Modeling of ABHD5 Structure and Ligand Recognition |
title_full_unstemmed | Molecular Modeling of ABHD5 Structure and Ligand Recognition |
title_short | Molecular Modeling of ABHD5 Structure and Ligand Recognition |
title_sort | molecular modeling of abhd5 structure and ligand recognition |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9274090/ https://www.ncbi.nlm.nih.gov/pubmed/35836935 http://dx.doi.org/10.3389/fmolb.2022.935375 |
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