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Chemokine Receptors—Structure-Based Virtual Screening Assisted by Machine Learning

Chemokines modulate the immune response by regulating the migration of immune cells. They are also known to participate in such processes as cell–cell adhesion, allograft rejection, and angiogenesis. Chemokines interact with two different subfamilies of G protein-coupled receptors: conventional chem...

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Autores principales: Dragan, Paulina, Merski, Matthew, Wiśniewski, Szymon, Sanmukh, Swapnil Ganesh, Latek, Dorota
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9965785/
https://www.ncbi.nlm.nih.gov/pubmed/36839838
http://dx.doi.org/10.3390/pharmaceutics15020516
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author Dragan, Paulina
Merski, Matthew
Wiśniewski, Szymon
Sanmukh, Swapnil Ganesh
Latek, Dorota
author_facet Dragan, Paulina
Merski, Matthew
Wiśniewski, Szymon
Sanmukh, Swapnil Ganesh
Latek, Dorota
author_sort Dragan, Paulina
collection PubMed
description Chemokines modulate the immune response by regulating the migration of immune cells. They are also known to participate in such processes as cell–cell adhesion, allograft rejection, and angiogenesis. Chemokines interact with two different subfamilies of G protein-coupled receptors: conventional chemokine receptors and atypical chemokine receptors. Here, we focused on the former one which has been linked to many inflammatory diseases, including: multiple sclerosis, asthma, nephritis, and rheumatoid arthritis. Available crystal and cryo-EM structures and homology models of six chemokine receptors (CCR1 to CCR6) were described and tested in terms of their usefulness in structure-based drug design. As a result of structure-based virtual screening for CCR2 and CCR3, several new active compounds were proposed. Known inhibitors of CCR1 to CCR6, acquired from ChEMBL, were used as training sets for two machine learning algorithms in ligand-based drug design. Performance of LightGBM was compared with a sequential Keras/TensorFlow model of neural network for these diverse datasets. A combination of structure-based virtual screening with machine learning allowed to propose several active ligands for CCR2 and CCR3 with two distinct compounds predicted as CCR3 actives by all three tested methods: Glide, Keras/TensorFlow NN, and LightGBM. In addition, the performance of these three methods in the prediction of the CCR2/CCR3 receptor subtype selectivity was assessed.
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spelling pubmed-99657852023-02-26 Chemokine Receptors—Structure-Based Virtual Screening Assisted by Machine Learning Dragan, Paulina Merski, Matthew Wiśniewski, Szymon Sanmukh, Swapnil Ganesh Latek, Dorota Pharmaceutics Article Chemokines modulate the immune response by regulating the migration of immune cells. They are also known to participate in such processes as cell–cell adhesion, allograft rejection, and angiogenesis. Chemokines interact with two different subfamilies of G protein-coupled receptors: conventional chemokine receptors and atypical chemokine receptors. Here, we focused on the former one which has been linked to many inflammatory diseases, including: multiple sclerosis, asthma, nephritis, and rheumatoid arthritis. Available crystal and cryo-EM structures and homology models of six chemokine receptors (CCR1 to CCR6) were described and tested in terms of their usefulness in structure-based drug design. As a result of structure-based virtual screening for CCR2 and CCR3, several new active compounds were proposed. Known inhibitors of CCR1 to CCR6, acquired from ChEMBL, were used as training sets for two machine learning algorithms in ligand-based drug design. Performance of LightGBM was compared with a sequential Keras/TensorFlow model of neural network for these diverse datasets. A combination of structure-based virtual screening with machine learning allowed to propose several active ligands for CCR2 and CCR3 with two distinct compounds predicted as CCR3 actives by all three tested methods: Glide, Keras/TensorFlow NN, and LightGBM. In addition, the performance of these three methods in the prediction of the CCR2/CCR3 receptor subtype selectivity was assessed. MDPI 2023-02-03 /pmc/articles/PMC9965785/ /pubmed/36839838 http://dx.doi.org/10.3390/pharmaceutics15020516 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dragan, Paulina
Merski, Matthew
Wiśniewski, Szymon
Sanmukh, Swapnil Ganesh
Latek, Dorota
Chemokine Receptors—Structure-Based Virtual Screening Assisted by Machine Learning
title Chemokine Receptors—Structure-Based Virtual Screening Assisted by Machine Learning
title_full Chemokine Receptors—Structure-Based Virtual Screening Assisted by Machine Learning
title_fullStr Chemokine Receptors—Structure-Based Virtual Screening Assisted by Machine Learning
title_full_unstemmed Chemokine Receptors—Structure-Based Virtual Screening Assisted by Machine Learning
title_short Chemokine Receptors—Structure-Based Virtual Screening Assisted by Machine Learning
title_sort chemokine receptors—structure-based virtual screening assisted by machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9965785/
https://www.ncbi.nlm.nih.gov/pubmed/36839838
http://dx.doi.org/10.3390/pharmaceutics15020516
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