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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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Pharmaceutics |
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| Online Access: | https://www.mdpi.com/1999-4923/15/2/516 |
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| author | Paulina Dragan Matthew Merski Szymon Wiśniewski Swapnil Ganesh Sanmukh Dorota Latek |
| author_facet | Paulina Dragan Matthew Merski Szymon Wiśniewski Swapnil Ganesh Sanmukh Dorota Latek |
| author_sort | Paulina Dragan |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T08:17:28Z |
| format | Article |
| id | doaj.art-ec6ca2e339d6461eb3f1f458c1bfe551 |
| institution | Directory Open Access Journal |
| issn | 1999-4923 |
| language | English |
| last_indexed | 2024-03-11T08:17:28Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceutics |
| spelling | doaj.art-ec6ca2e339d6461eb3f1f458c1bfe5512023-11-16T22:41:00ZengMDPI AGPharmaceutics1999-49232023-02-0115251610.3390/pharmaceutics15020516Chemokine Receptors—Structure-Based Virtual Screening Assisted by Machine LearningPaulina Dragan0Matthew Merski1Szymon Wiśniewski2Swapnil Ganesh Sanmukh3Dorota Latek4Faculty of Chemistry, University of Warsaw, 02-093 Warsaw, PolandFaculty of Chemistry, University of Warsaw, 02-093 Warsaw, PolandFaculty of Chemistry, University of Warsaw, 02-093 Warsaw, PolandFaculty of Chemistry, University of Warsaw, 02-093 Warsaw, PolandFaculty of Chemistry, University of Warsaw, 02-093 Warsaw, PolandChemokines 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.https://www.mdpi.com/1999-4923/15/2/516cheminformaticsmachine learningneural networkgradient-boosting machinemolecular dockingvirtual screening |
| spellingShingle | Paulina Dragan Matthew Merski Szymon Wiśniewski Swapnil Ganesh Sanmukh Dorota Latek Chemokine Receptors—Structure-Based Virtual Screening Assisted by Machine Learning Pharmaceutics cheminformatics machine learning neural network gradient-boosting machine molecular docking virtual screening |
| 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 | cheminformatics machine learning neural network gradient-boosting machine molecular docking virtual screening |
| url | https://www.mdpi.com/1999-4923/15/2/516 |
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