Evaluation of Docking Machine Learning and Molecular Dynamics Methodologies for DNA-Ligand Systems
DNA is a molecular target for the treatment of several diseases, including cancer, but there are few docking methodologies exploring the interactions between nucleic acids with DNA intercalating agents. Different docking methodologies, such as AutoDock Vina, DOCK 6, and Consensus, implemented into M...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Pharmaceuticals |
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| Online Access: | https://www.mdpi.com/1424-8247/15/2/132 |
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| author | Tiago Alves de Oliveira Lucas Rolim Medaglia Eduardo Habib Bechelane Maia Letícia Cristina Assis Paulo Batista de Carvalho Alisson Marques da Silva Alex Gutterres Taranto |
| author_facet | Tiago Alves de Oliveira Lucas Rolim Medaglia Eduardo Habib Bechelane Maia Letícia Cristina Assis Paulo Batista de Carvalho Alisson Marques da Silva Alex Gutterres Taranto |
| author_sort | Tiago Alves de Oliveira |
| collection | DOAJ |
| description | DNA is a molecular target for the treatment of several diseases, including cancer, but there are few docking methodologies exploring the interactions between nucleic acids with DNA intercalating agents. Different docking methodologies, such as AutoDock Vina, DOCK 6, and Consensus, implemented into Molecular Architect (MolAr), were evaluated for their ability to analyze those interactions, considering visual inspection, redocking, and ROC curve. Ligands were refined by Parametric Method 7 (PM7), and ligands and decoys were docked into the minor DNA groove (PDB code: 1VZK). As a result, the area under the ROC curve (AUC-ROC) was 0.98, 0.88, and 0.99 for AutoDock Vina, DOCK 6, and Consensus methodologies, respectively. In addition, we proposed a machine learning model to determine the experimental ∆T<sub>m</sub> value, which found a 0.84 R<sup>2</sup> score. Finally, the selected ligands mono imidazole lexitropsin (<b>42</b>), netropsin (<b>45</b>), and <i>N</i>,<i>N</i>′-(1H-pyrrole-2,5-diyldi-4,1-phenylene)dibenzenecarboximidamide (<b>51</b>) were submitted to Molecular Dynamic Simulations (MD) through NAMD software to evaluate their equilibrium binding pose into the groove. In conclusion, the use of MolAr improves the docking results obtained with other methodologies, is a suitable methodology to use in the DNA system and was proven to be a valuable tool to estimate the ∆T<sub>m</sub> experimental values of DNA intercalating agents. |
| first_indexed | 2024-03-09T21:16:10Z |
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| institution | Directory Open Access Journal |
| issn | 1424-8247 |
| language | English |
| last_indexed | 2024-03-09T21:16:10Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceuticals |
| spelling | doaj.art-adfe93ce80514a20b6cc6c00e53c21ca2023-11-23T21:33:31ZengMDPI AGPharmaceuticals1424-82472022-01-0115213210.3390/ph15020132Evaluation of Docking Machine Learning and Molecular Dynamics Methodologies for DNA-Ligand SystemsTiago Alves de Oliveira0Lucas Rolim Medaglia1Eduardo Habib Bechelane Maia2Letícia Cristina Assis3Paulo Batista de Carvalho4Alisson Marques da Silva5Alex Gutterres Taranto6Department of Bioengineering, Federal University of Sao Joao del-Rei, Praça Dom Helvecio, 74, Fabricas, Sao Joao del-Rei 36301-1601, MG, BrazilDepartment of Bioengineering, Federal University of Sao Joao del-Rei, Praça Dom Helvecio, 74, Fabricas, Sao Joao del-Rei 36301-1601, MG, BrazilFederal Center for Technological Education of Minas Gerais, Department of Informatics, Management and Design, CEFET MG, Campus Divinopolis, Rua Alvares de Azevedo, 400, Bela Vista, Divinopolis 35503-822, MG, BrazilDepartment of Bioengineering, Federal University of Sao Joao del-Rei, Praça Dom Helvecio, 74, Fabricas, Sao Joao del-Rei 36301-1601, MG, BrazilFeik School of Pharmacy, University of the Incarnate Word, 4301 Broadway, San Antonio, TX 78209, USAFederal Center for Technological Education of Minas Gerais, Department of Informatics, Management and Design, CEFET MG, Campus Divinopolis, Rua Alvares de Azevedo, 400, Bela Vista, Divinopolis 35503-822, MG, BrazilDepartment of Bioengineering, Federal University of Sao Joao del-Rei, Praça Dom Helvecio, 74, Fabricas, Sao Joao del-Rei 36301-1601, MG, BrazilDNA is a molecular target for the treatment of several diseases, including cancer, but there are few docking methodologies exploring the interactions between nucleic acids with DNA intercalating agents. Different docking methodologies, such as AutoDock Vina, DOCK 6, and Consensus, implemented into Molecular Architect (MolAr), were evaluated for their ability to analyze those interactions, considering visual inspection, redocking, and ROC curve. Ligands were refined by Parametric Method 7 (PM7), and ligands and decoys were docked into the minor DNA groove (PDB code: 1VZK). As a result, the area under the ROC curve (AUC-ROC) was 0.98, 0.88, and 0.99 for AutoDock Vina, DOCK 6, and Consensus methodologies, respectively. In addition, we proposed a machine learning model to determine the experimental ∆T<sub>m</sub> value, which found a 0.84 R<sup>2</sup> score. Finally, the selected ligands mono imidazole lexitropsin (<b>42</b>), netropsin (<b>45</b>), and <i>N</i>,<i>N</i>′-(1H-pyrrole-2,5-diyldi-4,1-phenylene)dibenzenecarboximidamide (<b>51</b>) were submitted to Molecular Dynamic Simulations (MD) through NAMD software to evaluate their equilibrium binding pose into the groove. In conclusion, the use of MolAr improves the docking results obtained with other methodologies, is a suitable methodology to use in the DNA system and was proven to be a valuable tool to estimate the ∆T<sub>m</sub> experimental values of DNA intercalating agents.https://www.mdpi.com/1424-8247/15/2/132computer drug designmolecular dockingmolecular dynamic simulationvirtual screeningMolArDNA intercalating agents |
| spellingShingle | Tiago Alves de Oliveira Lucas Rolim Medaglia Eduardo Habib Bechelane Maia Letícia Cristina Assis Paulo Batista de Carvalho Alisson Marques da Silva Alex Gutterres Taranto Evaluation of Docking Machine Learning and Molecular Dynamics Methodologies for DNA-Ligand Systems Pharmaceuticals computer drug design molecular docking molecular dynamic simulation virtual screening MolAr DNA intercalating agents |
| title | Evaluation of Docking Machine Learning and Molecular Dynamics Methodologies for DNA-Ligand Systems |
| title_full | Evaluation of Docking Machine Learning and Molecular Dynamics Methodologies for DNA-Ligand Systems |
| title_fullStr | Evaluation of Docking Machine Learning and Molecular Dynamics Methodologies for DNA-Ligand Systems |
| title_full_unstemmed | Evaluation of Docking Machine Learning and Molecular Dynamics Methodologies for DNA-Ligand Systems |
| title_short | Evaluation of Docking Machine Learning and Molecular Dynamics Methodologies for DNA-Ligand Systems |
| title_sort | evaluation of docking machine learning and molecular dynamics methodologies for dna ligand systems |
| topic | computer drug design molecular docking molecular dynamic simulation virtual screening MolAr DNA intercalating agents |
| url | https://www.mdpi.com/1424-8247/15/2/132 |
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