In Silico Modeling and Structural Analysis of Soluble Epoxide Hydrolase Inhibitors for Enhanced Therapeutic Design
Human soluble epoxide hydrolase (sEH), a dual-functioning homodimeric enzyme with hydrolase and phosphatase activities, is known for its pivotal role in the hydrolysis of epoxyeicosatrienoic acids. Inhibitors targeting sEH have shown promising potential in the treatment of various life-threatening d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/28/17/6379 |
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| author | Shuvam Sar Soumya Mitra Parthasarathi Panda Subhash C. Mandal Nilanjan Ghosh Amit Kumar Halder Maria Natalia D. S. Cordeiro |
| author_facet | Shuvam Sar Soumya Mitra Parthasarathi Panda Subhash C. Mandal Nilanjan Ghosh Amit Kumar Halder Maria Natalia D. S. Cordeiro |
| author_sort | Shuvam Sar |
| collection | DOAJ |
| description | Human soluble epoxide hydrolase (sEH), a dual-functioning homodimeric enzyme with hydrolase and phosphatase activities, is known for its pivotal role in the hydrolysis of epoxyeicosatrienoic acids. Inhibitors targeting sEH have shown promising potential in the treatment of various life-threatening diseases. In this study, we employed a range of in silico modeling approaches to investigate a diverse dataset of structurally distinct sEH inhibitors. Our primary aim was to develop predictive and validated models while gaining insights into the structural requirements necessary for achieving higher inhibitory potential. To accomplish this, we initially calculated molecular descriptors using nine different descriptor-calculating tools, coupled with stochastic and non-stochastic feature selection strategies, to identify the most statistically significant linear 2D-QSAR model. The resulting model highlighted the critical roles played by topological characteristics, 2D pharmacophore features, and specific physicochemical properties in enhancing inhibitory potential. In addition to conventional 2D-QSAR modeling, we implemented the Transformer-CNN methodology to develop QSAR models, enabling us to obtain structural interpretations based on the Layer-wise Relevance Propagation (LRP) algorithm. Moreover, a comprehensive 3D-QSAR analysis provided additional insights into the structural requirements of these compounds as potent sEH inhibitors. To validate the findings from the QSAR modeling studies, we performed molecular dynamics (MD) simulations using selected compounds from the dataset. The simulation results offered crucial insights into receptor–ligand interactions, supporting the predictions obtained from the QSAR models. Collectively, our work serves as an essential guideline for the rational design of novel sEH inhibitors with enhanced therapeutic potential. Importantly, all the in silico studies were performed using open-access tools to ensure reproducibility and accessibility. |
| first_indexed | 2024-03-10T23:16:24Z |
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| id | doaj.art-6fdc234186bb460092e9e2f7aaddae48 |
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| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-10T23:16:24Z |
| publishDate | 2023-08-01 |
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| series | Molecules |
| spelling | doaj.art-6fdc234186bb460092e9e2f7aaddae482023-11-19T08:35:09ZengMDPI AGMolecules1420-30492023-08-012817637910.3390/molecules28176379In Silico Modeling and Structural Analysis of Soluble Epoxide Hydrolase Inhibitors for Enhanced Therapeutic DesignShuvam Sar0Soumya Mitra1Parthasarathi Panda2Subhash C. Mandal3Nilanjan Ghosh4Amit Kumar Halder5Maria Natalia D. S. Cordeiro6Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, IndiaDepartment of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, IndiaDr. B. C. Roy College of Pharmacy and Allied Health Sciences, Campus Dr. Meghnad Saha Sarani, Durgapur 713206, IndiaDepartment of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, IndiaDepartment of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, IndiaDr. B. C. Roy College of Pharmacy and Allied Health Sciences, Campus Dr. Meghnad Saha Sarani, Durgapur 713206, IndiaLAQV@REQUIMTE—Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, PortugalHuman soluble epoxide hydrolase (sEH), a dual-functioning homodimeric enzyme with hydrolase and phosphatase activities, is known for its pivotal role in the hydrolysis of epoxyeicosatrienoic acids. Inhibitors targeting sEH have shown promising potential in the treatment of various life-threatening diseases. In this study, we employed a range of in silico modeling approaches to investigate a diverse dataset of structurally distinct sEH inhibitors. Our primary aim was to develop predictive and validated models while gaining insights into the structural requirements necessary for achieving higher inhibitory potential. To accomplish this, we initially calculated molecular descriptors using nine different descriptor-calculating tools, coupled with stochastic and non-stochastic feature selection strategies, to identify the most statistically significant linear 2D-QSAR model. The resulting model highlighted the critical roles played by topological characteristics, 2D pharmacophore features, and specific physicochemical properties in enhancing inhibitory potential. In addition to conventional 2D-QSAR modeling, we implemented the Transformer-CNN methodology to develop QSAR models, enabling us to obtain structural interpretations based on the Layer-wise Relevance Propagation (LRP) algorithm. Moreover, a comprehensive 3D-QSAR analysis provided additional insights into the structural requirements of these compounds as potent sEH inhibitors. To validate the findings from the QSAR modeling studies, we performed molecular dynamics (MD) simulations using selected compounds from the dataset. The simulation results offered crucial insights into receptor–ligand interactions, supporting the predictions obtained from the QSAR models. Collectively, our work serves as an essential guideline for the rational design of novel sEH inhibitors with enhanced therapeutic potential. Importantly, all the in silico studies were performed using open-access tools to ensure reproducibility and accessibility.https://www.mdpi.com/1420-3049/28/17/6379soluble epoxide hydrolaseQSARfeature selectionpharmacophoremolecular dynamicsTransformer-CNN |
| spellingShingle | Shuvam Sar Soumya Mitra Parthasarathi Panda Subhash C. Mandal Nilanjan Ghosh Amit Kumar Halder Maria Natalia D. S. Cordeiro In Silico Modeling and Structural Analysis of Soluble Epoxide Hydrolase Inhibitors for Enhanced Therapeutic Design Molecules soluble epoxide hydrolase QSAR feature selection pharmacophore molecular dynamics Transformer-CNN |
| title | In Silico Modeling and Structural Analysis of Soluble Epoxide Hydrolase Inhibitors for Enhanced Therapeutic Design |
| title_full | In Silico Modeling and Structural Analysis of Soluble Epoxide Hydrolase Inhibitors for Enhanced Therapeutic Design |
| title_fullStr | In Silico Modeling and Structural Analysis of Soluble Epoxide Hydrolase Inhibitors for Enhanced Therapeutic Design |
| title_full_unstemmed | In Silico Modeling and Structural Analysis of Soluble Epoxide Hydrolase Inhibitors for Enhanced Therapeutic Design |
| title_short | In Silico Modeling and Structural Analysis of Soluble Epoxide Hydrolase Inhibitors for Enhanced Therapeutic Design |
| title_sort | in silico modeling and structural analysis of soluble epoxide hydrolase inhibitors for enhanced therapeutic design |
| topic | soluble epoxide hydrolase QSAR feature selection pharmacophore molecular dynamics Transformer-CNN |
| url | https://www.mdpi.com/1420-3049/28/17/6379 |
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