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Fragment-based discovery of new potential DNMT1 inhibitors integrating multiple pharmacophore modeling, 3D-QSAR, virtual screening, molecular docking, ADME, and molecular dynamics simulation approaches

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Abstract

DNA methyl transferases (DNMTs) are one of the crucial epigenetic modulators associated with a wide variety of cancer conditions. Among the DNMT isoforms, DNMT1 is correlated with bladder, pancreatic, and breast cancer, as well as acute myeloid leukemia and esophagus squamous cell carcinoma. Therefore, the inhibition of DNMT1 could be an attractive target for combating cancers and other metabolic disorders. The disadvantages of the existing nucleoside and non-nucleoside DNMT1 inhibitors are the main motive for the discovery of novel promising inhibitors. Here, pharmacophore modeling, 3D-QSAR, and e-pharmacophore modeling of DNMT1 inhibitors were performed for the large fragment database screening. The resulting fragments with high dock scores were combined into molecules. The current study revealed several constitutional pharmacophoric features that can be essential for selective DNMT1 inhibition. The fragment docking and virtual screening identified 10 final hit molecules that exhibited good binding affinities in terms of docking score, binding free energies, and acceptable ADME properties. Also, the modified lead molecules (GL1b and GL2b) designed in this study showed effective binding with DNMT1 confirmed by their docking scores, binding free energies, 3D-QSAR predicted activities and acceptable drug-like properties. The MD simulation studies also suggested that leads (GL1b and GL2b) formed stable complexes with DNMT1. Therefore, the findings of this study can provide effective information for the development/identification of novel DNMT1 inhibitors as effective anticancer agents.

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Acknowledgements

The present work is supported by BUILDER grant from the Department of Biotechnology (DBT), India to BITS-Pilani, Hyderabad Campus (Grant No. BT/INF/22/SP42551/2021 dated 22.07.2021). Author L. Goverdhan sincerely thanks DBT-INDIA for the financial assistance as Research Associate-II under the DBT-BUILDER project. The authors are thankful to the High-Performance Computing (HPC) facility at BITS-Hyderabad Campus for providing access to run Schrodinger Software. The authors are thankful to Dr. K. Koushik, Senior Scientist, Schrodinger for his help and support during the execution of the project. The authors also acknowledge the Department of Pharmacy, BITS-Pilani, Hyderabad Campus and Department of Pharmaceutical Technology, Jadavpur University for providing research facilities to carry out the present work.

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Authors and Affiliations

  1. Epigenetic Research Laboratory, Department of Pharmacy, Birla Institute of Technology and Science-Pilani Hyderabad Campus, Shamirpet, Hyderabad, 500078, India

    Goverdhan Lanka & Balaram Ghosh

  2. Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, P. O. Box 17020, Kolkata, West Bengal, 700032, India

    Suvankar Banerjee & Nilanjan Adhikari

  3. Computer Aided Drug Design Laboratory, Department of Pharmacy, Birla Institute of Technology and Science-Pilani Hyderabad Campus, Shamirpet, Hyderabad, 500078, India

    Goverdhan Lanka & Balaram Ghosh

Authors
  1. Goverdhan Lanka
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  2. Suvankar Banerjee
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  3. Nilanjan Adhikari
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  4. Balaram Ghosh
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Contributions

GL contributed toward data curation, pharmacophore mapping, molecular docking, theoretical studies, data analysis, and manuscript drafting and writing; SB contributed toward data analysis, MD Simulation, and manuscript writing and reviewing; NA contributed toward conceptualization, data analysis, writing, editing, reviewing, overall organization of the manuscript, and supervision; BG contributed toward conceptualization, data analysis, writing, editing, reviewing, overall organization of the manuscript, and supervision.

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Correspondence to Balaram Ghosh.

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Lanka, G., Banerjee, S., Adhikari, N. et al. Fragment-based discovery of new potential DNMT1 inhibitors integrating multiple pharmacophore modeling, 3D-QSAR, virtual screening, molecular docking, ADME, and molecular dynamics simulation approaches. Mol Divers (2024). https://doi.org/10.1007/s11030-024-10837-5

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