Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures
The fat mass and obesity-associated (FTO) protein catalyzes metal-dependent modifications of nucleic acids, namely the demethylation of methyl adenosine inside mRNA molecules. The FTO protein has been identified as a potential target for developing anticancer therapies. Identifying a suitable ligand...
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MDPI AG
2024-02-01
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| Series: | BioMedInformatics |
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| Online Access: | https://www.mdpi.com/2673-7426/4/1/20 |
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| author | Kannan Mayuri Durairaj Varalakshmi Mayakrishnan Tharaheswari Chaitanya Sree Somala Selvaraj Sathya Priya Nagaraj Bharathkumar Renganathan Senthil Raja Babu Singh Kushwah Sundaram Vickram Thirunavukarasou Anand Konda Mani Saravanan |
| author_facet | Kannan Mayuri Durairaj Varalakshmi Mayakrishnan Tharaheswari Chaitanya Sree Somala Selvaraj Sathya Priya Nagaraj Bharathkumar Renganathan Senthil Raja Babu Singh Kushwah Sundaram Vickram Thirunavukarasou Anand Konda Mani Saravanan |
| author_sort | Kannan Mayuri |
| collection | DOAJ |
| description | The fat mass and obesity-associated (FTO) protein catalyzes metal-dependent modifications of nucleic acids, namely the demethylation of methyl adenosine inside mRNA molecules. The FTO protein has been identified as a potential target for developing anticancer therapies. Identifying a suitable ligand-targeting FTO protein is crucial to developing chemotherapeutic medicines to combat obesity and cancer. Scientists worldwide have employed many methodologies to discover a potent inhibitor for the FTO protein. This study uses deep learning-based methods and molecular docking techniques to investigate the FTO protein as a target. Our strategy involves systematically screening a database of small chemical compounds. By utilizing the crystal structures of the FTO complexed with ligands, we successfully identified three small-molecule chemical compounds (ZINC000003643476, ZINC000000517415, and ZINC000001562130) as inhibitors of the FTO protein. The identification process was accomplished by employing a combination of screening techniques, specifically deep learning (DeepBindGCN) and Autodock vina, on the ZINC database. These compounds were subjected to comprehensive analysis using 100 nanoseconds of molecular dynamics and binding free energy calculations. The findings of our study indicate the identification of three candidate inhibitors that might effectively target the human fat mass and obesity protein. The results of this study have the potential to facilitate the exploration of other chemicals that can interact with FTO. Conducting biochemical studies to evaluate these compounds’ effectiveness may contribute to improving fat mass and obesity treatment strategies. |
| first_indexed | 2024-04-24T18:30:54Z |
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| issn | 2673-7426 |
| language | English |
| last_indexed | 2024-04-24T18:30:54Z |
| publishDate | 2024-02-01 |
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| record_format | Article |
| series | BioMedInformatics |
| spelling | doaj.art-1fcb92c114334e6b840c446bb209f15e2024-03-27T13:27:29ZengMDPI AGBioMedInformatics2673-74262024-02-014134735910.3390/biomedinformatics4010020Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid ProceduresKannan Mayuri0Durairaj Varalakshmi1Mayakrishnan Tharaheswari2Chaitanya Sree Somala3Selvaraj Sathya Priya4Nagaraj Bharathkumar5Renganathan Senthil6Raja Babu Singh Kushwah7Sundaram Vickram8Thirunavukarasou Anand9Konda Mani Saravanan10Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai 602105, Tamil Nadu, IndiaDepartment of Biochemistry, Pondicherry University Community College, Pondicherry University, Pondicherry 605009, IndiaDepartment of Biochemistry, Pondicherry University Community College, Pondicherry University, Pondicherry 605009, IndiaB Aatral Biosciences Private Limited, Bangalore 560091, Karnataka, IndiaB Aatral Biosciences Private Limited, Bangalore 560091, Karnataka, IndiaB Aatral Biosciences Private Limited, Bangalore 560091, Karnataka, IndiaDepartment of Bioinformatics, Vels Institute of Science Technology and Advanced Studies, Pallavaram, Chennai 600117, Tamil Nadu, IndiaB Aatral Biosciences Private Limited, Bangalore 560091, Karnataka, IndiaDepartment of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai 602105, Tamil Nadu, IndiaB Aatral Biosciences Private Limited, Bangalore 560091, Karnataka, IndiaDepartment of Biotechnology, Bharath Institute of Higher Education and Research, Chennai 600073, Tamil Nadu, IndiaThe fat mass and obesity-associated (FTO) protein catalyzes metal-dependent modifications of nucleic acids, namely the demethylation of methyl adenosine inside mRNA molecules. The FTO protein has been identified as a potential target for developing anticancer therapies. Identifying a suitable ligand-targeting FTO protein is crucial to developing chemotherapeutic medicines to combat obesity and cancer. Scientists worldwide have employed many methodologies to discover a potent inhibitor for the FTO protein. This study uses deep learning-based methods and molecular docking techniques to investigate the FTO protein as a target. Our strategy involves systematically screening a database of small chemical compounds. By utilizing the crystal structures of the FTO complexed with ligands, we successfully identified three small-molecule chemical compounds (ZINC000003643476, ZINC000000517415, and ZINC000001562130) as inhibitors of the FTO protein. The identification process was accomplished by employing a combination of screening techniques, specifically deep learning (DeepBindGCN) and Autodock vina, on the ZINC database. These compounds were subjected to comprehensive analysis using 100 nanoseconds of molecular dynamics and binding free energy calculations. The findings of our study indicate the identification of three candidate inhibitors that might effectively target the human fat mass and obesity protein. The results of this study have the potential to facilitate the exploration of other chemicals that can interact with FTO. Conducting biochemical studies to evaluate these compounds’ effectiveness may contribute to improving fat mass and obesity treatment strategies.https://www.mdpi.com/2673-7426/4/1/20FTO proteindeep learning-based screeningmolecular dockingmolecular simulationsdrug screening |
| spellingShingle | Kannan Mayuri Durairaj Varalakshmi Mayakrishnan Tharaheswari Chaitanya Sree Somala Selvaraj Sathya Priya Nagaraj Bharathkumar Renganathan Senthil Raja Babu Singh Kushwah Sundaram Vickram Thirunavukarasou Anand Konda Mani Saravanan Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures BioMedInformatics FTO protein deep learning-based screening molecular docking molecular simulations drug screening |
| title | Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures |
| title_full | Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures |
| title_fullStr | Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures |
| title_full_unstemmed | Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures |
| title_short | Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures |
| title_sort | identifying potent fat mass and obesity associated protein inhibitors using deep learning based hybrid procedures |
| topic | FTO protein deep learning-based screening molecular docking molecular simulations drug screening |
| url | https://www.mdpi.com/2673-7426/4/1/20 |
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