Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures Kannan Mayuri Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai 602105, Tamil Nadu, India B Aatral Biosciences Private Limited, Bangalore 560091, Karnataka, India Durairaj Varalakshmi Department of Biochemistry, Pondicherry University Community College, Pondicherry University, Pondicherry 605009, India Mayakrishnan Tharaheswari Department of Biochemistry, Pondicherry University Community College, Pondicherry University, Pondicherry 605009, India Chaitanya Sree Somala B Aatral Biosciences Private Limited, Bangalore 560091, Karnataka, India Selvaraj Sathya Priya B Aatral Biosciences Private Limited, Bangalore 560091, Karnataka, India SRIIC Lab, Central Research Facility, Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, Tamil Nadu, India Nagaraj Bharathkumar B Aatral Biosciences Private Limited, Bangalore 560091, Karnataka, India Renganathan Senthil Department of Bioinformatics, Vels Institute of Science Technology and Advanced Studies, Pallavaram, Chennai 600117, Tamil Nadu, India http://orcid.org/0000-0002-8451-9832 Raja Babu Singh Kushwah B Aatral Biosciences Private Limited, Bangalore 560091, Karnataka, India http://orcid.org/0000-0002-9293-8981 Sundaram Vickram Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai 602105, Tamil Nadu, India Thirunavukarasou Anand B Aatral Biosciences Private Limited, Bangalore 560091, Karnataka, India SRIIC Lab, Central Research Facility, Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, Tamil Nadu, India Konda Mani Saravanan Department of Biotechnology, Bharath Institute of Higher Education and Research, Chennai 600073, Tamil Nadu, India http://orcid.org/0000-0002-5541-234X

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.
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