Identification of Anti-Tuberculosis Drugs Targeting DNA Gyrase A and Serine/Threonine Protein Kinase PknB: A Machine Learning-Assisted Drug-Repurposing Approach
Tuberculosis (TB) is a global health challenge associated with considerable levels of illness and mortality worldwide. The development of innovative therapeutic strategies is crucial to combat the rise of drug-resistant TB strains. DNA Gyrase A (GyrA) and serine/threonine protein kinase (PknB) are p...
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MDPI AG
2024-11-01
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| Series: | Tropical Medicine and Infectious Disease |
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| author | Dongwoo Lee Md Ataul Islam Sathishkumar Natarajan Dawood Babu Dudekula Hoyong Chung Junhyung Park Bermseok Oh |
| author_facet | Dongwoo Lee Md Ataul Islam Sathishkumar Natarajan Dawood Babu Dudekula Hoyong Chung Junhyung Park Bermseok Oh |
| author_sort | Dongwoo Lee |
| collection | DOAJ |
| description | Tuberculosis (TB) is a global health challenge associated with considerable levels of illness and mortality worldwide. The development of innovative therapeutic strategies is crucial to combat the rise of drug-resistant TB strains. DNA Gyrase A (GyrA) and serine/threonine protein kinase (PknB) are promising targets for new TB medications. This study employed techniques such as similarity searches, molecular docking analyses, machine learning (ML)-driven absolute binding-free energy calculations, and molecular dynamics (MD) simulations to find potential drug candidates. By combining ligand- and structure-based methods with ML principles and MD simulations, a novel strategy was proposed for identifying small molecules. Drugs with structural similarities to existing TB therapies were assessed for their binding affinity to GyrA and PknB through various docking approaches and ML-based predictions. A detailed analysis identified six promising compounds for each target, such as DB00199, DB01220, DB06827, DB11753, DB14631, and DB14703 for GyrA; and DB00547, DB00615, DB06827, DB14644, DB11753, and DB14703 for PknB. Notably, DB11753 and DB14703 show significant potential for both targets. Furthermore, MD simulations’ statistical metrics confirm the drug–target complexes’ stability, with MM-GBSA analyses underscoring their strong binding affinity, indicating their promise for TB treatment even though they were not initially designed for this disease. |
| format | Article |
| id | doaj-art-36be14e118e64740b4ea65bf021b26e2 |
| institution | OA Journals |
| issn | 2414-6366 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Tropical Medicine and Infectious Disease |
| spelling | doaj-art-36be14e118e64740b4ea65bf021b26e22025-08-20T02:01:19ZengMDPI AGTropical Medicine and Infectious Disease2414-63662024-11-0191228810.3390/tropicalmed9120288Identification of Anti-Tuberculosis Drugs Targeting DNA Gyrase A and Serine/Threonine Protein Kinase PknB: A Machine Learning-Assisted Drug-Repurposing ApproachDongwoo Lee0Md Ataul Islam1Sathishkumar Natarajan2Dawood Babu Dudekula3Hoyong Chung4Junhyung Park5Bermseok Oh6Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea3BIGS Omicscore Pvt., Ltd., 909 Lavelle Building, Richmond Circle, Bangalore 560025, India3BIGS Co., Ltd., B-831, Geumgang Penterium IX Tower, Hwaseong 18469, Republic of Korea3BIGS Omicscore Pvt., Ltd., 909 Lavelle Building, Richmond Circle, Bangalore 560025, India3BIGS Co., Ltd., B-831, Geumgang Penterium IX Tower, Hwaseong 18469, Republic of Korea3BIGS Co., Ltd., B-831, Geumgang Penterium IX Tower, Hwaseong 18469, Republic of KoreaDepartment of Biomedical Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of KoreaTuberculosis (TB) is a global health challenge associated with considerable levels of illness and mortality worldwide. The development of innovative therapeutic strategies is crucial to combat the rise of drug-resistant TB strains. DNA Gyrase A (GyrA) and serine/threonine protein kinase (PknB) are promising targets for new TB medications. This study employed techniques such as similarity searches, molecular docking analyses, machine learning (ML)-driven absolute binding-free energy calculations, and molecular dynamics (MD) simulations to find potential drug candidates. By combining ligand- and structure-based methods with ML principles and MD simulations, a novel strategy was proposed for identifying small molecules. Drugs with structural similarities to existing TB therapies were assessed for their binding affinity to GyrA and PknB through various docking approaches and ML-based predictions. A detailed analysis identified six promising compounds for each target, such as DB00199, DB01220, DB06827, DB11753, DB14631, and DB14703 for GyrA; and DB00547, DB00615, DB06827, DB14644, DB11753, and DB14703 for PknB. Notably, DB11753 and DB14703 show significant potential for both targets. Furthermore, MD simulations’ statistical metrics confirm the drug–target complexes’ stability, with MM-GBSA analyses underscoring their strong binding affinity, indicating their promise for TB treatment even though they were not initially designed for this disease.https://www.mdpi.com/2414-6366/9/12/288tuberculosisDNA Gyrase APknBdrug-repurposingmolecular dockingmachine learning |
| spellingShingle | Dongwoo Lee Md Ataul Islam Sathishkumar Natarajan Dawood Babu Dudekula Hoyong Chung Junhyung Park Bermseok Oh Identification of Anti-Tuberculosis Drugs Targeting DNA Gyrase A and Serine/Threonine Protein Kinase PknB: A Machine Learning-Assisted Drug-Repurposing Approach Tropical Medicine and Infectious Disease tuberculosis DNA Gyrase A PknB drug-repurposing molecular docking machine learning |
| title | Identification of Anti-Tuberculosis Drugs Targeting DNA Gyrase A and Serine/Threonine Protein Kinase PknB: A Machine Learning-Assisted Drug-Repurposing Approach |
| title_full | Identification of Anti-Tuberculosis Drugs Targeting DNA Gyrase A and Serine/Threonine Protein Kinase PknB: A Machine Learning-Assisted Drug-Repurposing Approach |
| title_fullStr | Identification of Anti-Tuberculosis Drugs Targeting DNA Gyrase A and Serine/Threonine Protein Kinase PknB: A Machine Learning-Assisted Drug-Repurposing Approach |
| title_full_unstemmed | Identification of Anti-Tuberculosis Drugs Targeting DNA Gyrase A and Serine/Threonine Protein Kinase PknB: A Machine Learning-Assisted Drug-Repurposing Approach |
| title_short | Identification of Anti-Tuberculosis Drugs Targeting DNA Gyrase A and Serine/Threonine Protein Kinase PknB: A Machine Learning-Assisted Drug-Repurposing Approach |
| title_sort | identification of anti tuberculosis drugs targeting dna gyrase a and serine threonine protein kinase pknb a machine learning assisted drug repurposing approach |
| topic | tuberculosis DNA Gyrase A PknB drug-repurposing molecular docking machine learning |
| url | https://www.mdpi.com/2414-6366/9/12/288 |
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