Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches
Cardiovascular diseases (CDs) are a major concern in the human race and one of the leading causes of death worldwide. β-Adrenergic receptors (β1-AR and β2-AR) play a crucial role in the overall regulation of cardiac function. In the present study, structure-based virtual screening, machine learning...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/22/20/11191 |
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| author | Md Ataul Islam V. P. Subramanyam Rallabandi Sameer Mohammed Sridhar Srinivasan Sathishkumar Natarajan Dawood Babu Dudekula Junhyung Park |
| author_facet | Md Ataul Islam V. P. Subramanyam Rallabandi Sameer Mohammed Sridhar Srinivasan Sathishkumar Natarajan Dawood Babu Dudekula Junhyung Park |
| author_sort | Md Ataul Islam |
| collection | DOAJ |
| description | Cardiovascular diseases (CDs) are a major concern in the human race and one of the leading causes of death worldwide. β-Adrenergic receptors (β1-AR and β2-AR) play a crucial role in the overall regulation of cardiac function. In the present study, structure-based virtual screening, machine learning (ML), and a ligand-based similarity search were conducted for the PubChem database against both β1- and β2-AR. Initially, all docked molecules were screened using the threshold binding energy value. Molecules with a better binding affinity were further used for segregation as active and inactive through ML. The pharmacokinetic assessment was carried out on molecules retained in the above step. Further, similarity searching of the ChEMBL and DrugBank databases was performed. From detailed analysis of the above data, four compounds for each of β1- and β2-AR were found to be promising in nature. A number of critical ligand-binding amino acids formed potential hydrogen bonds and hydrophobic interactions. Finally, a molecular dynamics (MD) simulation study of each molecule bound with the respective target was performed. A number of parameters obtained from the MD simulation trajectories were calculated and substantiated the stability between the protein-ligand complex. Hence, it can be postulated that the final molecules might be crucial for CDs subjected to experimental validation. |
| first_indexed | 2024-03-10T06:29:55Z |
| format | Article |
| id | doaj.art-839e944db4ec40a78336be3317ce483e |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T06:29:55Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-839e944db4ec40a78336be3317ce483e2023-11-22T18:35:19ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672021-10-0122201119110.3390/ijms222011191Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning ApproachesMd Ataul Islam0V. P. Subramanyam Rallabandi1Sameer Mohammed2Sridhar Srinivasan3Sathishkumar Natarajan4Dawood Babu Dudekula5Junhyung Park63BIGS Omicscore Pvt. Ltd., 1, O Shaughnessy Rd, Langford Gardens, Bengaluru, Karnataka 560025, India3BIGS Omicscore Pvt. Ltd., 1, O Shaughnessy Rd, Langford Gardens, Bengaluru, Karnataka 560025, India3BIGS Omicscore Pvt. Ltd., 1, O Shaughnessy Rd, Langford Gardens, Bengaluru, Karnataka 560025, India3BIGS Omicscore Pvt. Ltd., 1, O Shaughnessy Rd, Langford Gardens, Bengaluru, Karnataka 560025, India3BIGS Co., Ltd., 156, Gwanggyo-ro, Yeongtong-gu, Suwon-si 16506, Korea3BIGS Omicscore Pvt. Ltd., 1, O Shaughnessy Rd, Langford Gardens, Bengaluru, Karnataka 560025, India3BIGS Co., Ltd., 156, Gwanggyo-ro, Yeongtong-gu, Suwon-si 16506, KoreaCardiovascular diseases (CDs) are a major concern in the human race and one of the leading causes of death worldwide. β-Adrenergic receptors (β1-AR and β2-AR) play a crucial role in the overall regulation of cardiac function. In the present study, structure-based virtual screening, machine learning (ML), and a ligand-based similarity search were conducted for the PubChem database against both β1- and β2-AR. Initially, all docked molecules were screened using the threshold binding energy value. Molecules with a better binding affinity were further used for segregation as active and inactive through ML. The pharmacokinetic assessment was carried out on molecules retained in the above step. Further, similarity searching of the ChEMBL and DrugBank databases was performed. From detailed analysis of the above data, four compounds for each of β1- and β2-AR were found to be promising in nature. A number of critical ligand-binding amino acids formed potential hydrogen bonds and hydrophobic interactions. Finally, a molecular dynamics (MD) simulation study of each molecule bound with the respective target was performed. A number of parameters obtained from the MD simulation trajectories were calculated and substantiated the stability between the protein-ligand complex. Hence, it can be postulated that the final molecules might be crucial for CDs subjected to experimental validation.https://www.mdpi.com/1422-0067/22/20/11191cardiovascular diseasesβ-adrenergic receptorsvirtual screeningmachine learningsimilarity searchMD simulation |
| spellingShingle | Md Ataul Islam V. P. Subramanyam Rallabandi Sameer Mohammed Sridhar Srinivasan Sathishkumar Natarajan Dawood Babu Dudekula Junhyung Park Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches International Journal of Molecular Sciences cardiovascular diseases β-adrenergic receptors virtual screening machine learning similarity search MD simulation |
| title | Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches |
| title_full | Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches |
| title_fullStr | Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches |
| title_full_unstemmed | Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches |
| title_short | Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches |
| title_sort | screening of β1 and β2 adrenergic receptor modulators through advanced pharmacoinformatics and machine learning approaches |
| topic | cardiovascular diseases β-adrenergic receptors virtual screening machine learning similarity search MD simulation |
| url | https://www.mdpi.com/1422-0067/22/20/11191 |
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