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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Published in:International journal of molecular sciences Vol. 22; no. 20; p. 11191
Main Authors: Islam, Md Ataul, Rallabandi, V. P. Subramanyam, Mohammed, Sameer, Srinivasan, Sridhar, Natarajan, Sathishkumar, Dudekula, Dawood Babu, Park, Junhyung
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 17.10.2021
MDPI
Subjects:
Adrenergic receptors
Amino acids
Binding sites
Crystal structure
Drug Discovery
Humans
Kinases
Ligands
Machine Learning
Molecular Dynamics Simulation
Protein Binding
Proteins
R&D
Receptors, Adrenergic, beta-1 - chemistry
Receptors, Adrenergic, beta-2 - chemistry
Research & development
Signal transduction
MD simulation
similarity search
cardiovascular diseases
virtual screening
machine learning
β-adrenergic receptors
ISSN:1422-0067, 1661-6596, 1422-0067
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Abstract 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.
AbstractList 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.
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.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.
Author Dudekula, Dawood Babu
Mohammed, Sameer
Park, Junhyung
Natarajan, Sathishkumar
Srinivasan, Sridhar
Rallabandi, V. P. Subramanyam
Islam, Md Ataul
AuthorAffiliation 2 3BIGS Co., Ltd., 156, Gwanggyo-ro, Yeongtong-gu, Suwon-si 16506, Korea; sathish@3bigs.com
1 3BIGS Omicscore Pvt. Ltd., 1, O Shaughnessy Rd, Langford Gardens, Bengaluru, Karnataka 560025, India; ataul@3bigs.com (M.A.I.); subramanyam@3bigs.com (V.P.S.R.); sameer@3bigs.com (S.M.); sridhar@3bigs.com (S.S.); dawood@3bigs.com (D.B.D.)
AuthorAffiliation_xml – name: 1 3BIGS Omicscore Pvt. Ltd., 1, O Shaughnessy Rd, Langford Gardens, Bengaluru, Karnataka 560025, India; ataul@3bigs.com (M.A.I.); subramanyam@3bigs.com (V.P.S.R.); sameer@3bigs.com (S.M.); sridhar@3bigs.com (S.S.); dawood@3bigs.com (D.B.D.)
– name: 2 3BIGS Co., Ltd., 156, Gwanggyo-ro, Yeongtong-gu, Suwon-si 16506, Korea; sathish@3bigs.com
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  surname: Islam
  fullname: Islam, Md Ataul
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  fullname: Rallabandi, V. P. Subramanyam
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/34681845$$D View this record in MEDLINE/PubMed
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Keywords MD simulation
similarity search
cardiovascular diseases
virtual screening
machine learning
β-adrenergic receptors
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Snippet 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)...
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StartPage 11191
SubjectTerms Adrenergic receptors
Amino acids
Binding sites
Crystal structure
Drug Discovery
Humans
Kinases
Ligands
Machine Learning
Molecular Dynamics Simulation
Protein Binding
Proteins
R&D
Receptors, Adrenergic, beta-1 - chemistry
Receptors, Adrenergic, beta-2 - chemistry
Research & development
Signal transduction
Title Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches
URI https://www.ncbi.nlm.nih.gov/pubmed/34681845
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https://pubmed.ncbi.nlm.nih.gov/PMC8538848
Volume 22
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