Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches

Islam, Md Ataul; Rallabandi, V. P. Subramanyam; Mohammed, Sameer; Srinivasan, Sridhar; Natarajan, Sathishkumar; Dudekula, Dawood Babu; Park, Junhyung

doi:10.3390/ijms222011191

Open AccessArticle

Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches

by

Md Ataul Islam

¹

,

V. P. Subramanyam Rallabandi

¹,

Sameer Mohammed

¹,

Sridhar Srinivasan

¹,

Sathishkumar Natarajan

²

,

Dawood Babu Dudekula

¹ and

Junhyung Park

^2,*

¹

3BIGS Omicscore Pvt. Ltd., 1, O Shaughnessy Rd, Langford Gardens, Bengaluru, Karnataka 560025, India

²

3BIGS Co., Ltd., 156, Gwanggyo-ro, Yeongtong-gu, Suwon-si 16506, Korea

^*

Author to whom correspondence should be addressed.

Int. J. Mol. Sci. 2021, 22(20), 11191; https://doi.org/10.3390/ijms222011191

Submission received: 17 September 2021 / Revised: 14 October 2021 / Accepted: 14 October 2021 / Published: 17 October 2021

(This article belongs to the Special Issue Recent Advances in Virtual Screening 2.0)

Download

Browse Figures

Versions Notes

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.

Keywords: cardiovascular diseases; β-adrenergic receptors; virtual screening; machine learning; similarity search; MD simulation

Share and Cite

MDPI and ACS Style

Islam, M.A.; Rallabandi, V.P.S.; Mohammed, S.; Srinivasan, S.; Natarajan, S.; Dudekula, D.B.; Park, J. Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches. Int. J. Mol. Sci. 2021, 22, 11191. https://doi.org/10.3390/ijms222011191

AMA Style

Islam MA, Rallabandi VPS, Mohammed S, Srinivasan S, Natarajan S, Dudekula DB, Park J. Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches. International Journal of Molecular Sciences. 2021; 22(20):11191. https://doi.org/10.3390/ijms222011191

Chicago/Turabian Style

Islam, Md Ataul, V. P. Subramanyam Rallabandi, Sameer Mohammed, Sridhar Srinivasan, Sathishkumar Natarajan, Dawood Babu Dudekula, and Junhyung Park. 2021. "Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches" International Journal of Molecular Sciences 22, no. 20: 11191. https://doi.org/10.3390/ijms222011191

APA Style

Islam, M. A., Rallabandi, V. P. S., Mohammed, S., Srinivasan, S., Natarajan, S., Dudekula, D. B., & Park, J. (2021). Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches. International Journal of Molecular Sciences, 22(20), 11191. https://doi.org/10.3390/ijms222011191

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI