Journal Title
Title of Journal: J Mol Model
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Abbravation: Journal of Molecular Modeling
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Publisher
Springer-Verlag
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Authors: Gayathri Devi Chandramoorthi Shanmughavel Piramanayagam Parthiban Marimuthu
Publish Date: 2008/05/30
Volume: 14, Issue: 9, Pages: 849-856
Abstract
Knowledge of the threedimensional structures of protein targets from genomic data has the potential to accelerate researches pertaining to drug discovery Human β2 adrenergic receptor is a Gproteincoupled receptor with seven transmembrane helices and is important in pharmaceutical targeting on pulmonary and cardiovascular diseases The human β2 adrenergic receptor has been found to play a very important role in the pathogenesis of high altitude pulmonary edema HAPE In the present study a high quality of protein 3D structure has been predicted for the human β2 adrenergic receptor sequence with primary accession number P07550 Homologous template protein sequence with known 3D structure was identified and the templatequery protein sequence validation was done by multiple sequence alignment method The homology model was performed through Modeller and depended on the quality of the sequence alignment by BLAST template structure and the consolidated result performed by Gene silico metaserver The statistical verification of the generated model was evaluated by PROCHECK which revealed that the structure modeled through Modeller to be of good quality with 841 of residues in the most favored region Docking studies were carried out after modeling with two well known ligands namely Salmeterol and Nifedipine and the fitness score revealed that Salmeterol has a higher fitness score than Nifedipine Estimation of binding affinity by XScore revealed that Salmeterol had −1040 binding affinity while Nifedipine showed −962 binding affinity From the present study it can be concluded that the generated model of human β2 adrenergic receptor can be used for further studies related to this receptor and Salmeterol was found to have a high binding affinity with human β2 adrenergic receptor
Keywords:
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