Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/10495/25220
Título : A Computational Study of the Protein-Ligand Interactions in CDK2 Inhibitors: Using Quantum Mechanics/Molecular Mechanics Interaction Energy as a Predictor of the Biological Activity
Autor : Alzate Morales, Jans Humbeto
Contreras, Renato
Soriano, Alejandro
Tuñon, Iñaki
Silla, Estanislao
metadata.dc.subject.*: Simulación por Computador
Computer Simulation
Quinasa 2 Dependiente de la Ciclina
Cyclin-Dependent Kinase 2
Diseño de Fármacos
Drug Design
Modelos Químicos
Models, Chemical
Modelos Moleculares
Models, Molecular
Unión Proteica
Protein Binding
Conformación Proteica
Protein Conformation
Fecha de publicación : 2007
Editorial : Biophysical Society
Citación : Alzate-Morales JH, Contreras R, Soriano A, Tuñon I, Silla E. A computational study of the protein-ligand interactions in CDK2 inhibitors: using quantum mechanics/molecular mechanics interaction energy as a predictor of the biological activity. Biophys J. 2007 Jan 15;92(2):430-9. doi: 10.1529/biophysj.106.091512.
Resumen : ABSTRACT: We report a combined quantum mechanics/molecular mechanics (QM/MM) study to determine the protein-ligand interaction energy between CDK2 (cyclin-dependent kinase 2) and five inhibitors with the N2 -substituted 6-cyclohexylmethoxypurine scaffold. The computational results in this work show that the QM/MM interaction energy is strongly correlated to the biological activity and can be used as a predictor, at least within a family of substrates. A detailed analysis of the protein-ligand structures obtained from molecular dynamics simulations shows specific interactions within the active site that, in some cases, have not been reported before to our knowledge. The computed interaction energy gauges the strength of protein-ligand interactions. Finally, energy decomposition and multiple regression analyses were performed to check the contribution of the electrostatic and van der Waals energies to the total interaction energy and to show the capabilities of the computational model to identify new potent inhibitors.
metadata.dc.identifier.eissn: 1542-0086
ISSN : 0006-3495
metadata.dc.identifier.doi: 10.1529/biophysj.106.091512
Aparece en las colecciones: Artículos de Revista en Farmacéutica

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