Research Article Open Access

Identification of Pharmacological Targets Combining Docking and Molecular Dynamics Simulations

Ilizaliturri-Flores Ian1, Rosas-Trigueros Jorge Luis2, Carrillo-Vazquez Jonathan Pablo1, Vique-Sanchez Jose Luis3, Carrillo-Ibarra Normande1, Zamora-Lopez Beatriz4, Reyes-Lopez Cesar Augusto Sandino3, Benitez-Cardoza Claudia Guadalupe3, Correa-Basurto Jose5 and Zamorano-Carrillo Absalom6
  • 1 Doctorate in Science in Biotechnology-IPN, Mexico City, Mexico
  • 2 SEPI-ESCOM-IPN, Mexico City, Mexico
  • 3 Institutional Program of Molecular Biomedicine, ENMH-IPN, Mexico City, Mexico
  • 4 Department of Mental Health, Faculty of Medicine, UNAM, Mexico City, Mexico
  • 5 Interdisciplinary Group on Artificial Intelligence Applied to Protein Folding, Mexico
  • 6 Biochemistry Research Laboratory ENMH-IPN, Mexico City, Mexico


Studies that include both experimental data and computational simulations (in silico) have increased in number because the techniques are complementary. In silico methodologies are currently an essential component of drug design; moreover, identification and optimization of the best ligand based on the structures of biomolecules are common scientific challenges. Geometric structural properties of biomolecules explain their behavior and interactions and when this information is used by a combination of algorithms, a dynamic model based on atomic details can be produced. Docking studies enable researchers to determine the best position for a ligand to bind on a macromolecule, whereas Molecular Dynamics (MD) simulations describe the relevant interactions that maintain this binding. MD simulations have the advantage of illustrating the macromolecule movements in more detail. In the case of a protein, the side chain, backbone and domain movements can explain how ligands are trapped during different conformational states. Additionally, MD simulations can depict several binding sites of ligands that can be explored by docking studies, sampling many protein conformations. Following the previously mentioned strategy, it is possible to identify each binding site that might be able to accommodate different ligands through atomic motion. Another important advantage of MD is to explore the movement of side chains of key catalytic residues, which could provide information about the formation of transition states of a protein. All this information can be used to propose ligands and their most probable site of interaction, which are daily tasks of drug design. In this review, the most frequent criteria that are considered when determining pharmacological targets are gathered, particularly when docking and MD are combined.

American Journal of Agricultural and Biological Sciences
Volume 8 No. 1, 2013, 89-106


Submitted On: 18 September 2012 Published On: 21 March 2013

How to Cite: Ian, I., Luis, R. J., Pablo, C. J., Luis, V. J., Normande, C., Beatriz, Z., Sandino, R. C. A., Guadalupe, B. C., Jose, C. & Absalom, Z. (2013). Identification of Pharmacological Targets Combining Docking and Molecular Dynamics Simulations. American Journal of Agricultural and Biological Sciences, 8(1), 89-106.

  • 4 Citations



  • Docking
  • MD Simulations
  • In Silico
  • Theoretical Studies
  • Drug Design