TY - JOUR
T1 - Identification of pharmacological targets combining docking and molecular dynamics simulations
AU - Ian, Ilizaliturri Flores
AU - Luis, Rosas Trigueros Jorge
AU - Pablo, Carrillo Vazquez Jonathan
AU - Luis, Vique Sanchez Jose
AU - Normande, Carrillo Ibarra
AU - Beatriz, Zamora Lopez
AU - Sandino, Reyes Lopez Cesar Augusto
AU - Guadalupe, Benitez Cardoza Claudia
AU - Jose, Correa Basurto
AU - Absalom, Zamorano Carrillo
PY - 2013/3/21
Y1 - 2013/3/21
N2 - 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.
AB - 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.
KW - Docking
KW - Drug design
KW - In silico
KW - MD simulations
KW - Theoretical studies
UR - http://www.scopus.com/inward/record.url?scp=84876230130&partnerID=8YFLogxK
U2 - 10.3844/ajabssp.2013.89.106
DO - 10.3844/ajabssp.2013.89.106
M3 - Artículo de revisión
AN - SCOPUS:84876230130
SN - 1557-4989
VL - 8
SP - 89
EP - 106
JO - American Journal of Agricultural and Biological Science
JF - American Journal of Agricultural and Biological Science
IS - 1
ER -