Multicomplex-based pharmacophore modeling coupled with molecular dynamics simulations: An efficient strategy for the identification of novel inhibitors of PfDHODH.
J Mol Graph Model
; 75: 413-423, 2017 08.
Article
en En
| MEDLINE
| ID: mdl-28651831
ABSTRACT
Enormous efforts have been made in the past to identify novel scaffolds against the potential therapeutic target, Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH). Fourteen different organic molecules have been crystallized to understand the structural basis of the inhibition. However, the pharmacophoric studies carried out so far, have not exploited all the structural information simultaneously to identify the novel inhibitors. Therefore, an attempt was made to construct the pharmacophore hypotheses from the available PfDHODH structural proteome. Among the generated hypotheses, a representative hypothesis was employed as a primary filter to list the molecules with complimentary features accountable for inhibition. Moreover, the auxiliary evaluations of the filtered molecules were accomplished via docking and drug-likeness studies. Subsequently, the stability of the protein-ligand complex was evaluated by using molecular dynamics simulations (MDs). The molecular details of binding interactions of the potential hits were compared with the highly active experimental structure (5FI8) to seek the more potent candidates that can be targeted against PfDHODH. Overall, the combination of screening and stability procedures resulted in the identification of three potent candidates. The drug-likeness of these molecules lie within the acceptable range and consequently increased the opportunities for their development to new anti-malarials.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Plasmodium falciparum
/
Oxidorreductasas actuantes sobre Donantes de Grupo CH-CH
/
Inhibidores Enzimáticos
/
Simulación de Dinámica Molecular
/
Antimaláricos
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
J Mol Graph Model
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2017
Tipo del documento:
Article
País de afiliación:
India