Are protein-ligand docking programs good enough to predict experimental poses of noncovalent ligands bound to the SARS-CoV-2 main protease?
Drug Discov Today
; 29(10): 104137, 2024 Oct.
Article
en En
| MEDLINE
| ID: mdl-39151594
ABSTRACT
Hundreds of virtual screening (VS) studies have targeted the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) main protease (M-pro) to identify small molecules that inhibit its proteolytic action. Most studies use AutoDock Vina or Glide methodologies [high-throughput VS (HTVS), standard precision (SP), or extra precision (XP)], independently or in a VS workflow. Moreover, the Protein Data Bank (PDB) includes multiple complexes between M-pro and various noncovalent ligands, providing an excellent benchmark for assessing the predictive capabilities of docking programs. Here, we analyze the ability of the three Glide methodologies and AutoDock Vina by using various target structures/preparations to predict the experimental poses of these complexes. Our aims are to optimize target setup and docking methodologies, minimize false positives, and maximize the identification of various chemotypes in a SARS-CoV-2 M-pro noncovalent inhibitor VS campaign.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Antivirales
/
Inhibidores de Proteasas
/
Simulación del Acoplamiento Molecular
/
Proteasas 3C de Coronavirus
/
SARS-CoV-2
Límite:
Humans
Idioma:
En
Revista:
Drug Discov Today
Asunto de la revista:
FARMACOLOGIA
/
TERAPIA POR MEDICAMENTOS
Año:
2024
Tipo del documento:
Article
País de afiliación:
España
Pais de publicación:
Reino Unido