Enabling the hypothesis-driven prioritization of ligand candidates in big databases: Screenlamp and its application to GPCR inhibitor discovery for invasive species control.
J Comput Aided Mol Des
; 32(3): 415-433, 2018 03.
Article
em En
| MEDLINE
| ID: mdl-29383467
ABSTRACT
While the advantage of screening vast databases of molecules to cover greater molecular diversity is often mentioned, in reality, only a few studies have been published demonstrating inhibitor discovery by screening more than a million compounds for features that mimic a known three-dimensional (3D) ligand. Two factors contribute the general difficulty of discovering potent inhibitors, and the lack of free, user-friendly software to incorporate project-specific knowledge and user hypotheses into 3D ligand-based screening. The Screenlamp modular toolkit presented here was developed with these needs in mind. We show Screenlamp's ability to screen more than 12 million commercially available molecules and identify potent in vivo inhibitors of a G protein-coupled bile acid receptor within the first year of a discovery project. This pheromone receptor governs sea lamprey reproductive behavior, and to our knowledge, this project is the first to establish the efficacy of computational screening in discovering lead compounds for aquatic invasive species control. Significant enhancement in activity came from selecting compounds based on one of the hypotheses that matching two distal oxygen groups in the 3D structure of the pheromone is crucial for activity. Six of the 15 most active compounds met these criteria. A second hypothesis-that presence of an alkyl sulfate side chain results in high activity-identified another 6 compounds in the top 10, demonstrating the significant benefits of hypothesis-driven screening.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Receptores Acoplados a Proteínas G
/
Descoberta de Drogas
/
Bases de Dados de Compostos Químicos
Tipo de estudo:
Prognostic_studies
Limite:
Animals
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article