Hot-spots-guided receptor-based pharmacophores (HS-Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores.
The design of biologically active compounds from ligand-free protein structures using a structure-based approach is still a major challenge. In this paper, we present a fast knowledge-based approach (HS-Pharm) that allows the prioritization of cavity atoms that should be targeted for ligand binding, by training machine learning algorithms with atom-based fingerprints of known ligand-binding pockets. The knowledge of hot spots for ligand binding is here used for focusing structure-based pharmacophore models. Three targets of pharmacological interest (neuraminidase, beta2 adrenergic receptor, and cyclooxygenase-2) were used to test the evaluated methodology, and the derived structure-based pharmacophores were used in retrospective virtual screening studies. The current study shows that structure-based pharmacophore screening is a powerful technique for the fast identification of potential hits in a chemical library, and that it is a valid alternative to virtual screening by molecular docking.
Bioinformatics of the Drug, UMR 7175 CNRS-ULP (Universite Louis Pasteur-Strasbourg I), 74 route du Rhin, B.P. 24, F-67400 Illkirch, France.
Caterina
C
Barillari
Gilles
G
Marcou
Didier
D
Rognan
D008024:Ligands; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
D008024:Ligands; D011506:Proteins