Search for Novel Aminoglycosides by Combining Fragment-Based Virtual Screening and 3D-QSAR Scoring

Abstract

Aminoglycosides are antibiotics targeting the 16S RNA A site of the bacterial ribosome. There have been many efforts directed toward design of their synthetic derivatives, however with only few successes. As RNA binders, aminoglycosides are also a difficult target for computational drug design, since most of the existing methods were developed for protein ligands. Here, we present an approach that allows for evading the problems related to still poorly developed RNA docking and scoring algorithms. It is aimed at identification of new molecular scaffolds potentially binding to the A site. The considered molecules are based on the neamine core, which is common for all aminoglycosides and provides specificity toward the binding site, linked with diverse molecular fragments via its O5 or O6 oxygen atom. Suitable fragments are selected with the use of 3D searches of molecular fragments library against two distinct pharmacophores designed on the basis of available structural data for aminoglycoside-RNA complexes. The compounds resulting from fragments assembly with neamine are then scored with a 3D-QSAR model developed using the biological data for known aminoglycoside derivatives. Twenty-one new potential ligands are obtained, four of which have predicted activities comparable to less potent aminoglycoside antibiotics.