The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of experimentally validated quantitative structure-activity relationships (QSAR). We developed a feasible workflow that achieved quantitative prediction of both affinity and kinetic-based binding parameters of small molecules against the HIV-1 TAR model RNA system. A set of small molecules bearing diverse scaffolds was screened against the HIV-1-TAR construct using surface plasmon resonance, which provided binding kinetics and affinities. Introduction of Kennard-Stone sampling, lasso-based feature shrinkage and multiple linear regression to these data afforded predictive models as well as explicit interpretation of the contributing parameters. We expect the workflow described herein to be applicable to other RNA structures, ultimately providing essential insight into the small molecule descriptors that drive selective binding interactions and, consequently, exponentially increasing the efficiency of ligand design and optimization without the need for high-resolution structures.

Quantitative structure-activity relationship study for predicting binding parameters of RNA-targeted small molecules

ACS Spring 2022

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