Quantitative Structure–Activity Relationship (QSAR) Study Predicts Small-Molecule Binding to RNA Structure

Abstract

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 challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure–activity relationships (QSARs). Herein, we develop QSAR models that quantitatively predict both thermodynamic- and kinetic-based binding parameters of small molecules and the HIV-1 transactivation response (TAR) RNA model system. Small molecules bearing diverse scaffolds were screened against TAR using surface plasmon resonance. Multiple linear regression (MLR) combined with feature selection afforded robust models that allowed direct interpretation of the properties critical for both binding strength and kinetic rate constants. These models were validated with new molecules, and their accurate performance was confirmed via comparison to ensemble tree methods, supporting the general applicability of this platform.

Multiassay Profiling of a Focused Small Molecule Library Reveals Predictive Bidirectional Modulation of the lncRNA MALAT1 Triplex Stability In Vitro

“…Model fitness was calculated on the basis of the accuracy of the predicted affinity of the molecules ( R 2 ), and the model robustness was evaluated by leave-one-out cross validation (LOOCV, Q 2 ). A final predictive model with an R 2 of 0.87 and a Q 2 LOOCV of 0.74 was obtained, supporting the affinity predictions for the given small molecule set (Figure B) . One of the advantages of the chosen QSAR method over other machine-learning-based approaches is the open framework of model construction that allows identification of parameters that directly contribute to the binding event .…”

“…A final predictive model with an R 2 of 0.87 and a Q 2 LOOCV of 0.74 was obtained, supporting the affinity predictions for the given small molecule set (Figure B) . One of the advantages of the chosen QSAR method over other machine-learning-based approaches is the open framework of model construction that allows identification of parameters that directly contribute to the binding event . For example, in the IDA model described above, 3D parameters (molecular globularity, glob, and contact distances of vsurf_EDmin2 and vsurf_EDmin3, vsurf_DD23) were identified to contribute positively to the binding affinity, along with a 2D descriptor (total negative polar van der Waals surface area, Q_VSA_PNEG) .…”

“…Despite being some of the most commonly used descriptors, PMIs are not the only way to define small molecule 3D properties, and 2D parameters might also correlate to binding trends. To further expand the set of possible predictive parameters for small molecule affinity, we employed a recently reported QSAR method to gain insight into small molecule descriptors that might be driving molecular recognition events . Multiple linear regression models were iteratively generated by fitting combinations of small molecule descriptors to the small molecule’s response to the RNA target that, in this case, is affinity. − In this QSAR study, we calculated both 2D and 3D parameters, for a total of 339 descriptors for each ligand.…”

Nanomaterials for miRNA detection: the hybridization chain reaction strategy

“…29 However, it remains a challenge for researchers to ascertain the quantity of miRNA, mainly due to the short sequence and low abundance of miRNA. 30 However, various techniques, such as microarrays, Northern blotting, and quantitative reverse transcription-polymerase chain reaction (qRT-PCR), are commonly employed for quantitative RNA measurement, 31 but these typically all have limitations, such as lengthy procedures, the requirement for a large sample, and complicated and sophisticated instrumentation, that must be addressed. 32 The most critical issues, such as the sensitivity and performances of biosensors for miRNA detection, have been resolved using Au-loaded nanoporous superparamagnetic Fe 2 O 3 , which can provide an active surface for miRNA adsorption.…”

“…However, it remains a challenge for all the researchers to ascertain the quantity of miRNA, mainly due to the short sequence and low abundance of miRNA 30 . However, various techniques such as microarrays, Northern blotting, and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) can be commonly employed for quantitative RNA measurement 31 . But, many points like lengthy procedures, the requirement of a large sample, and complicated and sophisticated instrumentation are associated with these methods that must be addressed 32 .…”