Computational analysis and predictive modeling of small molecule modulators of microRNA

Salma Jamal,Vinod Scaria,Vinita Periwal,Opensourcedrugdiscovery Consortium

doi:10.1186/1758-2946-4-16

Abstract

BackgroundMicroRNAs (miRNA) are small endogenously transcribed regulatory RNA which modulates gene expression at a post transcriptional level. These small RNAs have now been shown to be critical regulators in a number of biological processes in the cell including pathophysiology of diseases like cancers. The increasingly evident roles of microRNA in disease processes have also motivated attempts to target them therapeutically. Recently there has been immense interest in understanding small molecule mediated regulation of RNA, including microRNA.ResultsWe have used publicly available datasets of high throughput screens on small molecules with potential to inhibit microRNA. We employed computational methods based on chemical descriptors and machine learning to create predictive computational models for biological activity of small molecules. We further used a substructure based approach to understand common substructures potentially contributing to the activity.ConclusionWe generated computational models based on Naïve Bayes and Random Forest towards mining small RNA binding molecules from large molecular datasets. We complement this with substructure based approach to identify and understand potentially enriched substructures in the active dataset. We use this approach to identify miRNA binding potential of a set of approved drugs, suggesting a probable novel mechanism of off-target activity of these drugs. To the best of our knowledge, this is the first and most comprehensive computational analysis towards understanding RNA binding activities of small molecules and predictive modeling of these activities.

Highlights

MicroRNAs are small endogenously transcribed regulatory RNA which modulates gene expression at a post transcriptional level
We describe a computational strategy for predictive modeling of small molecules with potential to inhibit specific microRNAs, based on machine learning from high-throughput screen dataset for modulators of microRNA mir-21 [13], a well studied oncomiR
We extend our study to analyze common chemical substructures shared between biologically active molecules using a Maximum Common Substructure (MCS) approach

Summary

Introduction

MicroRNAs (miRNA) are small endogenously transcribed regulatory RNA which modulates gene expression at a post transcriptional level. These small RNAs have been shown to be critical regulators in a number of biological processes in the cell including pathophysiology of diseases like cancers. There has been immense interest in understanding small molecule mediated regulation of RNA, including microRNA. Recent advancements in the availability of computational and experimental tools have triggered increasing levels of interest to predict and experimentally validate microRNAs and their biological targets and understand their regulatory roles in a wide variety of organisms [3,4,5]. The current literature points to a large number of classes of small molecules, including many therapeutically active classes of molecules which have RNA-binding potential [13,14]. Some of the reported molecules like aurintricarboxylic acid, suramin and oxidopamine modulate microRNA processing by inhibiting microRNA loading on the RNA Induced Silencing complex [16], while molecules like enoxacin, a fluoroquinolone antibacterial agent could potentially modulate microRNA biogenesis in a cancer-specific manner [14]

Methods

Results

Conclusion