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

RNATACs: Multispecific small molecules targeting RNA by induced proximity

RNA-targeting small molecules (rSMs) have become an attractive modality to tackle traditionally undruggable proteins and expand the druggable space. Among many innovative concepts, RNA-targeting chimeras (RNATACs) represent a new class of multispecific, induced proximity small molecules that act by chemically bringing RNA targets into proximity with an endogenous RNA effector, such as a ribonuclease (RNase). Depending on the RNA effector, RNATACs can alter the stability, localization, translation, or splicing of the target RNA. Although still in its infancy, this new modality has the potential for broad applications in the future to treat diseases with high unmet need. In this review, we discuss potential advantages of RNATACs, recent progress in the field, and challenges to this cutting-edge technology.

Advances in machine-learning approaches to RNA-targeted drug design

RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.

Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery

INTRODUCTION

Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted in large amounts of data on disease associated RNAs, as well as on small molecules that bind to such RNAs. Artificial intelligence (AI) approaches, including machine learning and deep learning, present an opportunity to speed up the discovery of RNA-targeted small molecules by improving decision-making efficiency and quality.

AREAS COVERED

The topics described in this review include the recent applications of AI in the identification of RNA targets, RNA structure determination, screening of chemical compound libraries, and hit-to-lead optimization. The impact and limitations of the recent AI applications are discussed, along with an outlook on the possible applications of next-generation AI tools for the discovery of novel RNA-targeted small molecule drugs.

EXPERT OPINION

Key areas for improvement include developing AI tools for understanding RNA dynamics and RNA - small molecule interactions. High-quality and comprehensive data still need to be generated especially on the biological activity of small molecules that target RNAs.

Quantitative structure-activity relationship model development for estimating the predicted No-effect concentration of petroleum hydrocarbon and derivatives in the ecological risk assessment

Quantitative structure-activity relationship (QSAR) is a cost-effective solution to directly and accurately estimating the environmental safety thresholds (ESTs) of pollutants in the ecological risk assessment due to the lack of toxicity data. In this study, QSAR models were developed for estimating the Predicted No-Effect Concentrations (PNECs) of petroleum hydrocarbons and their derivatives (PHDs) under dietary exposure, based on the quantified molecular descriptors and the obtained PNECs of 51 PHDs with given acute or chronic toxicity concentrations. Three high-reliable QSAR models were respectively developed for PHDs, aromatic hydrocarbons and their derivatives (AHDs), and alkanes, alkenes and their derivatives (ALKDs), with excellent fitting performance evidenced by high correlation coefficient (0.89-0.95) and low root mean square error (0.13-0.2 mg/kg), and high stability and predictive performance reflected by high internal and external verification coefficient (Q2LOO, 0.66-0.89; Q2F1, 0.62-0.78; Q2F2, 0.60-0.73). The investigated quantitative relationships between molecular structure and PNECs indicated that 18 autocorrelation descriptors, 3 information index descriptors, 4 barysz matrix descriptors, 6 burden modified eigenvalues descriptors, and 1 BCUT descriptor were important molecular descriptors affecting the PNECs of PHDs. The obtained results supported that PNECs of PHDs can be accurately estimated by the influencing molecular descriptors and the quantitative relationship from the developed QSAR models, that provided a new feasible solution for ESTs derivation in the ecological risk assessment.

De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning

One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides a powerful tool to overcome the challenge. Herein, a machine learning-based strategy is developed for de novo generation of novel compounds with drug efficacy termed DTLS (Deep Transfer Learning-based Strategy) by using dataset of disease-direct-related activity as input. DTLS is applied in two kinds of disease: colorectal cancer (CRC) and Alzheimer's disease (AD). In each case, novel compound is discovered and identified in in vitro and in vivo disease models. Their mechanism of actionis further explored. The experimental results reveal that DTLS can not only realize the generation and identification of novel compounds with drug efficacy but also has the advantage of identifying compounds by focusing on protein targets to facilitate the mechanism study. This work highlights the significant impact of machine learning on the design of novel compounds with drug efficacy, which provides a powerful new approach to drug discovery.

Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning

Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at: https://web.iitm.ac.in/bioinfo2/RSAPred/.

Hydroxyl-Directed Regio- and Diastereoselective Allylic Sulfone Reductions with [Sm(H2O)n]I2

Allylic 1,2- and 1,3-hydroxy phenyl sulfones undergo regioselective and diastereoselective desulfonylation with double bond migration upon treatment with [Sm(H2O)n]I2. Selectivity in these reactions is thought to arise from the formation of a chelated organosamarium intermediate followed by intramolecular protonation by samarium-bound water, which is supported by observed diastereoselectivity and stereospecificity trends along with deuterium labeling experiments. The reaction was then featured in the synthesis of the phenolic fragment of the thailandamide natural products.

Reactivity-based RNA profiling for analyzing transcriptome interactions of small molecules in human cells

Protein-targeted small-molecule drugs may unintentionally bind intracellular RNA, contributing to drug toxicity. Moreover, new drugs are actively sought for intentionally targeting RNA. Here, we present a protocol to globally profile RNA-drug interactions in human cells using acylating probes and next-generation sequencing. We describe steps for cell culture, target acylation, library preparation, and sequencing. Detailed bioinformatic analyses identify drug-binding RNA loci in ∼16,000 poly(A)+ human transcripts. This streamlined workflow identifies RNA-drug interactions at single-nucleotide resolution, revealing widespread transcriptome interactions of drugs. For complete details on the use and execution of this protocol, please refer to Fang et al.1.

Pervasive transcriptome interactions of protein-targeted drugs

The off-target toxicity of drugs targeted to proteins imparts substantial health and economic costs. Proteome interaction studies can reveal off-target effects with unintended proteins; however, little attention has been paid to intracellular RNAs as potential off-targets that may contribute to toxicity. To begin to assess this, we developed a reactivity-based RNA profiling methodology and applied it to uncover transcriptome interactions of a set of Food and Drug Administration-approved small-molecule drugs in vivo. We show that these protein-targeted drugs pervasively interact with the human transcriptome and can exert unintended biological effects on RNA functions. In addition, we show that many off-target interactions occur at RNA loci associated with protein binding and structural changes, allowing us to generate hypotheses to infer the biological consequences of RNA off-target binding. The results suggest that rigorous characterization of drugs' transcriptome interactions may help assess target specificity and potentially avoid toxicity and clinical failures.

Advances in computational frameworks in the fight against TB: The way forward

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA

RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.

Machine Learning Informs RNA-Binding Chemical Space

Small molecule targeting of RNA has emerged as a new frontier in medicinal chemistry, but compared to the protein targeting literature our understanding of chemical matter that binds to RNA is limited. In this study, we reported Repository Of BInders to Nucleic acids (ROBIN), a new library of nucleic acid binders identified by small molecule microarray (SMM) screening. The complete results of 36 individual nucleic acid SMM screens against a library of 24 572 small molecules were reported (including a total of 1 627 072 interactions assayed). A set of 2 003 RNA-binding small molecules was identified, representing the largest fully public, experimentally derived library of its kind to date. Machine learning was used to develop highly predictive and interpretable models to characterize RNA-binding molecules. This work demonstrates that machine learning algorithms applied to experimentally derived sets of RNA binders are a powerful method to inform RNA-targeted chemical space.

A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties

Identification of favorable biophysical properties for protein therapeutics as part of developability assessment is a crucial part of the preclinical development process. Successful prediction of such properties and bioassay results from calculated in silico features has potential to reduce the time and cost of delivering clinical-grade material to patients, but nevertheless has remained an ongoing challenge to the field. Here, we demonstrate an automated and flexible machine learning workflow designed to compare and identify the most powerful features from computationally derived physiochemical feature sets, generated from popular commercial software packages. We implement this workflow with medium-sized datasets of human and humanized IgG molecules to generate predictive regression models for two key developability endpoints, hydrophobicity and poly-specificity. The most important features discovered through the automated workflow corroborate several previous literature reports, and newly discovered features suggest directions for further research and potential model improvement.

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

The rapidly accelerating characterization of RNA tertiary structures has revealed their pervasiveness and active roles in human diseases. Small molecule-mediated modulation of RNA tertiary structures constitutes an attractive avenue for the development of tools for therapeutically targeting and/or uncovering the pathways associated with these RNA motifs. This potential has been highlighted by targeting of the triple helix present at the 3'-end of the noncoding RNA MALAT1, a transcript implicated in several human diseases. This triplex has been reported to decrease the susceptibility of the transcript to degradation and promote its cellular accumulation. While small molecules have been shown to bind to and impact the stability of the MALAT1 triple helix, the small molecule properties that lead to these structural modulations are not well understood. We designed a library utilizing the diminazene scaffold, which is underexplored but precedented for nucleic acid binding, to target the MALAT1 triple helix. We employed multiple assays to holistically assess what parameters, if any, could predict the small molecule affinity and effect on triplex stability. We designed and/or optimized competition, calorimetry, and thermal shift assays as well as an enzymatic degradation assay, the latter of which led to the discovery of bidirectional modulators of triple helix stability within the scaffold-centric library. Determination of quantitative structure-activity relationships afforded predictive models for both affinity- and stability-based assays. This work establishes a suite of powerful orthogonal biophysical tools for the evaluation of small molecule:RNA triplex interactions that generate predictive models and will allow small molecule interrogation of the growing body of disease-associated RNA triple helices.