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

An engineered DNA aptamer-based PROTAC for precise therapy of p53-R175H hotspot mutant-driven cancer

Targeting oncogenic mutant p53 represents an attractive strategy for cancer treatment due to the high frequency of gain-of-function mutations and ectopic expression in various cancer types. Despite extensive efforts, the absence of a druggable active site for small molecules has rendered these mutants therapeutically non-actionable. Here we develop a selective and effective proteolysis-targeting chimera (PROTAC) for p53-R175H, a common hotspot mutant with dominant-negative and oncogenic activity. Using a novel iterative molecular docking-guided post-SELEX (systematic evolution of ligands by exponential enrichment) approach, we rationally engineer a high-performance DNA aptamer with improved affinity and specificity for p53-R175H. Leveraging this resulting aptamer as a binder for PROTACs, we successfully developed a selective p53-R175H degrader, named dp53m. dp53m induces the ubiquitin-proteasome-dependent degradation of p53-R175H while sparing wildtype p53. Importantly, dp53m demonstrates significant antitumor efficacy in p53-R175H-driven cancer cells both in vitro and in vivo, without toxicity. Moreover, dp53m significantly and synergistically improves the sensitivity of these cells to cisplatin, a commonly used chemotherapy drug. These findings provide evidence of the potential therapeutic value of dp53m in p53-R175H-driven cancers.

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.

Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction

MOTIVATION

The diverse structures and functions inherent in RNAs present a wealth of potential drug targets. Some small molecules are anticipated to serve as leading compounds, providing guidance for the development of novel RNA-targeted therapeutics. Consequently, the determination of RNA-small molecule binding affinity is a critical undertaking in the landscape of RNA-targeted drug discovery and development. Nevertheless, to date, only one computational method for RNA-small molecule binding affinity prediction has been proposed. The prediction of RNA-small molecule binding affinity remains a significant challenge. The development of a computational model is deemed essential to effectively extract relevant features and predict RNA-small molecule binding affinity accurately.

RESULTS

In this study, we introduced RLaffinity, a novel deep learning model designed for the prediction of RNA-small molecule binding affinity based on 3D structures. RLaffinity integrated information from RNA pockets and small molecules, utilizing a 3D convolutional neural network (3D-CNN) coupled with a contrastive learning-based self-supervised pre-training model. To the best of our knowledge, RLaffinity was the first deep learning based method for the prediction of RNA-small molecule binding affinity. Our experimental results exhibited RLaffinity's superior performance compared to baseline methods, revealed by all metrics. The efficacy of RLaffinity underscores the capability of 3D-CNN to accurately extract both global pocket information and local neighbor nucleotide information within RNAs. Notably, the integration of a self-supervised pre-training model significantly enhanced predictive performance. Ultimately, RLaffinity was also proved as a potential tool for RNA-targeted drugs virtual screening.

AVAILABILITY AND IMPLEMENTATION

https://github.com/SaisaiSun/RLaffinity.