RPocket: an intuitive database of RNA pocket topology information with RNA-ligand data resources

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.

Dynamic geometry design of cyclic peptide architectures for RNA structure

Designing inhibitors for RNA is still challenging due to the bottleneck of maintaining the binding interaction of inhibitor-RNA accompanied by subtle RNA flexibility. Thus, the current approach usually needs to screen thousands of candidate inhibitors for binding. Here, we propose a dynamic geometry design approach to enrich the hits with only a tiny pool of designed geometrically compatible scaffold candidates. First, our method uses graph-based tree decomposition to explore the complementarity rigid binding cyclic peptide and design the amino acid side chain length and charge to fit the RNA pocket. Then, we perform an energy-based dynamical network algorithm to optimize the inhibitor-RNA hydrogen bonds. Dynamic geometry-guided design yields successful inhibitors with low micromolar binding affinity scaffolds and experimentally competes with the natural RNA chaperone. The results indicate that the dynamic geometry method yields higher efficiency and accuracy than traditional methods. The strategy could be further optimized to design the length and chirality by adopting nonstandard amino acids and facilitating RNA engineering for biological or medical applications.

Long Noncoding RNAs in Plant-Pathogen Interactions

Long noncoding RNAs (lncRNAs) are commonly defined as transcripts that lack protein-coding capacity and are longer than 200 nucleotides. Since the emergence of next-generation sequencing technologies in this century, thousands of lncRNAs have been identified from nearly all living organisms. Notably, various pathogens also express their own lncRNAs in host cells during infection. In plants, many lncRNAs exhibit dynamic expression patterns in response to environmental stimuli, including pathogen attacks. In contrast to well-established methods in identifying such lncRNAs, the current understanding of lncRNAs' functional mechanisms is in its infancy. Some lncRNAs serve as precursors for generating small RNAs or serve as target mimics to sequester functional small RNAs, which have been extensively reviewed in the literature. This review focuses on the emerging evidence supporting that certain lncRNAs function as negative or positive regulators of plant immunity. A common theme is that those regulations rely on specific interactions between lncRNAs and key regulatory proteins. Viroids as single-stranded circular noncoding RNAs provide a handle to investigate how RNA local motifs render interaction specificity between lncRNAs and regulatory proteins. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

RPflex: A Coarse-Grained Network Model for RNA Pocket Flexibility Study

RNA regulates various biological processes, such as gene regulation, RNA splicing, and intracellular signal transduction. RNA’s conformational dynamics play crucial roles in performing its diverse functions. Thus, it is essential to explore the flexibility characteristics of RNA, especially pocket flexibility. Here, we propose a computational approach, RPflex, to analyze pocket flexibility using the coarse-grained network model. We first clustered 3154 pockets into 297 groups by similarity calculation based on the coarse-grained lattice model. Then, we introduced the flexibility score to quantify the flexibility by global pocket features. The results show strong correlations between the flexibility scores and root-mean-square fluctuation (RMSF) values, with Pearson correlation coefficients of 0.60, 0.76, and 0.53 in Testing Sets I–III. Considering both flexibility score and network calculations, the Pearson correlation coefficient was increased to 0.71 in flexible pockets on Testing Set IV. The network calculations reveal that the long-range interaction changes contributed most to flexibility. In addition, the hydrogen bonds in the base–base interactions greatly stabilize the RNA structure, while backbone interactions determine RNA folding. The computational analysis of pocket flexibility could facilitate RNA engineering for biological or medical applications.

A generalized classification and coding system of Human Disease Animal Model Resource data with a case study to show improving database retrieval efficiency

BACKGROUND

Currently there is no unified data classification and coding standard for the existing human disease animal model resource data worldwide. Different data classification and coding systems produce different retrieval methods. Some of these methods are inefficient and difficult to use. This research investigated the rules for the classification and coding of such data based on the Replication Methodology of Animal Models for Human Disease, the Classification and Coding Rules for Health Information Data Set (WS/T 306-2009), the Science and Technology Resource Identification (GB/T 32843-2016), the Scientific Data Management Measures (000014349/2018-00052), and The Generic Description Specification for Natural Science and Technology Resources. This research aimed to develop a classification and coding system for data obtained from human disease animal model resource based on the Internet environment to provide a standardized and unified foundation for the collection, saving, retrieval, and sharing of data from this resource.

RESULTS

A complete data classification and coding table compiled in the form of letters and numbers was produced, with a classification infrastructure that expanded layer by layer according to the three dimensions (namely, system diseases, animal species, and modeling methods) and essential attributes. When necessary, it adopted the hierarchy of major, intermediate, and minor categories for certain layer and also one-to-one matched the code and classification result.

CONCLUSION

Through this study, a sharing and joint construction mechanism for data from this resource can be developed by all research institutes in this field. As a case study, this research also offered technical support for constructing the database for the National Human Disease Animal Model Resource Center. The technological innovation of this paper is to derive a research oriented retrieval method, which provides technical support for the research on the current COVID-19 epidemic and on possible future epidemics.

Brief considerations on targeting RNA with small molecules

For more than three decades, RNA has been known to be a relevant and attractive macromolecule to target but figuring out which RNA should be targeted and how remains challenging. Recent years have seen the confluence of approaches for screening, drug optimization, and target validation that have led to the approval of a few RNA-targeting therapeutics for clinical applications. This focused perspective aims to highlight - but not exhaustively review - key factors accounting for these successes while pointing at crucial aspects worth considering for further breakthroughs.

HARIBOSS: a curated database of RNA-small molecules structures to aid rational drug design

MOTIVATION

RNA molecules are implicated in numerous fundamental biological processes and many human pathologies, such as cancer, neurodegenerative disorders, muscular diseases and bacterial infections. Modulating the mode of action of disease-implicated RNA molecules can lead to the discovery of new therapeutical agents and even address pathologies linked to 'undruggable' protein targets. This modulation can be achieved by direct targeting of RNA with small molecules. As of today, only a few RNA-targeting small molecules are used clinically. One of the main obstacles that have hampered the development of a rational drug design protocol to target RNA with small molecules is the lack of a comprehensive understanding of the molecular mechanisms at the basis of RNA-small molecule (RNA-SM) recognition.

RESULTS

Here, we present Harnessing RIBOnucleic acid-Small molecule Structures (HARIBOSS), a curated collection of RNA-SM structures determined by X-ray crystallography, nuclear magnetic resonance spectroscopy and cryo-electron microscopy. HARIBOSS facilitates the exploration of drug-like compounds known to bind RNA, the analysis of ligands and pockets properties and ultimately the development of in silico strategies to identify RNA-targeting small molecules.

AVAILABILITY AND IMPLEMENTATION

HARIBOSS can be explored via a web interface available at http://hariboss.pasteur.cloud.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prediction of allosteric druggable pockets of cyclin-dependent kinases

Cyclin-dependent kinase (Cdk) proteins play crucial roles in the cell cycle progression and are thus attractive drug targets for therapy against such aberrant cell cycle processes as cancer. Since most of the available Cdk inhibitors target the highly conserved catalytic ATP pocket and their lack of specificity often lead to side effects, it is imperative to identify and characterize less conserved non-catalytic pockets capable of interfering with the kinase activity allosterically. However, a systematic analysis of these allosteric druggable pockets is still in its infancy. Here, we summarize the existing Cdk pockets and their selectivity. Then, we outline a network-based pocket prediction approach (NetPocket) and illustrate its utility for systematically identifying the allosteric druggable pockets with case studies. Finally, we discuss potential future directions and their challenges.

RPpocket: An RNA–Protein Intuitive Database with RNA Pocket Topology Resources

RNA–protein complexes regulate a variety of biological functions. Thus, it is essential to explore and visualize RNA–protein structural interaction features, especially pocket interactions. In this work, we develop an easy-to-use bioinformatics resource: RPpocket. This database provides RNA–protein complex interactions based on sequence, secondary structure, and pocket topology analysis. We extracted 793 pockets from 74 non-redundant RNA–protein structures. Then, we calculated the binding- and non-binding pocket topological properties and analyzed the binding mechanism of the RNA–protein complex. The results showed that the binding pockets were more extended than the non-binding pockets. We also found that long-range forces were the main interaction for RNA–protein recognition, while short-range forces strengthened and optimized the binding. RPpocket could facilitate RNA–protein engineering for biological or medical applications.

The TAR binding dynamics and its implication in Tat degradation mechanism

Human immunodeficiency virus (HIV) is a retrovirus that progressively attacks the human immune system. It is known that the HIV viral protein Tat recruits the host elongation factor, positive transcription elongation factor b (P-TEFb), onto the nascent HIV viral transactivation response element (TAR) RNA to overcome the elongation pause for active transcription of the entire viral genome. Interestingly, there exists an amplifying feedback loop between Tat and TAR-a reduction in Tat increases the elongation pause, resulting in more TAR RNA fragments instead of the entire viral genome transcript, and the TAR fragments as a scaffold for PRC2 complex in turn promote Tat ubiquitination and degradation. In this study, the structural ensembles and binding dynamics of various interfaces in the Tat/TAR/P-TEFb complex are probed by all-atom accelerated sampling molecular dynamics simulations. The results show that a protein-binding inhibitor F07#13 targeting the Tat/P-TEFb interface initiates the above feedback loop and shuts down the active transcription. Another RNA binding inhibitor, JB181, targeting the Tat/TAR interface, can prevent TAR from pulling down the Tat from P-TEFb protein and further reducing Tat degradation. The detailed mechanism of the complex dynamics helps elucidate how Tat and TAR coordinate the regulation between HIV genome transcription versus possible HIV latency.