RBind: computational network method to predict RNA binding sites

Predicting Small Molecule Binding Nucleotides in RNA Structures Using RNA Surface Topography

RNA small molecule interactions play a crucial role in drug discovery and inhibitor design. Identifying RNA small molecule binding nucleotides is essential and requires methods that exhibit a high predictive ability to facilitate drug discovery and inhibitor design. Existing methods can predict the binding nucleotides of simple RNA structures, but it is hard to predict binding nucleotides in complex RNA structures with junctions. To address this limitation, we developed a new deep learning model based on spatial correlation, ZHmolReSTasite, which can accurately predict binding nucleotides of small and large RNA with junctions. We utilize RNA surface topography to consider the spatial correlation, characterizing nucleotides from sequence and tertiary structures to learn a high-level representation. Our method outperforms existing methods for benchmark test sets composed of simple RNA structures, achieving precision values of 72.9% on TE18 and 76.7% on RB9 test sets. For a challenging test set composed of RNA structures with junctions, our method outperforms the second best method by 11.6% in precision. Moreover, ZHmolReSTasite demonstrates robustness regarding the predicted RNA structures. In summary, ZHmolReSTasite successfully incorporates spatial correlation, outperforms previous methods on small and large RNA structures using RNA surface topography, and can provide valuable insights into RNA small molecule prediction and accelerate RNA inhibitor design.

Research progress on prediction of RNA-protein binding sites in the past five years

Accurately predicting RNA-protein binding sites is essential to gain a deeper comprehension of the protein-RNA interactions and their regulatory mechanisms, which are fundamental in gene expression and regulation. However, conventional biological approaches to detect these sites are often costly and time-consuming. In contrast, computational methods for predicting RNA protein binding sites are both cost-effective and expeditious. This review synthesizes already existing computational methods, summarizing commonly used databases for predicting RNA protein binding sites. In addition, applications and innovations of computational methods using traditional machine learning and deep learning for RNA protein binding site prediction during 2018-2023 are presented. These methods cover a wide range of aspects such as effective database utilization, feature selection and encoding, innovative classification algorithms, and evaluation strategies. Exploring the limitations of existing computational methods, this paper delves into the potential directions for future development. DeepRKE, RDense, and DeepDW all employ convolutional neural networks and long and short-term memory networks to construct prediction models, yet their algorithm design and feature encoding differ, resulting in diverse prediction performances.

MultiModRLBP: A Deep Learning Approach for Multi-Modal RNA-Small Molecule Ligand Binding Sites Prediction

This study aims to tackle the intricate challenge of predicting RNA-small molecule binding sites to explore the potential value in the field of RNA drug targets. To address this challenge, we propose the MultiModRLBP method, which integrates multi-modal features using deep learning algorithms. These features include 3D structural properties at the nucleotide base level of the RNA molecule, relational graphs based on overall RNA structure, and rich RNA semantic information. In our investigation, we gathered 851 interactions between RNA and small molecule ligand from the RNAglib dataset and RLBind training set. Unlike conventional training sets, this collection broadened its scope by including RNA complexes that have the same RNA sequence but change their respective binding sites due to structural differences or the presence of different ligands. This enhancement enables the MultiModRLBP model to more accurately capture subtle changes at the structural level, ultimately improving its ability to discern nuances among similar RNA conformations. Furthermore, we evaluated MultiModRLBP on two classic test sets, Test18 and Test3, highlighting its performance disparities on small molecules based on metal and non-metal ions. Additionally, we conducted a structural sensitivity analysis on specific complex categories, considering RNA instances with varying degrees of structural changes and whether they share the same ligands. The research results indicate that MultiModRLBP outperforms the current state-of-the-art methods on multiple classic test sets, particularly excelling in predicting binding sites for non-metal ions and instances where the binding sites are widely distributed along the sequence. MultiModRLBP also can be used as a potential tool when the RNA structure is perturbed or the RNA experimental tertiary structure is not available. Most importantly, MultiModRLBP exhibits the capability to distinguish binding characteristics of RNA that are structurally diverse yet exhibit sequence similarity. These advancements hold promise in reducing the costs associated with the development of RNA-targeted drugs.

Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN

The rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Most in silico tools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here, we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics simulations and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identifies all the experimentally resolved pockets and ranks them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field.

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.

Structure-based virtual screening of unbiased and RNA-focused libraries to identify new ligands for the HCV IRES model system

Targeting RNA including viral RNAs with small molecules is an emerging field. The hepatitis C virus internal ribosome entry site (HCV IRES) is a potential target for translation inhibitor development to raise drug resistance mutation preparedness. Using RNA-focused and unbiased molecule libraries, a structure-based virtual screening (VS) by molecular docking and pharmacophore analysis was performed against the HCV IRES subdomain IIa. VS hits were validated by a microscale thermophoresis (MST) binding assay and a Förster resonance energy transfer (FRET) assay elucidating ligand-induced conformational changes. Ten hit molecules were identified with potencies in the high to medium micromolar range proving the suitability of structure-based virtual screenings against RNA-targets. Hit compounds from a 2-guanidino-quinazoline series, like the strongest binder, compound 8b with an EC50 of 61 μM, show low molecular weight, moderate lipophilicity and reduced basicity compared to previously reported IRES ligands. Therefore, it can be considered as a potential starting point for further optimization by chemical derivatization.

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.

A computational method for small molecule-RNA binding sites identification by utilizing position specificity and complex network information

Some computational methods have been given for small molecule-RNA binding site identification due to that it plays a significant role in revealing biology function researches. However, it is still challenging to design an accurate model, especially for MCC. We designed a feature extraction technology from two aspects (position specificity and complex network information). Specifically, complex network was employed to express the space topological structure and sequence position information for improving prediction effect. Then, the features fused position specificity and complex network information were input into random forest classifier for model construction. The AUC of 88.22%, 77.92% and 81.46% were obtained on three independent datasets (RB19, CS71, RB78). Compared with the existing method, the best MCC were obtained on three datasets, which were 8.19%, 0.59% and 4.35% higher than the state-of-the-art prediction methods, respectively. The outstanding performances show that our method is a powerful tool to identify RNA binding sites, helping to the design RNA-targeting small molecule drugs. The data and resource codes are available at https://github.com/Kangxiaoneuq/PCN_RNAsite.

RNet: a network strategy to predict RNA binding preferences

Determining the RNA binding preferences remains challenging because of the bottleneck of the binding interactions accompanied by subtle RNA flexibility. Typically, designing RNA inhibitors involves screening thousands of potential candidates for binding. Accurate binding site information can increase the number of successful hits even with few candidates. There are two main issues regarding RNA binding preference: binding site prediction and binding dynamical behavior prediction. Here, we propose one interpretable network-based approach, RNet, to acquire precise binding site and binding dynamical behavior information. RNetsite employs a machine learning-based network decomposition algorithm to predict RNA binding sites by analyzing the local and global network properties. Our research focuses on large RNAs with 3D structures without considering smaller regulatory RNAs, which are too small and dynamic. Our study shows that RNetsite outperforms existing methods, achieving precision values as high as 0.701 on TE18 and 0.788 on RB9 tests. In addition, RNetsite demonstrates remarkable robustness regarding perturbations in RNA structures. We also developed RNetdyn, a distance-based dynamical graph algorithm, to characterize the interface dynamical behavior consequences upon inhibitor binding. The simulation testing of competitive inhibitors indicates that RNetdyn outperforms the traditional method by 30%. The benchmark testing results demonstrate that RNet is highly accurate and robust. Our interpretable network algorithms can assist in predicting RNA binding preferences and accelerating RNA inhibitor design, providing valuable insights to the RNA research community.

Computational tools to study RNA-protein complexes

RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein–RNA interactions are still poorly derstood in contrast to protein–protein and protein–DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.

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.

Translating from proteins to ribonucleic acids for ligand-binding site detection

Identifying druggable ligand-binding sites on the surface of the macromolecular targets is an important process in structure-based drug discovery. Deep-learning models have been shown to successfully predict ligand-binding sites of proteins. As a step toward predicting binding sites in RNA and RNA-protein complexes, we employ three-dimensional convolutional neural networks. We introduce a dataset splitting approach to minimize structure-related bias in training data, and investigate the influence of protein-based neural network pre-training before fine-tuning on RNA structures. Models that were pre-trained on proteins considerably outperformed the models that were trained exclusively on RNA structures. Overall, 71% of the known RNA binding sites were correctly located within 4 Å of their true centres with a structural overlap of at least 25%.

RNA-ligand molecular docking: advances and challenges

With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.

RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures

The 3D architectures of RNAs are essential for understanding their cellular functions. While an accurate scoring function based on the statistics of known RNA structures is a key component for successful RNA structure prediction or evaluation, there are few tools or web servers that can be directly used to make comprehensive statistical analysis for RNA 3D structures. In this work, we developed RNAStat, an integrated tool for making statistics on RNA 3D structures. For given RNA structures, RNAStat automatically calculates RNA structural properties such as size and shape, and shows their distributions. Based on the RNA structure annotation from DSSR, RNAStat provides statistical information of RNA secondary structure motifs including canonical/non-canonical base pairs, stems, and various loops. In particular, the geometry of base-pairing/stacking can be calculated in RNAStat by constructing a local coordinate system for each base. In addition, RNAStat also supplies the distribution of distance between any atoms to the users to help build distance-based RNA statistical potentials. To test the usability of the tool, we established a non-redundant RNA 3D structure dataset, and based on the dataset, we made a comprehensive statistical analysis on RNA structures, which could have the guiding significance for RNA structure modeling. The python code of RNAStat, the dataset used in this work, and corresponding statistical data files are freely available at GitHub (https://github.com/RNA-folding-lab/RNAStat).

DFpin: Deep learning-based protein-binding site prediction with feature-based non-redundancy from RNA level

The interaction between proteins and RNA is closely related to various human diseases. Computer-aided drug design can be facilitated by detecting the RNA sites that bind proteins. However, due to the aggregation of binding sites in RNA sequences, high sample similarity occurs when extracting RNA fragments by using a sliding window. Considering these problems, we present a method, DFpin, to predict protein-interacting nucleotides in RNA. To retain more key nucleotide sites, we used the redundancy method based on feature similarity, that is, feature redundancy is removed based on the RNA mono-nucleotide composition to maintain the diversity of RNA samples and avoid the residue of redundant data. In addition, to extract key abstract features and avoid over-fitting, we used the cascade structure of a deep forest model to predict protein-interacting nucleotides. Overall, DFpin demonstrated excellent classification with 85.4% accuracy and 93.3% area under the curve. Compared with other methods, the accuracy of DFpin was better, suggesting that feature-based redundancy removal and deep forest can help predict nucleotides of protein interactions. The source code and all dataset are available at: https://github.com/zhaoxj-tech/DFpin.git.

Structure-based deep learning for binding site detection in nucleic acid macromolecules

Structure-based drug design (SBDD) targeting nucleic acid macromolecules, particularly RNA, is a gaining momentum research direction that already resulted in several FDA-approved compounds. Similar to proteins, one of the critical components in SBDD for RNA is the correct identification of the binding sites for putative drug candidates. RNAs share a common structural organization that, together with the dynamic nature of these molecules, makes it challenging to recognize binding sites for small molecules. Moreover, there is a need for structure-based approaches, as sequence information only does not consider conformation plasticity of nucleic acid macromolecules. Deep learning holds a great promise to resolve binding site detection problem, but requires a large amount of structural data, which is very limited for nucleic acids, compared to proteins. In this study we composed a set of ∼2000 nucleic acid-small molecule structures comprising ∼2500 binding sites, which is ∼40-times larger than previously used one, and demonstrated the first structure-based deep learning approach, BiteNet_N, to detect binding sites in nucleic acid structures. BiteNet_N operates with arbitrary nucleic acid complexes, shows the state-of-the-art performance, and can be helpful in the analysis of different conformations and mutant variants, as we demonstrated for HIV-1 TAR RNA and ATP-aptamer case studies.

Docking and scoring for nucleic acid-ligand interactions: Principles and current status

Nucleic acid (NA)-ligand interactions have crucial roles in many cellular processes and, thus, are increasingly attracting therapeutic interest in drug discovery. Molecular docking is a valuable tool for studying molecular interactions. However, because NAs differ significantly from proteins in both their physical and chemical properties, traditional docking algorithms and scoring functions for protein-ligand interactions might not be applicable to NA-ligand docking. Therefore, various sampling strategies and scoring functions for NA-ligand interactions have been developed. Here, we review the basic principles and current status of docking algorithms and scoring functions for DNA/RNA-ligand interactions. We also discuss challenges and limitations of current docking and scoring approaches.

Dissecting and predicting different types of binding sites in nucleic acids based on structural information

The biological functions of DNA and RNA generally depend on their interactions with other molecules, such as small ligands, proteins and nucleic acids. However, our knowledge of the nucleic acid binding sites for different interaction partners is very limited, and identification of these critical binding regions is not a trivial work. Herein, we performed a comprehensive comparison between binding and nonbinding sites and among different categories of binding sites in these two nucleic acid classes. From the structural perspective, RNA may interact with ligands through forming binding pockets and contact proteins and nucleic acids using protruding surfaces, while DNA may adopt regions closer to the middle of the chain to make contacts with other molecules. Based on structural information, we established a feature-based ensemble learning classifier to identify the binding sites by fully using the interplay among different machine learning algorithms, feature spaces and sample spaces. Meanwhile, we designed a template-based classifier by exploiting structural conservation. The complementarity between the two classifiers motivated us to build an integrative framework for improving prediction performance. Moreover, we utilized a post-processing procedure based on the random walk algorithm to further correct the integrative predictions. Our unified prediction framework yielded promising results for different binding sites and outperformed existing methods.

Mining for Ligandable Cavities in RNA

Identifying potential ligand binding cavities is a critical step in structure-based screening of biomolecular targets. Cavity mapping methods can detect such binding cavities; however, for ribonucleic acid (RNA) targets, determining which of the detected cavities are "ligandable" remains an unsolved challenge. In this study, we trained a set of machine learning classifiers to distinguish ligandable RNA cavities from decoy cavities. Application of our classifiers to two independent test sets demonstrated that we could recover ligandable cavities from decoys with an AUC > 0.83. Interestingly, when we applied our classifiers to a library of modeled structures of the HIV-1 transactivation response (TAR) element RNA, we found that several of the conformers that harbored cavities with high ligandability scores resembled known holo-TAR structures. On the basis of our results, we envision that our classifiers could find utility as a tool to parse RNA structures and prospectively mine for ligandable binding cavities and, in so doing, facilitate structure-based virtual screening efforts against RNA drug targets.

Recognition of small molecule-RNA binding sites using RNA sequence and structure

Motivation

RNA molecules become attractive small molecule drug targets to treat disease in recent years. Computer-aided drug design can be facilitated by detecting the RNA sites that bind small molecules. However, very limited progress has been reported for the prediction of small molecule–RNA binding sites.

Results

We developed a novel method RNAsite to predict small molecule–RNA binding sites using sequence profile- and structure-based descriptors. RNAsite was shown to be competitive with the state-of-the-art methods on the experimental structures of two independent test sets. When predicted structure models were used, RNAsite outperforms other methods by a large margin. The possibility of improving RNAsite by geometry-based binding pocket detection was investigated. The influence of RNA structure’s flexibility and the conformational changes caused by ligand binding on RNAsite were also discussed. RNAsite is anticipated to be a useful tool for the design of RNA-targeting small molecule drugs.

Availability and implementation

http://yanglab.nankai.edu.cn/RNAsite.

Supplementary information

Supplementary data are available at Bioinformatics online.

A computational study of Tat-CDK9-Cyclin binding dynamics and its implication in transcription-dependent HIV latency

HIV is a virus that attacks the T cells. HIV may either actively replicate or become latent within host cells for years. Since HIV uses its own protein Tat to hijack the host CDK9-Cyclin complex for transcription, Tat is implicated in transcription-dependent HIV latency. To quantify the impact of Tat binding, we propose a computational framework to probe the dynamics of the CDK9-Cyclin interface and the ATP pocket reorganization upon binding by different Tat mutants. Specifically, we focus on mutations at three Tat residues P10, W11, and N12 that are known to interact directly with CDK9 based on the crystal structure of the Tat-CDK9-Cyclin complex. Our molecular dynamics simulations show that the CDK9-Cyclin interface becomes slightly weaker for P10S and W11R mutants but tighter for the K12N mutant. Furthermore, the side chain orientation of residue K48 in the ATP pocket of CDK9 is similar to the inactive state in P10S and W11R simulations, but similar to the active state in K12N simulations. These are consistent with some existing but puzzling observations of latency for these mutants. This framework may hence help gain a better understanding of the role of Tat in the transcription-dependent HIV latency establishment.

RBinds: A user-friendly server for RNA binding site prediction

RNA performs various biological functions by interacting with other molecules. The knowledge of RNA binding sites is essential for the understanding of RNA-protein or RNA-ligand complex structures and their mechanisms. However, the RNA binding site prediction study requires tedious programming scripts and manual handling. One user-friendly bioinformatics tool for RNA binding site prediction has been missing. This limitation motivated us to develop the RBinds, a user-friendly web server, to predict the RNA binding site using a simple graphical user interface. Some advanced features implemented in RBinds are (1) transforming the RNA structure to a network automatically; (2) analyzing the structural network properties to predict binding site; (3) constructing one annotated force-directed network; (4) providing a visualization tool for users to scale and rotate the structure; (5) offering the related tools to predict or simulate RNA structures. RBinds web server is a reliable and user-friendly tool and facilitates the RNA binding site study without installing programs locally. RBinds is freely accessible at http://zhaoserver.com.cn/RBinds/RBinds.html.

Augmented base pairing networks encode RNA-small molecule binding preferences

RNA-small molecule binding is a key regulatory mechanism which can stabilize 3D structures and activate molecular functions. The discovery of RNA-targeting compounds is thus a current topic of interest for novel therapies. Our work is a first attempt at bringing the scalability and generalization abilities of machine learning methods to the problem of RNA drug discovery, as well as a step towards understanding the interactions which drive binding specificity. Our tool, RNAmigos, builds and encodes a network representation of RNA structures to predict likely ligands for novel binding sites. We subject ligand predictions to virtual screening and show that we are able to place the true ligand in the 71st-73rd percentile in two decoy libraries, showing a significant improvement over several baselines, and a state of the art method. Furthermore, we observe that augmenting structural networks with non-canonical base pairing data is the only representation able to uncover a significant signal, suggesting that such interactions are a necessary source of binding specificity. We also find that pre-training with an auxiliary graph representation learning task significantly boosts performance of ligand prediction. This finding can serve as a general principle for RNA structure-function prediction when data is scarce. RNAmigos shows that RNA binding data contains structural patterns with potential for drug discovery, and provides methodological insights for possible applications to other structure-function learning tasks. The source code, data and a Web server are freely available at http://rnamigos.cs.mcgill.ca.

NAPS update: network analysis of molecular dynamics data and protein-nucleic acid complexes

Network theory is now a method of choice to gain insights in understanding protein structure, folding and function. In combination with molecular dynamics (MD) simulations, it is an invaluable tool with widespread applications such as analyzing subtle conformational changes and flexibility regions in proteins, dynamic correlation analysis across distant regions for allosteric communications, in drug design to reveal alternative binding pockets for drugs, etc. Updated version of NAPS now facilitates network analysis of the complete repertoire of these biomolecules, i.e., proteins, protein–protein/nucleic acid complexes, MD trajectories, and RNA. Various options provided for analysis of MD trajectories include individual network construction and analysis of intermediate time-steps, comparative analysis of these networks, construction and analysis of average network of the ensemble of trajectories and dynamic cross-correlations. For protein–nucleic acid complexes, networks of the whole complex as well as that of the interface can be constructed and analyzed. For analysis of proteins, protein–protein complexes and MD trajectories, network construction based on inter-residue interaction energies with realistic edge-weights obtained from standard force fields is provided to capture the atomistic details. Updated version of NAPS also provides improved visualization features, interactive plots and bulk execution. URL: http://bioinf.iiit.ac.in/NAPS/

Computational study of switching mechanism in add A-riboswitch

Riboswitch can bind small molecules to regulate gene expression. Unlike other RNAs, riboswitch relies on its conformational switching for regulation. However, the understanding of the switching mechanism is still limited. Here, we focussed on the add A-riboswitch to illustrate the dynamical switching mechanism as an example. We performed molecular dynamics simulation, conservation and co-evolution calculations to infer the dynamical motions and evolutionary base pairings. The results suggest that the binding domain is stable for molecule recognition and binding, whereas the switching base pairings are co-evolutionary for translation. The understanding of the add A-riboswitch switching mechanism provides a potential solution for riboswitch drug design.

HKPocket: human kinase pocket database for drug design

Background

The kinase pocket structural information is important for drug discovery targeting cancer or other diseases. Although some kinase sequence, structure or drug databases have been developed, the databases cannot be directly used in the kinase drug study. Therefore, a comprehensive database of human kinase protein pockets is urgently needed to be developed.

Results

Here, we have developed HKPocket, a comprehensive Human Kinase Pocket database. This database provides sequence, structure, hydrophilic-hydrophobic, critical interactions, and druggability information including 1717 pockets from 255 kinases. We further divided these pockets into 91 pocket clusters using structural and position features in each kinase group. The pocket structural information would be useful for preliminary drug screening. Then, the potential drugs can be further selected and optimized by analyzing the sequence conservation, critical interactions, and hydrophobicity of identified drug pockets. HKPocket also provides online visualization and pse files of all identified pockets.

Conclusion

The HKPocket database would be helpful for drug screening and optimization. Besides, drugs targeting the non-catalytic pockets would cause fewer side effects. HKPocket is available at http://zhaoserver.com.cn/HKPocket/HKPocket.html.

DIRECT: RNA contact predictions by integrating structural patterns

Background

It is widely believed that tertiary nucleotide-nucleotide interactions are essential in determining RNA structure and function. Currently, direct coupling analysis (DCA) infers nucleotide contacts in a sequence from its homologous sequence alignment across different species. DCA and similar approaches that use sequence information alone typically yield a low accuracy, especially when the available homologous sequences are limited. Therefore, new methods for RNA structural contact inference are desirable because even a single correctly predicted tertiary contact can potentially make the difference between a correct and incorrectly predicted structure. Here we present a new method DIRECT (Direct Information REweighted by Contact Templates) that incorporates a Restricted Boltzmann Machine (RBM) to augment the information on sequence co-variations with structural features in contact inference.

Results

Benchmark tests demonstrate that DIRECT achieves better overall performance than DCA approaches. Compared to mfDCA and plmDCA, DIRECT produces a substantial increase of 41 and 18%, respectively, in accuracy on average for contact prediction. DIRECT improves predictions for long-range contacts and captures more tertiary structural features.

Conclusions

We developed a hybrid approach that incorporates a Restricted Boltzmann Machine (RBM) to augment the information on sequence co-variations with structural templates in contact inference. Our results demonstrate that DIRECT is able to improve the RNA contact prediction.

Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.