RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, current computational methods pose challenges to the cross-prediction of RNA-protein binding events across diverse cell lines and tissue contexts. Here, we develop HDRNet, an end-to-end deep learning-based framework to precisely predict dynamic RBP binding events under diverse cellular conditions. Our results demonstrate that HDRNet can accurately and efficiently identify binding sites, particularly for dynamic prediction, outperforming other state-of-the-art models on 261 linear RNA datasets from both eCLIP and CLIP-seq, supplemented with additional tissue data. Moreover, we conduct motif and interpretation analyses to provide fresh insights into the pathological mechanisms underlying RNA-RBP interactions from various perspectives. Our functional genomic analysis further explores the gene-human disease associations, uncovering previously uncharacterized observations for a broad range of genetic disorders. Predicting dynamic RNA-RBP interactions in diverse cell lines is an important challenge in unravelling RNA function and post-transcriptional regulatory mechanisms. Here, authors develop HDRNet, an end-to-end deep-learning-based framework for accurately predicting dynamic RBP binding events across various cellular conditions. Accurately identifying RBP binding states in specific cellular conditions is a significant challenge that required for unraveling the underlying regulatory mechanisms and understanding their biological function. Traditional experiment-based biological methods such as systematic evolution of ligands by exponential selection (SELEX)4, RNAcompete5, and RNA Bind-n-Seq6 have been developed to characterize the sequence preferences of RBPs in vitro, while RNA immunoprecipitation (RIP)7 and other immunoprecipitation-based technologies10 were proposed to identify RBP binding sites in vivo. Unfortunately, these laboratory-based experiments are time-consuming, labor-intensive, and susceptible to measurement errors. Therefore, developing high-throughput, accurate, and robust approaches to investigate RBP binding modes is of great importance11.RNA-binding proteins (RBPs) are an essential group of proteins that interact with RNA by recognizing specific RNA-binding domains, and are involved in post-transcriptional regulation of RNA splicing, translocation, sequence editing, intracellular localization, and translational control12, many RBP-RNA binding targets have been uncovered14, enabling us to develop effective data-driven computational methods15. These methods can be broadly classified into two categories16, either predicting RNA-binding sites on the protein surface17 or modeling the preferred RNA sequences of RNA-binding proteins18. From the protein perspective, several computational tools have been developed to predict the RBP-RNA binding sites at the protein level. For instance, SCRIBER, uses predictions of binding residues for several partner types to effectively reduce cross-prediction of the output protein-binding residues, combining novel and previously used input types19, while aPRBind, developed by Liu et al., combines protein sequence and structural features for RNA-binding residue prediction20. DRNApred21, a fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues was proposed by Yan et al., with regression that penalizes cross-predictions, and a two-layered architecture. However, these statistical or machine learning-based algorithms often encounter performance limitations as the size of the dataset continues to grow. This is primarily due to their inability to effectively capture complex patterns and relationships in large-scale data, which can lead to unsatisfactory predictive performance. Recently, to address such limitations, deep learning methods have been developed; for instance, DeepSite employs a 3D deep convolutional neural network to predict the binding site using protein structure22. Xia et al. proposed GraphBind, an end-to-end graph neural network that uses hierarchical graph neural networks to identify nucleic-acid-binding residues on proteins23. Zhang et al.24 presented DeepDISOBind, a deep multi-task architecture that accurately predicts DNA-, RNA- and protein-binding regions from protein sequences. Most recently, Lam et al. introduced NucleicNet, a deep learning model that predicts the binding preference of RNA backbone constituents and different bases from local physicochemical characteristics of the protein structure surface25. These methods have collectively enhanced our understanding of the binding properties of RBPs at the protein surface level.Thanks to the development of cross-linked immunoprecipitation sequencing technology26 was developed to understand RBP binding preferences in RNA sequence data using a deep neural network (DNN). Ilan et al. developed DLPRB27, a new DNN approach based on convolutional neural networks (CNN) and recurrent neural networks (RNN) for learning intrinsic RBP binding preferences and predicting novel interactions. Pan et al. developed iDeep28, a hybrid convolutional neural network and deep belief network-based model to predict the RBP interaction sites and motifs on RNAs. Daniel et al. proposed GraphProt29 that integrates sequence and computationally predicted RNA secondary structure information into graph-kernel features. Laverty et al. introduced PRIESSTESS30, a universal RNA motif-finding/scanning strategy capable of identifying enriched RNA sequence and/or structure motifs that are subsequently reduced to a set of core motifs by logistic regression with LASSO regularization. However, these methods only account for a specific cellular condition and are hence limited in their ability to predict RNA-protein interactions in other cell lines while considering the dynamic contexts.In parallel, the establishment of links between RBPs and their targets from the perspective of RNA sequences enables understanding of the regulation mechanism. A variety of efforts have been developed to address it in identical cellular conditions, referred to as static protein-RNA interactions; for instance, Deepbind31. In other words, the binding sites of the RBP that are exclusively present in specific cells or tissues can be designated as dynamic protein-RNA interactions. Recently, a new deep learning-based method called PrismNet32, was developed to accurately predict dynamic RBP binding in various cellular conditions by integrating in vivo experimental RNA structure information33. Nonetheless, unfortunately, we conceive that there are still room for improvements; for instance, the architectural design of the deep neural network within PrismNet can still be investigated in the context of neural network architecture search, indicating the potential for further improvements in RBP-RNA binding prediction. In addition, the one-hot encoding representation in PrismNet exhibits limitations that may hinder its ability to capture RNA features globally. Furthermore, the heterogeneity of transcripts results in widely sequential relationships across different contexts, such as various cell lines, tissues, and normal or disease conditions. The one-hot encoding may overlook this contextual information, treating each position independently and potentially missing crucial sequence patterns. Therefore, it is crucial to address these limitations by exploring alternative coding methods to leverage the contextual and sequential nature of RNA sequences.Indeed, the binding behavior of RBP-RNA interactions has been demonstrated to be dynamic in different cell types, as it is influenced by different cellular or tissue environments36 have proven to be effective frameworks for automatically encoding RNA sequences due to their syntactic, lexical, and semantic similarities to human language37. On this basis, much effort has been devoted to the application of advanced NLP techniques in RNA-related problems40. However, most existing research on the application of NLP techniques in RNA-related problems has relied on static embedding models such as Word2Vector41, GloVe35, Doc2Vector36, and FastText42. Similar to one-hot representation, static embedding methods maintain the same nucleotide encoding across all contexts, which leads to a failure to capture the context-based dynamic semantic information of RNA sequences and therefore cannot represent RNA sequence features globally. Moreover, static embedding does not capture the underlying information of nucleotide sequences in different cellular contexts, thus limiting the ability of dynamic prediction in other cellular conditions. To address this knowledge gap, we propose to adopt and customize the Bidirectional Encoder Representations from Transformers (BERT) model43 to learn the context-dependent information of RNA sequences, which can generate robust expressions containing the global contextual information by pre-training with a large-scale unlabeled text corpus in a self-supervised fashion. In contrast to static embeddings where nucleotide embeddings remain the same regardless of the context, the primary advantage of dynamic embeddings lies in their ability to generate context-specific features in different nucleotide contexts. Moreover, considering the heterogeneity of transcripts under different cellular, tissue, or physiological conditions , dynamic embeddings can overcome the limitation of a single representation for diverse contextual sequences, enriching the global features of the sequences and surpassing the performance bottleneck in dynamically predicting RBP binding sites. Therefore, by leveraging the advantages of the transformer model, we adopt the BERT model to encode RNA sequences in dynamic embedding vectors that then contain rich, global contextual semantic information for identifying dynamic RNA-binding events between different cell lines, tissues, or physiological conditions .The contextual relationship has been a focus of increasing attention, as word embedding techniquesHere, we propose HDRNet , a new end-to-end deep learning model for identifying RNA-binding interactions from eCLIP-seq data in various cellular conditions. To capture the hierarchical relationships between nucleotide sequences, we adopt multi-source biological information, including in vivo RNA secondary structure information and bio-language features, to characterize both the sequence and structural features of RNA. Then, we combine biological information from different sources using the unified alignment model to uncover possible relationships between nucleotide sequences and latent structural information. Additionally, hierarchical multi-scale residual networks (HMRN) are leveraged to comprehend the contextual dependencies between the nucleotides, and deep protein-RNA binding predictor (DPRBP) is developed to extract the contextual significance of the nucleotide sequences by stacking several pyramid convolutional blocks, and incorporating batch normalization and residual shortcut connections into the network to boost robustness and reduce overfitting. We evaluate HDRNet on 261 linear RNA datasets and compare it with other baseline methods. We demonstrated its validity and scalability in both static and dynamic cellular conditions. In addition, we performed motif and interpretation analyses to gain new insights into the pathological mechanisms of RNA-RBP interactions. Our functional genomic analysis revealed the association between genes and human diseases, leading to previously unknown observations on a wide range of inherited diseases.The HDRNet framework has the capability to perform accurate prediction of RBP binding events by leveraging robust features from multi-source biological information to aid in the identification of high-attention binding peaks and subsequent analysis of RBP binding data, as depicted in Fig. The HDRNet framework starts by generating multiple sources of biological information to represent the RNA sequence or structure, where the dynamic global contextual embedding representation comes from tagging the input RNA sequence as genetic codon tokens using the k-mer method and then encoding each token as a dynamic embedding vector using the pre-trained deep bidirectional transformer model (BERT). Dynamic embedding implies that the same token has different encoding in different contexts and therefore contains more nucleotide contextual information as well as long-distance dependencies. Meanwhile, the icSHAPE-pipeline is employed to analyze and generate the in-vivo RNA secondary structure profile, which is a numerical vector of the same length as the RNA sequence, providing the model with valuable information on the structural binding preferences of RBP.Afterward, HDRNet utilizes a unified alignment of multi-source feature representation, which consists of two convolutional neural network (CNN) modules, to preliminary extract the underlying features of both the sequence and structure, and to unify their feature dimensionality, respectively. HDRNet then adopts two HMRNs to analyze the sequence and structural features independently. Each extractor comprises of CNN modules with distinct kernel size to comprehend the contextual dependencies between the nucleotides and their structures at varying distance, for accurate interaction prediction. Thereafter, HDRNet amalgamates all the extracted features and implements the DPRBP with a multi-layer feature selector module, which learns the underlying representation and selects the crucial nucleotide tokens in each layer of the module. Furthermore, HDRNet employs a sigmoid activation function on the network’s output to forecast the binding of the RNA sequence to the protein.Finally, we can identify and align the binding motifs of RNA binding proteins by exploring the interpretability of HDRNet. Further, we delved into the underlying connections between the dynamic binding sites and human diseases by identifying the high-attention binding regions and their potential variants. To support this finding, our study also integrated transcriptomics to unveil disease regulatory mechanisms, including gene ontology enrichment, KEGG pathway analysis, protein-protein interaction network analysis, transcription factor-gene interaction analysis, miRNA-gene interaction analysis, and drug prediction analysis. Our analyses provide new insights into the interpretation of RBP datasets and the treatment of human diseases.32, PRIESSTESS30, DMSK39, iDeep28, DeepBind26, and Graphprot29 on 261 static RBP binding site datasets. The area under the receiver operating characteristic curve (AUC) was adopted as the performance metric for all computational methods. For each RBP dataset, we partitioned the binding sites into training and test sets. Then, we used the test set to benchmark all prediction methods32. Note that we used the data from a uniform pipeline32, which ensures that the data is accurate and consistent and that the comparison is fair.We evaluated the performance of our proposed HDRNet by comparing it with five other state-of-the-art computational methods, namely PrismNetCircos plot in Fig. The binding of RBPs is influenced by different cellular environments, and therefore, this binding is expected to be dynamic in diverse cell lines. BERT extracts the dynamic semantic information from RNA sequences globally, and is capable of revealing different RNA-protein interactions in particular cellular conditions. We evaluated the performance of HDRNet in predicting dynamic RNA-protein interactions on 62 RBP datasets obtained from K562 and HepG2 cell lines.MT-ND5 transcript. According to eCLIP, the MT-ND5 transcript contains 11 HNRNPA1 binding sites in HepG2 cells, and 6 binding sites in K562 cells. We found that HDRNet correctly predicted all 11 binding sites within the MT-ND5 transcript in HepG2 cells with no false positives, by using the model trained on K562 cells. In contrast, DeepBind and GraphProt, correctly predicted only 2 of the 11 sites, and iDeep and DMSK correctly predicted 4 of the 11 sites, and PrismNet and PRIESSTESS correctly predicted 7 of the 11 binding sites. Figure Specifically, we trained HDRNet on the RBP datasets of the K562 cell line. We then used the corresponding RNA sequence information generated by BERT and in vivo secondary structure information to predict the RBP binding sites in the HepG2 cell line, which was performed as an independent test set. Subsequently, we trained HDRNet on the HepG2 cell line data, and used data on K562 cell line as an independent test set. We then compared the dynamic prediction performance of HDRNet with other state-of-the-art deep learning methods, including PrismNet, PRIESSTESS, iDeep, DMSK, GraphProt, and Deepbind. Figure 44 (GEO accession: GSE68890) from POSTAR45, which studied the regulation of MBNL proteins in human brain tissues. In particular, a total of 5 datasets were obtained from autospy tissues  of patients with myotonic dystrophy type 1 , myotonic dystrophy type 2 , and control patients (2 datasets). HDRNet and the baseline models, including PrismNet, DMSK, iDeep, GraphProt, DeepBind, and PRIESSTESS were then trained on these datasets separately, and the trained models were tested on different tissues with different control context. As illustrated in Fig. 46, MBNL2 directly interacts with DM1 expanded CUG repeats and DM2 CCUG expansion RNAs in the brain, which functionally depletes the MBNL proteins. As illustrated in Fig. 47 to extract high-attention dynamic semantic information, we discovered that HDRNet successfully captured these disease-related high-attention regions. For example, in both DM1 datasets, HDRNet highlights continuous segments of CUG expansions, while in the DM2 dataset, HDRNet also detected significant regions of continuous CCUG expansions. These findings further confirm the superior biological interpretability of HDRNet and its potential to provide theoretical support for pathological research.We went on to explore the dynamic prediction capabilities of HDRNet between different in vivo tissue contexts, especially in normal and disease conditions. Specifically, we collected the MBNL2 (Muscleblind Like Splicing Regulator 2) binding data46 that studied the direct regulatory targets of MBNL1 in brain, heart, muscle, and myoblasts from mice. We obtained a total of five datasets (GEO accession: GSE39911), including two from the brain, one from muscle, one from heart, and one from myoblasts (C2C12 cells). As illustrated in Fig. Besides tissue-specific dynamic prediction, we asked whether the HDRNet model trained using cell line data is sufficient for predicting dynamic interactions in tissues. To clarify this, we retrieved two additional eCLIP RBP datasets from ENCODE, DGCR8, and HNRNPU, which were both derived from adrenal gland tissue. We then employed the models trained on K562 and HepG2 cell line data to validate these tissue RBP binding data. As expected, Supplementary Fig. lasts C2C cells. Alasts C2C cells. A41, Glove35, Doc2Vector36, FastText48, and the One-Hot coding scheme. Briefly speaking, the difference between dynamic and static methods is that dynamic encoding generates different embeddings in different contexts, while the embedding of each token is fixed in the static encoding methods. Figure We further discuss the superiority of HDRNet from the perspective of NLP methods and RNA secondary structure, respectively. We first conducted experiments comparing BERT with other existing static embedding methods, including Word2VectorThen, to further investigate the advantage of adding in vivo secondary structure information in vivo, we conducted several experiments to dissect how RNA sequence and structural information contribute to the accurate prediction by HDRNet. Firstly, we evaluated the performance of HDRNet with or without the secondary structure information in static prediction tasks, respectively. As expected, Fig. 49. We speculate that the reason for this is the powerful feature learning capability of HDRNet, able to extract important information from dynamic embeddings at different levels. After the Hierarchical Multi-scale Residual Network and before DPRBP, we also tried a different way to combine features by adding sequence features and structural features together instead of stringing them together. The experimental results are summarized in Fig. We explored the impact of several key parameters on HDRNet functioning. We first investigated the performance of different k-mer sizes in HDRNet, including 3bp to 6bp. As visualized in Fig. DUSO; (2) We employed only the DPRBP stage of HDRNet for prediction, called HDRNetDPRBP; (3) We tested HDRNet without the Hierarchical Multi-scale Residual Network, called HDRNetAVGpool. As depicted in Fig. DUSO is unable to provide accurate predictions, indicating that DPRBP has significant feature extraction and prediction ability. Furthermore, using only DPRBP, the performance of HDRNetDPRBP decreased by 4%, demonstrating that multiple deep learning mechanisms are effective in learning and integrating the underlying features of sequence and structure information. Moreover, the proposed HDRNet is higher than HDRNetThen, to assess the contribution of each component in our proposed deep network architecture, we ablated each component of HDRNet as follows: (1) We first removed the Hierarchical Multi-scale Residual Network and DPRBP and replaced them with a fully connected network, called HDRNett}NMVRS;  We replaFurthermore, after each DPRBP block, the sequence length is halved using pooling layers that preserve the most important features. We conducted an experiment to investigate the impact of various pooling layers, employing either the maximum pooling layer or the average pooling layer. Figure 50. To identify motifs by our HDRNet, we put all of the RNA sequences through BERT’s fine-tuning process and generated an attention vector for each sequence. Then, we used the attention vectors to find consecutive high-attention segments using sliding windows and aligned related subregions as the final binding motifs. Once all high-attention sequence fragments were recovered, their nucleotide occurrences were counted and transformed into position weight matrices (PWMs). As visualized in Fig. 51 in the MEME Suite to match the authenticated motifs in the ATtRACT database with the motifs discovered by the transformers. As can be seen in Supplementary Table The self-attentive mechanism can capture critical RNA sequence fragments, hence enhancing the ability to recognize motifs47 was employed to extract high-attention dynamic semantic information. We found unexpectedly that HDRNet captured the binding peaks associated with the extracted motifs and identified the structural preferences of the binding events. Taking the input sequence of RBP TIA1 in Hela cells as an example, Supplementary Fig. 32 , primarily including Single-Nucleotide Polymorphisms (SNPs) and Single-Nucleotide Variations (SNVs), are permanent changes in the nucleotides of DNA sequence that makes up a gene. The alternative alleles of a GV may confer different binding specificity to an RBP since each RBP has its own sequence specificity, implying that GVs may disrupt the identification of RNA substrates by the RBP, resulting in allele-specific functional consequences and leading to severe diseases53. Many studies have demonstrated that TDP-43 is associated with cancers54 and severe neurological disorders such as epilepsy, amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTLD) and Alzheimer’s disease (AD)58. To further elaborate the relationship between TDP-43 binding properties and disease-related genetic variants, we first used HDRNet to scan the TDP-43 dataset and obtained a total of 10589 TDP-43 binding sites . By comparing with the dbSNP59 and COSMIC databases60, we found that the TDP-43 binding sites were enriched with a substantial number of SNP and SNV mutant alleles, especially in the high-attention regions identified by HDRNet. To elucidate the correlation between diseases and potential genetic variants identified by HDRNet, we conducted experiments to observe binding behavior after applying a transformation to the variant alleles. As shown in Fig. ENST00000547986 from K562 cells shown in Fig. ENST00000533549 of K562 cells shown in Fig. ENST00000533549 of HEK293 cells, intron variant located at chr10:100517088 also reduced the binding tendency and is also associated with large intestine tumor.TARDBP, or TDP-43 (TAR DNA binding protein 43) protein that binds to DNA and RNA, plays an important function in intracellular RNA transcription, selective shearing, and regulation of mRNA stabilityNDUFB8 transcript were highly associated with the large intestine cancer, indicating that the disruption of such dynamic RNA-RBP interactions are potential links with carcinogenesis. In summary, we can conclude that these analyses validate the biological interpretability of HDRNet to identify potential genetic variants and reveal underlying associations of the RBP binding sites with human diseases.Notably, we show that the dynamic binding events of Fig. 61 in Fig. 62 to perform a gene ontology enrichment analysis on the 544 TDP-43 binding genes of K562 and HEK293 cells. This helped us to understand what they might mean biologically and which pathways were enriched. Then, we used the GO database as an annotation source to annotate three types of gene ontology analysis: biological processes, molecular function, and cellular components. The top 10 terms of these three categories based on p-value are depicted in Fig. 63, and is involved in other cellular processes such as microRNA biogenesis, apoptosis, and cell division64. In the molecular function experiment, we saw RNA binding (220 genes) and mRNA binding (55 genes) were the two top GO terms. It is known that RNA binding proteins (RBPs) are highly associated with neurological diseases65, corroborating the crucial need for studying and predicting RNA-RBP interactions. The nucleus (265 genes) and intracellular membrane-bounded organelles (283 genes) were the top GO terms for cellular components. Previous research has demonstrated a significant loss of neurons within the lateral part of the pedunculopontine nucleus in individuals with idiopathic Parkinson’s disease and in individuals with combined Parkinson’s and Alzheimer’s diseases66, revealing the association between the nucleus and the neurological disease. In addition, variations in intracellular membrane-bounded organelles are likewise a potential cause of neurological disorders68.After examining the latent relationship between RBP binding sites and human disease, we continued to study the link between RBP dynamic binding and human disease with a view of functional genomic and to find possible medical interventions from a transcriptomic angle. After obtaining TDP-43 binding sites, we mapped the binding transcript IDs to gene symbols and defined these genes as TDP-43-binding genes. In particular, the shared mapped binding sites were defined as dynamic binding genes. Firstly, we examined the interrelationships between the TDP-43 binding sites in K562 and HEK293 cells. Among the identified binding sites, 1063 binding genes (BGs) for K562 cells and 1894 BGs for HEK293 cells were recoginzed. The Venn diagram69. The most significant pathways of the genes in K562 cells and HEK293 cells of the TARDBP binding sites evaluated by p-value are summarized in Fig. 70, Amyotrophic lateral sclerosis (ALS)56, Huntington’s Disease (HD)71, Prion Disease (PD)72, and Alzheimer’s disease (AD)73.Further, we performed KEGG pathway analysis on the 544 shared TARDBP binding genes between K562 cells and HEK293 cells, revealing that the organism is capable of reacting to inherent modifications. KEGG pathway analysis can demonstrate the interaction between various diseases through basic molecular or biological processes74 to build a protein–protein interaction (PPI) network to visualize the interactions and adhesion pathway. As depicted in Supplementary Fig. 75 in Cytoscape was adopted to identify the most important modules, as shown in Fig. 76 using the MCC method76. As shown in Fig. NDUFA12, NDUFB9 and NDUFB8, and mutations in these genes are new causes of complex I deficiency79, which is known to be associated with Parkinson’s disease80. Moreover, focusing on these top hub TARDBP binding genes, we performed the pan-cancer analyses to characterize the differential expression of these genes in various cancers using TIMER81  binding patterns at the transcript level, accounting for the dynamic cellular conditions. However, current computational methods have limitations in considering the diversity of cellular conditions, which poses a significant challenge for predicting the cross-prediction events between RNAs and proteins in different cells. Here, we proposed an end-to-end deep learning-based framework to precisely predict dynamic RBP binding events across diverse cellular conditions. Specifically, we adopted multi-source biological information including the dynamic global contextual embedding and the in-vivo RNA secondary structure profile to characterize both the sequence and structural properties of RNA. Then, a unified alignment of multi-source feature representation was employed to generate potential feature maps with dimensional homogeneity for both biological representation. After that, we proposed a hierarchical multi-scale residual network to comprehend the contextual dependencies between the nucleotides and their structures at varying distances. Finally, a deep protein-RNA binding predictor with a multi-layer feature selector module was developed to learn and select the underlying key nucleotides tokens and employed a sigmoid function to forecast the binding of the proteins to RNA sequences.We compared HDRNet with five RBP prediction methods in static cellular protein-RNA interaction identification. The experimental results showed that HDRNet had the best prediction performance, outperforming all compared methods on most benchmark datasets. In particular, HDRNet was able to efficiently learn implicit representations of RNA sequences and structural information. Meanwhile, HDRNet brought new insights by characterizing RNA binding events between different cell lines in a dynamic manner. Specifically, we trained HDRNet on the RBP dataset of K562 or HepG2 cells, and then predicted the RBP binding sites in the other cell line as an independent test set. We compared the dynamic prediction performance of HDRNet with the 5 benchmark methods, and found that HDRNet outperformed other methods on both K562 and HepG2 cells. HDRNet successfully predicted all binding sites of RBP HNRNPA1 on the MT-ND5 transcript with no false positives. Moreover, HDRNet pioneeringly accomplished cross-tissue dynamic prediction tasks and successfully highlighted the significant binding regions, illustrating the robustness of HDRNet in dynamic prediction.In addition, HDRNet can also elucidate the underlying binding motifs from the dynamic global contextual embedding. We obtained a total of 172 motifs and discovered that RBPs involved in the RNA regulatory pathways were generally grouped together via hierarchical clustering. In addition, we found that HDRNet can detect the specific binding-peak by capturing the attention of the input data, thus enabling the identification of specific binding events in different cellular conditions. Then, through comparison with the genomic variants dataset, we identified that HDRNet captured high attention regions enriched with mutable alleles, and that alterations in these variants had significant changes in binding events in these high attention regions, revealing a potential link between RBP binding sites and human diseases. We also investigated the interrelationships between RBP binding sites and human diseases from a transcriptomic perspective by mapping the binding sites onto gene symbols and performing a series of genomic analyses, and provided possible drug molecules for disease treatment.In summary, HDRNet discards the traditional RNA sequence representation and uses multi-source biological information to characterize the binding patterns of RNA binding proteins. As a new deep-learning method, HDRNet can simultaneously perform both static and dynamic RBP binding prediction, potential feature extraction, binding-peak identification, and interpretability analyses.32. Among these, the length of each binding site was fixed at 101 nt while a region shorter than 101 nt was extended from the middle to both sides and a region longer than 101 nt was cut off from both sides. In addition, 10000 negative samples were generated by randomly selecting 101 nt from the whole transcriptome. Then, we labeled the positive samples as ’1’ and negative samples as ’0’. For each RBP dataset, we constructed a HDRNet model for each cell line separately. During the training of each RBP dataset, 20% of the samples were randomly selected as an independent test set to evaluate the performance of the model. Among the remaining 80% of the training samples, 20% of the training samples were randomly selected as the validated set to optimize the model parameters, while the rest of the data was considered as the training set. For convenience, we have provided all datasets and the BERT model on the website 10.6084/m9.figshare.24132423.For each RBP dataset, the resulting peaks were defined as binding sites, and the top 5000 binding sites with the most confident peaks were reserved for training and test sets as positive samples82. Therefore, it is essential to effectively identify RNA secondary structure features for precise RBP binding prediction. Although computational methods of structural analysis can also yield predictions of RNA structure from sequences with a certain degree of accuracy, the predicted RNA structures do not reflect the real situation under dynamic cellular conditions, which greatly limits dynamic RBP prediction. In vivo click selective 29-hydroxyl acylation and profiling experiment icSHAPE83 pipeline allows for the generation of RNA structure profiles, providing insights into the dynamic nature of RNA structure across the entire transcriptome from in vivo experiments, enabling the global and accurate characterization of the relationship between RNA structure and RNA binding protein (RBP) interactions85. In particular, icSHAPE is a chemical modification-based approach, which is known for providing the natural reflection on RNA folding.82. Lastly, another key factor is the widespread acceptance and usage within the research community of icSHAPE85, and this tool has garnered significant support and is backed by high-quality data. Thus, in our study, we adopted the icSHAPE technique to characterize the in vivo RNA secondary structure in different cellular conditions.In general, the function of RNA is closely related to its complex folded secondary and tertiary structures. In addition, RNA secondary structure motifs are essential for the regulation of several biological procedures, such as protein binding, subcellular localization, and RNA decayR is calculated based on reverse transcription (RT) counts and polymeric sequencing coverage  on individual bases, where each mapped read contributes an RT value for the first base upstream of the starting point of the mapping and a BD value for all bases covered. For each sequence, the window of size wSize is initialized in the 5’ direction and slid to the 3’ direction with window step wStep. Indeed, the RT and BD between two replicates of the DMSO library and the NAI library of each base are combined by direct addition as follows:α is the subtraction factor to measure the effect of background noise from the DMSO sample in the signal of the NAI-N3 sample. Finally, the resulting icSHAPE score is normalized to  as below:eq5 is the bottom 5% signal value and eq95 is the top 95% value. In the final icSHAPE score, it is noteworthy that only nucleotides with more than 200x coverage are considered as effective quality control. Accordingly, we process the RNA sequences using icSHAPE, and each RNA fragment generates an equal-length vector of real numbers representing secondary structural features as the input of the identification of RNA binding events in various cellular conditions.Briefly, the icSHAPE structure score 43, a self-attention-based language representation model, within the HDRNet framework to generate the dynamic global contextual information for RNA sequences in diverse cellular conditions. Benefitting from the multi-head self-attention mechanism, BERT has been widely used for capturing the long-range dependencies between tokens in sequential data  modelAfter obtaining multi-source biological characteristics of RNA sequences, an end-to-end hierarchical deep neural network was designed to simultaneously learn feature representation and identification via explicit modeling of those RBP binding data, as depicted in Fig. As expounded in the preceding section, we presented two distinct methods for characterizing RNA sequences. The first approach involved in vivo secondary structure representation produced by icSHAPE, while the second approach utilized dynamic global contextual sequence embedding generated by BERT. However, as the distributions of the two feature descriptors differ, a unified alignment model was developed to extend and extract structural and sequence features respectively, which can effectively capture global features of the in vivo secondary structure representation and local features of dynamic global contextual sequence embedding.d, which is defined as S = , where si denotes the i-th in-vivo RNA secondary structure score. We can clearly observe that the structural feature vector has a small number of channels, which limits the global structural information contained in the in-vivo RNA secondary structure representation. To address this limitation, we first fed such one-dimensional vector into a CNN block to enrich the global dependence of the structural information, as follows:FC is the customized sequential CNN block designed to detect the local sensitive regions of RBP binding. Conv(x) denotes a 1D-convolutional layer with a learnable convolutional kernel W ∈ Rd of c channels and a padding pl to ensure that the input and output sizes are identical. After that, to enhance the ability of the model to detect sensitive regions and to avoid overfitting, we add a batch normalization layer (BN)86 after the convolution layer as follows:γ and β are learnable parameters in the batch normalization layer. E(x) and Var(x) represent the mini-batch average and variance, respectively, and ε is added for numerical stability. During the training process, the batch normalization layer continuously updates running estimates of the mean and variance, which are used for normalization during evaluation. These running estimates have a default momentum value of m=0.1. The current estimate x, the previous estimate m using the formula:87 was employed to efficiently transfer the gradient, thus accelerating the convergence of the model as follows:Considering the structural information, we first embedded the RNA sequences of the RNA-RBP binding datasets into a one-dimensional vector of size 88 , residual neural networks (ResNet) have been shown to improve information flow and prevent the vanishing gradient problem, which often leads to overfitting in deep neural networksThrough multi-scale learning, the model is able to efficiently learn the different contextual dependencies of structural features at different distances to capture their unique binding properties. After that, we employed a residual mechanism to fuse the uniformly aligned secondary structural or dynamic global contextual embedding features with their corresponding multiscale biological features to enhance their local information, which is formulated as follows:89, which is capable of capturing global dependence of long-distance nucleotides at the token-level resolution. In our deep protein-RNA binding predictor, we set the sequence length halved after each layer, which specifically carries out a max-pooling layer and produces a new internal representation of the RNA sequence by taking the token-wise maximum over three consecutive internal vectors, which shortens the sequence while maintaining all its information. Since the number of feature channels is fixed, the computational complexity is also halved after each layer as the sequence length is halved. Moreover, unlike the original DPCNN89, in DPRBP we set up two independent CNN blocks instead of sharing their parameters to dynamically learn global context dependencies. The specific representation is as follows:t denotes two consecutive distinct CNN blocks C1 and C2, which are capable of dynamically learning the spatial relationships between sequences and structures. In addition, a residual connection r was added to enhance the perception of DPRBP in the high-attention region of sequences and structures. Subsequently, a max-pooling layer of size 3 with a step size of 2 was utilized to identify the specific binding of RBPs from the token-level loci, as follows:h is generated that is enriched with the structure-sequence space relationship and contains the global information of the whole sequence, denoted as follows:h will be fed into a fully connected neural network:d denotes the dimension of the vector h and σ(⋅) is the sigmoid activation function. Finally, the output P is considered as the probability of whether it is an RBP binding site or not.After applying the structural and sequence information to the hierarchical multi-scale residual network, we first fused the two enriched features by concatenating them to obtain a comprehensive feature matrix. However, the restriction on sequence length still makes it challenging to make precise predictions. To address this challenge, we developed the Deep protein-RNA binding predictor based on deep pyramid convolutional neural networkN denotes the mini batch size, yi is the true label and pi is the predicted binding probability. λ∣W∣2 denotes the L2 norm on all parameters, which acts as the weight decay term to reduce overfitting in the training model. In addition, to demonstrate the performance of our model, the area under the receiver operating characteristic curve (AUC) is chosen for performance evaluation.Generally, we trained an HDRNet model for identifying dynamic cellular RNA-binding events, including 261 RBP datasets for 172 human proteins. First, we used RNA sequences as the input data with BERT and icSHAPE secondary structures for multi-source feature representation. During the training process, we randomly selected 20% of the samples as the independent test set for performance evaluation, and the rest were considered as the training set, where 20% of the training set was randomly selected as the validation set. HDRNet was trained to learn the parameters that minimize the binary cross-entropy loss function:Kaiming initialization90. To prevent overfitting, dropout layers with a rate of 0.3 were added after each activation layer. The network was trained end-to-end using the Adam optimizer with a batch size of 32, a learning rate of 0.001, and a weight decay of 1e-6. In addition, we employed a linearly scaled warm-up scheme to calibrate the learning rate to overcome optimization challenges in the early training. Our model was trained on an NVIDIA GeForce RTX 3090 GPU with 24 GB of memory and early stops were controlled by validation loss, and taking ~80 h on all 261 RBP datasets.In the experimental section, we describe the implementation details of HDRNet. Sequence features obtained from BERT were first reduced in dimensionality using 128 one-dimensional convolutional kernels with a kernel size of 1. In addition, RNA secondary structure features were upsampled using 128 one-dimensional convolutional filters with a kernel size of 3. The resulting sequence and structural features were then separately fed into a Hierarchical multi-scale ResNet with identity maps for information enrichment, which contains one-dimensional convolutional layers with kernel sizes of 1, 3, 5 and 7 and CNN blocks of 1, 2, 3 and 4 in each view, totaling 32 channels. After concatenating the feature maps, they were passed through the deep protein-RNA binding predictor module, which had a kernel size of 5 and 256 channels, followed by a fully connected layer with one hidden unit and a Sigmoid activation function. All CNN layers were arranged in the order of Conv-BN-ReLU and had no bias. The model is trained on PyTorch 1.10 with parameters initialized using https://github.com/zhuhr213/HDRNet. To elucidate the effectiveness of our proposed model, we compared HDRNet with several deep learning models and machine learning algorithms as follows:32 (https://github.com/kuixu/PrismNet) is a recent study that developed a convolutional neural network (CNN)-based deep learning approach, which effectively incorporates in vivo RNA structure data and RNA-binding protein (RBP) binding data to make precise predictions of RBP binding sites. This method applied an “attention” strategy to precisely identify RBP binding nucleotides. Notably, PrismNet is the first tool designed for dynamic prediction tasks.PrismNet30 (https://github.com/kaitlin309/PRIESSTESS) introduces a universal RNA motif-finding/scanning strategy capable of identifying enriched RNA sequences and/or structure motifs. PRIESSTESS consists of two steps. The first step generates a large collection of enriched motifs encompassing both RNA sequence and structure. The second step produces an aggregate model, which combines the motif scores into a single value, and gauges the relative importance of each motif.PRIESSTESS28 (https://github.com/xypan1232/iDeep) is proposed and leverages a novel hybrid CNN network and deep belief network to predict the RBP interaction sites and motifs on RNAs by converting the original data into a high-level abstraction feature space using multiple layers of learning blocks, where the shared representations across different domains are integrated.iDeep39 (https://github.com/Rebecca3150/DMSK) is a novel identification method based on multi-view deep learning, subspace learning, and a multi-view classifier for the identification of circRNA-RBP interaction sites involved in computationally predicted RNA secondary structures.DMSK29 (https://github.com/dmaticzka/GraphProt) models the sequence and structure binding preferences of RBP binding sites using graph kernel features based on sequence and computationally predicted structure information.GraphProt26 (https://github.com/jisraeli/DeepBind) is a CNN-based deep learning model that predicts RBP binding sites based on only RNA sequences.DeepBindHDRNet was implemented in Python, and the core model was built on the PyTorch framework that is publicly available at 91, Random Forest (RF)92, Logistic Regression (LR)93, Artificial Neural Network (ANN)94, ExtraTreeClassifier (ETC)95, SGDClassifier (SGDC)96, and GaussianNB (GNB)97 using the same feature representation as HDRNet, including the dynamic contextual embedding and the icSHAPE secondary structure information. For the ANN implementation, we used a Pytorch environment and set the size of the hidden layers to 512, 32, and 1, respectively. XGBoost was implemented using the Python package of version 1.5.0. The remaining algorithms were implemented using the scikit-learn package98. Meanwhile, we applied the default parameter settings for Logistic Regression, Random Forest, and XGBoost.In addition, we conducted a comprehensive analysis of machine learning methods to compare performance to our proposed HDRNet. Specifically, we evaluated XGBoostThe raw data for tissue dataset is stored in the ’.bed’ format, which records the genomic positions of each binding peak. We employed the ’bedtools getfasta’ function or ’getFastaFromBed’ to obtain the corresponding nucleotide sequences based on the chromosomal coordinates. Specifically, for human tissue data, we utilized hg38 as the reference genome, while for mouse data, we employed mm9 as reference.i-th embedded k-mer token, denoted as Scorei through the summation, thereby identifying the transcript fragment in each RNA sequence that offers the most substantial contribution towards downstream classification, which can be formulated as follows:N symbolizes the number of attention heads, T represents the number of tokens in a given RNA sequence, Ki denotes the key vector of the i-th k-mer token with i ∈ {1, 2, . . . , L} where L denotes the number of input tokens, and d is the dimensionality of the feature vectors. After computing the attention score for each token, we classified RNA fragments as RBP-binding motifs based on the following three criteria: (1) The attention score of the fragment exceeds the average score of the sequence; (2) The attention score of the fragment is 10 times higher than the lowest attention score; and (3) The minimum length of the fragment is 6.The multi-head self-attention mechanism has the capacity to accurately identify and decode significant genomic regions. In our study, we leverage dynamic global contextual embedding to explore the biological functionalities of RNA sequences. More explicitly, we computed the attention score for the 47 to calculate the specific attention score for each token of the input RNA sequence. The ’GradientExplainer’ function was employed, where a score matrix of the same size as the input dynamic context matrix coule be obtained. Each numerical value in this matrix represents the attention value at the corresponding position in the input matrix. We then selected the maximum value of each token as the final attention score, and got an attention vector Att with length 99. Subsequently, we allocated the attention scores of each token evenly across every nucleotide, as demonstrated below:Scorei denotes the attention score of the i-th nucleotide, and i is in range .We adopted the SHAP tool99 to perform KEGG and GO enrichment analysis on the 544 TDP-43 binding genes. We utilized the function ’enrichKEGG’ for the KEGG analysis, where the parameters were set to “pAdjustMethod = fdr, pvalueCutoff = 0.01, qvalueCutoff = 0.05". The GO analysis was performed by function ’enrichGO’, where the parameters were set to “ont = ALL, pAdjustMethod = BH, pvalueCutoff = 0.01, qvalueCutoff = 0.05".We used the R package ClusterProfiler74 and the most significant disease-related genes, including a total of 28 genes were input to Cytoscape for visualization. To identify the most important modules in the PPI network, we adopt MCODE75 for the network and give the top three modules.We performed preliminary PPI network construction on the 544 shared TDP-43 binding genes using STRINGThe detailed statistical tests were explained in each figure legend. Sample data were obtained from public repositories. Sample size was not predetermined and is the maximum number of samples available for each datasets. No data were excluded from the analyses. No experimental groups were assigned. Our study does not involve group allocation that requires blinding. To reproduce the results, please find the Source Data file we provided.Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data


E. coli DEAD-Box helicase RhlB is responsible for ATP-dependent unwinding of structured mRNA to facilitate RNA degradation by the protein complex degradosome. The allosteric interaction with complex partner RNase E is necessary to stimulate both, RhlB’s ATPase and RNA unwinding activity to levels comparable with other DEAD-Box helicases. However, the structural changes of the helicase RhlB induced by binding of RNase E have not been characterized and how those lead to increased reaction rates has remained unclear. We investigated the origin of this activation for RNA substrates with different topologies. Using NMR spectroscopy and an RNA centered approach, we could show that RNase E binding increases the affinity of RhlB towards a subset of RNA substrates, which leads to increased ATP turnover rates. Most strikingly, our studies revealed that in presence of RNase E (694-790) RhlB induces a conformational change in an RNA duplex with 5’- overhang even in absence of ATP, leading to partial duplex opening. Those results indicate a unique and novel activation mode of RhlB among DEAD-Box helicases, as ATP binding is thought to be an essential prerequisite for RNA unwinding.The  E. coli is an interplay of endoribonuclease RNase E and 3’-5’-exoribonuclease PNPase. The nucleases assemble into a complex called degradosome, with the unstructured C-terminal domain of RNase E functioning as a binding platform for the other complex partners  was prepared as fusion-product with 5’-hammerhead ribozyme (51 nt) and 3’-HDV ribozyme (67 nt) by in vitro transcription from a SmaI linearized DNA template (modified pUC57-plasmid) and subsequently cleaved from the ribozymes. Therefore, 100 mM Tris/glutamic acid (pH 8.1), 2 mM spermidine, 40 mM Mg(OAc)2, 15 mM full 13C,15N-labelled rNTPs and 100 ng/µL DNA template were incubated for 30 min at 37°C before 20 mM DTT and 70 µg/mL T7-polymerase (P266L mutant) were added. The incubation was continued for 7.5 h with addition of 1 U/mL yeast inorganic pyrophosphatase (NEB) after 2 h. The RNA product was purified by self-packed anion exchange chromatography using 10 mL DEAE sepharose resin  with an elution gradient of 0.6–3 M NaOAc followed by a reversed-phase HPLC using Perfectsil RP18 300A 5 µm 10 × 250 mm column  at 60°C, buffer A  and buffer B (buffer A+ 60% acetonitrile) in the following sequence of gradients: 0%–37% in 5 min, 37%–40% in 30 min and 40%–100% in 5 min with a flowrate of 5 ml/min. After desalting with Vivaspin™ centrifugal concentrators (Sartorius) and precipitation with 2% (w/v) LiClO4 in acetone the RNA pellet was resolved in water and stored at −20°C.All unlabelled RNA constructs were purchased from Integrated DNA Technologies, Inc., dissolved in HWild type RhlB with C-terminal 6xHis-Tag and TEV cleavage sequence (ENLYFQG) in a pET11A expression vector was purchased by Dharmacon™. Both RNase E (694-790) and RNase E (628-843) were purchased by Dharmacon™ with N-terminal 6xHis-Tag and TEV cleavage sequence in pET21A and pET11A expression vectors, respectively.E. coli BL21 (DE3) cells at 37°C using terrific broth (TB) medium and induced at OD600 of 1.5 with 1 mM isopropyl β-d-1-thiogalactopyranoside (IPTG). Expression was continued for 3 h at 21°C before cells were harvested using centrifugation. The cells were resuspended in lysis buffer  and EDTA-free protease inhibitor tablet (Roche), lysed using high-pressure homogenization and clarified by centrifugation at 16,000 g for 45 min. After addition of 0.03% (w/v) polyethylenimine, the lysate was incubated for 15 min and cleared again using centrifugation. The lysate was loaded onto 5 mL Ni-NTA column , washed with lysis buffer first, followed by LiCl buffer  to remove protein-bound nucleic acids and after a second wash with lysis buffer the sample was eluted with a gradient of 100% Ni-NTA elution buffer over 100 mL . The His-Tag was cleaved off through TEV-cleavage during overnight dialysis against 5 L lysis buffer and subsequently removed via reverse Ni-NTA chromatography. The proteins were further purified with a HiLoad 26/60 Superdex 200 column  using size exclusion buffer . The proteins were concentrated to approximately 500 μM, flash-frozen in liquid nitrogen and stored at −80°C. RNase E (694-790) was overexpressed and purified in an analogous manner with the following alterations: 0.4 mM IPTG was used for induction of the pET21A plasmid, lysis and Ni-NTA elution buffer contained 200 mM NaCl, 100 mM KCl, 50 mM Tris/HCl, 5 mM MgCl2, 10 mM 2-mercaptoethanol at a pH of 8.0 and 5 mM or 500 mM imidazole, respectively, and a HiLoad 26/60 Superdex 75 column  was used for size exclusion chromatography.Both RhlB and RNase E (628-843) were overexpressed in 2, pH 8.3) using Vivaspin™ centrifugal concentrators.For both NMR experiments and assays, suitable amounts of proteins were thawed and rebuffered into NMR buffer  running at ∼70V in TA buffer  acetic acid, pH 8.0) for 5 h at 4°C. The gel was stained with GelRed staining solution (0.0001% (v/v) GelRed® Nucleid Acid Gel stain in water) for 15min and RNA bands visualized via UV-transillumination using a Gel iX20 Imager (Intas Science Imaging). For protein visualization, gels were afterwards stained with Coomassie staining solution (10% (v/v) ethanol, 5% (v/v) acetic acid, 0.0025% (w/v) Coomassie brilliant blue G250, 0.0025% (w/v) Coomassie brilliant blue R250) and the gel digitalized by Gel iX20 Imager. The fraction of bound RNA was calculated by quantifying the intensity of the free RNA band relative to the intensity of the “0” lane containing only the RNA using ImageJ (after subtracting the background). The fraction of bound RNA was plotted against protein concentration and the data were fitted for a simple single binding site function y=(B*x)/(KD+x), where B is the upper plateau of the binding curve.Binding reactions were carried out in 150 mM NaCl, 25 mM Tris, 1.5 mM MgCl2 and 0.1 mM sodium azide at pH 8.1 with 6 µM of the corresponding RNA substrate and 2.4 µM RhlB (and RNase E fragment). Following preincubation of 160 µL of the reaction components for 10 min at 22°C, the reaction was started through addition of 40 µL ATP to a final concentration of 0.2 mM. Reactions were monitored at 360 nm for 300 s with an Infinite® 200 PRO microplate reader (Tecan), measuring triplicates of the desired sample composition. The modified reaction volume and vessel required the conversion of the absorption raw data with the correct optical path length according to Lambert-Beer. The initial reaction rates were calculated via linear regression of the rate curve from 120 to 220 s and was converted into “mol phosphate min-1 mol helicase-1” with a phosphate standard curve.ATPase activity of RNA helicase RhlB was determined spectrophotometrically using the Molecular Probes EnzChek Phosphate Assay kit (Invitrogen), which is based on a method originally described by Webb . Here, t2, pH 8.3) with 6%–10% D2O and 100 µM sodium trimethylsilylpropanesulfonate (DSS) as well as 0.3–1 mM phosphocreatine (PCr) were used as chemical shift standards for 1H and 31P experiments. NMR experiments were performed at 288 K either on a Bruker AV 600 equipped with a TCI-HCP probe, a Bruker AV II 600 equipped with a TCI-HCN probe, a Bruker AV III 600 equipped with a TCI-HCN probe, a Bruker AV III 700 equipped with a QCI-HCNP probe or on a Bruker AV 800 equipped with either a TXO-HCN or TCI-HCN probe  and processed with Topspin 4.0.8 .Unless stated otherwise, all NMR samples were prepared in NMR buffer . The rapid-mixing setup, as illustrated in 31P 1D spectra were recorded as a pseudo 2D at 288 K, with the ATP injection being triggered after 128 scans. The amount of ATP was determined from peak integrals of ATPα and the resulting curves fitted with double exponential curve fit.For NMR real-time mixing experiments a 300 µL volume of 100 µM unlabelled 5’-OV RNA construct, 400 µM RhlB (+/−400 µM RNase E), 1 mM phosphocreatine, 100 µM DSS and 6% D1H and 13C HSQC titration experiments 180 µL of 100 µM unlabelled or double labelled RNA substrate , 100 µM DSS and 6%–10% D2O were prepared in NMR buffer in 3 mm NMR tubes and unlabelled protein was added in steps of 0.25, 0.5, 1, 2 and 4 equivalents over RNA. The total protein stock concentration varied between 0.75 and 2 mM depending on intrinsic stability of RhlB and RNase E fragments and dilution effects in the spectra were corrected with DSS reference signal.For D determination of 1H titration the normalized reciprocal peak intensities of non-overlapping imino proton resonances were plotted against protein concentration. The maximum decrease was determined in the experiment with the highest protein concentration. The resulting curves were fitted globally based on a single-site ligand binding equilibrium ; see With the addition of RNase E (628-843), a larger fragment encompassing the two RNA binding sites RBD and AR2 See , an evenIn total, RNase E (694-790) and (628–843) increase the ATP turnover rate by a factor of up to 6 and 27, respectively. From these experiments, it is apparent that the ATP hydrolysis reaction is driven towards the product side of the reaction either by single stranded RNAs or 5’-single stranded RNA fragments. This finding is corroborated by the finding that the RNase E (628-843) which contains binding sites for RNAs has an enhancing effect, that exceeds the pure allosteric enhancement that is exhibited by RNase E (694-790).31P 1D NMR spectra  is comparable with factors between four and five. However, for the RhlB complex with the larger fragment RNase E (628-843) is rather minute (facor of ≈2) in the 31P real time NMR experiment compared to the phosphate assay (factor of ≈10) (D (see following section). Further, RNAse E (628-843) contains two RNA binding sites flanking its RhlB binding site. As described in the following section, the affinity for RNA binding in RNAse E (628-843) is given by a KD of 162 ± 30 μM, so that under the given excess of proteins over RNA these RNA binding regions of RNase E (628-843) compete with RhlB for the RNA substrate. Therefore, we assume that this will lead to an decreased amount of RNA available for binding the helicase in the active site.NMR spectroscopic experiments are very different from UV/Vis based assays . Sample  spectra . Right a spectra . From th spectra . The exp spectra  and are  of ≈10) . We attrD but also reveals possible structural rearrangements and changes in base pairing within the RNA substrate during binding. For all of the five different RNAs the imino protons could unambiguously be assigned and therefore their conformation and topological configuration could be determined  complex. Since the control experiment with RNase E (694-790) alone provides evidence that this unstructured protein fragment does not bind RNA, we conclude that the interaction of RNase E (694-790) with RhlB allosterically affects the affinity of the helicase for RNA. Analogous experiments with RNase E (628-843) show that the RNA binding regions RBD and AR2 bind the 5’-OV RNA with a KD of 162.7 µM. Due to the unstructured nature of those binding sites as well as the high density of positively charged amino acids this is most likely a non-specific electrostatic interaction with the negatively charged RNA backbone  to the helicase does not increase the affinity for all RNA substrates in a similar manner. In fact, the affinity for both blunt end constructs as well as the single stranded construct decreased upon titration of the RhlB/RNase E (694-790) complex, in case of the 21 nt blunt end duplex even beyond detectability by NMR. Both 3’- and 5’-OV RNA on the other hand show a distinct increase in affinity, suggesting that interaction partner RNase E (694-790) narrows RhlB’s RNA substrate preferences.Blunt ended doubled stranded RNAs are accepted as substrate, but the proper native substrate are double stranded regions with 5’-single stranded overhangs as RNAs with this topology not only exhibit the highest ATP turnover rates but also the highest affinity.Besides monitoring the mere binding to the proteins, NMR is also capable to detect the induction of new conformational states within the RNA that is being bound to the helicase and its RNAse E complexes. NMR is thereby capable of discriminating the base pairing state at nucleotide resolution and even if the base pairing states are only populated transiently.Upon binding of RhlB, no site-specific effect affecting only a subset of the imino-proton resonances could be observed. This can indicate that either the imino-protons are too distant from RhlB’s actual binding site on the sugar-phosphate backbone or that structural changes like base pair opening are induced at the fraying blunt end of the RNA helix, that does not exhibit detectable imino-proton resonances.13C labelled RNA substrates (21 nt strand being fully 13C labelled) to gain structural insights into the effects of RNase E (694-790) on the RNA during binding. 13C-HSQC measurements give us a variety of possible RNA reporter signals in close proximity to the proposed interaction points whose peak intensity is unaffected of pH dependent solvent exchange. Furthermore, the chemical shifts of those resonances are sensitive to their chemical surrounding and the RNA’s conformation. We concentrated our evaluation on nucleobase resonances C6H6, C8H8 and C2H2, as the spectral overlap and resolution of other signals impede a complete tracking of the resonances.We therefore chose to perform 2D NMR titration experiments using Stepwise addition of up to 2 equivalents of RhlB to the labelled 5’-OV RNA construct showed a homogenous intensity decrease throughout all monitored resonances comparable to the previously measured imino protons. In 1H,13C-HSQC spectrum three of the new peaks could be identified as pyrimidines due to their doublet splitting. Two of those match the resonances of unpaired U16ss and U21ss, while the third pyrimidine arises in direct proximity to the C6H6 of unpaired U20ss. The three remaining new resonances can be assigned as purine nucleotides and match the number of new C2H2 adenine signals. Here we can also see a significant overlap of two resonances with the unpaired adenines A17ss, A18ss and A19ss. A third peak is slightly downfield shifted compared to the third unpaired adenine resonance. Slight deviations in the chemical shift overlap between the new resonances and the 3’-tail of the 3’-OV duplex might arise from the RNA being bound to the protein, where interactions with the amino acids in the binding pocket affect the nucleotides locally.Remarkably, we did observe significant differences in the titration with the RhlB/RNase E (694-790): while nucleobase resonances of the 5’-OV RNA duplex showed the same uniform intensity decrease as before, a new subset of peaks appeared, implying the formation of a conformationally distinct second population of nucleotide resonances upon binding . Taken tIt is known from various crystal structures of other DEAD-box helicases with single stranded RNA substrates that the amino acid coordination within the RNA binding pocket encompass 5 consecutive nucleotides . This al13C-HSQC NMR spectra revealed a conformational transition in a stretch of six nucleotides of the 5’-OV duplex from a base paired to an unpaired conformation only upon binding to RhlB in complex with RNase E (694-790). The other part of the duplex however remains intact, as 1H NMR titration spectra did not show a complete loss of imino proton resonances even at 4 equivalents of protein for any RNA construct, as would be expected for strand separation (data not shown). Those findings provide a novel insight into the pathway of communication between RhlB and RNase E and the structural effects that are at the basis of the elevated ATPase activity in presence of RNase E. Moreover, our results provide the first evidence of a DEAD-Box helicase structurally changing the RNA substrate in absence of ATP.In summary, the S. cerevisiae comprising a mix of different topologies of RNA substrates , a fragment that also contains RNA binding domains RBD and AR2 flanking the RhlB binding site. Earlier studies did not observe an increased ATPase activity through these parts of RNase E, as they worked with bulk RNA from bstrates , most prand CsdA ) and thaAs revealed by our titration experiments, the activity of RhlB is actually matched with the affinities towards substrate RNAs with different strand topologies. These results are corroborated by the findings of Chandran et al. who reported similar substrate preferences in RNA unwinding assays . Again, For these cases, affinity is increased by ∼4 fold up to ∼10 fold upon formation of tertiary complex with RNase E (694-790) or (628–843), respectively. The increase in affinity cannot be explained by a simple additive effect, as at least the RNase E (694-790) fragment alone only exhibit a negligible affinity for RNAs. The modulation in affinity therefore must result from an RNase E induced conformational change within RhlB’s RNA binding site.Consequently, we investigated the formation of wild type RhlB in complex with RNase E (694-790) with a 15 nt RNA duplex featuring a 6 nt 5’-single strand tail in solution utilizing NMR spectroscopy. While previous studies predominantly gained information from crystal structures of DEAD-Box helicases in complex with poly-U RNA single strands, very little investigations have been performed to capture the complex in solution and with a physiologically more functional RNA substrate. In conjunction with the results from the activity and affinity measurements, the structural investigations lead us to propose the following model .In the absence of protein interaction partner the RNA is bound to RhlB with reduced affinity and no opening of base pairing interactions can be observed. As RNAs with 5’-OV are preferentially bound compared to blunt ended double stranded RNAs or those featuring a 3’-OV, we propose that binding occurs at a binding site within C-terminal domain and further through an unspecific electrostatic but beneficial interaction between RhlB’s positively charged C-terminal extension (CTE) and the single stranded overhang. Although we cannot derive this interaction directly from our data, it is in line with earlier characterizations of multiple other DEAD-Box helicases also featuring a positively charged C-terminal extension (CTE) that demonstrate the assisting contribution of said extensions to RNA binding. Deletion of the CTE has also been shown to reduce RhlB’s RNA binding affinity, which strongly indicates its involvement in RNA binding . The resUpon interaction with RNase E (694-790), we infer an allosteric switch in the protein that ultimately leads to a conformational change in RhlB’s RNA binding pocket. The structural change in the binding pocket induces a partial opening of the blunt end of the 5’-OV RNA through the increased ability to interact with single stranded like conformations in the RNA. . With thThe allosteric activation of helicases is not unprecedented as, for example, a co-factor enhanced RNA unwinding could be shown for viral helicase NS3, that optimally performs its helicase activity on RNAs only upon binding to NS4a . In case


Low-density reef limestone is widely distributed in tropical oceans; exploring its mechanical properties is of significance to practices in marine foundation engineering. In this research, laboratory experiments on low-density reef limestones with two different types of porous structures were conducted using image recognition methods to study the special mechanical properties of low-reef limestone.  With the advances in technology and human exploitation and utilization of maritime resources, the number of construction projects on coral reefs has increased, and widespread concerns about the geological properties of coral reef have arisen ,2. CoralReef limestone is a type of rock with complicated sources. The remains of reef-building organisms including coral, foraminifera, and coral algae and the remains of lagoon sediments are major components of the skeleton of reef limestone. Reef limestone is mainly made up of minerals including aragonite, calcite, and dolomite, and its diagenesis is primarily subjected to effects of compaction, cementation, and corrosion ,7,8.Due to the complex constituents and diagenetic process, the mechanical properties of different types of reef limestone vary significantly. The uniaxial compressive strength (UCS) of reef limestone is a basic parameter for evaluating mechanical properties, which has been widely studied. Zhu et al. ,10,11 ex3 and a maximum density of 2.7 g/cm3. The mechanical properties of reef limestone samples with a lower density are rarely reported.According to the aforementioned analysis, the UCS characteristics of reef limestone have been extensively investigated: it is noteworthy that some of these studies have explored reef limestone samples with large density, e.g., reef limestone samples had a minimum density of 1.33 g/cmDiploria, as the one of three main types of coral in sites around Barbados, the young reef limestones developed had a minimum density of only 0.53 g/cm3, but achieved a relatively high UCS value in a mechanical test, which are largely different from results pertaining to other reef limestones with high densities in the same test. Burton et al. inferred that this type of reef limestone exhibits an especially efficient calcite structure. There is no other in-depth research relating to such topics being reported.For the literature on the UCS characteristics of low-density reef limestone, Burton et al. demonstrated an intriguing phenomenon: for Low-density reef limestone underwent slight compaction and cementation owing to their late diagenesis, showing significant primary porous structures. Hence, the study of physical properties for low-density reef limestone as a porous material warrants consideration in terms of the influence of porous structures . Zeng etAlthough use of X-ray CT can acquire detailed characteristic data of porous structures for reef limestone ,27,28, tAs the current factors influencing the UCS of low-density reef limestone remain unclear, this research quantified porous structures of low-density reef limestone by means of an image recognition technique based on testing results. On this basis, by referring to the research methods of other rocks ,36,37, tΦ 50 × 100 mm standard cylindrical specimens. In this test, two types of specimens with largely different porous structures were used as the control group, which were labelled S and M. The morphologies of two types of specimens are illustrated in The specimens used in the experiment were taken from the shoal of a tropical sea. Referring to the specifications suggested by the International Society for Rock Mechanics and Rock Engineering (ISRM) , the samAs shown in In this research, Image J 1.52i software, as a JAVA-based open-source image analysis software developed by the National Institute of Mental Health, in the US was adopted to identify the images of the pores for the specimens. In addition to fulfilling basic processing of images, Image J can be used to perform statistical analysis of specific image pixel zones, including number, size, and position. It has been widely applied in immunohistochemical quantification, grey-scale analysis of Western blot assay results, and cell counting .To acquire the data of pore geometry for reef limestones considering comparison of image recognition results for different reef limestone specimens, image recognition of all specimens was realized according to the process in As shown in As the grey-scale interval of image binarization in Step 5 is valued using manual selection, it is largely influenced by subjective consciousness; therefore, a reasonable grey-scale interval is selected by comparing pore image recognition effects at different grey-scale intervals to reduce the influence of subjectivity on interval selection.Although reef limestones, as typical porous media, demonstrate multiple types of pore structures, the pore structures in a given zone are similar. This is because the reef limestones in the zone originated from the same type of reef-building organisms. As image recognition results obtained are 2D geometric data pertaining to the pore structures in a given specimen, for convenience of analysis, this research used the area as the parameter quantifying geometry of individual pores by neglecting shape differences in different single pores. The average area was used as the parameter quantifying the pore geometric characteristics at specimen level. Thus, The experiment was conducted following conventional UCS research and experimental process of rock. The experimental steps are as follows: (1) Porosity and density of reef limestone specimens were measured in the laboratory. (2) The pores of each specimen are identified through image recognition according to the method described in In this research, image binarization aims to distinguish between porous zones and skeleton zones in images to lay a foundation for automatic statistics of pores. In the following part, the manual recognition results are taken as the benchmark, and the image recognition performances at different grey-scale intervals were compared to determine the optimal grey-scale interval in the experiment.A be the upper limit of the selected grey-scale interval to be processed in the image binarization, while parameter B is the lower limit thereof. Testing and analysis show that when B is below 40, and A is within the range from 115 to 140, the automatic image recognition performance of pores is better. For this reason, the image recognition results when B is 25 and A is set to 115, 120, 125, 130, 135, and 140 were compared with the original grey-scale image (A reef limestone specimen (S-1) was chosen to be processed following the steps in le image .A increases, image recognition performance is shown to be good at first and then becomes poor; when A is small . This finding arises because, as A increases, the recognition zone for a single pore is gradually enlarged; when A = 125, image recognition results can match those achieved using manual recognition; hence, in the following research, 25~125 is chosen as the grey-scale interval selected for image binarization.As can be seen from 2 to 1 mm2 match data achieved via manual recognition, while in the interval from 0 mm2 to 0.1 mm2, the degree of fit between the image recognition results and manual recognition results is poor. Using the particle tracking function in Image J, it is found that in automatic statistical analysis, the statistics pertaining to noisy points with small size in images can be extracted . To reduce the error caused by noisy points and consider that areas of different pores in a given specimen are of the same order of magnitude, image recognition results were processed following the steps below to optimize the interval used to calculate the parameter (1)According to the difference in the order of magnitude, first-level statistical intervals are classified;(2)In each first-level interval, 10 second-level statistical intervals are proportionally classified;(3)The first-level statistical interval in which all porous areas account for the largest proportion of the total area of the image and all of its second-level statistical intervals are non-zero is seen as an effective interval;(4)According to Equation (1), the data related to the effective interval are calculated to deduce the pore geometric parameter The effect of this optimization method is verified below. Taking images chosen in 2, optimized from 3.701 mm2 beforehand; the deviation in number of pores is decreased to 22, optimized from 148 beforehand; the deviation of the parameter As can be seen from The results for each specimen are summarized in 3 and the minimum density is 0.76 g/cm3. The maximum pore geometric parameter 2, while the minimum value of 2, which are both within the same order of magnitude. Moreover, the maximum UCS is 4.730 MPa, and the minimum value is 1.467 MPa. The maximum porosity of M-type reef limestone is 65.61% and the minimum porosity is 48.16%, while the maximum density is 1.44 g/cm3 and the minimum density is 0.94 g/cm3. The maximum value of 2, while the minimum 2, which are also both within the same order of magnitude. In addition, the maximum UCS is 15.160 MPa, and the minimum UCS is 7.686 MPa. Comparing the testing results of S and M-type specimens, it was found that low-density reef limestones with different porous structures show smaller differences in terms of porosity and density, while they differ significantly in terms of parameter As can be seen from As shown in According to Given that there are order-of-magnitude differences in parameter Based on the results for the goodness of fit, there is an unfavorable linear correlation between the UCS and lg plotted .The subscripts of the UCS in Equations (3) and (4) refer to the types of low-density reef limestones. Judging from the degree of fit obtained, there are poor linear correlations between the density and UCS of the two types of low-density reef limestones, among which S-type specimens show the poorest linear correlation for the density and UCS. Based on Referring to research by Burton et al. , when thAccording to the aforementioned research, when reef limestone has a low density, there is a poor linear correlation between the UCS and density; meanwhile, the UCS shows a better linear correlation with lgThe test data thus obtained were used to verify the fitting performance of Equation (6). The  plotted .It can be found from The degree of fit in Equation (6) is 0.91, showing a good degree of fit. In comparison with analysis results for the UCS correlation, the use of Equation (6) for the pore geometric parameter The mechanical properties of rock are generally closely related to factors including loading rate, rock type, and density. Previous research into the factors influencing the UCS of reef limestone shows that the influence of pore structure is negligible owing to the large density of the specimens, so relevant conclusions can be obtained by only considering the basic physical parameters of rock. Low-density reef limestone exhibits the significant feature of being a porous material, and the influence of pore structure on its mechanical properties is worthy of investigation.In the present work, an image recognition technique was used to acquire geometric data of pore structures for low-density reef limestone. By defining pore geometric parameter n et al. . d is the particle size, p is the porosity, and Recently, Wei et al.  analyzedEquation (6) is similar to Equation (8) in derivation process and form and also fits better than the single-factor analysis model. This suggests that it is feasible to utilize an image recognition technique to investigate the characteristics of pore geometry and investigate the mechanical properties of low-density reef limestone based on the theory of the mechanics of porous media.Furthermore, the physico-mechanical properties, relative density, and pore structure of skeletal materials also influence the mechanical properties of porous materials . The lim(1)The pore structure of low-density reef limestone is significant: different types of pore structures demonstrate significant differences in terms of their shapes and sizes. By means of image recognition techniques, 2D geometric data pertaining to the pores within low-density reef limestone can be readily acquired. Through using unified photographic methods and comparing them with manual identification results, batch identification of the porous structure of specimens can be realized by optimizing the parameters involved in the image recognition process and image recognition results.(2)Low-density reef limestones with different porous structures show small difference in porosity and density, while they exhibit large differences in pore sizes and UCS.(3)By introducing the pore geometric parameter The UCS of low-density reef limestone was investigated using image recognition techniques. Conclusions can be drawn as follows:(4)The UCS of low-density reef limestones is influenced by the combined effects of the pore geometric parameter (5)The image recognition technology and data optimization method in this paper have significance for the rapid estimation of the strength of low-density reef limestone in engineering practice.


Salmonella Typhimurium. Surprisingly, bidirectional promoters occur three times more frequently in plasmid components of the genome compared to chromosomal DNA. Implications for the evolution of promoter sequences are discussed.Transcription of the DNA template, to generate an RNA message, is the first step in gene expression. The process initiates at DNA sequences called promoters. Conventionally, promoters have been considered to drive transcription in a specific direction. However, in recent work, we showed that many prokaryotic promoters can drive divergent transcription. This is a consequence of key DNA sequences for transcription initiation being inherently symmetrical. Here, we used global transcription start site mapping to determine the prevalence of such bidirectional promoters in  The instructions encoded by DNA are organized into discrete packets of information called genes . To utilSalmonella Typhimurium strain SL1344 using cappable-seq [S. Typhimurium gene control. Consistent with our prior work, we detect thousands of transcripts resulting from divergent transcription at bidirectional promoters. Surprisingly, such transcripts are found to originate much more frequently from plasmid, rather than chromosomally, encoded promoters. Previously, we speculated that recently acquired DNA sequences are more likely to contain bidirectional promoters. This model is discussed with reference to our current observations.In this work, we have mapped TSSs genome wide in able-seq . We idenS. Typhimurium strain SL1344 was grown to mid-log phase in LB medium and RNA was extracted using the SV Total RNA Isolation System (Promega). Generation of cappable-seq libraries, and Illumina sequencing, was done by Vertis Biotechnologie AG on 5 µg of extracted RNA. Raw sequencing reads are available from ArrayExpress using accession number E-MTAB-12506. The analysis was done twice and those TSSs detected were present in both replicates.Cappable-sequencing (cappable-seq) experiments were done as previously described . BrieflyS. Typhimurium SL1344 reference genome  using Bowtie2 and SAMtools (version 1.3.1). TSSs were identified using the software of Ettwiller et al. [Sequencing reads were mapped to the r et al. . The falS. Typhimurium SL1344. This strain has a 4 878 012 bp chromosome and two plasmids, pCol1B9 and pSLT, that are 86 908 and 93 842 bp in length respectively. Our isolate lacks plasmid pRSF1010 described in prior work [The 5′ end of primary RNA transcripts is triphosphorylated. Conversely, processed RNAs have monophosphorylated 5′ ends. Hence, methods for identifying TSSs aim to identify triphosphorylated RNA termini. Many previous TSS mapping studies relied on the use of Xrn1 exonuclease that preferentially degrades RNAs with a monophosphorylated 5′ end. However, RNA folding can prevent nuclease access to its target. Consequently, RNA samples with and without Xrn1 treatment are compared. This is known as differential RNA-seq (dRNA-seq). More recently, cappable-seq has been used to identify TSSs. This method exploits the ability of vaccinia capping enzyme to specifically modify triphosphorylated RNA 5′ ends with biotinylated GTP. The reaction is highly specific, allows primary unprocessed transcripts to be isolated using streptavidin beads, and removes the need to generate a second sample for comparison. We used cappable-seq to map TSSs in ior work . We idenPreviously, Kröger and co-workers used dRNA-seq to map chromosomal TSSs in the same strain and identified 3 838 RNA 5′ ends . Of thesEscherichia coli, most bidirectional promoters allowed transcription initiation at sites separated by 18 bp on opposite DNA strands (Previously, we defined bidirectional promoters as being associated with divergent pairs of TSSs separated by between 7 and 25 bp . In such strands .We selected DNA sequences corresponding to all sites of divergent transcription initiation separated by 18 bp. These sequences were aligned, according to the position of the TSS in the forward orientation, and used to generate DNA sequence logos . As expeS. Typhimurium SL1344 using cappable-seq. Our analysis identifies over 80 % of TSSs previously defined by Kröger et al. in the same organism [et al. demonstrated that non-coding intragenic transcription provides stochastic relief from gene silencing [We report high-resolution mapping of TSSs in organism  (Fig. 2aorganism . Whilst organism . In receilencing . In otheilencing . Hence, Importantly, promoter sequences associated with RNAs specifically detected by either cappable-seq or dRNA-seq are very similar . This isS. Typhimurium genome (E. coli [The most surprising aspect of our analysis is the 3-fold enrichment for bidirectional promoters within plasmid components of the m genome . This wam genome . In the . coli . ConversClick here for additional data file.