The Identification and Function of Linc01615 on Influenza Virus Infection and Antiviral Response.

Influenza virus infection poses a great threat to human health globally each year. Non-coding RNAs (ncRNAs) in the human genome have been reported to participate in the replication process of the influenza virus, among which there are still many unknowns about Long Intergenic Non-Coding RNAs (LincRNAs) in the cell cycle of viral infections. Here, we observed an increased expression of Linc01615 in A549 cells upon influenza virus PR8 infection, accompanied by the successful activation of the intracellular immune system. The knockdown of Linc01615 using the shRNAs promoted the proliferation of the influenza A virus, and the intracellular immune system was inhibited, in which the expressions of IFN-ß, IL-28A, IL-29, ISG-15, MX1, and MX2 were decreased. Predictions from the catRAPID website suggested a potential interaction between Linc01615 and DHX9. Also, knocking down Linc01615 promoted influenza virus proliferation. The subsequent transcriptome sequencing results indicated a decrease in Linc01615 expression after influenza virus infection when DHX9 was knocked down. Further analysis through cross-linking immunoprecipitation and high-throughput sequencing (CLIP-seq) in HEK293 cells stably expressing DHX9 confirmed the interaction between DHX9 and Linc01615. We speculate that DHX9 may interact with Linc01615 to partake in influenza virus replication and that Linc01615 helps to activate the intracellular immune system. These findings suggest a deeper connection between DHX9 and Linc01615, which highlights the significant role of Linc01615 in the influenza virus replication process. This research provides valuable insights into understanding influenza virus replication and offers new targets for preventing influenza virus infections.

Untacking small RNA profiling and RNA fragment footprinting: Approaches and challenges in library construction.

Small RNAs (sRNAs) with sizes ranging from 15 to 50 nucleotides (nt) are critical regulators of gene expression control. Prior studies have shown that sRNAs are involved in a broad range of biological processes, such as organ development, tumorigenesis, and epigenomic regulation; however, emerging evidence unveils a hidden layer of diversity and complexity of endogenously encoded sRNAs profile in eukaryotic organisms, including novel types of sRNAs and the previously unknown post-transcriptional RNA modifications. This underscores the importance for accurate, unbiased detection of sRNAs in various cellular contexts. A multitude of high-throughput methods based on next-generation sequencing (NGS) are developed to decipher the sRNA expression and their modifications. Nonetheless, distinct from mRNA sequencing, the data from sRNA sequencing suffer frequent inconsistencies and high variations emanating from the adapter contaminations and RNA modifications, which overall skew the sRNA libraries. Here, we summarize the sRNA-sequencing approaches, and discuss the considerations and challenges for the strategies and methods of sRNA library construction. The pros and cons of sRNA sequencing have significant implications for implementing RNA fragment footprinting approaches, including CLIP-seq and Ribo-seq. We envision that this review can inspire novel improvements in small RNA sequencing and RNA fragment footprinting in future. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA Processing > Processing of Small RNAs Regulatory RNAs/RNAi/Riboswitches > Biogenesis of Effector Small RNAs.

ProQ-dependent activation of Salmonella virulence genes mediated by post-transcriptional control of PhoP synthesis.

Gastrointestinal disease caused by Salmonella enterica is associated with the pathogen's ability to replicate within epithelial cells and macrophages. Upon host cell entry, the bacteria express a type-three secretion system encoded within Salmonella pathogenicity island 2, through which host-manipulating effector proteins are secreted to establish a stable intracellular niche. Transcription of this intracellular virulence program is activated by the PhoPQ two-component system that senses the low pH and the reduced magnesium concentration of host cell vacuoles. In addition to transcriptional control, Salmonella commonly employ RNA-binding proteins (RBPs) and small regulatory RNAs (sRNAs) to regulate gene expression at the post-transcriptional level. ProQ is a globally acting RBP in Salmonella that promotes expression of the intracellular virulence program, but its RNA repertoire has previously been characterized only under standard laboratory growth conditions. Here, we provide a high-resolution ProQ interactome during conditions mimicking the environment of the Salmonella-containing vacuole (SCV), revealing hundreds of previously unknown ProQ binding sites in sRNAs and mRNA 3'UTRs. ProQ positively affected both the levels and the stability of many sRNA ligands, some of which were previously shown to associate with the well-studied and infection-relevant RBP Hfq. We further show that ProQ activates the expression of PhoP at the post-transcriptional level, which, in turn, leads to upregulation of the intracellular virulence program. IMPORTANCE: Salmonella enterica is a major pathogen responsible for foodborne gastroenteritis, and a leading model organism for genetic and molecular studies of bacterial virulence mechanisms. One key trait of this pathogen is the ability to survive within infected host cells. During infection, the bacteria employ a type three secretion system that deliver effector proteins to target and manipulate host cell processes. The transcriptional regulation of this virulence program is well understood. By contrast, the factors and mechanisms operating at the post-transcriptional level to control virulence gene expression are less clear. In this study, we have charted the global RNA ligand repertoire of the RNA-binding protein ProQ during in vitro conditions mimicking the host cell environment. This identified hundreds of binding sites and revealed ProQ-dependent stabilization of intracellular-specific small RNAs. Importantly, we show that ProQ post-transcriptionally activates the expression of PhoP, a master transcriptional activator of intracellular virulence in Salmonella.

Transfer Learning Allows Accurate RBP Target Site Prediction with Limited Sample Sizes.

RNA-binding proteins are vital regulators in numerous biological processes. Their disfunction can result in diverse diseases, such as cancer or neurodegenerative disorders, making the prediction of their binding sites of high importance. Deep learning (DL) has brought about a revolution in various biological domains, including the field of protein-RNA interactions. Nonetheless, several challenges persist, such as the limited availability of experimentally validated binding sites to train well-performing DL models for the majority of proteins. Here, we present a novel training approach based on transfer learning (TL) to address the issue of limited data. Employing a sophisticated and interpretable architecture, we compare the performance of our method trained using two distinct approaches: training from scratch (SCR) and utilizing TL. Additionally, we benchmark our results against the current state-of-the-art methods. Furthermore, we tackle the challenges associated with selecting appropriate input features and determining optimal interval sizes. Our results show that TL enhances model performance, particularly in datasets with minimal training data, where satisfactory results can be achieved with just a few hundred RNA binding sites. Moreover, we demonstrate that integrating both sequence and evolutionary conservation information leads to superior performance. Additionally, we showcase how incorporating an attention layer into the model facilitates the interpretation of predictions within a biologically relevant context.

Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning.

We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and miCLIP assays, outperforming state-of-the-art classifiers. RBPNet performs bias correction by modeling the raw signal as a mixture of the protein-specific and background signal. Through model interrogation via Integrated Gradients, RBPNet identifies predictive sub-sequences that correspond to known and novel binding motifs and enables variant-impact scoring via in silico mutagenesis. Together, RBPNet improves imputation of protein-RNA interactions, as well as mechanistic interpretation of predictions.

Oncogenic K-Ras suppresses global miRNA function.

K-Ras frequently acquires gain-of-function mutations (K-RasG12D being the most common) that trigger significant transcriptomic and proteomic changes to drive tumorigenesis. Nevertheless, oncogenic K-Ras-induced dysregulation of post-transcriptional regulators such as microRNAs (miRNAs) during oncogenesis is poorly understood. Here, we report that K-RasG12D promotes global suppression of miRNA activity, resulting in the upregulation of hundreds of targets. We constructed a comprehensive profile of physiological miRNA targets in mouse colonic epithelium and tumors expressing K-RasG12D using Halo-enhanced Argonaute pull-down. Combining this with parallel datasets of chromatin accessibility, transcriptome, and proteome, we uncovered that K-RasG12D suppressed the expression of Csnk1a1 and Csnk2a1, subsequently decreasing Ago2 phosphorylation at Ser825/829/832/835. Hypo-phosphorylated Ago2 increased binding to mRNAs while reducing its activity to repress miRNA targets. Our findings connect a potent regulatory mechanism of global miRNA activity to K-Ras in a pathophysiological context and provide a mechanistic link between oncogenic K-Ras and the post-transcriptional upregulation of miRNA targets.

Mechanistic insight into the efficient packaging of antigenomic S RNA into Rift Valley fever virus particles.

Rift Valley fever virus (RVFV), a bunyavirus, has a single-stranded, negative-sense tri-segmented RNA genome, consisting of L, M and S RNAs. An infectious virion carries two envelope glycoproteins, Gn and Gc, along with ribonucleoprotein complexes composed of encapsidated viral RNA segments. The antigenomic S RNA, which serves as the template of the mRNA encoding a nonstructural protein, NSs, an interferon antagonist, is also efficiently packaged into RVFV particles. An interaction between Gn and viral ribonucleoprotein complexes, including the direct binding of Gn to viral RNAs, drives viral RNA packaging into RVFV particles. To understand the mechanism of efficient antigenomic S RNA packaging in RVFV, we identified the regions in viral RNAs that directly interact with Gn by performing UV-crosslinking and immunoprecipitation of RVFV-infected cell lysates with anti-Gn antibody followed by high-throughput sequencing analysis (CLIP-seq analysis). Our data suggested the presence of multiple Gn-binding sites in RVFV RNAs, including a prominent Gn-binding site within the 3' noncoding region of the antigenomic S RNA. We found that the efficient packaging of antigenomic S RNA was abrogated in a RVFV mutant lacking a part of this prominent Gn-binding site within the 3' noncoding region. Also, the mutant RVFV, but not the parental RVFV, triggered the early induction of interferon-ß mRNA expression after infection. These data suggest that the direct binding of Gn to the RNA element within the 3' noncoding region of the antigenomic S RNA promoted the efficient packaging of antigenomic S RNA into virions. Furthermore, the efficient packaging of antigenomic S RNA into RVFV particles, driven by the RNA element, facilitated the synthesis of viral mRNA encoding NSs immediately after infection, resulting in the suppression of interferon-ß mRNA expression.

Analyzing RNA-Protein Interactions by Cross-Link Rates and CLIP-seq Libraries.

UV cross-linking-based methods are the most common tool to explore in vivo RNA-protein interactions. UV cross-linking enables the freezing of direct interactions in the cell, which can then be mapped by high-throughput sequencing through a family of methods termed CLIP-seq. CLIP-seq measures the distribution of cross-link events by purifying a protein of interest and sequencing the covalently bound RNA fragments. However, there are disagreements and ambiguities as to which proteins are RNA-binding proteins and what interactions are significant as all proteins contact all RNAs at some frequency. Here we describe a protocol for both determining RNA-protein interactions through a combination of RNA library preparation and the measurement of absolute cross-link rates, which helps determine what proteins are RNA-binding proteins and what interactions are significant. This protocol, comprising an updated form of the easyCLIP protocol, describes guidelines for RNA library preparation, oligo and protein standard construction, and the measurement of cross-link rates. These methods are easily visualizable through their fluorescent labels and can be adapted to study RNA-binding properties of both functional, high affinity RNA-binding proteins, and the accidental RNA interactions of non-RNA-binding proteins. © 2023 Wiley Periodicals LLC. Basic Protocol 1: RNA library construction Basic Protocol 2: Determining UV cross-link rates Support Protocol 1: Cross-linking and lysing cells Support Protocol 2: Adapter preparation Support Protocol 3: Preparation of cross-linked RBP standard.

Overview of Computational and Experimental Methods to Identify Tissue-Specific MicroRNA Targets.

As ubiquitous posttranscriptional regulators of gene expression, microRNAs (miRNAs) play key roles in cell physiology and function across taxa. In the last two decades, we have gained a good understanding about miRNA biogenesis pathways, modes of action, and consequences of miRNA-mediated gene regulation. More recently, research has focused on exploring causes for miRNA dysregulation, miRNA-mediated crosstalk between genes and signaling pathways, and the role of miRNAs in disease.This chapter discusses methods for the identification of miRNA-target interactions and causes for tissue-specific miRNA-target regulation. Computational approaches for predicting miRNA target sites and assessing tissue-specific target regulation are discussed. Moreover, there is an emphasis on features that affect miRNA target recognition and how high-throughput sequencing protocols can help in assessing miRNA-mediated gene regulation on a genome-wide scale. In addition, this chapter introduces some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA-target interactions.

Distinct binding pattern of EZH2 and JARID2 on RNAs and DNAs in hepatocellular carcinoma development.

Hepatocellular carcinoma (HCC) is one of the most malignant cancers worldwide, with high mortality. However, the molecular regulatory mechanisms of liver cancer, especially transcriptional and post-transcriptional mechanisms, should be further studied. Here we used chromatin and cross-linking immunoprecipitation with high throughput sequencing methods (ChIP-seq and CLIP-seq) to capture the global binding profiles on RNAs and DNAs of Enhancer of zeste homolog 2 (EZH2) and its partner Jumonji And AT-Rich Interaction Domain Containing 2 (JARID2) in liver carcinoma cell lines (HepG2) and normal liver cell line (THLE-2), respectively. We also integrated HCC transcriptome data from the TCGA to analyze the expression pattern of bound genes. We found that EZH2 and JARID2 both showed distinct binding profiles between HepG2 and THLE-2 cells. By binding to the primary RNAs, bound transcripts of EZH2 and JARID2 in HepG2 showed significantly increased transcriptional levels in HCC patients. By performing gene set enrichment analysis (GSEA), the bound transcripts were also highly related to HCC development. We also found EZH2 and JARID2 could specifically bind to several long noncoding RNAs (lncRNAs), including H19. By exploring the DNA binding profile, we detected a dramatically repressed DNA binding ability of EZH2 in HepG2 cells. We also found that the EZH2-bound genes showed slightly increased transcriptional levels in HepG2 cells. Integrating analysis of the RNA and DNA binding profiles suggests EZH2 and JARID2 shift their binding ability from DNA to RNA in HepG2 cells to promote cancer development in HCC. Our study provided a comprehensive and distinct binding profile on RNAs and DNAs of EZH2 and JARID2 in liver cancer cell lines, suggesting their potential novel functional manners to promote HCC development.

A family of C. elegans VASA homologs control Argonaute pathway specificity and promote transgenerational silencing.

Germline Argonautes direct transcriptome surveillance within perinuclear membraneless organelles called nuage. In C. elegans, a family of Vasa-related Germ Line Helicase (GLH) proteins localize in and promote the formation of nuage. Previous studies have implicated GLH proteins in inherited silencing, but direct roles in small-RNA production, Argonaute binding, or mRNA targeting have not been identified. Here we show that GLH proteins compete with each other to control Argonaute pathway specificity, bind directly to Argonaute target mRNAs, and promote the amplification of small RNAs required for transgenerational inheritance. We show that the ATPase cycle of GLH-1 regulates direct binding to the Argonaute WAGO-1, which engages amplified small RNAs. Our findings support a dynamic and direct role for GLH proteins in inherited silencing beyond their role as structural components of nuage.

Solid-Support Directional (SSD) RNA-Seq as a Companion Method to CLIP-Seq.

CLIP-Seq (Deep Sequencing after in vivo Crosslinking and Immunoprecipitation, HITS-CLIP) has emerged as a key method for the study of RNA-binding proteins (RBPs), as it can scrutinize the RNAs bound by an RBP in vivo, with minimum manipulation of biological samples. CLIP-Seq is best used to reveal changes of the RNA cargo of an RBP and differences on binding patterns of the bound RNAs in living cells in different genetic backgrounds or after experimental treatment, rather than simply identifying RNA species. It is therefore crucial that a reference of the steady state levels of the RNAs present in the samples used for the CLIP-Seq experiment is included in the bioinformatic analysis. A simple directional RNA-Seq method was developed that uses the same oligonucleotides and the same PCR amplification steps as our CLIP-Seq method, which therefore can be analyzed using the same bioinformatic pipeline as the CLIP-Seq data. This greatly simplifies and streamlines the analysis process, and at the same time reduces the chances of protocol-specific artifacts and biases interfering with data interpretation. Some considerations on ways to integrate CLIP-Seq and RNA-Seq analyses are also provided herein.

Identification of RNA-RBP Interactions in Subcellular Compartments by CLIP-Seq.

Cross-linking immunoprecipitation and high-throughput sequencing (CLIP-seq) allows the identification of RNA targets bound by a specific RNA-binding protein (RBP) in in vivo and ex vivo experimental models with high specificity. Due to the little RNA yield obtained after cross-linking, immunoprecipitation, polyacrylamide gel electrophoresis, membrane transfer, and RNA extraction, CLIP-seq is usually performed from relatively large amounts of starting material, like cell lysates or tissue homogenates. However, RBP binding of its specific RNA targets depends on its subcellular localization, and a different set of RNAs may be bound by the same RBP within distinct subcellular sites. To uncover these RNA subsets, preparation of CLIP-seq libraries from specific subcellular compartments and comparison to CLIP-seq datasets from total lysates is necessary, yet there are currently no available protocols for this. Here we describe the adaptation of CLIP-seq to identify the specific RNA targets of an RBP (FUS) at a small subcompartment, that is, neuronal synapses, including subcompartment isolation, RBP-RNA complex enrichment, and upscaling steps.

Post-transcriptional control of fungal cell wall synthesis.

Pathogenic fungi hide from their hosts by camouflage, obscuring immunogenic cell wall components such as beta-glucan with innocuous coverings such as mannoproteins and alpha-glucan that are less readily recognised by the host. Attempts to understand how such processes are regulated have met with varying success. Typically studies focus on understanding the transcriptional response of fungi to either their reservoir environment or the host. However, such approaches do not fully address this research question, due to the layers of post-transcriptional and post-translational regulation that occur within a cell. Although in animals the impact of post-transcriptional and post-translational regulation has been well characterised, our knowledge of these processes in the fungal kingdom is more limited. Mutations in RNA-binding proteins, like Ssd1 and Candida albicans Slr1, affect cell wall composition and fungal virulence indicating that post-transcriptional regulation plays a key role in these processes. Here, we review the current state of knowledge of fungal post-transcriptional regulation, and link this to potential mechanisms of immune evasion by drawing on studies from model yeast and plant pathogenic fungi. We highlight several RNA-binding proteins that regulate cell wall synthesis and could be involved in local translation of cell wall components. Expanding our knowledge on post-transcriptional regulation in human fungal pathogens is essential to fully comprehend fungal virulence strategies and for the design of novel antifungal therapies.

Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data.

Identifying cis-regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) binding sites and inferring gene regulatory mechanisms for any organism. Since 2015, deep learning (DL) methods have been widely applied to identify TF binding sites and predict motif patterns, with the strengths of offering a scalable, flexible and unified computational approach for highly accurate predictions. As far as we know, 20 DL methods have been developed. However, without a clear and systematic assessment, users will struggle to choose the most appropriate tool for their specific studies. In this manuscript, we evaluated 20 DL methods for cis-regulatory motif prediction using 690 ENCODE ChIP-seq, 126 cancer ChIP-seq and 55 RNA CLIP-seq data. Four metrics were investigated, including the accuracy of motif finding, the performance of DNA/RNA sequence classification, algorithm scalability and tool usability. The assessment results demonstrated the high complementarity of the existing DL methods. It was determined that the most suitable model should primarily depend on the data size and type and the method's outputs.

Two novel RNA-binding proteins identification through computational prediction and experimental validation.

Since RBPs play important roles in the cell, it's particularly important to find new RBPs. We performed iRIP-seq and CLIP-seq to verify two proteins, CLIP1 and DMD, predicted by RBPPred whether are RBPs or not. The experimental results confirm that these two proteins have RNA-binding activity. We identified significantly enriched binding motifs UGGGGAGG, CUUCCG and CCCGU for CLIP1 (iRIP-seq), DMD (iRIP-seq) and DMD (CLIP-seq), respectively. The computational KEGG and GO analysis show that the CLIP1 and DMD share some biological processes and functions. Besides, we found that the SNPs between DMD and its RNA partners may be associated with Becker muscular dystrophy, Duchenne muscular dystrophy, Dilated cardiomyopathy 3B and Cardiovascular phenotype. Among the thirteen cancers data, CLIP1 and another 300 oncogenes always co-occur, and 123 of these 300 genes interact with CLIP1. These cancers may be associated with the mutations occurred in both CLIP1 and the genes it interacts with.

DeepA-RBPBS: A hybrid convolution and recurrent neural network combined with attention mechanism for predicting RBP binding site.

It's important to infer the binding site of RNA-binding proteins (RBP) for understanding the interaction between RBP and its RNA targets and decipher the mechanisms of transcriptional regulation. However, experimental detection of RBP binding sites is still time-intensive and expensive. Algorithms based on machine learning can speed up detection of RBP binding sites. In this article, we propose a new deep learning method, DeepA-RBPBS, which can use RNA sequences and structural features to predict RBP binding site. DeepA-RBPBS uses CNN and BiGRU to extract sequences and structural features without long-term dependence issues. It also utilizes an attention mechanism to enhance the contribution of key features. The comparison shows that the performance of DeepA-RBPBS is better than that of the state-of-the-art predictors. In the testing on 31 datasets of CLIP-seq experiments over 19 proteins, MCC (AUC) is 8% (5%) higher than those of the latest method based on deep learning, iDeepS. We also apply DeepA-RBPBS to the target RNA data of RBPs related to diabetes (LIN28, RBFOX2, FTO, IGF2BP2, CELF1 and HuR). The results show that DeepA-RBPBS correctly predicted 41,693 samples, where iDeepS predicted 31,381 samples.Communicated by Ramaswamy H. Sarma.

Time-Resolved Analysis of N-RNA Interactions during RVFV Infection Shows Qualitative and Quantitative Shifts in RNA Encapsidation and Packaging.

Rift Valley fever virus (RVFV) is a negative-sense, tripartite RNA virus that is endemic to Africa and the Arabian Peninsula. It can cause severe disease and mortality in humans and domestic livestock and is a concern for its potential to spread more globally. RVFV's nucleocapsid protein (N) is an RNA-binding protein that is necessary for viral transcription, replication, and the production of nascent viral particles. We have conducted crosslinking, immunoprecipitation, and sequencing (CLIP-seq) to characterize N interactions with host and viral RNAs during infection. In parallel, to precisely measure intracellular N levels, we employed multiple reaction monitoring mass spectrometry (MRM-MS). Our results show that N binds mostly to host RNAs at early stages of infection, yielding nascent virus particles of reduced infectivity. The expression of N plateaus 10 h post-infection, whereas the intracellular viral RNA concentration continues to increase. Moreover, the virions produced later in infection have higher infectivity. Taken together, the detailed examination of these N-RNA interactions provides insight into how the regulated expression of N and viral RNA produces both infectious and incomplete, noninfectious particles.

CLIP-Seq to identify targets and interactions of RNA binding proteins and RNA modifying enzymes.

The study of RNA chemical modifications is currently one of the most rapid-growing fields. Many types of RNA modifications in diverse RNA species have been shown to play versatile roles in a wide array of cellular processes. These modifications are installed and erased by writer and eraser enzymes, respectively. Additionally, RNA chemical modifications have downstream biological effects through either influencing changes in the chemistry or structure of RNA molecules or through recognition of the modification; these functions are primarily executed by the modification reader proteins. Reader proteins may bind to the modification site and cause a downstream signal cascade. One of the essential tools for studying erasers, writers, and readers is cross-linking immunoprecipitation followed by high-throughput sequencing (CLIP-seq). This method can detect the sites on endogenous RNAs bound by RNA-binding proteins or RNA modifying enzymes. Essentially, this strategy allows for snapshots of the epitranscriptome and molecular events occurring within the cell. In this article, we go through in detail the various steps involved in CLIP-seq.

RNAProt: an efficient and feature-rich RNA binding protein binding site predictor.

BACKGROUND: Cross-linking and immunoprecipitation followed by next-generation sequencing (CLIP-seq) is the state-of-the-art technique used to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). However, it relies on gene expression, which can be highly variable between conditions and thus cannot provide a complete picture of the RBP binding landscape. This creates a demand for computational methods to predict missing binding sites. Although there exist various methods using traditional machine learning and lately also deep learning, we encountered several problems: many of these are not well documented or maintained, making them difficult to install and use, or are not even available. In addition, there can be efficiency issues, as well as little flexibility regarding options or supported features. RESULTS: Here, we present RNAProt, an efficient and feature-rich computational RBP binding site prediction framework based on recurrent neural networks. We compare RNAProt with 1 traditional machine learning approach and 2 deep-learning methods, demonstrating its state-of-the-art predictive performance and better run time efficiency. We further show that its implemented visualizations capture known binding preferences and thus can help to understand what is learned. Since RNAProt supports various additional features (including user-defined features, which no other tool offers), we also present their influence on benchmark set performance. Finally, we show the benefits of incorporating additional features, specifically structure information, when learning the binding sites of an hairpin loop binding RBP. CONCLUSIONS: RNAProt provides a complete framework for RBP binding site predictions, from data set generation over model training to the evaluation of binding preferences and prediction. It offers state-of-the-art predictive performance, as well as superior run time efficiency, while at the same time supporting more features and input types than any other tool available so far. RNAProt is easy to install and use, comes with comprehensive documentation, and is accompanied by informative statistics and visualizations. All this makes RNAProt a valuable tool to apply in future RBP binding site research.