Sequence-Based Prediction of RNA-Binding Residues in Proteins

Reconstructing the sequence specificities of RNA-binding proteins across eukaryotes

RNA-binding proteins (RBPs) are key regulators of gene expression. Here, we introduce EuPRI (Eukaryotic Protein-RNA Interactions) - a freely available resource of RNA motifs for 34,736 RBPs from 690 eukaryotes. EuPRI includes in vitro binding data for 504 RBPs, including newly collected RNAcompete data for 174 RBPs, along with thousands of reconstructed motifs. We reconstruct these motifs with a new computational platform - Joint Protein-Ligand Embedding (JPLE) - which can detect distant homology relationships and map specificity-determining peptides. EuPRI quadruples the number of known RBP motifs, expanding the motif repertoire across all major eukaryotic clades, and assigning motifs to the majority of human RBPs. EuPRI drastically improves knowledge of RBP motifs in flowering plants. For example, it increases the number of Arabidopsis thaliana RBP motifs 7-fold, from 14 to 105. EuPRI also has broad utility for inferring post-transcriptional function and evolutionary relationships. We demonstrate this by predicting a role for 12 Arabidopsis thaliana RBPs in RNA stability and identifying rapid and recent evolution of post-transcriptional regulatory networks in worms and plants. In contrast, the vertebrate RNA motif set has remained relatively stable after its drastic expansion between the metazoan and vertebrate ancestors. EuPRI represents a powerful resource for the study of gene regulation across eukaryotes.

Interferon alpha inducible protein 6 is a negative regulator of innate immune responses by modulating RIG-I activation

Interferons (IFNs), IFN-stimulated genes (ISGs), and inflammatory cytokines mediate innate immune responses, and are essential to establish an antiviral response. Within the innate immune responses, retinoic acid-inducible gene I (RIG-I) is a key sensor of virus infections, mediating the transcriptional induction of IFNs and inflammatory proteins. Nevertheless, since excessive responses could be detrimental to the host, these responses need to be tightly regulated. In this work, we describe, for the first time, how knocking-down or knocking-out the expression of IFN alpha-inducible protein 6 (IFI6) increases IFN, ISG, and pro-inflammatory cytokine expression after the infections with Influenza A Virus (IAV), Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and Sendai Virus (SeV), or poly(I:C) transfection. We also show how overexpression of IFI6 produces the opposite effect, in vitro and in vivo, indicating that IFI6 negatively modulates the induction of innate immune responses. Knocking-out or knocking-down the expression of IFI6 diminishes the production of infectious IAV and SARS-CoV-2, most likely because of its effect on antiviral responses. Importantly, we report a novel interaction of IFI6 with RIG-I, most likely mediated through binding to RNA, that affects RIG-I activation, providing a molecular mechanism for the effect of IFI6 on negatively regulating innate immunity. Remarkably, these new functions of IFI6 could be targeted to treat diseases associated with an exacerbated induction of innate immune responses and to combat viral infections, such as IAV and SARS-CoV-2.

The IFN-stimulated gene IFI27 counteracts innate immune responses after viral infections by interfering with RIG-I signaling

The recognition of viral nucleic acids by host pattern recognition receptors (PRRs) is critical for initiating innate immune responses against viral infections. These innate immune responses are mediated by the induction of interferons (IFNs), IFN-stimulated genes (ISGs) and pro-inflammatory cytokines. However, regulatory mechanisms are critical to avoid excessive or long-lasting innate immune responses that may cause detrimental hyperinflammation. Here, we identified a novel regulatory function of the ISG, IFN alpha inducible protein 27 (IFI27) in counteracting the innate immune responses triggered by cytoplasmic RNA recognition and binding. Our model systems included three unrelated viral infections caused by Influenza A virus (IAV), Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), and Sendai virus (SeV), and transfection with an analog of double-stranded (ds) RNA. Furthermore, we found that IFI27 has a positive effect on IAV and SARS-CoV-2 replication, most likely due to its ability to counteract host-induced antiviral responses, including in vivo. We also show that IFI27 interacts with nucleic acids and PRR retinoic acid-inducible gene I (RIG-I), being the interaction of IFI27 with RIG-I most likely mediated through RNA binding. Interestingly, our results indicate that interaction of IFI27 with RIG-I impairs RIG-I activation, providing a molecular mechanism for the effect of IFI27 on modulating innate immune responses. Our study identifies a molecular mechanism that may explain the effect of IFI27 in counterbalancing innate immune responses to RNA viral infections and preventing excessive innate immune responses. Therefore, this study will have important implications in drug design to control viral infections and viral-induced pathology.

ProbeRating: a recommender system to infer binding profiles for nucleic acid-binding proteins

MOTIVATION

The interaction between proteins and nucleic acids plays a crucial role in gene regulation and cell function. Determining the binding preferences of nucleic acid-binding proteins (NBPs), namely RNA-binding proteins (RBPs) and transcription factors (TFs), is the key to decipher the protein-nucleic acids interaction code. Today, available NBP binding data from in vivo or in vitro experiments are still limited, which leaves a large portion of NBPs uncovered. Unfortunately, existing computational methods that model the NBP binding preferences are mostly protein specific: they need the experimental data for a specific protein in interest, and thus only focus on experimentally characterized NBPs. The binding preferences of experimentally unexplored NBPs remain largely unknown.

RESULTS

Here, we introduce ProbeRating, a nucleic acid recommender system that utilizes techniques from deep learning and word embeddings of natural language processing. ProbeRating is developed to predict binding profiles for unexplored or poorly studied NBPs by exploiting their homologs NBPs which currently have available binding data. Requiring only sequence information as input, ProbeRating adapts FastText from Facebook AI Research to extract biological features. It then builds a neural network-based recommender system. We evaluate the performance of ProbeRating on two different tasks: one for RBP and one for TF. As a result, ProbeRating outperforms previous methods on both tasks. The results show that ProbeRating can be a useful tool to study the binding mechanism for the many NBPs that lack direct experimental evidence. and implementation.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available at <https://github.com/syang11/ProbeRating>.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

SNB-PSSM: A spatial neighbor-based PSSM used for protein-RNA binding site prediction

Protein-RNA interactions play essential roles in a wide variety of biological processes. Recognition of RNA-binding residues on proteins has been a challenging problem. Most of methods utilize the position-specific scoring matrix (PSSM). It has been found that considering the evolutionary information of sequence neighboring residues can improve the prediction. In this work, we introduce a novel method SNB-PSSM (spatial neighbor-based PSSM) combined with the structure window scheme where the evolutionary information of spatially neighboring residues is considered. The results show our method consistently outperforms the standard and smoothed PSSM methods. Tested on multiple datasets, this approach shows an encouraging performance compared with RNABindRPlus, BindN+, PPRInt, xypan, Predict_RBP, SpaPF, PRNA, and KYG, although is inferior to RNAProSite, RBscore, and aaRNA. In addition, since our method is not sensitive to protein structure changes, it can be applied well on binding site predictions of modeled structures. Thus, the result also suggests the evolution of binding sites is spatially cooperative. The proposed method as an effective tool of considering evolutionary information can be widely used for the nucleic acid-/protein-binding site prediction and functional motif finding.

Prediction of Ion Channels and their Types from Protein Sequences: Comprehensive Review and Comparative Assessment

BACKGROUND

Ion channels are a large and growing protein family. Many of them are associated with diseases, and consequently, they are targets for over 700 drugs. Discovery of new ion channels is facilitated with computational methods that predict ion channels and their types from protein sequences. However, these methods were never comprehensively compared and evaluated.

OBJECTIVE

We offer first-of-its-kind comprehensive survey of the sequence-based predictors of ion channels. We describe eight predictors that include five methods that predict ion channels, their types, and four classes of the voltage-gated channels. We also develop and use a new benchmark dataset to perform comparative empirical analysis of the three currently available predictors.

RESULTS

While several methods that rely on different designs were published, only a few of them are currently available and offer a broad scope of predictions. Support and availability after publication should be required when new methods are considered for publication. Empirical analysis shows strong performance for the prediction of ion channels and modest performance for the prediction of ion channel types and voltage-gated channel classes. We identify a substantial weakness of current methods that cannot accurately predict ion channels that are categorized into multiple classes/types.

CONCLUSION

Several predictors of ion channels are available to the end users. They offer practical levels of predictive quality. Methods that rely on a larger and more diverse set of predictive inputs (such as PSIONplus) are more accurate. New tools that address multi-label prediction of ion channels should be developed.

PSIONplusm Server for Accurate Multi-Label Prediction of Ion Channels and Their Types

Computational prediction of ion channels facilitates the identification of putative ion channels from protein sequences. Several predictors of ion channels and their types were developed in the last quindecennial. While they offer reasonably accurate predictions, they also suffer a few shortcomings including lack of availability, parallel prediction mode, single-label prediction (inability to predict multiple channel subtypes), and incomplete scope (inability to predict subtypes of the voltage-gated channels). We developed a first-of-its-kind PSIONplus^m method that performs sequential multi-label prediction of ion channels and their subtypes for both voltage-gated and ligand-gated channels. PSIONplus^m sequentially combines the outputs produced by three support vector machine-based models from the PSIONplus predictor and is available as a webserver. Empirical tests show that PSIONplus^m outperforms current methods for the multi-label prediction of the ion channel subtypes. This includes the existing single-label methods that are available to the users, a naïve multi-label predictor that combines results produced by multiple single-label methods, and methods that make predictions based on sequence alignment and domain annotations. We also found that the current methods (including PSIONplus^m) fail to accurately predict a few of the least frequently occurring ion channel subtypes. Thus, new predictors should be developed when a larger quantity of annotated ion channels will be available to train predictive models.