Analysis of CLIP and iCLIP methods for nucleotide-resolution studies of protein-RNA interactions

ABSTRACT: UV cross-linking and immunoprecipitation (CLIP) and individual-nucleotide resolution CLIP (iCLIP) are methods to study protein-RNA interactions in untreated cells and tissues. Here, we analyzed six published and two novel data sets to confirm that both methods identify protein-RNA cross-link sites, and to identify a slight uridine preference of UV-C-induced cross-linking. Comparing Nova CLIP and iCLIP data revealed that cDNA deletions have a preference for TTT motifs, whereas iCLIP cDNA truncations are more likely to identify clusters of YCAY motifs as the primary Nova binding sites. In conclusion, we demonstrate how each method impacts the analysis of protein-RNA binding specificity.     

PRI-Modeler: extracting RNA structural elements from PDB files of protein-RNA complexes

A complete understanding of protein and RNA structures and their interactions is important for determining the binding sites in protein-RNA complexes. Computational approaches exist for identifying secondary structural elements in proteins from atomic coordinates. However, similar methods have not been developed for RNA, due in part to the very limited structural data so far available. We have developed a set of algorithms for extracting and visualizing secondary and tertiary structures of RNA and for analyzing protein-RNA complexes. These algorithms have been implemented in a web-based program called PRI-Modeler (protein-RNA interaction modeler). Given one or more protein data bank files of protein-RNA complexes, PRI-Modeler analyzes the conformation of the RNA, calculates the hydrogen bond (H bond) and van der Waals interactions between amino acids and nucleotides, extracts secondary and tertiary RNA structure elements, and identifies the patterns of interactions between the proteins and RNAs. This paper presents PRI-Modeler and its application to the hydrogen bond and van der Waals interactions in the most representative set of protein-RNA complexes. The analysis reveals several interesting interaction patterns at various levels. The information provided by PRI-Modeler should prove useful for determining the binding sites in protein-RNA complexes. PRI-Modeler is accessible at http://wilab.inha.ac.kr/primodeler/, and supplementary materials are available in the analysis results section at http://wilab.inha.ac.kr/primodeler/.     

Partner-specific prediction of RNA-binding residues in proteins: A critical assessment

RNA-protein interactions play essential roles in regulating gene expression. While some RNA-protein interactions are “specific”, that is, the RNA-binding proteins preferentially bind to particular RNA sequence or structural motifs, others are “non-RNA specific.” Deciphering the protein-RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein-RNA interfaces, there is a need for computational methods to identify RNA-binding residues in proteins. While most of the existing computational methods for predicting RNA-binding residues in RNA-binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner-specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner-specific protein-RNA interface prediction tools, PS-PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA-specificity metric (RSM), for quantifying the RNA-specificity of the RNA binding residues predicted by such tools. Our results show that the RNA-binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner-agnostic metrics, RNA partner-specific methods are outperformed by the state-of-the-art partner-agnostic methods. We conjecture that either (a) the protein-RNA complexes in PDB are not representative of the protein-RNA interactions in nature, or (b) the current methods for partner-specific prediction of RNA-binding residues in proteins fail to account for the differences in RNA partner-specific versus partner-agnostic protein-RNA interactions, or both.     

RNase-mediated protein footprint sequencing reveals protein-binding sites throughout the human transcriptome

Background

Sequence specific RNA binding proteins are important regulators of gene expression. Several related crosslinking-based, high-throughput sequencing methods, including PAR-CLIP, have recently been developed to determine direct binding sites of global protein-RNA interactions. However, no studies have quantitatively addressed the contribution of background binding to datasets produced by these methods.

Results

We measured non-specific RNA background in PAR-CLIP data, demonstrating that covalently crosslinked background binding is common, reproducible and apparently universal among laboratories. We show that quantitative determination of background is essential for identifying targets of most RNA-binding proteins and can substantially improve motif analysis. We also demonstrate that by applying background correction to an RNA binding protein of unknown binding specificity, Caprin1, we can identify a previously unrecognized RNA recognition element not otherwise apparent in a PAR-CLIP study.

Conclusions

Empirical background measurements of global RNA-protein crosslinking are a necessary addendum to other experimental controls, such as performing replicates, because covalently crosslinked background signals are reproducible and otherwise unavoidable. Recognizing and quantifying the contribution of background extends the utility of PAR-CLIP and can improve mechanistic understanding of protein-RNA specificity, protein-RNA affinity and protein-RNA association dynamics.     

Studies on the kinetic sequence of in vitro ribosome assembly using cibacron blue F3GA as a general assembly inhibitor.

We have found that all E. coli ribosomal proteins strongly bind to an agarose affinity column derivatized with the dye Cibacron Blue F3GA. We have also shown that the capacity to bind the dye is lost when the proteins are organized within the structure of the ribosome or are members of pre-formed protein-RNA complexes. We conclude that the binding of ribosomal proteins to this dye involves specific protein-RNA recognition sites. These observations led us to discover that Cibacron Blue can be used to inhibit in vitro ribosome assembly at any stage of the assembly process. This has allowed us to determine a kinetic order of ribosome assembly.     

Structure-based prediction of RNA-binding domains and RNA-binding sites and application to structural genomics targets

Mechanistic understanding of many key cellular processes often involves identification of RNA binding proteins (RBPs) and RNA binding sites in two separate steps. Here, they are predicted simultaneously by structural alignment to known protein-RNA complex structures followed by binding assessment with a DFIRE-based statistical energy function. This method achieves 98% accuracy and 91% precision for predicting RBPs and 93% accuracy and 78% precision for predicting RNA-binding amino-acid residues for a large benchmark of 212 RNA binding and 6761 non-RNA binding domains (leave-one-out cross-validation). Additional tests revealed that the method makes no false positive prediction from 311 DNA binding domains but correctly detects six domains binding with both DNA and RNA. In addition, it correctly identified 31 of 75 unbound RNA-binding domains with 92% accuracy and 65% precision for predicted binding residues and achieved 86% success rate in its application to SCOP RNA binding domain superfamily (Structural Classification Of Proteins). It further predicts 25 targets as RBPs in 2076 structural genomics targets: 20 of 25 predicted ones (80%) are putatively RNA binding. The superior performance over existing methods indicates the importance of dividing structures into domains, using a Z-score to measure relative structural similarity, and a statistical energy function to measure protein-RNA binding affinity.     

Stoichiometry and specificity of binding of Rauscher oncovirus 10,000-dalton (p10) structural protein to nucleic acids.

A structural protein of Rauscher oncovirus of about 8,000 to 10,000 daltons (p10), encoded by the gag gene, has been purified in high yield to apparent homogeneity by a simple three-step procedure. The purified protein was highly basic, with an isoelectric point of more than 9.0, and its immunological antigenicity was chiefly group specific. A distinctive property of the protein was the binding to nucleic acids. The stoichiometry of p10 binding to Rauscher virus RNA was analyzed using both 125I-labeled p10 and 3H-labeled RNA. The protein-RNA complex, cross-linked by formaldehyde, was separated from free RNA and free protein by velocity sedimentation and density gradient centrifugation. A maximum of about 140 mol of p10 was bound per mol of 35S RNA, or about one molecule of p10 per 70 nucleotides. This protein-RNA complex banded at a density of about 1.55 g/ml. The number of nucleic acid sites bound and the affinity of p10 binding differed significantly among the other polynucleotides tested. The protein bound to both RNA and DNA with a preference for single-stranded molecules. Rauscher virus RNA and single-stranded phage fd DNA contained the highest number of binding sites. Binding to fd DNA was saturated with about 30 mol of p10 per mol of fd DNA, an average of about one p10 molecule per 180 nucleotides. The apparent binding constant was 7.3 X 10(7) M(-1). The properties of the p10 place it in a category with other nucleic acid binding proteins that achieve a greater binding density on single-stranded than on double-stranded molecules and appear to act by facilitating changes in polynucleotide conformation.     

Computational methods for prediction of protein-RNA interactions

Understanding the molecular mechanism of protein-RNA recognition and complex formation is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes by X-ray crystallography and nuclear magnetic resonance spectroscopy (NMR) is tedious and difficult. Alternatively, protein-RNA interactions can be predicted by computational methods. Although less accurate than experimental observations, computational predictions can be sufficiently accurate to prompt functional hypotheses and guide experiments, e.g. to identify individual amino acid or nucleotide residues. In this article we review 10 methods for predicting protein-RNA interactions, seven of which predict RNA-binding sites from protein sequences and three from structures. We also developed a meta-predictor that uses the output of top three sequence-based primary predictors to calculate a consensus prediction, which outperforms all the primary predictors. In order to fully cover the software for predicting protein-RNA interactions, we also describe five methods for protein-RNA docking. The article highlights the strengths and shortcomings of existing methods for the prediction of protein-RNA interactions and provides suggestions for their further development.     

Computational analysis of hydrogen bonds in protein-RNA complexes for interaction patterns

Structural analysis of protein-RNA complexes is labor-intensive, yet provides insight into the interaction patterns between a protein and RNA. As the number of protein-RNA complex structures reported has increased substantially in the last few years, a systematic method is required for automatically identifying interaction patterns. This paper presents a computational analysis of the hydrogen bonds in the most representative set of protein-RNA complexes. The analysis revealed several interesting interaction patterns. (1) While residues in the beta-sheets favored unpaired nucleotides, residues in the helices showed no preference and residues in turns favored paired nucleotides. (2) The backbone hydrogen bonds were more dominant than the base hydrogen bonds in the paired nucleotides, but the reverse was observed in the unpaired nucleotides. (3) The protein-RNA complexes contained more paired nucleotides than unpaired nucleotides, but the unpaired nucleotides were observed more frequently interacting with the proteins. And (4) Arg-U, Thr-A, Lys-A, and Asn-U were the most frequently observed pairs. The interaction patterns discovered from the analysis will provide us with useful information in predicting the structure of the RNA binding protein and the structure of the protein binding RNA.     

Prediction of interacting single-stranded RNA bases by protein-binding patterns

Prediction of protein-RNA interactions at the atomic level of detail is crucial for our ability to understand and interfere with processes such as gene expression and regulation. Here, we investigate protein binding pockets that accommodate extruded nucleotides not involved in RNA base pairing. We observed that most of the protein-interacting nucleotides are part of a consecutive fragment of at least two nucleotides whose rings have significant interactions with the protein. Many of these share the same protein binding cavity and more than 30% of such pairs are π-stacked. Since these local geometries cannot be inferred from the nucleotide identities, we present a novel framework for their prediction from the properties of protein binding sites.First, we present a classification of known RNA nucleotide and dinucleotide protein binding sites and identify the common types of shared 3-D physicochemical binding patterns. These are recognized by a new classification methodology that is based on spatial multiple alignment. The shared patterns reveal novel similarities between dinucleotide binding sites of proteins with different overall sequences, folds and functions. Given a protein structure, we use these patterns for the prediction of its RNA dinucleotide binding sites. Based on the binding modes of these nucleotides, we further predict an RNA fragment that interacts with those protein binding sites. With these knowledge-based predictions, we construct an RNA fragment that can have a previously unknown sequence and structure. In addition, we provide a drug design application in which the database of all known small-molecule binding sites is searched for regions similar to nucleotide and dinucleotide binding patterns, suggesting new fragments and scaffolds that can target them.