Identification of protein-interacting nucleotides in a RNA sequence using composition profile of tri-nucleotides

Panwar, Bharat; Raghava, Gajendra P. S.

doi:10.1016/j.ygeno.2015.01.005

Cited by 24 publications

(24 citation statements)

References 47 publications

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“…A few methods have been reported for three purposes: 1) the investigation of associations between proteins and RNAs, such as RPI-Pred 36 , RPIseq 37 , and lncPro 38 ; 2) the prediction of binding sites on either RNAs or proteins, such as the sequenced-based methods: BindN 39 , RNABindR 40 , RNAProB 41 , PPRint 42 , RNApin 43 , PRINTR 44 , RISP 45 , PiRaNhA 46 , BindN+ 47 , NAPS 48 , PRBR 49 , SRCPred 50 , Predict_RBP 51 , RNABindRPlus 52 and RBRIdent 53 ; the structure-based methods: KYG 54 , RsiteDB 55 , PRIP 56 , OPRA 57 , DRNA 58 , PRNA 59 , aaRNA 60 , RBRDetector 61 and RBscore 62 ; and 3) the residue-nucleotide contacts prediction, such as catRAPID 63 . The catRAPID method is the only available method and different from those are specially designed to determine residue-nucleotide interactions of different known DNA-binding domain families 64,65 .…”

Section: Introductionmentioning

confidence: 99%

RPI-Bind: a structure-based method for accurate identification of RNA-protein binding sites

Luo

Liang

Venkateswaran

et al. 2017

Sci Rep

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RNA and protein interactions play crucial roles in multiple biological processes, while these interactions are significantly influenced by the structures and sequences of protein and RNA molecules. In this study, we first performed an analysis of RNA-protein interacting complexes, and identified interface properties of sequences and structures, which reveal the diverse nature of the binding sites. With the observations, we built a three-step prediction model, namely RPI-Bind, for the identification of RNA-protein binding regions using the sequences and structures of both proteins and RNAs. The three steps include 1) the prediction of RNA binding regions on protein, 2) the prediction of protein binding regions on RNA, and 3) the prediction of interacting regions on both RNA and protein simultaneously, with the results from steps 1) and 2). Compared with existing methods, most of which employ only sequences, our model significantly improves the prediction accuracy at each of the three steps. Especially, our model outperforms the catRAPID by >20% at the 3rd step. All of these results indicate the importance of structures in RNA-protein interactions, and suggest that the RPI-Bind model is a powerful theoretical framework for studying RNA-protein interactions.

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Section: Introductionmentioning

confidence: 99%

RPI-Bind: a structure-based method for accurate identification of RNA-protein binding sites

Luo

Liang

Venkateswaran

et al. 2017

Sci Rep

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“…Based on these researches [7, 9–11, 14–24], we combined a variety of features of the amino acids to represent the specific interaction attributes of protein residues with RNA nucleotides. In this work, some of the site characteristics, such as relative accessible surface area, secondary structure and interaction propensity, can be calculated only after the protein structure information is available.…”

Section: Resultsmentioning

confidence: 99%

“…1. A residue is defined as an RNA-binding site if there exists at least one atom in the protein with a distance cutoff < 5.0Å from an atom of the binding RNA [7, 9–11, 14–24]. RBP170 contains 6,754 (14.47%) RNA-binding sites and 39,933 (85.53%) non-binding sites.…”

Section: Methodsmentioning

confidence: 99%

“…A series of machine learning methods [6] such as Naive Bayes, support vector machine (SVM), and random forest (RF), combined with amino acid sequence or protein three-dimensional structural characteristics [4, 7], have been proposed to identify RNA-binding residues. Jeong et al [8] build a neural network classifier to predict RNA-binding residues based on protein sequence and structural information.…”

Section: Introductionmentioning

confidence: 99%

“…Although existing studies [7, 9–24] have made remarkable progress to explore the interfaces of protein-RNA interactions, there is still great room for improvement. First, precise biological properties for precisely recognizing RNA-binding sites are not fully uncovered; no single feature can effectively identify protein-RNA interaction residues.…”

Section: Introductionmentioning

confidence: 99%

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A boosting approach for prediction of protein-RNA binding residues

et al. 2017

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BackgroundRNA binding proteins play important roles in post-transcriptional RNA processing and transcriptional regulation. Distinguishing the RNA-binding residues in proteins is crucial for understanding how protein and RNA recognize each other and function together as a complex.ResultsWe propose PredRBR, an effectively computational approach to predict RNA-binding residues. PredRBR is built with gradient tree boosting and an optimal feature set selected from a large number of sequence and structure characteristics and two categories of structural neighborhood properties. In cross-validation experiments on the RBP170 data set show that PredRBR achieves an overall accuracy of 0.84, a sensitivity of 0.85, MCC of 0.55 and AUC of 0.92, which are significantly better than that of other widely used machine learning algorithms such as Support Vector Machine, Random Forest, and Adaboost. We further calculate the feature importance of different feature categories and find that structural neighborhood characteristics are critical in the recognization of RNA binding residues. Also, PredRBR yields significantly better prediction accuracy on an independent test set (RBP101) in comparison with other state-of-the-art methods.ConclusionsThe superior performance over existing RNA-binding residue prediction methods indicates the importance of the gradient tree boosting algorithm combined with the optimal selected features.

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

et al. 2018

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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.

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Identification of protein-interacting nucleotides in a RNA sequence using composition profile of tri-nucleotides

Cited by 24 publications

References 47 publications

RPI-Bind: a structure-based method for accurate identification of RNA-protein binding sites

RPI-Bind: a structure-based method for accurate identification of RNA-protein binding sites

A boosting approach for prediction of protein-RNA binding residues

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

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