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

Zhao, Huiying; Yang, Yuedong; Zhou, Yaoqi

doi:10.1093/nar/gkq1266

Cited by 105 publications

(130 citation statements)

References 49 publications

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“…More recently, a template-based approach was developed by utilizing known protein–RNA complex structures. 28,29 In this method, a target structure is aligned to the templates in the template library and an RBP is predicted if the structural similarity between the target and a template is higher than a certain threshold. Several structural alignment programs were tested.…”

Section: Low Resolution Function Prediction: Two-state Prediction (Rbmentioning

confidence: 99%

Prediction of RNA binding proteins comes of age from low resolution to high resolution

2013

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Networks of protein-RNA interactions is likely to be larger than protein-protein and protein-DNA interaction networks because RNA transcripts are encoded tens of times more than proteins (e.g. only 3% of human genome coded for proteins), have diverse function and localization, and are controlled by proteins from birth (transcription) to death (degradation). This massive network is evidenced by several recent experimental discoveries of large numbers of previously unknown RNA-binding proteins (RBPs). Meanwhile, more than 400 non-redundant protein-RNA complex structures (at 25% sequence identity or less) have been deposited into the protein databank. These sequences and structural resources for RBPs provide ample data for the development of computational techniques dedicated to RBP prediction, as experimentally determining RNAbinding functions is time-consuming and expensive. This review compares traditional machinelearning based approaches with emerging template-based methods at several levels of prediction resolution ranging from two-state binding/non-binding prediction, to binding residue prediction and protein-RNA complex structure prediction. The analysis indicates that the two approaches are complementary and their combinations may lead to further improvements.

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Section: Low Resolution Function Prediction: Two-state Prediction (Rbmentioning

confidence: 99%

Prediction of RNA binding proteins comes of age from low resolution to high resolution

2013

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“…To complement experimental methods, computational approaches are highly desirable for predicting the RNP interactome (Bellucci et al, 2011; Li et al, 2012a; Pons et al, 2010; Puton et al, 2012; Setny and Zacharias, 2011; Tuszynska and Bujnicki, 2011; Zhao et al, 2011; Zheng et al, 2007). No current method, however, can provide accurate genome-wide predictions of RNPs without a priori geometrical restraints or assumptions on the protein motifs that contact the RNA.…”

Section: Introductionmentioning

confidence: 99%

Discovering RNA-Protein Interactome by Using Chemical Context Profiling of the RNA-Protein Interface

Parisien¹,

Wang²,

Perdrizet³

et al. 2013

Cell Reports

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SUMMARY RNA-protein (RNP) interactions generally are required for RNA function. At least 5% of human genes code for RNA-binding proteins. Whereas many approaches can identify the RNA partners for a specific protein, finding the protein partners for a specific RNA is difficult. We present a machine-learning method that scores a protein’s binding potential for an RNA structure by utilizing the chemical context profiles of the interface from known RNP structures. Our approach is applicable even when only a single RNP structure is available. We examined 801 mammalian proteins and find that 37 (4.6%) potentially bind transfer RNA (tRNA). Most are enzymes involved in cellular processes unrelated to translation and were not known to interact with RNA. We experimentally tested six positive and three negative predictions for tRNA binding in vivo, and all nine predictions were correct. Our computational approach provides a powerful complement to experiments in discovering new RNPs.

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“…Based on the classical assumption that structure infers function, homology modeling and threading approaches have been shown to be very effective in classifying DNA- and RNA-binding proteins 10,11 , as well as in predicting the nucleic acid binding sites (e.g., 12,13 ). However, while homology-based approaches are very valuable for function prediction, they are limited to cases for which a structural homologue is available 14–16 .…”

Section: Introductionmentioning

confidence: 99%

Predicting nucleic acid binding interfaces from structural models of proteins

et al. 2011

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The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared to patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure.

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

Cited by 105 publications

References 49 publications

Prediction of RNA binding proteins comes of age from low resolution to high resolution

Prediction of RNA binding proteins comes of age from low resolution to high resolution

Discovering RNA-Protein Interactome by Using Chemical Context Profiling of the RNA-Protein Interface

Predicting nucleic acid binding interfaces from structural models of proteins

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