Struct-NB: predicting protein-RNA binding sites using structural features

Towfic, Fadi; Caragea, Cornelia; Gemperline, David C; Dobbs, Drena; Honavar, Vasant

doi:10.1504/ijdmb.2010.030965

Cited by 41 publications

(33 citation statements)

References 27 publications

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“…The binding elements are often classified using machine‐learning methods such as Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB). For further information about the algorithms, we refer the Reader to reviews describing features and techniques in detail . In the following text we will provide a short description of the most used algorithms (Table ) and their published performances.…”

Section: Computational Methods For Detection Of Protein–rna Interactionsmentioning

confidence: 99%

“…For further information about the algorithms, we refer the Reader to reviews describing features and techniques in detail. [81][82][83][84][85][86][87][88][89][90][91][92][93] In the following text we will provide a short description of the most used algorithms ( Table 2) and their published performances.…”

Section: (See Rna-centric Methods In Section Experimental Methods Formentioning

confidence: 99%

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Advances in the characterization of RNA‐binding proteins

et al. 2016

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From transcription, to transport, storage, and translation, RNA depends on association with different RNA‐binding proteins (RBPs). Methods based on next‐generation sequencing and protein mass‐spectrometry have started to unveil genome‐wide interactions of RBPs but many aspects still remain out of sight. How many of the binding sites identified in high‐throughput screenings are functional? A number of computational methods have been developed to analyze experimental data and to obtain insights into the specificity of protein–RNA interactions. How can theoretical models be exploited to identify RBPs? In addition to oligomeric complexes, protein and RNA molecules can associate into granular assemblies whose physical properties are still poorly understood. What protein features promote granule formation and what effects do these assemblies have on cell function? Here, we describe the newest in silico, in vitro, and in vivo advances in the field of protein–RNA interactions. We also present the challenges that experimental and computational approaches will have to face in future studies. WIREs RNA 2016, 7:793–810. doi: 10.1002/wrna.1378For further resources related to this article, please visit the WIREs website.

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Section: Computational Methods For Detection Of Protein–rna Interactionsmentioning

confidence: 99%

Section: (See Rna-centric Methods In Section Experimental Methods Formentioning

confidence: 99%

Advances in the characterization of RNA‐binding proteins

et al. 2016

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“…The algorithms Struct‐NB,39 PRIP,40 PatchFinderPlus,41 SPOT,42 and OPRA43 predict RNA‐binding using properties of protein surfaces. SVM and Naïve Bayes Classifiers (NBCs) trained on structural data are employed to analyze surface features.…”

Section: Structure‐based Predictions Of Rna‐binding Sites (Rnabindr mentioning

confidence: 99%

“…Struct‐NB (http://www.public.iastate.edu/~ftowfic) uses an ensemble of NBCs and a structural‐based Gaussian Naïve Bayes classifier (GNBC) to predict RNA‐binding sites 39. The NBC is trained on sequence‐based features present in the RNABindR algorihm,37 whereas the structural‐based GNBC exploits two main structural features: the surface roughness (i.e., degree of irregularity on the surface) and the CX value (i.e., the ratio of the volume of atoms that occupy a 6 Å sphere compared to the empty volume within the sphere).…”

Section: Structure‐based Predictions Of Rna‐binding Sites (Rnabindr mentioning

confidence: 99%

Predictions of protein–RNA interactions

Cirillo

Federico

Tartaglia

2012

WIREs Comput Mol Sci

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Ribonucleoprotein interactions play important roles in a wide variety of cellular processes, ranging from transcriptional and posttranscriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions provide information about the complexity of interaction networks, but require time and considerable efforts. Thus, there is need for reliable computational methods for predicting ribonucleoprotein interactions. In this review, we discuss a number of approaches that have been developed to predict the ability of proteins and RNA molecules to associate.

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“…The predictive success rates of different methods for identifying RNA-binding residues are summarized in Table 4 (23,25,30,32,51,(53)(54)(55)(56)(57)(58)(59)(60)(61)(62). The efficiency of the methods is usually reported as negative specificity and sensitivity values.…”

Section: Sequence-based Versus Structure-based Computational Methodsmentioning

confidence: 99%

Dissection and prediction of RNA-binding sites on proteins

Pérez-Cano

Fernández‐Recio

2010

BioMolecular Concepts

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RNA-binding proteins are involved in many important regulatory processes in cells and their study is essential for a complete understanding of living organisms. They show a large variability from both structural and functional points of view. However, several recent studies performed on protein-RNA crystal structures have revealed interesting common properties. RNA-binding sites usually constitute patches of positively charged or polar residues that make most of the specific and non-specific contacts with RNA. Negatively charged or aliphatic residues are less frequent at protein-RNA interfaces, although they can also be found either forming aliphatic and positive-negative pairs in protein RNA-binding sites or contacting RNA through their main chains. Aromatic residues found within these interfaces are usually involved in specific base recognition at RNA single-strand regions. This specific recognition, in combination with structural complementarity, represents the key source for specificity in protein-RNA association. From all this knowledge, a variety of computational methods for prediction of RNA-binding sites have been developed based either on protein sequence or on protein structure. Some reported methods are really successful in the identification of RNA-binding proteins or the prediction of RNA-binding sites. Given the growing interest in the field, all these studies and prediction methods will undoubtedly contribute to the identification and comprehension of protein-RNA interactions.

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Struct-NB: predicting protein-RNA binding sites using structural features

Cited by 41 publications

References 27 publications

Advances in the characterization of RNA‐binding proteins

Advances in the characterization of RNA‐binding proteins

Predictions of protein–RNA interactions

Dissection and prediction of RNA-binding sites on proteins

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