Structural characterization of RNA-binding sites of proteins: Preliminary results

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

doi:10.1109/bibmw.2007.4425401

Cited by 3 publications

(2 citation statements)

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“…We focus on two of the six structural properties of amino acid residues used in a recent analysis of protein-protein interfaces by Wu et al [Wu et al, 2007], namely, surface roughness [Lewis and Rees, 1985] and CX value [Pintar et al, 2002]. Solid Angle [Connolly, 1986] was also used early in this study (see [Towfic et al, 2007]). However, it was deemed unnecessary to include in this study since the results from Solid Angle overlap with those of Roughness [Lewis and Rees, 1985] with a correlation of 0.88 (roughness and CX overlap with a correlation of −0.56).…”

Section: Introductionmentioning

confidence: 99%

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

Towfic

Caragea

Gemperline

et al. 2010

IJDMB

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We explore whether protein-RNA interfaces differ from non-interfaces in terms of their structural features and whether structural features vary according to the type of the bound RNA (e.g., mRNA, siRNA, etc.), using a non-redundant dataset of 147 protein chains extracted from protein-RNA complexes in the Protein Data Bank. Furthermore, we use machine learning algorithms for training classifiers to predict protein-RNA interfaces using information derived from the sequence and structural features. We develop the Struct-NB classifier that takes into account structural information. We compare the performance of Naïve Bayes and Gaussian Naïve Bayes with that of Struct-NB classifiers on the 147 protein-RNA dataset using sequence and structural features respectively as input to the classifiers. The results of our experiments show that Struct-NB outperforms Naïve Bayes and Gaussian Naïve Bayes on the problem of predicting the protein-RNA binding interfaces in a protein sequence in terms of a range of standard measures for comparing the performance of classifiers.

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Section: Introductionmentioning

confidence: 99%

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

Towfic

Caragea

Gemperline

et al. 2010

IJDMB

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“…Although their attempt could describe the entire process in unambiguous numerical terms, the effective surface area of nucleic‐acid‐binding surface patches was not found to be significantly higher than those not binding to nucleic acids. However a recent study (Towfic et al ., ) contradicted their findings and, when the same FD‐based procedures are applied, reported a higher population of residues with rough surfaces in protein–RNA interfaces. One finds a similar result from another study (Shazman and Mandel‐Gutfreund, ), at least in the paradigm of t‐RNA binding proteins.…”

Section: An Overview Of Successful Application Of Protein Surface Framentioning

confidence: 86%

Fractal nature of protein surface roughness: a note on quantification of change of surface roughness in active sites, before and after binding

Banerji

Navare²

2013

J of Molecular Recognition

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Year 2010 marked the 25th year since we came to know that roughness of a protein surface has fractal symmetry. Ever since the publication of Lewis and Rees' paper, hundreds of works from a spectrum of perspectives have established that fractal dimension (FD) can be considered as a reliable marker that describes roughness of protein surface objectively. In this article, we introduce readers to the fundamentals of fractals and present categorical biophysical and geometrical reasons as to why FD-based constructs can describe protein surface roughness more accurately. We then review the commonality (and the lack of it) between numerous approaches that have attempted to investigate protein surface with fractal measures, before exploring the patterns in the results that they have produced. Apart from presenting the genealogy of approaches and results, we present an analysis that quantifies the difference in surface roughness in stretches of protein surface containing the active site, before and after binding to ligands, to underline the utility of FD-based measures further. It has been found that surface stretches containing the active site, in general, undergo a significant increment in its roughness after binding. After presenting the entire repertoire of FD-based surface roughness studies, we talk about two yet-unexplored problems where application of FD-based techniques can help in deciphering underlying patterns of surface interactions. Finally, we list the limitations of FD-based constructs and put down several precautions that one must take while working with them.

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Predicting RNA-binding residues from evolutionary information and sequence conservation

et al. 2010

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BackgroundRNA-binding proteins (RBPs) play crucial roles in post-transcriptional control of RNA. RBPs are designed to efficiently recognize specific RNA sequences after it is derived from the DNA sequence. To satisfy diverse functional requirements, RNA binding proteins are composed of multiple blocks of RNA-binding domains (RBDs) presented in various structural arrangements to provide versatile functions. The ability to computationally predict RNA-binding residues in a RNA-binding protein can help biologists reveal important site-directed mutagenesis in wet-lab experiments.ResultsThe proposed prediction framework named “ProteRNA” combines a SVM-based classifier with conserved residue discovery by WildSpan to identify the residues that interact with RNA in a RNA-binding protein. Although these conserved residues can be either functionally conserved residues or structurally conserved residues, they provide clues on the important residues in a protein sequence. In the independent testing dataset, ProteRNA has been able to deliver overall accuracy of 89.78%, MCC of 0.2628, F-score of 0.3075, and F0.5-score of 0.3546.ConclusionsThis article presents the design of a sequence-based predictor aiming to identify the RNA-binding residues in a RNA-binding protein by combining machine learning and pattern mining approaches. RNA-binding proteins have diverse functions while interacting with different categories of RNAs because these proteins are composed of multiple copies of RNA-binding domains presented in various structural arrangements to expand the functional repertoire of RNA-binding proteins. Furthermore, predicting RNA-binding residues in a RNA-binding protein can help biologists reveal important site-directed mutagenesis in wet-lab experiments.

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Structural characterization of RNA-binding sites of proteins: Preliminary results

Cited by 3 publications

References 23 publications

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

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

Fractal nature of protein surface roughness: a note on quantification of change of surface roughness in active sites, before and after binding

Predicting RNA-binding residues from evolutionary information and sequence conservation

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