Exploiting structural and topological information to improve prediction of RNA-protein binding sites

Maetschke, Stefan; Zheng, Yawen

doi:10.1186/1471-2105-10-341

Cited by 54 publications

(54 citation statements)

References 31 publications

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“…Betweenness centrality, when averaged over a patch of residues, was found to improve the predictive power of a model for identifying residues involved in binding RNA [48]. Heme-binding residue prediction [49] was shown to work well using standardised network descriptors in combination with a sequence profile descriptor and several additional structural descriptors, including solvent accessibility, measures of local concavity and convexity of the protein surface.…”

Section: Active Site Analysis and Protein-ligand Bindingmentioning

confidence: 99%

Small World Network Strategies for Studying Protein Structures and Binding

Taylor¹

2013

Computational and Structural Biotechnology Journal

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Small world network concepts provide many new opportunities to investigate the complex three dimensional structures of protein molecules. This mini-review explores the published literature on using small-world network approaches to study protein structure, with emphasis on the different combinations of descriptors that have been tested, on studies involving ligand binding in protein-ligand complexes, and on protein-protein complexes. The benefits and success of small world network approaches, which change the focus from specific interactions to the local environment, even to non-local phenomenon, are described. The purpose is to show the different ways that small world network concepts have been used for building new computational models for studying protein structure and function, and for extending and improving existing modelling approaches.

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Section: Active Site Analysis and Protein-ligand Bindingmentioning

confidence: 99%

Small World Network Strategies for Studying Protein Structures and Binding

Taylor¹

2013

Computational and Structural Biotechnology Journal

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“…At this stage, no classifier is built so that no cross-validation scheme is required to calculate the AUC scores [19]. The AUC for an amino acid type is calculated in the same way as it would be for a classifier output.…”

Section: Methodsmentioning

confidence: 99%

Prediction of heme binding residues from protein sequences with integrative sequence profiles

et al. 2012

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BackgroundThe heme-protein interactions are essential for various biological processes such as electron transfer, catalysis, signal transduction and the control of gene expression. The knowledge of heme binding residues can provide crucial clues to understand these activities and aid in functional annotation, however, insufficient work has been done on the research of heme binding residues from protein sequence information.MethodsWe propose a sequence-based approach for accurate prediction of heme binding residues by a novel integrative sequence profile coupling position specific scoring matrices with heme specific physicochemical properties. In order to select the informative physicochemical properties, we design an intuitive feature selection scheme by combining a greedy strategy with correlation analysis.ResultsOur integrative sequence profile approach for prediction of heme binding residues outperforms the conventional methods using amino acid and evolutionary information on the 5-fold cross validation and the independent tests.ConclusionsThe novel feature of an integrative sequence profile achieves good performance using a reduced set of feature vector elements.

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“…At this stage, no classifier is built so that no cross-validation scheme is required to calculate the AUC scores [13]. Then, we designed a greedy approach in combination with correlation analysis for feature selection by constructing and assessing a series of heme binding sites predictors using 5-fold cross validation in Pheme-75.…”

Section: B Sequence-based Featuresmentioning

confidence: 99%

Prediction of Heme Binding Sites in Heme Proteins Using an Integrative Sequence Profile Coupling Evolutionary Information with Physicochemical Properties

Xiong

Zhang

Zeng

et al. 2011

2011 IEEE International Conference on Bioinformatics and Biomedicine

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Heme-protein interactions are essential for various biological processes such as electron transfer, catalysis, signal transduction and the control of gene expression. The knowledge of heme binding residues can provide crucial clues to understand the mechanism of hemeprotein interactions and aid in functional annotation. In the present work, we propose a sequence-based approach for the accurate prediction of heme binding residues by a novel integrative sequence profile coupling position specific scoring matrices with heme specific physicochemical properties. Particularly, we design an intuitive feature selection scheme for informative physicochemical properties. As shown in the primary results, our integrative sequence profile approach for prediction of heme binding residues outperforms the conventional methods using amino acid and evolutionary information on the 5-fold cross validation and the independent test.

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Exploiting structural and topological information to improve prediction of RNA-protein binding sites

Cited by 54 publications

References 31 publications

Small World Network Strategies for Studying Protein Structures and Binding

Small World Network Strategies for Studying Protein Structures and Binding

Prediction of heme binding residues from protein sequences with integrative sequence profiles

Prediction of Heme Binding Sites in Heme Proteins Using an Integrative Sequence Profile Coupling Evolutionary Information with Physicochemical Properties

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