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

ABSTRACT: BACKGROUND:The 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. METHODS:We 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. 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 tests. CONCLUSIONS:The novel feature of an integrative sequence profile achieves good performance using a reduced set of feature vector elements.

SUBMITTER: Xiong Y

PROVIDER: S-EPMC3380730 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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

Xiong Yi Y Liu Juan J Zhang Wen W Zeng Tao T

Proteome science 20120621

<h4>Background</h4>The 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.<h4>Methods</h4>We propose a sequence-based approach for accurat ...[more]

PMID: 22759579

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Project description:MotivationAccurate predictions of protein-binding residues (PBRs) enhances understanding of molecular-level rules governing protein-protein interactions, helps protein-protein docking and facilitates annotation of protein functions. Recent studies show that current sequence-based predictors of PBRs severely cross-predict residues that interact with other types of protein partners (e.g. RNA and DNA) as PBRs. Moreover, these methods are relatively slow, prohibiting genome-scale use.ResultsWe propose a novel, accurate and fast sequence-based predictor of PBRs that minimizes the cross-predictions. Our SCRIBER (SeleCtive pRoteIn-Binding rEsidue pRedictor) method takes advantage of three innovations: comprehensive dataset that covers multiple types of binding residues, novel types of inputs that are relevant to the prediction of PBRs, and an architecture that is tailored to reduce the cross-predictions. The dataset includes complete protein chains and offers improved coverage of binding annotations that are transferred from multiple protein-protein complexes. We utilize innovative two-layer architecture where the first layer generates a prediction of protein-binding, RNA-binding, DNA-binding and small ligand-binding residues. The second layer re-predicts PBRs by reducing overlap between PBRs and the other types of binding residues produced in the first layer. Empirical tests on an independent test dataset reveal that SCRIBER significantly outperforms current predictors and that all three innovations contribute to its high predictive performance. SCRIBER reduces cross-predictions by between 41% and 69% and our conservative estimates show that it is at least 3 times faster. We provide putative PBRs produced by SCRIBER for the entire human proteome and use these results to hypothesize that about 14% of currently known human protein domains bind proteins.Availability and implementationSCRIBER webserver is available at http://biomine.cs.vcu.edu/servers/SCRIBER/.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

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

Publications

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

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