Dataset Information

Delineation of lipopolysaccharide (LPS)-binding sites on hemoglobin: from in silico predictions to biophysical characterization.

ABSTRACT: Hemoglobin (Hb) functions as a frontline defense molecule during infection by hemolytic microbes. Binding to LPS induces structural changes in cell-free Hb, which activates the redox activity of the protein for the generation of microbicidal free radicals. Although the interaction between Hb and LPS has implications for innate immune defense, the precise LPS-interaction sites on Hb remain unknown. Using surface plasmon resonance, we found that both the Hb ? and ? subunits possess high affinity LPS-binding sites, with K(D) in the nanomolar range. In silico analysis of Hb including phospho-group binding site prediction, structure-based sequence comparison, and docking to model the protein-ligand interactions showed that Hb possesses evolutionarily conserved surface cationic patches that could function as potential LPS-binding sites. Synthetic Hb peptides harboring predicted LPS-binding sites served to validate the computational predictions. Surface plasmon resonance analysis differentiated LPS-binding peptides from non-binders. Binding of the peptides to lipid A was further substantiated by a fluorescent probe displacement assay. The LPS-binding peptides effectively neutralized the endotoxicity of LPS in vitro. Additionally, peptide B59 spanning residues 59-95 of Hb? attached to the surface of Gram-negative bacteria as shown by flow cytometry and visualized by immunogold-labeled scanning electron microscopy. Site-directed mutagenesis of the Hb subunits further confirmed the function of the predicted residues in binding to LPS. In summary, the integration of computational predictions and biophysical characterization has enabled delineation of multiple LPS-binding hot spots on the Hb molecule.

SUBMITTER: Bahl N

PROVIDER: S-EPMC3199521 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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Delineation of lipopolysaccharide (LPS)-binding sites on hemoglobin: from in silico predictions to biophysical characterization.

Bahl Neha N Du Ruijuan R Winarsih Imelda I Ho Bow B Tucker-Kellogg Lisa L Tidor Bruce B Ding Jeak Ling JL

The Journal of biological chemistry 20110906 43

Hemoglobin (Hb) functions as a frontline defense molecule during infection by hemolytic microbes. Binding to LPS induces structural changes in cell-free Hb, which activates the redox activity of the protein for the generation of microbicidal free radicals. Although the interaction between Hb and LPS has implications for innate immune defense, the precise LPS-interaction sites on Hb remain unknown. Using surface plasmon resonance, we found that both the Hb α and β subunits possess high affinity L ...[more]

PMID: 21900232

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Project description:Protein-DNA interactions are crucial to many cellular activities such as expression-control and DNA-repair. These interactions between amino acids and nucleotides are highly specific and any aberrance at the binding site can render the interaction completely incompetent. In this study, we have three aims focusing on DNA-binding residues on the protein surface: to develop an automated approach for fast and reliable recognition of DNA-binding sites; to improve the prediction by distance-dependent refinement; use these predictions to identify DNA-binding proteins. We use a support vector machines (SVM)-based approach to harness the features of the DNA-binding residues to distinguish them from non-binding residues. Features used for distinction include the residue's identity, charge, solvent accessibility, average potential, the secondary structure it is embedded in, neighboring residues, and location in a cationic patch. These features collected from 50 proteins are used to train SVM. Testing is then performed on another set of 37 proteins, much larger than any testing set used in previous studies. The testing set has no more than 20% sequence identity not only among its pairs, but also with the proteins in the training set, thus removing any undesired redundancy due to homology. This set also has proteins with an unseen DNA-binding structural class not present in the training set. With the above features, an accuracy of 66% with balanced sensitivity and specificity is achieved without relying on homology or evolutionary information. We then develop a post-processing scheme to improve the prediction using the relative location of the predicted residues. Balanced success is then achieved with average sensitivity, specificity and accuracy pegged at 71.3%, 69.3% and 70.5%, respectively. Average net prediction is also around 70%. Finally, we show that the number of predicted DNA-binding residues can be used to differentiate DNA-binding proteins from non-DNA-binding proteins with an accuracy of 78%. Results presented here demonstrate that machine-learning can be applied to automated identification of DNA-binding residues and that the success rate can be ameliorated as more features are added. Such functional site prediction protocols can be useful in guiding consequent works such as site-directed mutagenesis and macromolecular docking.

Dataset Information

Delineation of lipopolysaccharide (LPS)-binding sites on hemoglobin: from in silico predictions to biophysical characterization.

Publications

Delineation of lipopolysaccharide (LPS)-binding sites on hemoglobin: from in silico predictions to biophysical characterization.

Similar Datasets

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