Quantitative parameters for amino acid-base interaction: implications for prediction of protein-DNA binding sites.

Inspection of the amino acid-base interactions in protein-DNA complexes is essential to the understanding of specific recognition of DNA target sites by regulatory proteins. The accumulation of information on protein-DNA co-crystals challenges the derivation of quantitative parameters for amino acid-base interaction based on these data. Here we use the coordinates of 53 solved protein-DNA complexes to extract all non-homologous pairs of amino acid-base that are in close contact, including hydrogen bonds and hydrophobic interactions. By comparing the frequency distribution of the different pairs to a theoretical distribution and calculating the log odds, a quantitative measure that expresses the likelihood of interaction for each pair of amino acid-base could be extracted. A score that reflects the compatibility between a protein and its DNA target can be calculated by summing up the individual measures of the pairs of amino acid-base involved in the complex, assuming additivity in their contributions to binding. This score enables ranking of different DNA binding sites given a protein binding site and vice versa and can be used in molecular design protocols. We demonstrate its validity by comparing the predictions using this score with experimental binding results of sequence variants of zif268 zinc fingers and their DNA binding sites.     

ProPred: prediction of HLA-DR binding sites.

ProPred is a graphical web tool for predicting MHC class II binding regions in antigenic protein sequences. The server implement matrix based prediction algorithm, employing amino-acid/position coefficient table deduced from literature. The predicted binders can be visualized either as peaks in graphical interface or as colored residues in HTML interface. This server might be a useful tool in locating the promiscuous binding regions that can bind to several HLA-DR alleles. AVAILABILITY: The server is available at http://www.imtech.res.in/raghava/propred/ CONTACT: raghava@imtech.res.in SUPPLEMENTARY INFORMATION: http://www.imtech.res.in/raghava/propred/page3.html     

Analysis and prediction of carbohydrate binding sites

An analysis of the characteristic properties of sugar binding sites was performed on a set of 19 sugar binding proteins. For each site six parameters were evaluated: solvation potential, residue propensity, hydrophobicity, planarity, protrusion and relative accessible surface area. Three of the parameters were found to distinguish the observed sugar binding sites from the other surface patches. These parameters were then used to calculate the probability for a surface patch to be a carbohydrate binding site. The prediction was optimized on a set of 19 non-homologous carbohydrate binding structures and a test prediction was carried out on a set of 40 protein-carbohydrate complexes. The overall accuracy of prediction achieved was 65%. Results were in general better for carbohydrate-binding enzymes than for the lectins, with a rate of success of 87%.     

ProPred1: prediction of promiscuous MHC Class-I binding sites

SUMMARY: ProPred1 is an on-line web tool for the prediction of peptide binding to MHC class-I alleles. This is a matrix-based method that allows the prediction of MHC binding sites in an antigenic sequence for 47 MHC class-I alleles. The server represents MHC binding regions within an antigenic sequence in user-friendly formats. These formats assist user in the identification of promiscuous MHC binders in an antigen sequence that can bind to large number of alleles. ProPred1 also allows the prediction of the standard proteasome and immunoproteasome cleavage sites in an antigenic sequence. This server allows identification of MHC binders, who have the cleavage site at the C terminus. The simultaneous prediction of MHC binders and proteasome cleavage sites in an antigenic sequence leads to the identification of potential T-cell epitopes. AVAILABILITY: Server is available at http://www.imtech.res.in/raghava/propred1/. Mirror site of this server is available at http://bioinformatics.uams.edu/mirror/propred1/ Supplementary information: Matrices and document on server are available at http://www.imtech.res.in/raghava/propred1/page2.html     

Text mining improves prediction of protein functional sites

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.     

Dicer partners expand the repertoire of miRNA targets

Processing of pre-miRNAs by Dicer is regulated by its dsRNA-binding protein partner, and leads to the generation of alternative miRNA forms with distinct target sets.     

The human miRNA repertoire of different blood compounds

Background

MiRNAs from body fluids gain more and more attraction as biomarker candidates. Besides serum, patterns from whole blood are increasingly considered as markers for human pathologies. Usually, the contribution of different cell types to the respective signature remains however unknown. In this study we provide insights into the human miRNome of different compounds of the blood including CD3, CD14, CD15, CD19, CD56 positive cells as well as exosomes.

Methods

We measured the miRNA repertoire for each cell type and whole blood for two individuals at three time points over the course of one year in order to provide evidence that the cell type miRNomes can be reproducibly detected.

Results

For measurements repeated after 24 hours we found on average correlation of 0.97, even after one year profiles still correlated with 0.96, demonstrating the enormous stability of the cell type specific miRNomes. Highest correlation was found for CD15 positive cells, exceeding Pearson correlation of 0.99. For exosomes a significantly higher variability of miRNA expression was detected. In order to estimate the complexity and variability of the cell type specific miRNomes, we generated profiles for all considered cell types in a total of seven unaffected individuals. While CD15 positive cells showed the most complex miRNome consisting of 328 miRNAs, we detected significantly less miRNAs (186, p = 1.5*10^-5) in CD19 positive cells. Moreover, our analysis showed functional enrichment in many relevant categories such as onco-miRNAs and tumor miRNA suppressors. Interestingly, exosomes were enriched just for onco-miRNAs but not for miRNA tumor suppressors.

Conclusion

In sum, our results provide evidence that blood cell type specific miRNomes are very consistent between individuals and over time.     

miRTour: Plant miRNA and target prediction tool

MicroRNAs (miRNAs) are important negative regulators of gene expression in plant and animals, which are endogenously produced from their own genes. Computational comparative approach based on evolutionary conservation of mature miRNAs has revealed a number of orthologs of known miRNAs in different plant species. The homology-based plant miRNA discovery, followed by target prediction, comprises several steps, which have been done so far manually. Here, we present the bioinformatics pipeline miRTour which automates all the steps of miRNA similarity search, miRNA precursor selection, target prediction and annotation, each of them performed with the same set of input sequences. AVAILABILITY: The database is available for free at http://bio2server.bioinfo.uni-plovdiv.bg/miRTour/     

Heme binding by hemopexin: evidence for multiple modes of binding and functional implications

Hemopexin binds 1 mol of heme per mol with high affinity (K _d < 1 pM) in a low-spin complex and acts as a transport vehicle for the heme. Circular dichroism (CD) spectroscopy was used to examine the heme environment in the ferri-, ferro-, and CO-ferro complexes of four iron tetrapyrroles [meso-, proto-, deutero-, and (2-vinyl, 4-hydroxymethyl)-deutero-heme] with three species (human, rabbit, and rat) of hemopexin. All ferri-heme-hemopexin complexes exhibit a band of positive ellipticity near the Soret maximum, except for the human ferri-protoheme hemopexin complex, which has a bisignate spectrum. The ferro-heme and CO-ferro-heme complexes display a variety of spectra, demonstrating redox- and ligand-linked shifts in conformation that alter the environment of the heme. The rabbit mesoheme-N-domain complexes have absorbance spectra almost indistinguishable from those of intact hemopexin, but present CD spectra that are distinctly different. However, adding the C-domain to mesoheme-N-domain restores most of the CD characteristics of the intact hemopexin complexes.     

pkaPS: prediction of protein kinase A phosphorylation sites with the simplified kinase-substrate binding model

Background  

Protein kinase A (cAMP-dependent kinase, PKA) is a serine/threonine kinase, for which ca. 150 substrate proteins are known. Based on a refinement of the recognition motif using the available experimental data, we wished to apply the simplified substrate protein binding model for accurate prediction of PKA phosphorylation sites, an approach that was previously successful for the prediction of lipid posttranslational modifications and of the PTS1 peroxisomal translocation signal.     

Studies of cadmium binding to hexokinase: structural and functional implications

The interaction between cadmium and yeast hexokinase was studied. Cadmium produces changes in the aggregation state of the protein and large structures with a large molecular mass were formed. This phenomenon occurs without large modifications to the secondary structure. During this change the enzyme maintains a high level of activity in the monomer as well as in aggregate form. This implies that the enzyme function is not greatly affected by the change and it maintains its active sites without significant modifications. According to kinetic measurements with both glucose and ATP as a variable substrate, cadmium causes a mixed-type inhibition with a main uncompetitive component. Binding experiments show that the protein presents negative cooperative binding with cadmium at various temperatures (298, 303 and 313 K) and a progressive loss in metal union with concentration depending on the temperature. The total union percentage decreases as the metal concentration increases. This is probably due to the aggregation process, which affects the binding sites for the metal and also for the substrate. Labile interactions are more persistent than specific interactions in accordance with the solvation parameter.