152 research outputs found

    PSP_MCSVM: brainstorming consensus prediction of protein secondary structures using two-stage multiclass support vector machines

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    Secondary structure prediction is a crucial task for understanding the variety of protein structures and performed biological functions. Prediction of secondary structures for new proteins using their amino acid sequences is of fundamental importance in bioinformatics. We propose a novel technique to predict protein secondary structures based on position-specific scoring matrices (PSSMs) and physico-chemical properties of amino acids. It is a two stage approach involving multiclass support vector machines (SVMs) as classifiers for three different structural conformations, viz., helix, sheet and coil. In the first stage, PSSMs obtained from PSI-BLAST and five specially selected physicochemical properties of amino acids are fed into SVMs as features for sequence-to-structure prediction. Confidence values for forming helix, sheet and coil that are obtained from the first stage SVM are then used in the second stage SVM for performing structure-to-structure prediction. The two-stage cascaded classifiers (PSP_MCSVM) are trained with proteins from RS126 dataset. The classifiers are finally tested on target proteins of critical assessment of protein structure prediction experiment-9 (CASP9). PSP_MCSVM with brainstorming consensus procedure performs better than the prediction servers like Predator, DSC, SIMPA96, for randomly selected proteins from CASP9 targets. The overall performance is found to be comparable with the current state-of-the art. PSP_MCSVM source code, train-test datasets and supplementary files are available freely in public domain at: http://sysbio.icm.edu.pl/secstruct and http://code.google.com/p/cmater-bioinfo

    Dissecting Molecular Differences between Wnt Coreceptors LRP5 and LRP6

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    Low-density lipoprotein receptor-related proteins 5 and 6 (LRP5 and LRP6) serve as Wnt co-receptors for the canonical β-catenin pathway. While LRP6 is essential for embryogenesis, both LRP5 and LRP6 play critical roles for skeletal remodeling, osteoporosis pathogenesis and cancer formation, making LRP5 and LRP6 key therapeutic targets for cancer and disease treatment. LRP5 and LRP6 each contain in the cytoplasmic domain five conserved PPPSPxS motifs that are pivotal for signaling and serve collectively as phosphorylation-dependent docking sites for the scaffolding protein Axin. However existing data suggest that LRP6 is more effective than LRP5 in transducing the Wnt signal. To understand the molecular basis that accounts for the different signaling activity of LRP5 and LRP6, we generated a series of chimeric receptors via swapping LRP5 and LRP6 cytoplasmic domains, LRP5C and LRP6C, and studied their Wnt signaling activity using biochemical and functional assays. We demonstrate that LRP6C exhibits strong signaling activity while LRP5C is much less active in cells. Recombinant LRP5C and LRP6C upon in vitro phosphorylation exhibit similar Axin-binding capability, suggesting that LRP5 and LRP6 differ in vivo at a step prior to Axin-binding, likely at receiving phosphorylation. We identified between the two most carboxyl PPPSPxS motifs an intervening “gap4” region that appears to account for much of the difference between LRP5C and LRP6C, and showed that alterations in this region are sufficient to enhance LRP5 PPPSPxS phosphorylation and signaling to levels comparable to LRP6 in cells. In addition we provide evidence that binding of phosphorylated LRP5 or LRP6 to Axin is likely direct and does not require the GSK3 kinase as a bridging intermediate as has been proposed. Our studies therefore uncover a new and important molecular tuning mechanism for differential regulation of LRP5 and LRP6 phosphorylation and signaling activity

    “El Sexo no es Malo”: Maternal Values Accompanying Contraceptive Use Advice to Young Latina Adolescent Daughters

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    In this study, we utilized observational methods to identify maternal values and concerns accompanying contraceptive use advice in Latina mother–daughter sexuality conversations. The sample included non-sexually active early adolescents around 12 years of age and their mostly Spanish-speaking Latina mothers. Videotaped conversations were coded for the prevalence of messages related to four sexual values (abstinence, delay sex until older, sex is “normal”, sex is “improper”) and concerns about pregnancy and STD transmission. We examined whether the duration of time spent conversing about these messages was associated with participant characteristics, general communication openness, and the amount of time the dyads spent discussing contraceptive use. Results indicated that Latina mothers who had fewer years of education and lower family income talked longer to their daughters about the need to delay sex, avoid risky situations that would increase their chances of getting pregnant or acquiring an STD, and engage in self-protective practices. Less perceived openness in general communication as reported by both the mothers and the daughters was associated with increased time discussing that sex is improper. Although the duration of contraceptive use messages was brief, mothers and daughters who discussed the fact that sex is normal, and who communicated more about the importance of delaying sex, talked longer about contraceptive use practices compared to mothers and daughters who engaged in minimal discussion of these sexual values

    Identifying Cognate Binding Pairs among a Large Set of Paralogs: The Case of PE/PPE Proteins of Mycobacterium tuberculosis

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    We consider the problem of how to detect cognate pairs of proteins that bind when each belongs to a large family of paralogs. To illustrate the problem, we have undertaken a genomewide analysis of interactions of members of the PE and PPE protein families of Mycobacterium tuberculosis. Our computational method uses structural information, operon organization, and protein coevolution to infer the interaction of PE and PPE proteins. Some 289 PE/PPE complexes were predicted out of a possible 5,590 PE/PPE pairs genomewide. Thirty-five of these predicted complexes were also found to have correlated mRNA expression, providing additional evidence for these interactions. We show that our method is applicable to other protein families, by analyzing interactions of the Esx family of proteins. Our resulting set of predictions is a starting point for genomewide experimental interaction screens of the PE and PPE families, and our method may be generally useful for detecting interactions of proteins within families having many paralogs

    MultiRTA: A simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes

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    abstract: Background The binding of peptide fragments of antigens to class II MHC is a crucial step in initiating a helper T cell immune response. The identification of such peptide epitopes has potential applications in vaccine design and in better understanding autoimmune diseases and allergies. However, comprehensive experimental determination of peptide-MHC binding affinities is infeasible due to MHC diversity and the large number of possible peptide sequences. Computational methods trained on the limited experimental binding data can address this challenge. We present the MultiRTA method, an extension of our previous single-type RTA prediction method, which allows the prediction of peptide binding affinities for multiple MHC allotypes not used to train the model. Thus predictions can be made for many MHC allotypes for which experimental binding data is unavailable. Results We fit MultiRTA models for both HLA-DR and HLA-DP using large experimental binding data sets. The performance in predicting binding affinities for novel MHC allotypes, not in the training set, was tested in two different ways. First, we performed leave-one-allele-out cross-validation, in which predictions are made for one allotype using a model fit to binding data for the remaining MHC allotypes. Comparison of the HLA-DR results with those of two other prediction methods applied to the same data sets showed that MultiRTA achieved performance comparable to NetMHCIIpan and better than the earlier TEPITOPE method. We also directly tested model transferability by making leave-one-allele-out predictions for additional experimentally characterized sets of overlapping peptide epitopes binding to multiple MHC allotypes. In addition, we determined the applicability of prediction methods like MultiRTA to other MHC allotypes by examining the degree of MHC variation accounted for in the training set. An examination of predictions for the promiscuous binding CLIP peptide revealed variations in binding affinity among alleles as well as potentially distinct binding registers for HLA-DR and HLA-DP. Finally, we analyzed the optimal MultiRTA parameters to discover the most important peptide residues for promiscuous and allele-specific binding to HLA-DR and HLA-DP allotypes. Conclusions The MultiRTA method yields competitive performance but with a significantly simpler and physically interpretable model compared with previous prediction methods. A MultiRTA prediction webserver is available at http://bordnerlab.org/MultiRTA.The electronic version of this article is the complete one and can be found online at: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-48

    Sequence-Specific Binding of Recombinant Zbed4 to DNA: Insights into Zbed4 Participation in Gene Transcription and Its Association with Other Proteins

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    Zbed4, a member of the BED subclass of Zinc-finger proteins, is expressed in cone photoreceptors and glial Müller cells of human retina whereas it is only present in Müller cells of mouse retina. To characterize structural and functional properties of Zbed4, enough amounts of purified protein were needed. Thus, recombinant Zbed4 was expressed in E. coli and its refolding conditions optimized for the production of homogenous and functionally active protein. Zbed4’s secondary structure, determined by circular dichroism spectroscopy, showed that this protein contains 32% α-helices, 18% β-sheets, 20% turns and 30% unordered structures. CASTing was used to identify the target sites of Zbed4 in DNA. The majority of the DNA fragments obtained contained poly-Gs and some of them had, in addition, the core signature of GC boxes; a few clones had only GC-boxes. With electrophoretic mobility shift assays we demonstrated that Zbed4 binds both not only to DNA and but also to RNA oligonucleotides with very high affinity, interacting with poly-G tracts that have a minimum of 5 Gs; its binding to and GC-box consensus sequences. However, the latter binding depends on the GC-box flanking nucleotides. We also found that Zbed4 interacts in Y79 retinoblastoma cells with nuclear and cytoplasmic proteins Scaffold Attachment Factor B1 (SAFB1), estrogen receptor alpha (ERα), and cellular myosin 9 (MYH9), as shown with immunoprecipitation and mass spectrometry studies as well as gel overlay assays. In addition, immunostaining corroborated the co-localization of Zbed4 with these proteins. Most importantly, in vitro experiments using constructs containing promoters of genes directing expression of the luciferase gene, showed that Zbed4 transactivates the transcription of those promoters with poly-G tracts

    Towards Universal Structure-Based Prediction of Class II MHC Epitopes for Diverse Allotypes

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    The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632–0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register
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