Conformation of antibiotic X-537A (lasalocid-A) and its calcium complex in acetonitrile solvent using circular dichroism spectroscopy

The calcium binding characteristics of antibiotic X-537A (lasalocid-A) in a lipophilic solvent, acetonitrile (CH3CN), have been studied using circular dichroism (CD) spectroscopy. The analysis of the data indicated that in this medium polar solvent, X-537A forms predominantly the charged complexes of stoichiometries 2:1 and 1:1, the relative amounts of the two being dependent on [Ca2+]. The conformations of the complexes, arrived at on the basis of the data, seem to indicate a rigid part encompassing Ca2+, liganded to 3 oxygens of the molecule, viz., the carbonyl, the substituted tetrahydrofuran ring and the substituted pyran ring oxygens (apart from, possibly, the liganding provided by nitrogen atoms of the solvent molecules), and a flexible part consisting of the salicylic acid group of the molecule.     

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5–3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson–Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/.     

RetroPred: A tool for prediction, classification and extraction of non-LTR retrotransposons (LINEs & SINEs) from the genome by integrating PALS, PILER, MEME and ANN

The problem of predicting non-long terminal repeats (LTR) like long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs) from the DNA sequence is still an open problem in bioinformatics. To elevate the quality of annotations of LINES and SINEs an automated tool "RetroPred" was developed. The pipeline allowed rapid and thorough annotation of non-LTR retrotransposons. The non-LTR retrotransposable elements were initially predicted by Pairwise Aligner for Long Sequences (PALS) and Parsimonious Inference of a Library of Elementary Repeats (PILER). Predicted non-LTR elements were automatically classified into LINEs and SINEs using ANN based on the position specific probability matrix (PSPM) generated by Multiple EM for Motif Elicitation (MEME). The ANN model revealed a superior model (accuracy = 78.79 +/- 6.86 %, Q(pred) = 74.734 +/- 17.08 %, sensitivity = 84.48 +/- 6.73 %, specificity = 77.13 +/- 13.39 %) using four-fold cross validation. As proof of principle, we have thoroughly annotated the location of LINEs and SINEs in rice and Arabidopsis genome using the tool and is proved to be very useful with good accuracy. Our tool is accessible at http://www.juit.ac.in/RepeatPred/home.html.     

Prediction, conservation analysis, and structural characterization of mammalian mucin-type O-glycosylation sites

O-GalNAc-glycosylation is one of the main types of glycosylation in mammalian cells. No consensus recognition sequence for the O-glycosyltransferases is known, making prediction methods necessary to bridge the gap between the large number of known protein sequences and the small number of proteins experimentally investigated with regard to glycosylation status. From O-GLYCBASE a total of 86 mammalian proteins experimentally investigated for in vivo O-GalNAc sites were extracted. Mammalian protein homolog comparisons showed that a glycosylated serine or threonine is less likely to be precisely conserved than a nonglycosylated one. The Protein Data Bank was analyzed for structural information, and 12 glycosylated structures were obtained. All positive sites were found in coil or turn regions. A method for predicting the location for mucin-type glycosylation sites was trained using a neural network approach. The best overall network used as input amino acid composition, averaged surface accessibility predictions together with substitution matrix profile encoding of the sequence. To improve prediction on isolated (single) sites, networks were trained on isolated sites only. The final method combines predictions from the best overall network and the best isolated site network; this prediction method correctly predicted 76% of the glycosylated residues and 93% of the nonglycosylated residues. NetOGlyc 3.1 can predict sites for completely new proteins without losing its performance. The fact that the sites could be predicted from averaged properties together with the fact that glycosylation sites are not precisely conserved indicates that mucin-type glycosylation in most cases is a bulk property and not a very site-specific one. NetOGlyc 3.1 is made available at www.cbs.dtu.dk/services/netoglyc.     

First partial three-dimensional model of human monoamine oxidase A

A survey of the major known structural aspects of monoamine oxidase (MAO) is given and a first partial model of human MAO A is presented. This 3D model has been established using secondary structure predictions and fold recognition methods. It shows two α/β domains (the FAD-binding N-terminal and central domains) and an α+β domain. The C-terminal region is predicted to be responsible for anchoring the protein into the mitochondrial membrane and was not modeled. The covalent binding of the flavin cofactor to a cysteine residue is well predicted. The model is validated with experimental data from the literature and should be useful in designing new experimental studies (site-directed mutagenesis, chemical modification, specific antibodies). This first step towards the 3D structure of monoamine oxidase should contribute to a better understanding of the mechanisms of action and inhibition of this drug target in the treatment of clinical depression. Proteins 32:97–110, 1998. © 1998 Wiley-Liss, Inc.     

Mapping hidden potential identity elements by computing the average discriminating power of individual tRNA positions

The recently published discrete mathematical method, extended consensus partition (ECP), identifies nucleotide types at each position that are strictly absent from a given sequence set, while occur in other sets. These are defined as discriminating elements (DEs). In this study using the ECP approach, we mapped potential hidden identity elements that discriminate the 20 different tRNA identities. We filtered the tDNA data set for the obligatory presence of well-established tRNA features, and then separately for each identity set, the presence of already experimentally identified strictly present identity elements. The analysis was performed on the three kingdoms of life. We determined the number of DE, e.g. the number of sets discriminated by the given position, for each tRNA position of each tRNA identity set. Then, from the positional DE numbers obtained from the 380 pairwise comparisons of the 20 identity sets, we calculated the average excluding value (AEV) for each tRNA position. The AEV provides a measure on the overall discriminating power of each position. Using a statistical analysis, we show that positional AEVs correlate with the number of already identified identity elements. Positions having high AEV but lacking published identity elements predict hitherto undiscovered tRNA identity elements.     

The effect of the polyproline II (PPII) conformation on the denatured state entropy

Polyproline II (PPII) is reported to be a dominant conformation in the unfolded state of peptides, even when no prolines are present in the sequence. Here we use isothermal titration calorimetry (ITC) to investigate the PPII bias in the unfolded state by studying the binding of the SH3 domain of SEM-5 to variants of its putative PPII peptide ligand, Sos. The experimental system is unique in that it provides direct access to the conformational entropy change of the substituted amino acids. Results indicate that the denatured ensemble can be characterized by at least two thermodynamically distinct states, the PPII conformation and an unfolded state conforming to the previously held idea of the denatured state as a random collection of conformations determined largely by hard-sphere collision. The probability of the PPII conformation in the denatured states for Ala and Gly were found to be significant, approximately 30% and approximately 10%, respectively, resulting in a dramatic reduction in the conformational entropy of folding.     

Dosiomics-based prediction of radiation-induced hypothyroidism in nasopharyngeal carcinoma patients

PurposeTo predict the incidence of radiation-induced hypothyroidism (RHT) in nasopharyngeal carcinoma (NPC) patients, dosiomics features based prediction models were established.Materials and methodsA total of 145 NPC patients treated with radiotherapy from January 2012 to January 2015 were included. Dosiomics features of the dose distribution within thyroid gland were extracted. The minimal-redundancy-maximal-relevance (mRMR) criterion was used to rank the extracted features and selected the most relevant features. Machine learning (ML) algorithms including logistic regression (LR), support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN) were utilized to establish prediction models, respectively. Nested sampling and hyper-tuning methods were adopted to train and validate the prediction models. The dosiomics-based (DO) prediction models were evaluated through comparing with the dose-volume factor-based (DV) models in terms of the area under the receiver operating characteristic (ROC) curve (AUC). The demographics factors (age and gender) were included in both DO model and DV model.ResultsAge, V45 and 37 dosiomics features exhibited significant correlations with RHT in univariate analysis. For prediction performance, DO prediction models exhibited better results with the best AUC value 0.7 while DV prediction models 0.61. In DO prediction models, the AUC values displayed a trend from ascending to descending with the increasing of selected features. The highest AUC value was achieved when the number of selected features was 3. In DV prediction model, similar trend was not observed.ConclusionThis study established a prediction model based on the dosiomics features with better performance than conventional dose-volume factors, leading to early predict the possible RHT among NPC patients who had received radiotherapy and take precaution measures for NPC patients.     

Individualized medicine 2010

Molecular and cell biology have revolutionized not only diagnosis, therapy and prevention of human diseases but also greatly contributed to the understanding of their pathogenesis. Based on modern molecular and biochemical methods it is possible to identify on the one hand point mutations and single nucleotide polymorphisms. On the other hand, using high throughput array technologies, it is possible to analyse thousands of genes or gene products simultaneously, resulting in an individual gene or gene expression profile (signature). These data increasingly allow to define the individual risk for a given disease and to predict the individual prognosis of a disease as well as the efficacy of therapeutic strategies (individualized medicine). In the following sections some of the recent advances of predictive medicine and their clinical relevance will be addressed.