Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information

Background The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/trans isomerization is known to play a critical role in protein folding, splicing, cell signaling and transmembrane active transport. Accurate prediction of proline cis/trans isomerization in proteins would have many important applications towards the understanding of protein structure and function. Results In this paper, we propose a new approach to predict the proline cis/trans isomerization in proteins using support vector machine (SVM). The preliminary results indicated that using Radial Basis Function (RBF) kernels could lead to better prediction performance than that of polynomial and linear kernel functions. We used single sequence information of different local window sizes, amino acid compositions of different local sequences, multiple sequence alignment obtained from PSI-BLAST and the secondary structure information predicted by PSIPRED. We explored these different sequence encoding schemes in order to investigate their effects on the prediction performance. The training and testing of this approach was performed on a newly enlarged dataset of 2424 non-homologous proteins determined by X-Ray diffraction method using 5-fold cross-validation. Selecting the window size 11 provided the best performance for determining the proline cis/trans isomerization based on the single amino acid sequence. It was found that using multiple sequence alignments in the form of PSI-BLAST profiles could significantly improve the prediction performance, the prediction accuracy increased from 62.8% with single sequence to 69.8% and Matthews Correlation Coefficient (MCC) improved from 0.26 with single local sequence to 0.40. Furthermore, if coupled with the predicted secondary structure information by PSIPRED, our method yielded a prediction accuracy of 71.5% and MCC of 0.43, 9% and 0.17 higher than the accuracy achieved based on the singe sequence information, respectively. Conclusion A new method has been developed to predict the proline cis/trans isomerization in proteins based on support vector machine, which used the single amino acid sequence with different local window sizes, the amino acid compositions of local sequence flanking centered proline residues, the position-specific scoring matrices (PSSMs) extracted by PSI-BLAST and the predicted secondary structures generated by PSIPRED. The successful application of SVM approach in this study reinforced that SVM is a powerful tool in predicting proline cis/trans isomerization in proteins and biological sequence analysis.

Predicting residue-wise contact orders in proteins by support vector regression

Background The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Pharmacological consequence of the A118G μ opioid receptor polymorphism on morphine-and fentanyl-mediated modulation of Ca2+ channels in humanized mouse sensory neurons

Background: The most common functional single nucleotide polymorphism of the human OPRM1 gene, A118G, has been shown to be associated with interindividual differences in opioid analgesic requirements, particularly with morphine, in patients with acute postoperative pain. The purpose of this study was to examine whether this polymorphism would modulate the morphine and fentanyl pharmacological profile of sensory neurons isolated from a humanized mouse model homozygous for either the 118A or 118G allele. Methods: The coupling of wild-type and mutant μ opioid receptors to voltage-gated Ca channels after exposure to either ligand was examined by employing the whole cell variant of the patch-clamp technique in acutely dissociated trigeminal ganglion neurons. Morphine-mediated antinociception was measured in mice carrying either the 118AA or 118GG allele. RESULTS:: The biophysical parameters (cell size, current density, and peak current amplitude potential) measured from both groups of sensory neurons were not significantly different. In 118GG neurons, morphine was approximately fivefold less potent and 26% less efficacious than that observed in 118AA neurons. On the other hand, the potency and efficacy of fentanyl were similar for both groups of neurons. Morphine-mediated analgesia in 118GG mice was significantly reduced compared with the 118AA mice. Conclusions: This study provides evidence to suggest that the diminished clinical effect observed with morphine in 118G carriers results from an alteration of the receptor's pharmacology in sensory neurons. In addition, the impaired analgesic response with morphine may explain why carriers of this receptor variant have an increased susceptibility to become addicted to opioids. © 2011 the American Society of Anesthesiologists, Inc. Lippincott Williams & Wilkins. Anesthesiology.

Thermal stability of the ‘cave’ mineral ardealite Ca2(HPO4)(SO4)•4H2O

Thermogravimetry combined with evolved gas mass spectrometry has been used to characterise the mineral ardealite and to ascertain the thermal stability of this ‘cave’ mineral. The mineral ardealite Ca2(HPO4)(SO4)•4H2O is formed through the reaction of calcite with bat guano. The mineral shows disorder and the composition varies depending on the origin of the mineral. Thermal analysis shows that the mineral starts to decompose over the temperature range 100 to 150°C with some loss of water. The critical temperature for water loss is around 215°C and above this temperature the mineral structure is altered. It is concluded that the mineral starts to decompose at 125°C, with all waters of hydration being lost after 226°C. Some loss of sulphate occurs over a broad temperature range centred upon 565°C. The final decomposition temperature is 823°C with loss of the sulphate and phosphate anions.

Handling incomplete data in survival analysis with multiple covariates

This paper studies the missing covariate problem which is often encountered in survival analysis. Three covariate imputation methods are employed in the study, and the effectiveness of each method is evaluated within the hazard prediction framework. Data from a typical engineering asset is used in the case study. Covariate values in some time steps are deliberately discarded to generate an incomplete covariate set. It is found that although the mean imputation method is simpler than others for solving missing covariate problems, the results calculated by it can differ largely from the real values of the missing covariates. This study also shows that in general, results obtained from the regression method are more accurate than those of the mean imputation method but at the cost of a higher computational expensive. Gaussian Mixture Model (GMM) method is found to be the most effective method within these three in terms of both computation efficiency and predication accuracy.

Two SusD-like proteins encoded within a polysaccharide utilisation locus of an uncultured ruminant bacteroidetes bind strongly to cellulose

We demonstrate that two characteristic Sus-like proteins encoded within a Polysaccharide Utilisation Locus (PUL) bind strongly to cellulosic substrates and interact with plant primary cell walls. This shows associations between uncultured Bacteroidetes-affiliated lineages and cellulose in the rumen, and thus presents new PUL-derived targets to pursue regarding plant biomass degradation.