Insertion reactions of organic anhydrides into the Cu-O bond of [CuOBu]: syntheses and structures of functionalised copper (I) and copper (II) carboxylates

The insertion reaction of maleic anhydride into the Cu-O bond in [CuO^tBu] produced the complexes [Cu₂(CO₂C₂H₂CO₂^tBu)₄ · dme] (1), [Cu(CO₂C₂H₂CO₂^tBu)₂ · tmeda] (2) and [Cu₂(CO₂C₂H₂CO₂^tBu)₂ · dppm]₂ (3) (dme = 1,2-dimethoxyethane; tmeda = N¹,N¹,N²,N²-tetramethylethane-1,2-diamine; dppm = bis(diphenylphosphino)methane). This reaction represents a useful synthetic strategy for a range of functionalised Cu(I) and Cu(II) carboxylates.     

Sequence Representation and Prediction of Protein Secondary Structure for Structural Motifs in Twilight Zone Proteins

Characterizing and classifying regularities in protein structure is an important element in uncovering the mechanisms that regulate protein structure, function and evolution. Recent research concentrates on analysis of structural motifs that can be used to describe larger, fold-sized structures based on homologous primary sequences. At the same time, accuracy of secondary protein structure prediction based on multiple sequence alignment drops significantly when low homology (twilight zone) sequences are considered. To this end, this paper addresses a problem of providing an alternative sequences representation that would improve ability to distinguish secondary structures for the twilight zone sequences without using alignment. We consider a novel classification problem, in which, structural motifs, referred to as structural fragments (SFs) are defined as uniform strand, helix and coil fragments. Classification of SFs allows to design novel sequence representations, and to investigate which other factors and prediction algorithms may result in the improved discrimination. Comprehensive experimental results show that statistically significant improvement in classification accuracy can be achieved by: (1) improving sequence representations, and (2) removing possible noise on the terminal residues in the SFs. Combining these two approaches reduces the error rate on average by 15% when compared to classification using standard representation and noisy information on the terminal residues, bringing the classification accuracy to over 70%. Finally, we show that certain prediction algorithms, such as neural networks and boosted decision trees, are superior to other algorithms.This research was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC).     

Metabolic memory - the implications for diabetic complications

Many important biochemical mechanisms are activated in the presence of high levels of glucose, which occur in diabetes. Large randomised studies have established that early intensive glycaemic control reduces the risk of diabetic complications. This phenomenon has recently been dubbed 'metabolic memory'. It has been suggested that early glycaemia normalisation can halt the hyperglycaemia-induced pathological processes associated with enhanced oxidative stress and glycation of cellular proteins and lipids. The phenomenon of metabolic memory suggests that early aggressive treatment and strict glycaemic control could prevent chronic diabetic complications.     

Computational design of a new hydrogen bond network and at least a 300-fold specificity switch at a protein-protein interface

The redesign of protein-protein interactions is a stringent test of our understanding of molecular recognition and specificity. Previously we engineered a modest specificity switch into the colicin E7 DNase-Im7 immunity protein complex by identifying mutations that are disruptive in the native complex, but can be compensated by mutations on the interacting partner. Here we extend the approach by systematically sampling alternate rigid body orientations to optimize the interactions in a binding mode specific manner. Using this protocol we designed a de novo hydrogen bond network at the DNase-immunity protein interface and confirmed the design with X-ray crystallographic analysis. Subsequent design of the second shell of interactions guided by insights from the crystal structure on tightly bound water molecules, conformational strain, and packing defects yielded new binding partners that exhibited specificities of at least 300-fold between the cognate and the non-cognate complexes. This multi-step approach should be applicable to the design of polar protein-protein interactions and contribute to the re-engineering of regulatory networks mediated by protein-protein interactions.     

The characterization of the exposure to immune mediated apoptosis and the regulation of immune cytotoxic activity in the environment of a neoplasm and in decidua

Acquiring the immune-mediated apoptosis and the ability to regulate the cytotoxic immune response are the main phenomena playing fundamental roles in such situations as neoplasm survival and creation of immune tolerance during pregnancy. The aim of this study was to investigate these phenomena through the evaluation of metallothionein and RCAS1 proteins in neoplasm and its healthy environment (clear surgical margin), physiological conditions in placenta and its environment (decidua) and the comparison to non-neoplasmatic lesions originating from the environment (nasal polyps, endometriosis). We have shown that the growth of RCAS1 expression was simultaneous to the infiltration of activated immunological cells of tumor environment as well as decidua. The activity of immunological cells was in our study selectively suppressed. Metallothionein expression growth was also observed in healthy tumors stroma and in decidua probably in response to the growing cytotoxic activity and tumor spread. Alterations in RCAS1 and Metallothionein expression seem to be associated with local immune dysfunction in nasal polyps and endometriosis. In conclusion, the ability to compensate the growing cytotoxic immune response is physiologically observed in decidua, the lost of this ability in tumor environment might participate in the development of tumor spread.     

Classifier ensembles for protein structural class prediction with varying homology

Structural class characterizes the overall folding type of a protein or its domain. A number of computational methods have been proposed to predict structural class based on primary sequences; however, the accuracy of these methods is strongly affected by sequence homology. This paper proposes, an ensemble classification method and a compact feature-based sequence representation. This method improves prediction accuracy for the four main structural classes compared to competing methods, and provides highly accurate predictions for sequences of widely varying homologies. The experimental evaluation of the proposed method shows superior results across sequences that are characterized by entire homology spectrum, ranging from 25% to 90% homology. The error rates were reduced by over 20% when compared with using individual prediction methods and most commonly used composition vector representation of protein sequences. Comparisons with competing methods on three large benchmark datasets consistently show the superiority of the proposed method.