Symmetric K+ and Mg2+ ion-binding sites in the 5S rRNA loop E inferred from molecular dynamics simulations

Potassium binding to the 5 S rRNA loop E motif has been studied by molecular dynamics at high (1.0 M) and low (0.2 M) concentration of added KCl in the presence and absence of Mg²⁺. A clear pattern of seven deep groove K⁺ binding sites or regions, in all cases connected with guanine N7/O6 atoms belonging to GpG, GpA, and GpU steps, was identified, indicating that the LE deep groove is significantly more ionophilic than the equivalent groove of regular RNA duplexes. Among all, two symmetry-related sites (with respect to the central G·A pair) were found to accommodate K⁺ ions with particularly long residence times. In a preceding molecular dynamics study by Auffinger et al. in the year 2003, these two sites were described as constituting important Mg²⁺ binding locations. Altogether, the data suggest that these symmetric sites correspond to the loop E main ion binding regions. Indeed, they are located in the deep groove of an important ribosomal protein binding motif associated with a fragile pattern of non-Watson-Crick pairs that has certainly to be stabilized by specific Mg²⁺ ions in order to be efficiently recognized by the protein. Besides, the other sites accommodate monovalent ions in a more diffuse way pointing out their lesser significance for the structure and function of this motif. Ion binding to the shallow groove and backbone atoms was generally found to be of minor importance since, at the low concentration, no well defined binding site could be characterized while high K⁺ concentration promoted mostly unspecific potassium binding to the RNA backbone. In addition, several K⁺ binding sites were located in positions equivalent to water molecules from the first hydration shell of divalent ions in simulations performed with magnesium, indicating that ion binding regions are able to accommodate both mono- and divalent ionic species. Overall, the simulations provide a more precise but, at the same time, a more intricate view of the relations of this motif with its ionic surrounding.     

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.