Engineering protein therapeutics: predictive performances of a structure-based virtual affinity maturation protocol

The implementation of a structure based virtual affinity maturation protocol and evaluation of its predictivity are presented. The in silico protocol is based on conformational sampling of the interface residues (using the Dead End Elimination/A* algorithm), followed by the estimation of the change of free energy of binding due to a point mutation, applying MM/PBSA calculations. Several implementations of the protocol have been evaluated for 173 mutations in 7 different protein complexes for which experimental data were available: the use of the Boltzamnn averaged predictor based on the free energy of binding (ΔΔG(*)) combined with the one based on its polar component only (ΔΔE(pol*)) led to the proposal of a subset of mutations out of which 45% would have successfully enhanced the binding. When focusing on those mutations that are less likely to be introduced by natural in vivo maturation methods (99 mutations with at least two base changes in the codon), the success rate is increased to 63%. In another evaluation, focusing on 56 alanine scanning mutations, the in silico protocol was able to detect 89% of the hot-spots.     

Estimating time-varying conditional correlations between stock and foreign exchange markets

This study explores the dynamic interaction between stock market returns and changes in nominal exchange rates. Many financial variables are known to exhibit fat tails and autoregressive variance structure. It is well-known that unconditional covariance and correlation coefficients also vary significantly over time and multivariate generalized autoregressive model (MGARCH) is able to capture the time-varying variance-covariance matrix for stock market returns and changes in exchange rates. The model is applied to daily Euro-Dollar exchange rates and two stock market indexes from the US economy: Dow-Jones Industrial Average Index and S&P500 Index. The news impact surfaces are also drawn based on the model estimates to see the effects of idiosyncratic shocks in respective markets.     

Comparison of stability predictions and simulated unfolding of rhodopsin structures

Developing a better mechanistic understanding of membrane protein folding is urgently needed because of the discovery of an increasing number of human diseases, where membrane protein instability and misfolding is involved. Towards this goal, we investigated folding and stability of 7-transmembrane (TM) helical bundles by computational methods. We compared the results of three different algorithms for predicting changes in stability of proteins against an experimental mutation dataset obtained for bacteriorhodopsin (BR) and mammalian rhodopsin and find that 61.6% and 70.6% of the mutation results can potentially be explained by known local contributors to the stability of the folded state of BR and mammalian rhodopsin, respectively. To obtain further information on the predicted folding pathway of 7-TM proteins, we conducted simulated thermal unfolding experiments of all available rhodopsin structures with resolution better than 3 angstroms using the Floppy Inclusions and Rigid Substructure Topography (FIRST) method (Jacobs, D. J., A. J. Rader, L. A. Kuhn and M. F. Thorpe [2001] Proteins 44, 150) described previously for a single mammalian rhodopsin structure (Rader et al. [2004] PNAS 101, 7246). In statistical comparison we found that structures of mammalian rhodopsin have a stability core that is characterized by long-range interactions involving amino acids close in space but distant in sequence comprising positions from both extracellular loop and TM regions. In contrast, BR-simulated unfolding does not reveal such a core but is dominated by interactions within individual and groups of TM helices, consistent with the two-stage hypothesis of membrane protein folding. Similar results were obtained for halo- and sensory rhodopsins as for BRs. However, the average folding core energies of sensory rhodopsins were in between those observed for mammalian rhodopsins and BRs hinting at a possible evolution of these structures toward a rhodopsin-like behavior. These results support the conclusion that although the two-stage model can explain the mechanisms of folding and stability of BR, it fails to account for the folding and stability of mammalian rhodopsin, even though the two proteins are structurally related.     

Fatty Acid Composition and Antioxidant Activity of Ranunculus constantinopolitanus

Chemistry of Natural Compounds -     

The Structural Basis of Mycobacterium tuberculosis RpoB Drug-Resistant Clinical Mutations on Rifampicin Drug Binding

Tuberculosis (TB), caused by the Mycobacterium tuberculosis infection, continues to be a leading cause of morbidity and mortality in developing countries. Resistance to the first-line anti-TB drugs, isoniazid (INH) and rifampicin (RIF), is a major drawback to effective TB treatment. Genetic mutations in the β-subunit of the DNA-directed RNA polymerase (rpoB) are reported to be a major reason of RIF resistance. However, the structural basis and mechanisms of these resistant mutations are insufficiently understood. In the present study, thirty drug-resistant mutants of rpoB were initially modeled and screened against RIF via a comparative molecular docking analysis with the wild-type (WT) model. These analyses prioritized six mutants (Asp441Val, Ser456Trp, Ser456Gln, Arg454Gln, His451Gly, and His451Pro) that showed adverse binding affinities, molecular interactions, and RIF binding hinderance properties, with respect to the WT. These mutant models were subsequently analyzed by molecular dynamics (MD) simulations. One-hundred nanosecond all-atom MD simulations, binding free energy calculations, and a dynamic residue network analysis (DRN) were employed to exhaustively assess the impact of mutations on RIF binding dynamics. Considering the global structural motions and protein–ligand binding affinities, the Asp441Val, Ser456Gln, and His454Pro mutations generally yielded detrimental effects on RIF binding. Locally, we found that the electrostatic contributions to binding, particularly by Arg454 and Glu487, might be adjusted to counteract resistance. The DRN analysis revealed that all mutations mostly distorted the communication values of the critical hubs and may, therefore, confer conformational changes in rpoB to perturb RIF binding. In principle, the approach combined fundamental molecular modeling tools for robust “global” and “local” level analyses of structural dynamics, making it well suited for investigating other similar drug resistance cases.     

Immobilization of laccase on polymer grafted polytetrafluoroethylene membranes for biosensor construction

In this study, Trametes versicolor laccase was immobilized on polytetrafluoroethylene (PTFE) membranes using two different techniques, entrapment to gelatin and covalent immobilization to the surface. For surface immobilization, functional groups were formed on PTFE surface by radiofrequency (RF) plasma treatment followed by polymer grafting. Two different polymers, polyacrylamide (pAAm) and polyacrylic acid (pAAc) were tried. For polyacrylamide grafted PTFE, a two-step polymerization process was used. The membranes were first treated with hydrogen plasma and pAAm grafted PTFE (pAAm-g-PTFE) was then formed by argon plasma treatment. To produce pAAc grafted PTFE (pAAc-g-PTFE), the surface was first treated with argon plasma and AAc was then attached to the surface by heat treatment (70 °C, 6 h). For both cases, an optimized carbodiimide coupling reaction was used for laccase immobilization. Enzyme activity was measured by an oxygen electrode using guaiacol as substrate. All three biosensing membranes were characterized and compared in terms of optimum working conditions, storage stability and reusability. Our study concluded that although a higher activity was obtained by gelatin entrapped laccase, its mechanical instability and poor storage life makes the gelatin biosensor unattractive for multiple usages and for field measurements. pAAc-g-PTFE biosensor was found to be more stable and highly reusable (ca. 50 times) when compared with the other two biosensors. In addition, its sensitivity was suitable for field applications. Therefore, the pAAc-g-PTFE biosensor could be proposed as an alternative on-site detection tool for phenolic compound monitoring.