Development of an artificial metalloenzyme from a nicotinamide-dependent enzymatic scaffold

Transfer hydrogenation reactions can yield a diverse range of chiral products to be used as such, or as building block for the synthesis of more complex molecules. Biocatalysts and artificial metalloenzymes (ArMs) are sustainable alternatives to chemical routes. ArMs are the result of the insertion of a metal ion or complex into a biological scaffold. Most of the ArMs use proteins as a biological hosts to provide a highly functionalised environment where reactants are brought together and activated in very specific ways. The incorporation of the metal catalyst provides a synthetic activity, not necessarily accessible by natural biocatalysts. Most of the ArMs are based on nonenzymatic scaffolds, containing large binding sites generally evolved to accommodate hydrophobic molecules. As such, their design requires efforts to improve the specific chemical reaction targeted. Here we present the development of new ArMs that could be used as a biocatalytic platform for transfer hydrogenation reactions. In previous work, our group generated an artificial mutant of Thermoanaerobacter brockii alcohol dehydrogenase (TbADH) by bioconjugation of a piano-stool rhodium catalyst to cysteine 37 in the active site. In the present work, a computational design approach was used to investigate more suitable positions for the catalyst to be anchored within the catalytic site of the enzyme, using a covalent anchoring strategy. Four positions were found where thiol sidechains would be more accessible than the reported labelled position 37. Single cysteine mutations were prepared at those positions and compared with two other protein variants, where all but one native cysteine residues in the binding pocket were removed. After optimisation of the bioconjugation process with three different ligands bearing a bidentate N-N motif, two enzymatic scaffolds were identified and used for the development of ArMs, using covalent anchoring of Rh and Ir complexes. Five different ArMs were subsequently created and their activity towards the regeneration of nicotinamide cofactors and derivatives was assessed. One mutant bearing a rhodium complex bound at position 243 showed catalytic activity for the reduction of nicotinamides, with a discrimination between the natural cofactors and their mimics. This ArM could potentially be used in a cofactor recycling system compatible with both nicotinamide cofactors. Whilst some of the ArMs suffered from the dissociation of the Cp*Rh functionality from its complex, resulting in unspecific binding and catalysis, one of the constructs, possessing a Cp*Rh(bipyridine) catalyst, showed very promising results for the recycling of NADPH and of benzyl nicotinamide. Besides a covalent anchoring approach, which requires tedious work for the development of ArMs, a supramolecular approach was also investigated, by taking advantage of one feature of ADH, their nicotinamide binding pocket. Benzyl nicotinamide derivatives were synthesised and their capacity to interact with two ADHs, in a similar fashion to their natural cofactor, was assessed with the aim of using these as anchors for synthetic metal catalysts into ADHs. The lack of interactions observed with both enzymes, TbADH and horse liver alcohol dehydrogenase (HLADH), suggested that more hydrophilic structures would be required to yield a suitable supramolecular anchor. To provide initial data for further investigation on the development of ArMs by supramolecular anchoring with ADH enzymatic scaffolds, two separate studies were performed in silico. The first one focused on the anchor structure and its capability to mimic interactions within the cofactor binding site and led to the design of three potential supramolecular anchors where the benzyl substituent was replaced with more hydrophilic functionalities. The second study focused on finding enzymatic scaffolds capable of accommodating the hydrophobic benzyl nicotinamide derivatives. This study led to three potential ADH candidates. To assess the validity of the computational models from the 2 studies, experimental work will be required

Benzophenone derivatives : From natural source to synthetic one. Synthesis and anti-AGEs activity

International audienc

An analysis of the relevance of off-balance sheet items in explaining productivity change in European banking

The 1990s have witnessed a significant growth in bank income generated through non-traditional activities, especially for large EU universal banking institutions. Using the non-parametric Malmquist methodology this study analyses the impact of the inclusion of off-balance sheet (OBS) business in the definition of banks' output when estimating total factor productivity change indexes. Whereas the results reinforce the prevalent view in the recent literature, indicating that the exclusion of non-traditional activities leads to a misspecification of banks' output, the impact of the inclusion of these activities varies. Overall, the inclusion of OBS items results in an increase in estimated productivity levels for all countries under study. However, the impact seems to be the biggest on technological change rather than efficiency change. © 2005 Taylor & Francis

Volatility forecasting for risk management

Recent research has suggested that forecast evaluation on the basis of standard statistical loss functions could prefer models which are sub-optimal when used in a practical setting. This paper explores a number of statistical models for predicting the daily volatility of several key UK financial time series. The out-of-sample forecasting performance of various linear and GARCH-type models of volatility are compared with forecasts derived from a multivariate approach. The forecasts are evaluated using traditional metrics, such as mean squared error, and also by how adequately they perform in a modern risk management setting. We find that the relative accuracies of the various methods are highly sensitive to the measure used to evaluate them. Such results have implications for any econometric time series forecasts which are subsequently employed in financial decisionmaking

Prediction of photoperiodic regulators from quantitative gene circuit models

Photoperiod sensors allow physiological adaptation to the changing seasons. The external coincidence hypothesis postulates that a light-responsive regulator is modulated by a circadian rhythm. Sufficient data are available to test this quantitatively in plants, though not yet in animals. In Arabidopsis, the clock-regulated genes CONSTANS (CO) and FLAVIN, KELCH, F-BOX (FKF1) and their lightsensitive proteins are thought to form an external coincidence sensor. We use 40 timeseries of molecular data to model the integration of light and timing information by CO, its target gene FLOWERING LOCUS T (FT), and the circadian clock. Among other predictions, the models show that FKF1 activates FT. We demonstrate experimentally that this effect is independent of the known activation of CO by FKF1, thus we locate a major, novel controller of photoperiodism. External coincidence is part of a complex photoperiod sensor: modelling makes this complexity explicit and may thus contribute to crop improvement

Structure and assembly of the S-layer determine virulence in C. difficile

Many bacteria and archaea possess a cell surface layer – S-layer – made of a 2D protein array that covers the entire cell. As the outermost component of the cell envelope, S-layers play crucial roles in many aspects of cell physiology. Importantly, many clinically relevant bacterial pathogens possess a distinct S-layer that forms an initial interface with the host, making it a potential target for development of species-specific antimicrobials. Targeted therapeutics are particularly important for antibiotic resistant pathogens such as Clostridioides difficile, the most frequent cause of hospital acquired diarrhea, which relies on disruption of normal microbiota through antibiotic usage. Despite the ubiquity of S-layers, only partial structural information from a very limited number of species is available and their function and organization remains poorly understood. Here we report the first complete atomic level structure and in situ assembly model of an S-layer from a bacterial pathogen and reveal its role in disease severity. SlpA, the main C. difficile S-layer protein, assembles through tiling of triangular prisms abutting the cell wall, interlocked by distinct ridges facing the environment. This forms a tightly packed array, unlike the more porous S-layer models previously described. We report that removing one of the SlpA ridge features dramatically reduces disease severity, despite being dispensable for overall SlpA structure and S-layer assembly. Remarkably, the effect on disease severity is independent of toxin production and bacterial colonization within the mouse model of disease. Our work combines X-ray and electron crystallography to reveal a novel S-layer organization in atomic detail, highlighting the need for multiple technical approaches to obtain structural information on these paracrystalline arrays. These data also establish a direct link between specific structural elements of S-layer and virulence for the first time, in a crucial paradigm shift in our understanding of C. difficile disease, currently largely attributed to the action of potent toxins. This work highlights the crucial role of S-layers in pathogenicity and the importance of detailed structural information for providing new therapeutic avenues, targeting the S-layer. Understanding the interplay between S-layer and other virulence factors will further enhance our ability to tackle pathogens carrying an S-layer. We anticipate that this work provides a solid basis for development of new, C. difficile-specific therapeutics, targeting SlpA structure and S-layer assembly to reduce the healthcare burden of these infections.

An extreme value theory approach to calculating minimum capital risk requirements

This paper investigates the frequency of extreme events for three LIFFE futures contracts for the calculation of minimum capital risk requirements (MCRRs). We propose a semiparametric approach where the tails are modelled by the Generalized Pareto Distribution and smaller risks are captured by the empirical distribution function. We compare the capital requirements form this approach with those calculated from the unconditional density and from a conditional density - a GARCH(1,1) model. Our primary finding is that both in-sample and for a hold-out sample, our extreme value approach yields superior results than either of the other two models which do not explicitly model the tails of the return distribution. Since the use of these internal models will be permitted under the EC-CAD II, they could be widely adopted in the near future for determining capital adequacies. Hence, close scrutiny of competing models is required to avoid a potentially costly misallocation capital resources while at the same time ensuring the safety of the financial system