Cell-Free Synthesis of Selenoproteins in High Yield and Purity for Selective Protein Tagging

The selenol group of selenocysteine is much more nucleophilic than the thiol group of cysteine. Selenocysteine residues in proteins thus offer reactive points for rapid post-translational modification. Herein, we show that selenoproteins can be expressed in high yield and purity by cell-free protein synthesis by global substitution of cysteine by selenocysteine. Complete alkylation of solvent-exposed selenocysteine residues was achieved in 10 minutes with 4-chloromethylene dipicolinic acid (4Cl-MDPA) under conditions that left cysteine residues unchanged even after overnight incubation. Gd^III−Gd^III distances measured by double electron–electron resonance (DEER) experiments of maltose binding protein (MBP) containing two selenocysteine residues tagged with 4Cl-MDPA-Gd^III were indistinguishable from Gd^III−Gd^III distances measured of MBP containing cysteine reacted with 4Br-MDPA tags.     

Chemical-Shift Perturbations Reflect Bile Acid Binding to Norovirus Coat Protein: Recognition Comes in Different Flavors

Bile acids have been reported as important cofactors promoting human and murine norovirus (NoV) infections in cell culture. The underlying mechanisms are not resolved. Through the use of chemical shift perturbation (CSP) NMR experiments, we identified a low-affinity bile acid binding site of a human GII.4 NoV strain. Long-timescale MD simulations reveal the formation of a ligand-accessible binding pocket of flexible shape, allowing the formation of stable viral coat protein–bile acid complexes in agreement with experimental CSP data. CSP NMR experiments also show that this mode of bile acid binding has a minor influence on the binding of histo-blood group antigens and vice versa. STD NMR experiments probing the binding of bile acids to virus-like particles of seven different strains suggest that low-affinity bile acid binding is a common feature of human NoV and should therefore be important for understanding the role of bile acids as cofactors in NoV infection.     

State-space ARIMA for supply-chain forecasting

ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.