Transferable interactions of Li+ and Mg2+ ions in polarizable models

Therapeutic implications of Li(+), in many cases, stem from its ability to inhibit certain Mg(2+)-dependent enzymes, where it interacts with or substitutes for Mg(2+). The underlying details of its action are, however, unknown. Molecular simulations can provide insights, but their reliability depends on how well they describe relative interactions of Li(+) and Mg(2+) with water and other biochemical groups. Here, we explore, benchmark, and recommend improvements to two simulation approaches: the one that employs an all-atom polarizable molecular mechanics (MM) model and the other that uses a hybrid quantum and MM implementation of the quasi-chemical theory (QCT). The strength of the former is that it describes thermal motions explicitly and that of the latter is that it derives local contributions from electron densities. Reference data are taken from the experiment, and also obtained systematically from CCSD(T) theory, followed by a benchmarked vdW-inclusive density functional theory. We find that the QCT model predicts relative hydration energies and structures in agreement with the experiment and without the need for additional parameterization. This implies that accurate descriptions of local interactions are essential. Consistent with this observation, recalibration of local interactions in the MM model, which reduces errors from 10.0 kcal/mol to 1.4 kcal/mol, also fixes aqueous phase properties. Finally, we show that ion–ligand transferability errors in the MM model can be reduced significantly from 10.3 kcal/mol to 1.2 kcal/mol by correcting the ligand’s polarization term and by introducing Lennard-Jones cross-terms. In general, this work sets up systematic approaches to evaluate and improve molecular models of ions binding to proteins

Climate Change and Crop Choice in Zambia: A Mathematical Programming Approach

While climate change is widely regarded as a threat to food security in southern Africa, few studies attempt to link the science of climate change impacts on agriculture with the specificities of smallholder livelihoods. In this paper, we build a series of linear programming (LP) farm-household models in Zambia in order to assess the impact of climate change on rural households and likely changes in land use and crop management. The LP models represent three household types (smallholders, emergent farmers, and female-headed households) in three agro-ecological zones with divergent cropping patterns and climate trends. Model parameters are drawn from several nationally representative rural household surveys, local meteorological records, and downscaled climate predictions of the Hadley (HadCM3) and CCSM models for the year 2050. The calorie-maximizing LP models are calibrated to best reflect baseline crop distributions at each site. Statistical analyses of crop yields over nine years reveal that crops in Zambia exhibit varying levels of sensitivity to climate shocks, and under climate change scenarios, the LP models indicate that farmers will shift their choices of technologies and crops. Among smallholder farms, calorie production from field crops changes by -13.56 to +5.13% under the Hadley predictions and -10.61 to +9.79% under the CCSM predictions. Although farm-households are expected to meet their consumption requirements even under climate change scenarios, the probability of falling below a minimum threshold of calorie production increases in two of our three study sites, and this is particularly true for smallholder farmers who face binding land constraints. Given the current choice set, autonomous on-farm adaptation generally will not be enough to offset the negative yield effects of climate change. Zambia therefore needs larger-scale institutional developments and agricultural research to provide farmers with additional adaptation options