Many important properties of clay minerals are defined by the species of charge-balancing cation. Phenomena such as clay swelling and cation exchange depend on the cation species present, and understanding how the cations bind with the mineral surface at a fundamental level is important. In the present study the binding affinities of several different charge-balancing cations with the basal surface of the smectite mineral, montmorillonite, have been calculated using molecular dynamics in conjunction with the well-tempered metadynamics algorithm. The results follow a Hofmeister series of preferred ion adsorption to the smectite basal surfaces of the form:

K+ > Na+ > Ca2+ > Cs+ > Ba2+

The results also revealed the energetically favorable position of the ions above the clay basal surfaces. Key features of the free-energy profiles are illustrated by Boltzmann population inversions and analyses of the water structures surrounding the ion and clay surface. The results show that weakly hydrated cations (K+ and Cs+) preferentially form inner-sphere surface complexes (ISSC) above the ditrigonal siloxane cavities of the clay, while the more strongly hydrated cations (Na+) are able to form ISSCs above the basal O atoms of the clay surface. The strongly hydrated cations (Na+, Ca2+, and Ba2+), however, preferentially form outer-sphere surface complexes. The results provide insight into the adsorption mechanisms of several ionic species on montmorillonite and are relevant to many phenomena thought to be affected by cation exchange, such as nuclear waste disposal, herbicide/pesticide-soil interactions, and enhanced oil recovery.

A laboratory experiment was conducted to compare the adsorption and desorption of fluometuron between two soils, one collected from an eastern gamagrass filter strip and the other from a cropped field. Fluometuron adsorption to soil collected from the filter strip was higher than to soil collected from the cropped field. Kd values for fluometuron ranged from 1.9 to 3.6 for soil from a cropped area, compared with 2.9 to 5.3 for soil from the filter strip, indicating a weak to moderate binding affinity for fluometuron. The total fluometuron desorbed ranged from 48 to 79% of that adsorbed, most of which (50 to 59% of the total amount desorbed) occurred during the first desorption cycle with both soils. Approximately 11% less fluometuron desorbed when a filter strip was present. Results indicate that eastern gamagrass filter strips can influence adsorption–desorption processes between fluometuron and a Brooksville silty clay soil by altering the soil properties, specifically soil organic matter.

This is a brief review of the computational modeling of protein-ligand interactions using a recently developed fully polarizable continuum model (FPCM) and rational drug design. Computational modeling has become a powerful tool in understanding detailed protein-ligand interactions at molecular level and in rational drug design. To study the binding of a protein with multiple molecular species of a ligand, one must accurately determine both the relative free energies of all of the molecular species in solution and the corresponding microscopic binding free energies for all of the molecular species binding with the protein. In this paper, we aim to provide a brief overview of the recent development in computational modeling of the solvent effects on the detailed protein-ligand interactions involving multiple molecular species of a ligand related to rational drug design. In particular, we first briefly discuss the main challenges in computational modeling of the detailed protein-ligand interactions involving the multiple molecular species and then focus on the FPCM model and its applications. The FPCM method allows accurate determination of the solvent effects in the first-principles quantum mechanism (QM) calculations on molecules in solution. The combined use of the FPCM-based QM calculations and other computational modeling and simulations enables us to accurately account for a protein binding with multiple molecular species of a ligand in solution. Based on the computational modeling of the detailed protein-ligand interactions, possible new drugs may be designed rationally as either small-molecule ligands of the protein or engineered proteins that bind/metabolize the ligand. The computational drug design has successfully led to discovery and development of promising drugs.

Differences in insulin binding affinity and in concentrations of insulin receptor, were found in a variety of tissues taken, at slaughter, from mature steers (701 (s.d. 23) kg) and growing lambs (47 (s.d. 2·1) kg). In both species, liver had lower insulin binding affinity than skeletal muscles m. pectineus m. longissimus dorsi and m. rectus capitis (all P < 0·001) and subcutaneous, omental and perirenal adipose depots (all P < 0·001). Site-specific differences in affinity for insulin existed between adipose depots (subcutaneous < omental, P < 0·05; subcutaneous < perirenal, P < 0·001) and between tissue-types (subcutaneous fat < m. pectineus skeletal muscle, P < 0·05; m. rectus capitis < perirenal fat, P < 0·05) in steers. In lambs also, receptor affinity for insulin differed between tissue-type (m. longissimus dorsi < perirenal fat, P < 0·05; m. rectus capitis < subcutaneous fat, P < 0·05 and m. rectus capitis < perirenal fat, P < 0·001) but lambs did not show the adipose depot-specific differences in insulin affinity observed in steers. Insulin receptor concentration differed between adipose depots (subcutaneous < omental, P < 0·05; subcutaneous < perirenal, P < 0·01) and between tissue-type (m. pectineus < perirenal fat P < 0·05) in steers and perirenal and subcutaneous adipose depots of lambs had higher receptor concentrations than m. longissimus dorsi and m. pectineusP < 0·001). This is the first study to demonstrate, in any species, differences in insulin receptor binding affinity and receptor concentration in a wide range of tissues (liver, skeletal muscles and adipose depots) from the same individual. Such differences in meat-producing animals could, through effects on tissue sensitivity and/or responsiveness to insulin, influence nutrient partitioning to tissues and affect overall rates of lipid storage and net protein synthesis.

A popular approach to the computational modeling of ligand/receptor interactions is to use an empirical free energy like model with adjustable parameters. Parameters are learned from one set of complexes, then used to predict another set. To improve these empirical methods requires an independent way to study their inherent errors. We introduce a toy model of ligand/receptor binding as a workbench for testing such errors. We study the errors incurred from the two state binding assumption—the assumption that a ligand is either bound in one orientation, or unbound. We find that the two state assumption can cause large errors in free energy predictions, but it does not affect rank order predictions significantly. We show that fitting parameters using data from high affinity ligands can reduce two state errors; so can using more physical models that do not use the two state assumption. We also find that when using two state models to predict free energies, errors are more severe on high affinity ligands than low affinity ligands. And we show that two state errors can be diagnosed by systematically adding new binding modes when predicting free energies: if predictions worsen as the modes are added, then the two state assumption in the fitting step may be at fault.