The solvation layer surrounding a protein is clearly an intrinsic part of protein structure–dynamics–function, and our understanding of how the hydration dynamics influences protein function is emerging. We have recently reported simulations indicating a correlation between regional hydration dynamics and the structure of the solvation layer around different regions of the enzyme Candida antarctica lipase B, wherein the radial distribution function (RDF) was used to calculate the pairwise entropy, providing a link between dynamics (diffusion) and thermodynamics (excess entropy) known as Rosenfeld scaling. Regions with higher RDF values/peaks in the hydration layer (the first peak, within 6 Å of the protein surface) have faster diffusion in the hydration layer. The finding thus hinted at a handle for rapid evaluation of hydration dynamics at different regions on the protein surface in molecular dynamics simulations. Such an approach may move the analysis of hydration dynamics from a specialized venture to routine analysis, enabling an informatics approach to evaluate the role of hydration dynamics in biomolecular function. This paper first confirms that the correlation between regional diffusive dynamics and hydration layer structure (via water center of mass around protein side-chain atom RDF) is observed as a general relationship across a set of proteins. Second, it seeks to devise an approach for rapid analysis of hydration dynamics, determining the minimum amount of information and computational effort required to get a reliable value of hydration dynamics from structural data in MD simulations based on the protein–water RDF. A linear regression model using the integral of the hydration layer in the water–protein RDF was found to provide statistically equivalent apparent diffusion coefficients at the 95% confidence level for a set of 92 regions within five different proteins. In summary, RDF analysis of 10 ns of data after simulation convergence is sufficient to accurately map regions of fast and slow hydration dynamics around a protein surface. Additionally, it is anticipated that a quick look at protein–water RDFs, comparing peak heights, will be useful to provide a qualitative ranking of regions of faster and slower hydration dynamics at the protein surface for rapid analysis when investigating the role of solvent dynamics in protein function.

Histidine C–H bonds observed in protein structures include (clockwise from top left): myoglobin, β-lactamase, and photoactive yellow protein; calculations indicate that tautomeric/protonation state influences H-bonding ability (bottom left).

Three direct complexometric titration methods for bismuth subsalicylate determination in over-the-counter stomach relief caplets and liquids are presented, as well as a UV–vis assay, for use in student laboratories. The main difference between titration methods is the choice of indicator; either xylenol orange (XO), pyrocatechol violet (PV), or potassium iodide (KI) is used. These methods are simple and safe, making this experiment desirable in a general chemistry or analytical laboratory to relate real-world sample analysis to theoretical titrimetric concepts. Learning outcomes, student feedback, and student data from Wichita State University (WSU) Chemistry II Honors lab, Bethel College Introduction to Chemistry lab, and University of Missouri (Mizzou) Quantitative Analysis lab are presented. The majority of students enjoyed this real-world sample analysis; however, titration results are more precise for more advanced students. The titration with EDTA using XO as an indicator is the method of choice for caplet analysis by the authors, but methods utilizing PV or KI as indicator are also acceptable, although the KI method may generate large amounts of iodide waste depending on class size. All titration methods presented are selective at low pH (∼1.5–1.6) and do not suffer interferences from commonly occurring ions such as magnesium and calcium. Using the titrimetric procedures presented in this work, all tested caplet and liquid brands appear to meet United States Pharmacopeia (USP) standards, where the allowable range of active ingredient is within 90–110% of label value.