HighlightsTo assign use-related information to chemicals to help prioritize which will be given more scrutiny relative to human exposure potential.Categorical chemical use and functional information are presented through the Chemical/Product Categories Database (CPCat).CPCat contains information on >43,000 unique chemicals mapped to ∼800 terms categorizing their usage or function.The CPCat database is useful for modeling and prioritizing human chemical exposures.

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Of the 1,693 pesticides considered in this review, 1,594 are organic chemicals, 47 are inorganic chemicals, 53 are of biological origin (largely non chemical; insect,fungus, bacteria, virus, etc.), and 2 have an undetermined structure. Considering that the EPA's Office of Pesticide Programs found 1,252 pesticide active ingredients(EPA Pesticides Customer Service 2011), we consider this dataset to be comprehensive; however, no direct comparison of the compound lists was undertaken. Of all pesticides reviewed, 482 (28%) are chiral; 30% are chiral when considering only the organic chemical pesticides. A graph of this distribution is shown in Fig. 7a. Each pesticide is classified with up to three pesticidal utilities (e.g., fungicide, plant growth regulator, rodenticide, etc.), taken first from the Pesticide Manual as a primary source, and the Compendium of Common Pesticide Names website as a secondary source. Of the chiral pesticides, 195 (34%) are insecticides (including attractants, pheromones, and repellents), 150 (27%) are herbicides (including plant growth regulators and herbicide safeners), 104 (18%) are fungicides, and 55 (10%)are acaricides. The distribution of chiral pesticides by utility is shown in Fig. 7b,including categories of pesticides that make up 3%t or less of the usage categories.Figure 7c shows a similar distribution of non chiral pesticide usage categories. Of the chiral pesticides, 270 (56%) have one chiral feature, 105 (22%) have two chiral features, 30 (6.2%) have three chiral features, and 29 (6.0%) have ten or more chiral features.Chiral chemicals pose many difficulties in stereospecific synthesis, characterization, and analysis. When these compounds are purposely put into the environment,even more interesting complications arise in tracking, monitoring, and predicting their fate and risks. More than 475 pesticides are chiral, as are other chiral contaminants such as pharmaceuticals, polychlorinated biphenyls, brominated flame retardants, synthetic musks, and their degradates (Kallenborn and Hiihnerfuss 2001;Heeb et al. 2007; Hihnerfuss and Shah 2009). The stereoisomers of pesticides can have widely different efficacy, toxicity to nontarget organisms, and metabolic rates in biota. For these reasons, it is important to first be aware of likely fate and effect differences, to incorporate molecular asymmetry insights into research projects, and to study the individual stereoisomers of the applied pesticide material.With the advent of enantioselective chromatography techniques, the chirality of pesticides has been increasingly studied. While the ChirBase (Advanced ChemistryDevelopment 1997-2010) database does not include all published chiral analytical separations, it does contain more than 3,500 records for 146 of the 482 chiral pesticides (30%). The majority of the records are found in the liquid chromatography database (2,677 or 76%), followed by the gas chromatography database (652 or 18%),and the capillary electrophoresis database (203 or 6%). The finding that only 30% of the chiral pesti...

binding to the C-domain of CaM. Specifically, PEP-19 accelerates the rates of both association and dissociation of Ca 2ϩ without greatly affecting the overall K Ca of the C-domain (5). RC3 accelerates the rate of Ca 2ϩ dissociation from CaM, but has a lesser effect on the association rate, thereby decreasing the affinity of binding Ca 2ϩ to the C-domain of CaM (6). Importantly, both PEP-19 and RC3 exert these effects even when CaM is bound to CaM-dependent protein kinase II (CKII␣) (5, 6).These results suggest that PEP-19 and RC3 could have broad extrinsic effects on CaM-related signaling pathways by modulating the Ca 2ϩ binding properties of free or enzyme-bound CaM. This is consistent with the phenotype of RC3 knock-out mice, which show defects in synaptic plasticity (7), attenuated phosphorylation of hippocampal protein kinase A and C substrates (8), and altered Ca 2ϩ dynamics in cortical neurons (9). Both PEP-19 and RC3 contain an IQ motif. This rather loose consensus sequence (IQXXXRGXXXR) was first identified as the light chain binding site in conventional myosins, but was subsequently recognized as a CaM binding sequence in numerous other proteins (10). IQ motif proteins exhibit diverse modes of interaction with CaM that include Ca 2ϩ -dependent or independent binding (10), binding to both or only one domain of CaM (5, 11-13), binding multiple CaMs to multiple IQ motifs (14), and exchange of CaM between the IQ motif and other sites in the same protein (15, 16).These intriguing structure-function relationships of IQ motifs led us to identify amino acids in PEP-19 that are required to modulate Ca 2ϩ binding to CaM. We show here that the consensus IQ CaM binding motif is necessary, but not sufficient to mimic the effect of intact PEP-19 on CaM. An adjacent highly acidic amino acid sequence acts in synergy with the IQ motif to modulate Ca 2ϩ binding to the C-domain of CaM. We propose that this acidic/IQ sequence constitutes a new CaM regulatory motif. EXPERIMENTAL PROCEDURESRecombinant Proteins and Peptides-Recombinant CaM, CaM(K75C), CaM(T110C), CaM(T34C), CaM(T34C,T110C), PEP19, and RC3 were cloned, expressed, and purified as described previously (5, 6, 16 -18). The expression plasmid for the C-domain of CaM (residues 78 -148) was a generous gift * This work was supported in part by National Institutes of Health Grants GM069611 and NS038310 and Robert A. Welch Foundation Grant AU1144. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. -bound calmodulin; CaM ACR , acrylodan labeled CaM(K75C); CaM DANS , IAEDANS labeled CaM(K75C); CKII, CaM-dependent protein kinase II; RC3, neurogranin; FRET, fluorescence resonance energy transfer; MOPS, 4-morpholinepropanesulfonic acid; acrylodan, 6-acryloyl-2-dimethylaminonaphthalene; IAEDANS, 5-((((2-iodoacetyl)amino)ethyl)amino)naphthalene-1-sulfonic acid; DDPM,maleimide; HPLC, high performance liq...

Ochratoxin A (OTA), a fungal metabolite of strains of Penicillium and Aspergillus, binds in its dianion form to Sudlow site I of human serum albumin (HSA) with high affinity. In this study, isothermal calorimetry (ITC) is used to study the binding of OTA and its O-methyl derivative (MOA). Calculations of the equilibrium geometry of the monoanion and dianion of OTA reveal only small structural changes among the lowest energy conformers. The ITC data show the binding of MOA, which lacks the phenolic proton of OTA, is accompanied by the uptake of a proton from the surrounding solvent. At pH 7.13, the binding of OTA is accompanied by uptake of 0.43 ( 0.15 protons from the solvent. At this pH, the monoanion (0.54) and dianion (0.46) forms of OTA are both present in solution. However, the pK a of the phenolic group of OTA decreases by more than three units upon protein binding, and so all available OTA is bound to the protein as the dianion. To account for the ITC data, a model is proposed in which the proton is provided by the phenolic moiety of OTA in the case of initial binding of the monoanion, and a proton is taken up from the surrounding solvent for initial binding of the dianion. The binding constant of MOA is 2 orders of magnitude smaller than that of OTA, indicating the ion pair between the phenoxide group of OTA and the protonated amino acid is a major contributor to the high binding affinity of OTA to HSA. To identify the specific amino acid involved, the binding of OTA to bovine, rat, and porcine serum albumins was examined. Deprotonation of the monoanion of OTA occurred upon binding to all species. Assuming the amino acid is conserved between species and taking into account crystal structures of ligands bound to site I of HSA and their ability to displace OTA from HSA, either R218 or R257 is involved in the ion pairing with OTA. These two amino acids sit across the binding cavity from one other in site I.

The binding site of ochratoxin A (OTA) within domain 2A of human serum albumin (HSA) is examined by theoretical simulations and site-directed mutagenesis experiments. The calculated binding constant, based on docking experiments and theoretical affinity constants derived from the empirical free energy of binding as implemented in AutoDock 3.0, for the OTA dianion (3.7 × 10 6 M -1 ) is in good agreement with experimental value of 5.2 × 10 6 M -1 . The carboxy terminus of OTA associates with R218 and R222 of the protein. Binding is reduced by over an order of magnitude for the mutant R218A in both experiments and theoretical simulations. The carbonyl of the lactone and the phenolic group of OTA are in close proximity to R257. The experimental binding constant of OTA to the R257A mutant is 1.6 × 10 5 M -1 , over an order of magnitude smaller than for the wild-type protein. The predicted binding constant based on a comparison of the lowest-energy conformer from docking studies performed in AutoDock 3.0 of OTA to the R257A mutant (8.3 × 10 4 M -1 ) is also in good agreement with the experimental result. R257 clearly plays an important role in the binding of the isocoumarin ring of OTA by serving as a proton acceptor and stabilizing the binding through the creation of an ion pair with the phenoxide group on OTA.

The trichochromes are a class of small molecules present in pheomelanin (the red melanin) and absent in eumelanin (the black melanin). Herein trichochrome F (TF) and decarboxytrichochrome C (dTC) are examined. Both trichochromes are characterized by a visible absorption band, which is shown to be the result of overlapping transitions of the cis and trans isomers. The temperature dependence of the absorption spectrum of dTC suggests the additional presence of equilibrium between the enol and keto forms of the molecule. These conclusions are supported by ground-state energies of these isomers obtained using a continuum solvation model. Near-infrared emission measurements were not able to detect photoproduction of 1O2, and spin-trapping experiments revealed formation of O2*-. DNA nicking assays also revealed a low level of light-induced aerobic activity of dTC, suggesting a quantum efficiency of at most 5 x 10(-6) for the photogeneration of O2*-. These results are consistent with pump-probe optical experiments, which reveal efficient and nearly complete ground-state recovery within a few picoseconds of excitation. Both trichochromes are efficient quenchers of 1O2, exhibiting a bimolecular rate constant comparable with vitamin C. These results suggest that trichochromes could serve a protective role in pheomelanin pigments.

Biomonitoring is the process by which biomarkers are measured in human tissues and specimens to evaluate exposures. Given the growing number of population-based biomonitoring surveys, there is now an escalated interest in using biomarker data to reconstruct exposures for supporting risk assessment and risk management. While detection of biomarkers is de facto evidence of exposure and absorption, biomarker data cannot be used to reconstruct exposure unless other information is available to establish the external exposure-biomarker concentration relationship. In this review, the process of using biomarker data and other information to reconstruct human exposures is examined. Information that is essential to the exposure reconstruction process includes (1) the type of biomarker based on its origin (e.g., endogenous vs. exogenous), (2) the purpose/design of the biomonitoring study (e.g., occupational monitoring), (3) exposure information (including product/chemical use scenarios and reasons for expected contact, the physicochemical properties of the chemical and nature of the residues, and likely exposure scenarios), and (4) an understanding of the biological system and mechanisms of clearance. This review also presents the use of exposure modeling, pharmacokinetic modeling, and molecular modeling to assist in integrating these various types of information.