This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation or the creation of derivative works without specific permission.Mutations in the gene autoimmune regulator (AIRE) cause autoimmune polyendocrinopathy candidiasis ectodermal dystrophy. AIRE is expressed in thymic medullary epithelial cells, where it promotes the expression of tissue-restricted antigens. By the combined use of biochemical and biophysical methods, we show that AIRE selectively interacts with histone H3 through its first plant homeodomain (PHD) finger (AIRE-PHD1) and preferentially binds to non-methylated H3K4 (H3K4me0). Accordingly, in vivo AIRE binds to and activates promoters containing low levels of H3K4me3 in human embryonic kidney 293 cells. We conclude that AIRE-PHD1 is an important member of a newly identified class of PHD fingers that specifically recognize H3K4me0, thus providing a new link between the status of histone modifications and the regulation of tissue-restricted antigen expression in thymus.

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We present an extended QSPR modeling of solubilities of about 500 substances in series of up to 69 diverse solvents. The models are obtained with our new software package, CODESSA PRO, which is furnished with an advanced variable selection procedure and a large pool of theoretically derived molecular descriptors. The squared correlation coefficients and squared standard deviations (variances) range from 0.837 and 0.1 for 2-pyrrolidone to 0.998 and 0.02 for dipropyl ether, respectively. The predictive power of the models was verified by using the "leave-one-out" cross-validation procedure. The QSPR models presented are suitable for the rapid evaluation of solvation free energies of organic compounds. BACKGROUND TO THE PRESENT SERIES OF PAPERSSolubility is of the utmost significance in numerous areas of human endeavor and interest. Solubility in water is fundamental to environmental issues such as pollution, erosion, and mass transfer. Solubility in organic solvents forms much of the basis of the chemical industry. Solubility determines shelf life and cross contamination. It is critically linked to bioavailability and thus to the effectiveness of pharmaceuticals, biodegradation, suitability of gaseous anesthetics, blood substitutes, oxygen carriers, etc. Toxicity is critically dependent on solubility.Very extensive studies have been carried out on the solubilities of various solute-solvent pairs resulting in diverse theories of solute-solvent interactions that form the basis of our knowledge for the understanding of solubility. 1 These theories are based on concepts ranging from quantitative analysis to statistical mechanics and quantum mechanics. Quantitative treatments of solute-solvent interactions in series of compounds have gained wide attraction and have led to various models for explaining solute-solvent behavior. 2 Most of this work has involved studying a series of solutes dissolved in a single solvent. There are some instances in which the solubilities of a solute in a series of solvents have been examined, as reviewed elsewhere. 3,4 Many of the previous studies provide valuable contributions to the understanding of the general phenomena of solute-solvent interactions. In depth comparisons of published data series have revealed that many gaps exist, which render impossible any general comparison of solvent-solute pairs utilizing only experimental data. Therefore we have proposed the combination of quantitatiVe structure-property/actiVity relationship analysis and subsequent principal component analysis for the general treatment of solubility. 5 A common procedure in quantitative structure-property/ activity relationships (QSPR/QSAR) analysis is the application of variable selection methods such as stepwise forward selection, 6,7 genetic algorithms, 8,9 and simulated annealing 10,11 for the reduction of descriptor space in order to keep the only most influential descriptors for the prediction of a property (in the present instance solubility). In this first version of our general treatment of solubility w...

The importance of melting points in characterization, in the estimation of other physical properties and toxicity, and in practical applications such as ionic liquids is summarized, as are difficulties in the systematic treatment of melting points in terms of QSPR. Classical correlations of melting points of congeneric and diverse sets are discussed together with group contribution methods, combined approaches, and computer simulations.

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

Plant gas exchange is regulated by guard cells that form stomatal pores. Stomatal adjustments are crucial for plant survival; they regulate uptake of CO2 for photosynthesis, loss of water, and entrance of air pollutants such as ozone. We mapped ozone hypersensitivity, more open stomata, and stomatal CO2-insensitivity phenotypes of the Arabidopsis thaliana accession Cvi-0 to a single amino acid substitution in MITOGEN-ACTIVATED PROTEIN (MAP) KINASE 12 (MPK12). In parallel, we showed that stomatal CO2-insensitivity phenotypes of a mutant cis (CO2-insensitive) were caused by a deletion of MPK12. Lack of MPK12 impaired bicarbonate-induced activation of S-type anion channels. We demonstrated that MPK12 interacted with the protein kinase HIGH LEAF TEMPERATURE 1 (HT1)—a central node in guard cell CO2 signaling—and that MPK12 functions as an inhibitor of HT1. These data provide a new function for plant MPKs as protein kinase inhibitors and suggest a mechanism through which guard cell CO2 signaling controls plant water management.

A successful interpretation of the complex manner by which the GC retention indexes of methylalkanes produced by insects are related to chemical structure was achieved using the quantitative structure-property relationship (QSPR) method. A general QSPR model including mainly topological descriptors was obtained for 178 data points. The error of the model is similar to the experimental error. The model was supported by (i) leave-one-out cross validation and (ii) division into three sets and prediction of each set from the other two. As a further test of the utility of the model, retention indexes were successfully predicted for an external set of 30 methyl-branched hydrocarbons not involved in the deduction of the correction equation from the main data set. General trends of the structural variation of compounds in any given range of retention index are discussed. The average error was 4.6 overall and 4.3 for the 165 compounds remaining after leaving out small monomethyl alkanes.

A phenomenological study of solubility has been conducted using a combination of quantitative structureproperty relationship (QSPR) and principal component analysis (PCA). A solubility database of 4540 experimental data points was used that utilized available experimental data into a matrix of 154 solvents times 397 solutes. Methodology in which QSPR and PCA are combined was developed to predict the missing values and to fill the data matrix. PCA on the resulting filled matrix, where solutes are observations and solvents are variables, shows 92.55% of coverage with three principal components. The corresponding transposed matrix, in which solvents are observations and solutes are variables, showed 62.96% of coverage with four principal components.