Modeling The Bioconcentration Factors and Bioaccumulation Factors of Polychlorinated Biphenyls with Posetic Quantitative Super Structure/Activity Relationship

Summary During bioconcentration, chemical pollutants from water are absorbed by aquatic animals via the skin or a respiratory surface, while the entry routes of chemicals during bioaccumulation are both directly from the environment (skin or a respiratory surface) and indirectly from food. The bioconcentration factor (BCF) and the bioaccumulation factor (BAF) for a particular chemical compound are defined as the ratio of the concentration of a chemical inside an organism to the concentration in the surrounding environment. Because the experimental determination of BAF and BCF is time-consuming and expensive, it is efficacious to develop models to provide reliable activity predictions for a large number of chemical compounds. Polychlorinated biphenyls (PCBs) released from industrial activities are persistent pollutants of the environment thereby producing widespread contamination of water and soil. PCBs can bioaccumulate in the food chain, constituting a potential source of exposure for the general population. To predict the bioconcentration and bioaccumulation factors for PCBs we make use of the biphenyl substitution-reaction network for the sequential substitution of H-atoms by Cl-atoms. Each PCB structure then occurs as a node of this reaction network, which is some sort of super-structure, turning out mathematically to be a partially ordered set (poset). Rather than dealing with the molecular structure via ordinary QSAR we use only this poset, making different quantitative super-structure/activity relationships (QSSAR). Thence we developed cluster expansion and splinoid QSSAR for PCB bioconcentration and bioaccumulation factors. The predictive ability of the BAF and BCF models generated for 20 data sets (representing different conditions and fish species) was evaluated with the leave-one-out cross-validation, which shows that the splinoid QSSAR (r between 0.903 and 0.935) are better than models computed with the cluster expansion (r between 0.745 and 0.887). The splinoid QSSAR models for BAF and BCF yield predictions for the missing PCBs in the investigated data sets

SDAP: database and computational tools for allergenic proteins

SDAP (Structural Database of Allergenic Proteins) is a web server that provides rapid, cross-referenced access to the sequences, structures and IgE epitopes of allergenic proteins. The SDAP core is a series of CGI scripts that process the user queries, interrogate the database, perform various computations related to protein allergenic determinants and prepare the output HTML pages. The database component of SDAP contains information about the allergen name, source, sequence, structure, IgE epitopes and literature references and easy links to th

Prediction of Environmental Properties for Chlorophenols with Posetic Quantitative Super-Structure/Property Relationships (QSSPR)

Due to their widespread use in bactericides, insecticides, herbicides, andfungicides, chlorophenols represent an important source of soil contaminants. Theenvironmental fate of these chemicals depends on their physico-chemical properties. In theabsence of experimental values for these physico-chemical properties, one can use predictedvalues computed with quantitative structure-property relationships (QSPR). As analternative to correlations to molecular structure we have studied the super-structure of areaction network, thereby developing three new QSSPR models (poset-average, cluster-expansion, and splinoid poset) that can be applied to chemical compounds which can behierarchically ordered into a reaction network. In the present work we illustrate these posetQSSPR models for the correlation of the octanol/water partition coefficient (log Kow) and thesoil sorption coefficient (log KOC) of chlorophenols. Excellent results are obtained for allQSSPR poset models to yield: log Kow, r = 0.991, s = 0.107, with the cluster-expansionQSSPR; and log KOC, r = 0.938, s = 0.259, with the spline QSSPR. Thus, the poset QSSPRmodels predict environmentally important properties of chlorophenols

Building-block Computation of Wiener-type Indices for the Virtual Screening of Combinatorial Libraries

Screening Virtual and synthetic combinatorial libraries may facilitate rapid drug lead discovery by selecting subsets of molecules ac-cording to their similarity or dissimilarity toward specific compound collections. Topological indices computed from atomic connectivities or graph distances are increasingly used as structural descriptors in order to maximize the molecular diversity of libraries or to quantify the drug-like character of compounds. In this paper we present efficient equations for the computation of several distance-based topological indices of a molecular graph from the distance invariants of its subgraphs. These equations offer an effective way to compute for non-weighted molecular graphs the Wiener index, even/odd Wiener index, resistance distance index, Wiener polynomial, and even/odd Wiener polynomial. Using a simple and fast algorithm one can compute these topological indices for very large virtual combinatorial libraries without computing the indices from the atomic scale up for each individual compound - rather only distance-based indices of the building blocks are needed to generate the topological indices of the compound assembled from the building blocks

Design of Topological Indices. Part 6.1,2 A New Topological Parameter for the Steric Effect of Alkyl Substituents

The steric effect of alkyl groups is characterized by the Steric Vertex Topological Index (SVTI). SVTI is equal to the sum of the distances not greater than three between the attachment site and the carbon atoms of the alkly group. The correlations presented, either with reaction rates of acid-catalyzed esterification of carboxylic acids or with various steric parameters, i.e. Es, E\u27s, v, and E, indicated that SVTI can serve as a useful steric parameter in reactivity studies

Characterization of Chemical Structures by the Atomic Counts of Self-Returning Walks: On the Construction of Isocodal Graphs

A characterization of chemical structures based on counting selfreturning walks in a molecular graph is found to be degenerate for certain pairs of isospectral graphs. On the basis of endospectral graphs, we present (without proof) two theorems for constructing pairs of nonisomorphic graphs with identical atomic counts of self-returning walks

Reverse Wiener Indices

By subtracting from the graph diameter all topological distances one obtains a new symmetrical matrix, reverse Wiener RW, with zeroes on the main diagonal, whose sums over rows or columns give rise to new integer-number graph invariants σi whose half-sum is a novel topological index (TI), the reverse Wiener index Λ. Analytical forms for values of σi and Λ of several classes of graphs are presented. Relationships with other TIs are discussed. Unlike distance sums, σi values increase from the periphery towards the center of the graph, and they are equal to the graph vertex degrees when the diameter of the graph is equal to 2. Structural descriptors computed from the reverse Wiener matrix were tested in a large number of quantitative structure-property relationship models, demonstrating the usefulness of the new molecular matrix