Use of mathematical structural invariants in analyzing combinatorial libraries: a case study with psoralen derivatives.

In this paper, calculated topological indices have been used to cluster a large virtual library of 12⁵ psoralen derivatives into 25 clusters in an effort to select a subset of mutually dissimilar structures from a large collection of molecules. Inspection of the 25 structures, one closest to the respective centroid of each cluster, shows that the molecules are structurally more diverse as compared to a subset of 25 selected randomly. It is expected that such methods based on easily calculated descriptors may find applications in new drug discovery from the analysis of libraries of interesting lead compounds.

Computational study of dispersion and extent of mutated and duplicated sequences of the H5N1 influenza neuraminidase over the period 1997-2008.

Study of mutational changes in neuraminidase (NA) gene sequences is important to track the effectiveness of the inhibitors to the H5N1 avian flu virus that targets this component of the viral apparatus. Our analysis based on numerical characterization studies of 682 complete neuraminidase gene and protein sequences available in the database, updated to March 2009, and which extends our previous work based on a sample of 173 sequences has revealed several interesting features. We have noticed that identical sequences have appeared over significant distances in space and time, raising the need for a deeper understanding of the longevity of such viral strains in the environment.

Mathematical biodescriptors of proteomics maps: background and applications.

This article reviews recent developments in the formulation and application of biodescriptors to characterize proteomics maps. Such biodescriptors can be derived by applying techniques from discrete mathematics (graph theory, linear algebra and information theory). This review focuses on the development of biodescriptors for proteomics maps derived from 2D gel electrophoresis.

Graphical representation and numerical characterization of H5N1 avian flu neuraminidase gene sequence.

The high degree of virulence and potential for development of a pandemic strain of the H5N1 avian flu has resulted in wide interest in characterization of the various genes of the H5N1 virus genome. We have considered for our analysis all 173 available complete sequences, as of February 2006, of the neuraminidase gene, which is the target of the most effective treatment regimen comprising the inhibitors oseltamivir and zanamivir. We have used a 2D graphical representation of the neuraminidase RNA sequences of H5N1 strains to identify a few distinct structural motifs.

Canonical labeling of proteome maps.

We propose a canonical labeling of proteome maps, which enables one to sort and catalog the maps in a simple way. The canonical label of a proteome map is based on the canonical labeling of vertexes of Hasse diagram embedded in the map resulting in the adjacency matrix, the rows of which when viewed as binary numbers are the smallest possible such numbers. The use of the approach in documentation is illustrated with the proteome maps of liver cells of healthy male Fisher F344 rats and the rats treated with different peroxisome proliferators.

Prediction of halocarbon toxicity from structure: a hierarchical QSAR approach.

Mathematical structural invariants and quantum theoretical descriptors have been used extensively in quantitative structure-activity relationships (QSARs) for the estimation of pharmaceutical activities, biological properties, physicochemical properties, and the toxicities of chemicals. Recently our research team has explored the relative importance of various levels of chemodescriptors, i.e.

Prediction of cellular toxicity of halocarbons from computed chemodescriptors: a hierarchical QSAR approach.

A hierarchical quantitative structure-activity relationship (HiQSAR) approach was used to estimate toxicity and genetic toxicity for a set of 55 halocarbons using computed chemodescriptors. The descriptors consisted of topostructural (TS), topochemical (TC), geometrical, semiempirical (AM1) quantum chemical, and ab initio (STO-3G, 6-31G(d), 6-311G, 6-311G(d), and aug-cc-pVTZ) quantum chemical indices. For the two toxicity endpoints investigated, ARR and D(37), the TC indices gave the best cross-validated R(2) values.