Canonical Labeling of Proteome Maps

A new bioinformatics approach to natural protein collections: permutation structure contrasts of viral and cellular systems

Biological cells and viruses operate by different replication and symmetry paradigms. Cells are able to replicate independently and express little spatial symmetry; viruses require cells for replication while manifesting high symmetry. The author inquired whether different paradigms were reflected in the permutations of amino acid sequences. The hypothesis was that the permutation structure level and symmetry within viral protein collections exceed that of living cells. The rationale was that one symmetry aspect generally accompanies and promotes others in a system. The inquiry was readily answered given abundant sequence archives for proteins. The analysis of collections from diverse viral and cellular sources lends strong support. Additional insights into protein primary structure, the design of collections, and the role of information are provided as well.

On graphical representation of trans-membrane proteins

In the first part of this paper, we present a novel graphical representation of proteins, which starts with constructing a map of a protein that is obtained from a matrix, the elements of which record the adjacencies of pairs of amino acids in the primary structure of a protein. Starting with the novel protein map, one interprets its matrix elements as vertices of a graph, which are labelled in sequential order as in the protein sequence. The nearest vertices are connected to the nearest neighbour which has a smaller label. In the second part of this paper, we describe the construction of protein binary codes that can serve as protein descriptors. This novel graphical representation of proteins is illustrated on segments of trans-membrane proteins, which are embedded in the membrane.

Study of proteome maps using partial ordering

This paper describes numerical characterization of proteome maps based on partial ordering of protein spots with respect to the mass and the charge. The partial ordering diagram is embedded directly over the 2D map and the corresponding adjacency matrix is constructed. The adjacency matrix is augmented by including the information on the abundance of proteins in a gel as suitably scaled diagonal entries of the matrix. The approach is illustrated on proteome maps of Anderson et al. (1996) based on experimental results from liver cells of rats exposed to four peroxisome proliferators. We used the leading eigenvectors of the adjacency matrices as maps descriptors in order to determine the degree of similarity between proteome maps.

QSAR for RNases and theoretic-experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein

The toxicity and low success of current treatments for Leishmaniosis determines the search of new peptide drugs and/or molecular targets in Leishmania pathogen species (L. infantum and L. major). For example, Ribonucleases (RNases) are enzymes relevant to several biologic processes; then, theoretical and experimental study of the molecular diversity of Peptide Mass Fingerprints (PMFs) of RNases is useful for drug design. This study introduces a methodology that combines QSAR models, 2D-Electrophoresis (2D-E), MALDI-TOF Mass Spectroscopy (MS), BLAST alignment, and Molecular Dynamics (MD) to explore PMFs of RNases. We illustrate this approach by investigating for the first time the PMFs of a new protein of L. infantum. Here we report and compare new versus old predictive models for RNases based on Topological Indices (TIs) of Markov Pseudo-Folding Lattices. These group of indices called Pseudo-folding Lattice 2D-TIs include: Spectral moments pi ( k )(x,y), Mean Electrostatic potentials xi ( k )(x,y), and Entropy measures theta ( k )(x,y). The accuracy of the models (training/cross-validation) was as follows: xi ( k )(x,y)-model (96.0%/91.7%)>pi ( k )(x,y)-model (84.7/83.3) > theta ( k )(x,y)-model (66.0/66.7). We also carried out a 2D-E analysis of biological samples of L. infantum promastigotes focusing on a 2D-E gel spot of one unknown protein with M<20, 100 and pI <7. MASCOT search identified 20 proteins with Mowse score >30, but not one >52 (threshold value), the higher value of 42 was for a probable DNA-directed RNA polymerase. However, we determined experimentally the sequence of more than 140 peptides. We used QSAR models to predict RNase scores for these peptides and BLAST alignment to confirm some results. We also calculated 3D-folding TIs based on MD experiments and compared 2D versus 3D-TIs on molecular phylogenetic analysis of the molecular diversity of these peptides. This combined strategy may be of interest in drug development or target identification.

Classification of voltage-gated K(+) ion channels from 3D pseudo-folding graph representation of protein sequences using genetic algorithm-optimized support vector machines

Voltage-gated K(+) ion channels (VKCs) are membrane proteins that regulate the passage of potassium ions through membranes. This work reports a classification scheme of VKCs according to the signs of three electrophysiological variables: activation threshold voltage (V(t)), half-activation voltage (V(a50)) and half-inactivation voltage (V(h50)). A novel 3D pseudo-folding graph representation of protein sequences encoded the VKC sequences. Amino acid pseudo-folding 3D distances count (AAp3DC) descriptors, calculated from the Euclidean distances matrices (EDMs) were tested for building the classifiers. Genetic algorithm (GA)-optimized support vector machines (SVMs) with a radial basis function (RBF) kernel well discriminated between VKCs having negative and positive/zero V(t), V(a50) and V(h50) values with overall accuracies about 80, 90 and 86%, respectively, in crossvalidation test. We found contributions of the "pseudo-core" and "pseudo-surface" of the 3D pseudo-folded proteins to the discrimination between VKCs according to the three electrophysiological variables.