Association algorithm to mine the rules that govern enzyme definition and to classify protein sequences

BACKGROUND: The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity. RESULTS: There were five datasets collected from the Swiss-Prot for establishing the annotation rules. These were treated as the training sets. The TrEMBL entries were treated as the testing set. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. The fungus training dataset which lacks an enzyme class description was also used to evaluate the fungus candidate rules. A total of 88 out of 5085 test entries were matched with the fungus rule set. These were otherwise poorly annotated using their functional descriptions. CONCLUSION: The feasibility of using the method presented here to classify enzyme classes based on the enzyme domain rules is evident. The rules may be also employed by the protein annotators in manual annotation or implemented in an automatic annotation flowchart

Phenomenological scaling laws for ‘‘semidilute’’ macromolecule solutions from light scattering by optical probe particles

Polymer solution dynamics may be inferred from light scattering spectra of dissolved optical probe particles. We compare a variety of probes in solutions of several polymers. In the ‘‘overlapping’’ concentration/molecular weight regime, the Stokes–Einstein equation fails by up to a factor of 2, while the probe diffusion coefficient D follows a scaling law D/D0=exp(−aMγcνRδ) (c, M, and R are the polymer concentration, molecular weight, and the probe radius, respectively). Experimentally, γ=0.8±0.1, ν=0.6–1.0, and δ=−0.1 to 0, contrary to the theoretical predictions γ=0 and δ=1. With very high molecular‐weight polymers, we observe a further ‘‘entangled’’ regime, characterized by huge (104) failures of the Stokes–Einstein equation and the appearance of ‘‘fast’’ modes in the scattering spectrum.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/70873/2/JCPSA6-82-11-5242-1.pd

Complete genomic sequence of the temperate bacteriophage ΦAT3 isolated from Lactobacillus casei ATCC 393

AbstractThe complete genomic sequence of a temperate bacteriophage ΦAT3 isolated from Lactobacillus (Lb.) casei ATCC 393 is reported. The phage consists of a linear DNA genome of 39,166 bp, an isometric head of 53 nm in diameter, and a flexible, noncontractile tail of approximately 200 nm in length. The number of potential open reading frames on the phage genome is 53. There are 15 unpaired nucleotides at both 5′ ends of the ΦAT3 genome, indicating that the phage uses a cos-site for DNA packaging. The ΦAT3 genome was grouped into five distinct functional clusters: DNA packaging, morphogenesis, lysis, lysogenic/lytic switch, and replication. The amino acid sequences at the NH2-termini of some major proteins were determined. An in vivo integration assay for the ΦAT3 integrase (Int) protein in several lactobacilli was conducted by constructing an integration vector including ΦAT3 int and the attP (int-attP) region. It was found that ΦAT3 integrated at the tRNAArg gene locus of Lactobacillus rhamnosus HN 001, similar to that observed in its native host, Lb. casei ATCC 393

Modeling Ligand-Receptor Interaction for Some MHC Class II HLA-DR4 Peptide Mimetic Inhibitors Using Several Molecular Docking and 3D QSAR Techniques

The ligand-receptor interaction between some peptidomimetic inhibitors and a class II MHC peptide presenting molecule, the HLA-DR4 receptor, was modeled using some three-dimensional (3D) quantitative structure-activity relationship (QSAR) methods such as the Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Indices Analysis (CoMSIA), and a pharmacophore building method, the Catalyst program. The structures of these peptidomimetic inhibitors were generated theoretically, and the conformations used in the 3D QSAR studies were defined by docking them into the known structure of HLA-DR4 receptor through the GOLD, GLIDE Rigidly, GLIDE Flexible, and Xscore programs. Some of the parameters used in these docking programs were selected by docking an X-ray ligand into the receptor and comparing the root-means-square difference (RMSD) computed between the coordinates of the X-ray and docked structure. However, the goodness of a docking result for docking a series of peptidomimetic inhibitors into the HLA-DR4 receptor was judged by comparing the Spearman's rank correlation coefficient computed between each docking result and the activity data taken from the literature. The best CoMFA and CoMSIA models were constructed using the aligned structures of the best docking result. The CoMSIA was conducted in a stepwise manner to identify some important molecular features that were further employed in a pharmacophore building process by the Catalyst program. It was found that most inhibitors of the training set were accurately predicted by the best pharmacophore model, the Hypo1 hypothesis constructed. The deviation or conflict found between the actual and predicted activities of some inhibitors of both the training and the test sets were also investigated by mapping the Hypo1 hypothesis onto the corresponding structures of the inhibitors

Diffusion of TiO 2 probe particles through a poly(ethylene oxide) melt

Quasi-elastic light scattering (QELS) spectroscopy was for the first time used to measure the mutual diffusion coefficient D m of titanium dioxide (TiO 2 ) particles of 0,37 Μm and 0,31 Μm diameters in a poly(ethylene oxide) melt (weight-average mol. wt. 7 500) between 85 °C and 160 °C. D m exhibits a strong dependence on the temperature. The effective shear imposed by diffusing particles on the polymer melt was estimated through a comparison of the viscosity obtained by a cone-and-plate viscometer with the microviscosity calculated from the diffusion coefficients and hydrodynamic radii of the particles through the Stokes-Einstein equation. The rate of shear found was ≥ 10 4 · s −1 .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/38630/1/021870516_ftp.pd

Diffusion of Macroparticulate Probes in Poly-(Acrylic)Acid: Water Solutions and Polyethylene Oxide Melt.

Quasi-elastic light scattering spectroscopy was used to study the diffusion of various probe species dissolved in poly-(acrylic)acid:water solutions and in a polyethylene oxide melt. The diffusion coefficient for different size spheres (204 (ANGSTROM) to 1.5(mu)) was measured as a function of polymer concentration in polymer solutions of three different molecular weight (5(.)10('4), 3(.)10('5) and 1(.)10('6) amu). In the 3(.)10('5) solutions, the apparent hydrodynamic radius of the spheres increases markedly with increasing polymer concentration. At high polymer concentrations (C > 30 g/l), D and the viscosity were both described by functions of the form exp(-aC('(nu))); non-linear least-square fits gave (nu) (TURNEQ) 2/3 for spheres in the 3(.)10('5) and 1(.)10('6) solutions and (nu) (TURNEQ) 8/9 for spheres in the 5(.)10('4) solutions. With the smaller spheres (204 (ANGSTROM) and 800 (ANGSTROM)), polymer adsorption to a thickness of 100-150 (ANGSTROM) was apparent. In solutions of the 5(.)10('4) polymer, D followed the Stokes-Einstein equation. The Stokes-Einstein equation fails badly (by up to 10('4)) for spheres in high molecular weight polymer solutions. The decrease in the apparent hydrodynamic radii of larger spheres in high molecular weight solutions was ascribed to the shear thinning of a non-Newtonian polymer solution over the time and distance scales probed by spheres. The diffusion coefficient of 0.34(mu) and 0.31(mu) titanium dioxide particle in a poly-(ethylene oxide) melt (MW 7,500) as measured over the temperature range 85(DEGREES)C-160(DEGREES)C. By comparing the viscosity measured by a cone- and -plate viscometer with the microviscosity calculated through the Stokes-Einstein equation from the diffusion coefficients and hydrodynamic radii of the particles, the effective shear imposed by diffusing particles on the polymer melt was found to be in the range of 10('6)-10('8) sec('-1).Ph.D.Polymer chemistryUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/160173/1/8422278.pd

Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular Descriptors.

As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO) also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM) on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D) are computed and classified using the support vector machine (SVM) for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes. The condensed Fukui function fA- representing the nucleophilicity of central atom A and the attributes from circular fingerprints accounting the influence of neighbors on the central atom. The total number of FMO substrates and non-substrates collected in the study is 85 and they are equally divided into the training and test sets with each carrying roughly the same number of potential SOMs. However, only N-oxidation and S-oxidation features were considered in the prediction since the available C-oxidation data was scarce. In the training process, the LibSVM package of WEKA package and the option of 10-fold cross validation are employed. The prediction performance on the test set evaluated by accuracy, Matthews correlation coefficient and area under ROC curve computed are 0.829, 0.659, and 0.877 respectively. This work reveals that the SVM model built can accurately predict the potential SOMs for drug molecules that are metabolizable by the FMO enzymes