The quenching of T526GST intrinsic fluorescence allowed for the determination of the dissociation constants (K-D) for all ligands. Obtained data indicate that T526GST binds to all ligands but with different affinity. Porphyrins and lipid peroxide products inhibited T526GST catalytic activity up to 100% in contrast with only 20-30% inhibition observed for bile acids and two saturated fatty acids. Non-competitive type inhibition was observed for all enzyme inhibitor ligands except for transtrans-2,4-decadienal, which exhibited uncompetitive type inhibition. The dissociation constant value K-D = 0.7 mu M A for the hematin ligand, determined

Bafilomycin A1 in vitro by competitive fluorescence assays with ANS, was in good agreement with its inhibition kinetic value Ki 0.3 mu M and its intrinsic fluorescence quenching K-D = 0.7 mu M. The remaining ligands did not displace ANS from the enzyme suggesting the existence of different binding sites. In addition to the catalytic

activity of Ts26GST the results obtained suggest that the enzyme exhibits a ligandin function with broad specificity towards nonsubstrate ligands. (C) 2014 Elsevier Inc. All rights reserved.”
“Protein-protein interactions defined by affinity purification and mass spectrometry (APMS) suffer from high false discovery rates. Consequently, lists of potential interactions must be pruned of contaminants before network construction and interpretation, historically an expensive, time-intensive, and error-prone

task. In recent ICG-001 years, numerous computational methods were developed to identify genuine interactions from the hundreds of candidates. Here, comparative analysis of three popular algorithms, click here HGSCore, CompPASS, and SAINT, revealed complementarity in their classification accuracies, which is supported by their divergent scoring strategies. We improved each algorithm by an average area under a receiver operating characteristics curve increase of 16% by integrating a variety of indirect data known to correlate with established protein-protein interactions, including mRNA coexpression, gene ontologies, domain-domain binding affinities, and homologous protein interactions. Each APMS scoring approach was incorporated into a separate logistic regression model along with the indirect features; the resulting three classifiers demonstrate improved performance on five diverse APMS data sets. To facilitate APMS data scoring within the scientific community, we created Spotlite, a user-friendly and fast web application. Within Spotlite, data can be scored with the augmented classifiers, annotated, and visualized (http://cancer.unc.edu/majorlab/software.php).