A signature for immune response correlates with HCV treatment outcome in Caucasian subjects.

This data article contains Supplementary material for a published research article describing a whole-blood proteomic signature that predicts treatment outcome for subjects infected with hepatitis C virus (HCV) [1]. The proteomic signature is derived from whole-blood samples from subjects infected with HCV. The article includes detailed experimental and computational methods used in the analysis. The article also includes tables of demographic and other information about the subjects. Finally, the article includes several figures and tables showing detailed results of the analyses (e.g. lists of identified proteins and coefficients/ROC curves for the regression models).

A signature for immune response correlates with HCV treatment outcome in Caucasian subjects.

Broad proteomic profiling was performed on serum samples of phase 2 studies (PROVE1, PROVE2, and PROVE3) of the direct-acting antiviral drug telaprevir in combination with peg-interferon and ribavirin in subjects with HCV. Using only profiling data from subjects treated with peg-interferon and ribavirin, a signature composed of pretreatment levels of 13 components was identified that correlated well (R(2)=0.68) with subjects' underlying immune response as measured by week 4 viral decline and was highly predictive of sustained virologic response in non-African American subjects (AUC=0.99). The signature was validated by predicting in an independent cohort of non-African American subjects treated with telaprevir, peg-interferon and ribavirin (AUC=0.854). Samples from extreme responders were over-represented in these analyses. Proteins identified as differentially-expressed between responders and non-responders to HCV treatment were quantified using multiple reaction monitoring in samples from all Caucasian subjects in the peg-interferon and ribavirin arms of PROVE1 and PROVE2, revealing 15 proteins that were significantly differentially expressed between treatment responders and non-responders. Seven of the proteins are part of focal adhesions or other macromolecular assemblies that form structural links between integrins and the actin cytoskeleton and are involved in antiviral response. BIOLOGICAL SIGNIFICANCE: HCV is a significant health problem. We describe a novel approach for identifying markers that predicts HCV treatment response different treatment regimens and use this approach to identify a novel HCV treatment response signature. The signature has potential to guide optimization of HCV treatment regimens.

Classifying protein kinase structures guides use of ligand-selectivity profiles to predict inactive conformations: structure of lck/imatinib complex.

We report a clustering of public human protein kinase structures based on the conformations of two structural elements, the activation segment and the C-helix, revealing three discrete clusters. One cluster includes kinases in catalytically active conformations. Each of the other clusters contains a distinct inactive conformation. Typically, kinases adopt at most one of the inactive conformations in available X-ray structures, implying that one of the conformations is preferred for many kinases. The classification is consistent with selectivity profiles of several well-characterized kinase inhibitors. We show further that inhibitor selectivity profiles guide kinase classification. For example, selective inhibition of lck among src-family kinases by imatinib (Gleevec) suggests that the relative stabilities of inactive conformations of lck are different from other src-family kinases. We report the X-ray structure of the lck/imatinib complex, confirming that the conformation adopted by lck is distinct from other structurally-characterized src-family kinases and instead resembles kinases abl1 and kit in complex with imatinib. Our classification creates new paths for designing small-molecule inhibitors.

A structurally unrelated mimic of a Pseudomonas aeruginosa acyl-homoserine lactone quorum-sensing signal.

The pathogenic bacterium Pseudomonas aeruginosa uses acyl-homoserine lactone quorum-sensing signals to coordinate the expression of a battery of virulence genes in a cascade of regulatory events. The quorum-sensing signal that triggers the cascade is N-3-oxo-dodecanoyl homoserine lactone (3OC12-HSL), which interacts with two signal receptor-transcription factors, LasR and QscR. This signal is base labile, and it is degraded by mammalian PON lactonases. We have identified a structurally unrelated triphenyl mimic of 3OC12-HSL that is base-insensitive and PON-resistant. The triphenyl mimic seems to interact specifically with LasR but not with QscR. In silico analysis suggests that the mimic fits into the 3OC12-HSL-binding site of LasR and makes key contacts with LasR. The triphenyl mimic is an excellent scaffold for developing quorum-sensing inhibitors, and its stability and potency make it ideal for biotechnology uses such as heterologous gene expression.

CORES: an automated method for generating three-dimensional models of protein/ligand complexes.

We describe a new, automated method for building 3D models of small-molecule ligands complexed with proteins. Modeling templates are constructed from frameworks (i.e., ring systems and linkers) of ligands extracted from 3D structures of ligands complexed with proteins that are structurally related to the target protein. These templates are typically substructures of the target ligand and are used to build models that constrain the ligand's conformation and binding orientation in the active site of the target protein. The practical utility of the method is shown by demonstrating that most ligands containing related frameworks bind protein kinases in the same orientation. Moreover, models for 15 of 19 cdk2/ligand complexes in the protein data bank built using our method deviate from the X-ray structure by less than 2 A (rms). Finally, we show that over 70% of small-molecule protein kinase inhibitors published in J. Med. Chem. since 1993 can be modeled using a template extracted from a 3D protein kinase structure in the protein data bank.