Active Site Similarity between PrgI and Bcl-xL.

<p>(A) CPASS alignment of the <i>S. typhimurium</i> PrgI active-site complexed to DDAB with the active-site of human Bcl-2 protein (Bcl-xL) complexed with acyl-sulfonamide-based inhibitor. The residues aligned by CPASS are labeled and colored blue in the structures. The active site sequence alignment is also shown below the structures. The ligands are colored yellow. (B) Overlay of the human Bcl-2 protein (red) with <i>S. typhimurium</i> PrgI (turquoise) based on a DaliLite <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007442#pone.0007442-Holm1" target="_blank">[29]</a> alignment. (C) Multiple-sequence alignment of the three known T3SS structures of <i>S. typhimurium</i> PrgI, <i>B. pseudomallei</i> BsaL, and <i>S. flexneri</i> MxiH with the human Bcl-2 protein (Bcl-xL). The reliability of the each amino acid alignment is color-coded from blue (poor) to red (good) using the CORE index <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007442#pone.0007442-Notredame1" target="_blank">[35]</a>. The consensus alignment received a score of 69, where a perfect alignment receives a score of 100.</p

The two PrgI ligand binding sites identified using FAST-NMR.

<p>The two PrgI ligand binding sites are highlighted on an electrostatic potential surface (blue positive charge, red negative charge) calculated with DelPhi <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007442#pone.0007442-Nicholls1" target="_blank">[79]</a> implemented in Chimera <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007442#pone.0007442-Pettersen1" target="_blank">[80]</a>. The didecyldimethylammonium bromide binding site (A) is found in a region responsible for needle formation while the chelerythrine binding site (B) is found on the opposite face.</p

Verification that the Bcl-xL inhibitor chelerythrine also binds PrgI.

<p>(A). Expanded overlay of the 2D ¹⁵N-¹H HSQC spectra for free PrgI (black) and PrgI bound to chelerythrine (blue). CSPs greater than one standard deviation are boxed. (B) An AutoDock/ADF docked structure of PrgI complexed with chelerythrine based on the observed CSPs from (A). (C) The Bcl-xL region shown to bind chelerythrine is highlighted while the remaining protein structure is transparent. Chelerythrine is colored yellow and is drawn with licorice bonds. Side-chains for Y173 and V135 are shown as licorice bonds and colored grey. (D) A ribbon diagram of the AutoDock/ADF docked PrgI-chelerythrine co-structure. The PrgI-chelerythrine binding region that overlaps with Bcl-xL is highlighted. Chelerythrine is colored yellow and is drawn with licorice bonds. Side-chains for Y57 and K15 are shown as licorice bonds and colored grey. (E) An expanded view of the overlay of Bcl-xL (red) with PrgI (blue) illustrating the structural similarity of the chelerythrine binding sites.</p

Identification of PrgI Binding Ligands.

<p>(A) DDAB NMR spectra in the absence (<i>top</i>) and presence (<i>bottom</i>) of PrgI illustrating changes in NMR intensities (boxed) upon binding PrgI. Both free and bound 1D ¹H NMR spectra were normalized to a constant DMSO signal intensity. (B) Expanded view of the superimposed 2D ¹⁵N-¹H HSQC spectra of the free and DDAB bound PrgI NMR samples. Residues that incur a chemical shift perturbation are boxed. (C) Expanded view of PrgI surface rendered in VMD <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007442#pone.0007442-Humphrey1" target="_blank">[78]</a> where residues that incur a chemical shift change are colored blue and DDAB is colored yellow. Co-structure based on NMR determined ligand binding site using AutoDock <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007442#pone.0007442-Morris1" target="_blank">[27]</a> and our AutoDockFilter program <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007442#pone.0007442-Stark1" target="_blank">[24]</a>.</p

Predicted Interaction and Associated Carbonyl C Chemical Shifts.

<p>(<b>A</b>) Residues Asn155 and Phe189 from the x-ray structure of <i>Bacillus amyloliquefaciens</i> subtillisin BPN’ (PDB ID: 1v5i) illustrating the structural features for an optimal interaction between carbonyl groups. (<b>B</b>) 2D contour plot of carbonyl C chemical shift differences relative to random coil values as a function of the distance () and angle () between carbonyls. A Gaussian smoothing function was applied to the data with and of 0.3 Å and 1.5°, respectively. A transparency mask based on the density of experimental data (see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042075#pone.0042075.s001" target="_blank">Figure S1</a>) is overlaid on the contour plot. Regions lacking experimental data are white. Positive values indicate downfield shifts.</p

Carbonyl C Chemical Shifts and Dipole-Dipole Potential.

<p>Carbonyl C chemical shift differences relative to random coil are plotted against calculated dipole-dipole potential (). The dipole-dipole potential is calculated from the high-resolution x-ray structure using Equation 1. Pairs of carbonyls with and values within the optimal limits for an interaction are colored red.</p

Predicting the <i>in Vivo</i> Mechanism of Action for Drug Leads Using NMR Metabolomics

New strategies are needed to circumvent increasing outbreaks of resistant strains of pathogens and to expand the dwindling supply of effective antimicrobials. A common impediment to drug development is the lack of an easy approach to determine the <i>in vivo</i> mechanism of action and efficacy of novel drug leads. Toward this end, we describe an unbiased approach to predict <i>in vivo</i> mechanisms of action from NMR metabolomics data. <i>Mycobacterium smegmatis</i>, a non-pathogenic model organism for <i>Mycobacterium tuberculosis</i>, was treated with 12 known drugs and 3 chemical leads identified from a cell-based assay. NMR analysis of drug-induced changes to the <i>M. smegmatis</i> metabolome resulted in distinct clustering patterns correlating with <i>in vivo</i> drug activity. The clustering of novel chemical leads relative to known drugs provides a mean to identify a protein target or predict <i>in vivo</i> activity

Summary of Quantum Chemical Calculations.

<p>Plot of calculated (<b>A</b>) carbonyl C chemical shielding () and (<b>B</b>) dipole-dipole interaction energy () as a function of the distance between donor oxygen and acceptor carbon () and the angle between carbonyl groups (). See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042075#pone.0042075.s003" target="_blank">Figure S3</a>.</p

Carbonyl C Chemical Shifts and Hydrogen Bonds.

<p>Contour plot of C carbonyl chemical shift differences as a function of calculated dipole-dipole potential () and calculated hydrogen bond length (). See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042075#pone.0042075.s002" target="_blank">Figure S2</a>.</p

Potential Metabolite Biomarkers of Multiple Sclerosis from Multiple Biofluids

Multiple sclerosis (MS) is a chronic and progressive neurological disorder without a cure, but early intervention can slow disease progression and improve the quality of life for MS patients. Obtaining an accurate diagnosis for MS is an arduous and error-prone task that requires a combination of a detailed medical history, a comprehensive neurological exam, clinical tests such as magnetic resonance imaging, and the exclusion of other possible diseases. A simple and definitive biofluid test for MS does not exist, but is highly desirable. To address this need, we employed NMR-based metabolomics to identify potentially unique metabolite biomarkers of MS from a cohort of age and sex-matched samples of cerebrospinal fluid (CSF), serum, and urine from 206 progressive MS (PMS) patients, 46 relapsing-remitting MS (RRMS) patients, and 99 healthy volunteers without a MS diagnosis. We identified 32 metabolites in CSF that varied between the control and PMS patients. Utilizing patient-matched serum samples, we were able to further identify 31 serum metabolites that may serve as biomarkers for PMS patients. Lastly, we identified 14 urine metabolites associated with PMS. All potential biomarkers are associated with metabolic processes linked to the pathology of MS, such as demyelination and neuronal damage. Four metabolites with identical profiles across all three biofluids were discovered, which demonstrate their potential value as cross-biofluid markers of PMS. We further present a case for using metabolic profiles from PMS patients to delineate biomarkers of RRMS. Specifically, three metabolites exhibited a variation from healthy volunteers without MS through RRMS and PMS patients. The consistency of metabolite changes across multiple biofluids, combined with the reliability of a receiver operating characteristic classification, may provide a rapid diagnostic test for MS