Helical Conformation of the SEVI Precursor Peptide PAP248-286, a Dramatic Enhancer of HIV Infectivity, Promotes Lipid Aggregation and Fusion

AbstractIn previous in vivo studies, amyloid fibers formed from a peptide ubiquitous in human seminal fluid (semen-derived enhancer of viral infection (SEVI)) were found to dramatically enhance the infectivity of the HIV virus (3–5 orders of magnitude by some measures). To complement those studies, we performed in vitro assays of PAP248-286, the most active precursor to SEVI, and other polycationic polymers to investigate the physical mechanisms by which the PAP248-286 promotes the interaction with lipid bilayers. At acidic (but not at neutral) pH, freshly dissolved PAP248-286 catalyzes the formation of large lipid flocculates in a variety of membrane compositions, which may be linked to the promotion of convective transport in the vaginal environment rather than transport by a random Brownian motion. Furthermore, PAP248-286 is itself fusiogenic and weakens the integrity of the membrane in such a way that may promote fusion by the HIV gp41 protein. An α-helical conformation of PAP248-286, lying parallel to the membrane surface, is implicated in promoting bridging interactions between membranes by the screening of the electrostatic repulsion that occurs when two membranes are brought into close contact. This suggests that nonspecific binding of monomeric or small oligomeric forms of SEVI in a helical conformation to lipid membranes may be an additional mechanism by which SEVI enhances the infectivity of the HIV virus

Imaging of Glucose Metabolism by 13C-MRI Distinguishes Pancreatic Cancer Subtypes in Mice

Metabolic differences among and within tumors can be an important determinant in cancer treatment outcome. However, methods for determining these differences non-invasively in vivo is lacking. Using pancreatic ductal adenocarcinoma as a model, we demonstrate that tumor xenografts with a similar genetic background can be distinguished by their differing rates of the metabolism of 13C labeled glucose tracers, which can be imaged without hyperpolarization by using newly developed techniques for noise suppression. Using this method, cancer subtypes that appeared to have similar metabolic profiles based on steady state metabolic measurement can be distinguished from each other. The metabolic maps from 13C-glucose imaging localized lactate production and overall glucose metabolism to different regions of some tumors. Such tumor heterogeneity would not be not detectable in FDG-PET

Dynamic Imaging of Glucose and Lactate Metabolism by C-13-MRS without Hyperpolarization

Abstract Metabolic reprogramming is one of the defining features of cancer and abnormal metabolism is associated with many other pathologies. Molecular imaging techniques capable of detecting such changes have become essential for cancer diagnosis, treatment planning, and surveillance. In particular, 18F-FDG (fluorodeoxyglucose) PET has emerged as an essential imaging modality for cancer because of its unique ability to detect a disturbed molecular pathway through measurements of glucose uptake. However, FDG-PET has limitations that restrict its usefulness in certain situations and the information gained is limited to glucose uptake only.13C magnetic resonance spectroscopy theoretically has certain advantages over FDG-PET, but its inherent low sensitivity has restricted its use mostly to single voxel measurements unless dissolution dynamic nuclear polarization (dDNP) is used to increase the signal, which brings additional complications for clinical use. We show here a new method of imaging glucose metabolism in vivo by MRI chemical shift imaging (CSI) experiments that relies on a simple, but robust and efficient, post-processing procedure by the higher dimensional analog of singular value decomposition, tensor decomposition. Using this procedure, we achieve an order of magnitude increase in signal to noise in both dDNP and non-hyperpolarized non-localized experiments without sacrificing accuracy. In CSI experiments an approximately 30-fold increase was observed, enough that the glucose to lactate conversion indicative of the Warburg effect can be imaged without hyper-polarization with a time resolution of 12s and an overall spatial resolution that compares favorably to 18F-FDG PET

The use of single-molecule detection in the resolution of complex enzyme kinetics.

The bacterial enzyme para-hydroxybenzoate hydroxylase (PHBH) catalyzes a multiple step reaction that requires precise control of solvent access to the catalytic site during the reaction cycle and strict differentiation between structurally similar substrates. Previous studies on PHBH have indicated conformational changes in the active site are essential for some steps in the reaction. However, detection of the hypothesized conformational changes in the substrate binding phase of the reaction has remained elusive because of the need to synchronize the reaction to distinguish the conformational species existing at equilibrium. Single molecule spectroscopy allows one to follow each individual molecule in time, thus eliminating the need to synchronize and offering a way of detecting these conformational changes at equilibrium. In this thesis we report single-molecule fluorescence studies of PHBH in the absence of substrate that support the hypothesis that a critical step in substrate binding is the movement of the isoalloxazine between an in conformation and a more exposed open conformation. The conformational switches are followed directly by changes in fluorescence intensity, confirmed by studies with the Y222A mutant form of PHBH which suggest that the exposed conformation is fluorescent while the in-conformation is quenched. We note that many of the single-molecule fluorescence trajectories reveal a conformational heterogeneity, with populations of the enzyme characterized by either fast or slow switching between the in- and open-conformations. Our data also allow us to construct a model in which one flavin in the dimer inhibits the motion of the other. This model is supported by single-molecule studies of PHBH in sucrose, an osmolyte that favors dimer formation by minimizing exposed protein surface area. Sucrose also minimizes flavin dissociation from the enzyme, which has possible applications for the extension of the available observation time in future single-molecule experiments. Finally, the conformational changes in the subsequent stage of the catalytic cycle were followed by observing the interaction of PHBH with three substrate analogues, each of which stabilizes a different conformational state of PHBH upon binding. Single-molecule fluorescence allows direct observation of the conformational switching upon substrate binding, which previously had been inferred through kinetic studies and crystallographic structures.Ph.D.BiochemistryPure SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/125969/2/3224826.pd

Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles.

The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies

Breakdown of the performance of the interface profile score compared to other potentials for different types of interface residues.

<p>See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004494#pcbi.1004494.g006" target="_blank">Fig 6</a> for the definition of the interface residue types.</p

Pipeline of BindProf for predicting protein-binding affinity using features derived from interface structural profiles, wild type (WT) and mutant sequences, and physics based scoring of the structures of the WT and mutant complexes.

<p>(<b>1</b>) Interface profile scores and Interface profile scores features are derived by profile scoring structural alignment of structurally similar interface using an interface similarity cutoff to define the aligned sequences that are used to build the profile. (<b>2</b>) Physics based scores are formed at the residue or atomic level formed by modeling the mutant monomeric protein and complex and evaluating the difference in energy. (<b>3</b>) Sequence features are formed by the difference between the WT and mutant sequences in the number of hydrophobic (V, I, L, M, F, W, or C), aromatic (Y, F, or W), charged (R, K, D, or E), hydrogen bond acceptors (D, E, N, H, Q, S, T, or Y), and hydrogen bond donating residues (R, K, W, N, Q, H, S, T, or Y) along with the difference in amino acid volume calculated from the sequence.</p

Prediction of ΔΔG value by different combinations of the interface profile scores.

<p>(A) Interface profile only; (B) Interface profile and residue level potentials; (C) Interface potential, residue level potentials, and atomic level potentials. In each picture, the right panel shows the overall correlation between predicted and experimental ΔΔG values; the right penal shows different features from random forest model as sorted by their effect on the residual error (right) or the node purity (a measure of the efficiency of splitting on feature during the construction of the decision tree) (left). Correlation values are for 10 fold cross-validation repeated three times.</p