Biomolecular NMR: a chaperone to drug discovery

Spy vs. spy: selecting the best reporter for 19F NMR competition experiments

Systematic characterization of a series of fluorinated VHL ligands, varying binding affinity and position of the trifluoromethyl group, qualifies a spy molecule for competitive 19F NMR screening and reveals guiding principles to develop highly sensitive assays with low material consumption.

Low Rank Enhanced Matrix Recovery of Hybrid Time and Frequency Data in Fast Magnetic Resonance Spectroscopy

GOAL

The two dimensional magnetic resonance spectroscopy (MRS) possesses many important applications in bioengineering but suffers from long acquisition duration. Non-uniform sampling has been applied to the spatiotemporally encoded ultrafast MRS, but results in missing data in the hybrid time and frequency plane. An approach is proposed to recover this missing signal, of which enables high quality spectrum reconstruction. M ethods: The natural exponential characteristic of MRS is exploited to recover the hybrid time and frequency signal. The reconstruction issue is formulated as a low rank enhanced Hankel matrix completion problem and is solved by a fast numerical algorithm.

RESULTS

Experiments on synthetic and real MRS data show that the proposed method provides faithful spectrum reconstruction, and outperforms the state-of-the-art compressed sensing approach on recovering low-intensity spectral peaks and robustness to different sampling patterns. C onclusion: The exponential signal property serves as an useful tool to model the time-domain MRS signals and even allows missing data recovery. The proposed method has been shown to reconstruct high quality MRS spectra from non-uniformly sampled data in the hybrid time and frequency plane.

SIGNIFICANCE

Low-intensity signal reconstruction is generally challenging in biological MRS and we provide a solution to this problem. The proposed method may be extended to recover signals that generally can be modeled as a sum of exponential functions in biomedical engineering applications, e.g., signal enhancement, feature extraction, and fast sampling.

A comparative study of family-specific protein-ligand complex affinity prediction based on random forest approach

The assessment of binding affinity between ligands and the target proteins plays an essential role in drug discovery and design process. As an alternative to widely used scoring approaches, machine learning methods have also been proposed for fast prediction of the binding affinity with promising results, but most of them were developed as all-purpose models despite of the specific functions of different protein families, since proteins from different function families always have different structures and physicochemical features. In this study, we proposed a random forest method to predict the protein-ligand binding affinity based on a comprehensive feature set covering protein sequence, binding pocket, ligand structure and intermolecular interaction. Feature processing and compression was respectively implemented for different protein family datasets, which indicates that different features contribute to different models, so individual representation for each protein family is necessary. Three family-specific models were constructed for three important protein target families of HIV-1 protease, trypsin and carbonic anhydrase respectively. As a comparison, two generic models including diverse protein families were also built. The evaluation results show that models on family-specific datasets have the superior performance to those on the generic datasets and the Pearson and Spearman correlation coefficients (R p and Rs) on the test sets are 0.740, 0.874, 0.735 and 0.697, 0.853, 0.723 for HIV-1 protease, trypsin and carbonic anhydrase respectively. Comparisons with the other methods further demonstrate that individual representation and model construction for each protein family is a more reasonable way in predicting the affinity of one particular protein family.

Studies of metabolite-protein interactions: a review

The study of metabolomics can provide valuable information about biochemical pathways and processes at the molecular level. There have been many reports that have examined the structure, identity and concentrations of metabolites in biological systems. However, the binding of metabolites with proteins is also of growing interest. This review examines past reports that have looked at the binding of various types of metabolites with proteins. An overview of the techniques that have been used to characterize and study metabolite-protein binding is first provided. This is followed by examples of studies that have investigated the binding of hormones, fatty acids, drugs or other xenobiotics, and their metabolites with transport proteins and receptors. These examples include reports that have considered the structure of the resulting solute-protein complexes, the nature of the binding sites, the strength of these interactions, the variations in these interactions with solute structure, and the kinetics of these reactions. The possible effects of metabolic diseases on these processes, including the impact of alterations in the structure and function of proteins, are also considered.

1H NMR detection of small molecules in human urine with a deep cavitand synthetic receptor

A water-soluble deep cavitand recognized alkylammonium salts, including the drug amantadine hydrochloride, in spiked samples of human urine. The signals of the guests are detected by (1)H NMR upfield of 0 ppm and so occur in a spectroscopic window that is outside of the normal region and distinct from the signals of the biofluid components.

Investigating metabolite-protein interactions: an overview of available techniques

Metabolites comprise the molar majority of chemical substances in living cells, and metabolite-protein interactions are expected to be quite common. Many interactions have already been identified and have been shown to be involved in the regulation of different types of cellular processes including signaling events, enzyme activities, protein localizations and interactions. Recent technological advances have greatly facilitated the detection of metabolite-protein interactions at high sensitivity and some of these have been applied on a large scale. In this manuscript, we review the available in vitro, in silico and in vivo technologies for mapping small-molecule-protein interactions. Although some of these were developed for drug-protein interactions they can be applied for mapping metabolite-protein interactions. Information gained from the use of these approaches can be applied to the manipulation of cellular processes and therapeutic applications.

NMR metabolomics and drug discovery

NMR is an integral component of the drug discovery process with applications in lead discovery, validation, and optimization. NMR is routinely used for fragment-based ligand affinity screens, high-resolution protein structure determination, and rapid protein-ligand co-structure modeling. Because of this inherent versatility, NMR is currently making significant contributions in the burgeoning area of metabolomics, where NMR is successfully being used to identify biomarkers for various diseases, to analyze drug toxicity and to determine a drug's in vivo efficacy and selectivity. This review describes advances in NMR-based metabolomics and discusses some recent applications.

One-sample approach to determine the relative orientations of proteins in ternary and binary complexes from residual dipolar coupling measurements

We present a procedure that supports the acquisition of (1)H-(15)N residual dipolar coupling (RDC) values for individual subunits in binary or ternary protein assemblies from a single experimental sample. Our method relies on asymmetric labeling of each subunit with the following scheme: species A uniformly with (15)N, species B uniformly with (15)N and (13)C, and species C uniformly with (15)N but selectively with (13)C' or (13)C(alpha). Because only a single sample is required, the approach obviates the need for preparing multiple samples and eliminates potential errors introduced from differences in sample conditions. Because numerous biological processes rely on protein assemblies or transient interactions, this method should be well suited for a wide range of future applications.

Use of residual dipolar couplings in structural analysis of protein-ligand complexes by solution NMR spectroscopy

Investigation of structure-function relationships in protein complexes, specifically protein-ligand interactions, carry great significance in elucidating the structural and mechanistic bases of molecular recognition events and their role in regulating cell processes. Nuclear magnetic resonance (NMR) spectroscopy is one of the leading structural and analytical techniques in in-depth studies of protein-ligand interactions. Recent advances in NMR methodology such as transverse relaxation-optimized spectroscopy (TROSY) and residual dipolar couplings (RDCs) measured in liquid crystalline alignment medium, offer a viable alternative to traditional nuclear Overhauser enhancement (NOE)-based approaches for structure determination of large protein complexes. RDCs provide a way to constrain the relative orientation of two molecules in complex with each other by aligning their independently determined order tensors. The potential for utilization of RDCs can be extended to proteins with multiple ligands or even multimeric protein-ligand complexes, where symmetry properties of the protein can be taken advantage of. Availability of effective RDC data collection and analysis protocols can certainly aid this process by their incorporation into structure calculation protocols using intramolecular and intermolecular orientational restraints. This chapter discusses in detail some of these protocols including methods for sample preparation in liquid crystalline media, NMR experiments for RDC data collection, as well as software tools for RDC data analysis and protein-ligand complex structure determination.

Simultaneous detection and deconvolution of congested NMR spectra containing three isotopically labeled species

We present a procedure for isolating subspectra corresponding to individual protein or peptide components in a ternary mixture or complex. Each of the three-component species is labeled differently: species A uniformly with 15N, species B uniformly with 15N and 13C, and species C uniformly with 15N but selectively with 13C' or 13Calpha. By using the dual carbon label selective HSQC (DCLS-HSQC) pulse sequence and exploiting differences in 1J 15N-13C coupling patterns to filter selected 15N resonances from detection during a constant time period, a subspectrum from each species can be generated from three spectra acquired from a single sample. Many important biological pathways involve dynamic interactions among members of multicomponent protein assemblies, and this approach offers a powerful way to monitor such processes.

ICAM-1 peptide inhibitors of T-cell adhesion bind to the allosteric site of LFA-1. An NMR characterization

We have used nuclear magnetic resonance to characterize the binding site of two intercellular adhesion molecule-1 derived cyclic peptides, cIBC and cIBR, to the I-domain of leukocyte function-associated antigen-1. These peptides inhibit the leukocyte function-associated antigen-1/intercellular adhesion molecule-1 interaction known to play a key role in autoimmune diseases and cancer metastasis. Perturbation of the chemical shifts and intensities of the nuclear magnetic resonance signals corresponding to a number of residues of the I-domain of leukocyte function-associated antigen-1 show that both peptides bind to the I-domain allosteric site, the binding site of I-domain allosteric inhibitors such as lovastatin, and therefore the peptides probably also act as allosteric inhibitors of leukocyte function-associated antigen-1. Molecular models of the interaction of these two cyclic peptides with leukocyte function-associated antigen-1 I-domain show that the binding mode of the three molecules are analogous: the hydrophobic residues of the peptides remain buried and occupy the same positions as the apolar groups of lovastatin, while the peptides regions containing the most polar residues are flexible and primarily exposed to the solvent. These results suggest an allosteric mechanism for the inhibitory effect on T-cell adhesion displayed by both peptides, which exhibit potential as therapeutic agents.