PROLIX: rapid mining of protein-ligand interactions in large crystal structure databases.

A central problem in structure-based drug design is understanding protein-ligand interactions quantitatively and qualitatively. Several recent studies have highlighted from a qualitative perspective the nature of these interactions and their utility in drug discovery. However, a common limitation is a lack of adequate tools to mine these interactions comprehensively, since exhaustive searches of the protein data bank are time-consuming and difficult to perform. Consequently, fundamental questions remain unanswered: How unique or how common are the protein-ligand interactions observed in a given drug design project when compared to all complexed structures in the protein data bank? Which interaction patterns might explain the affinity of a tool compound toward unwanted targets? To answer these questions and to enable the systematic and comprehensive study of protein-ligand interactions, we introduce PROLIX (Protein Ligand Interaction Explorer), a tool that uses sophisticated fingerprint representations of protein-ligand interaction patterns for rapid data mining in large crystal structure databases. Our implementation strategy pursues a branch-and-bound technique that enables mining against thousands of complexes within a few seconds. Key elements of PROLIX include (i) an intuitive interface that enables users to formulate complex queries easily, (ii) exceptional speed for results retrieval, and (iii) a sophisticated results summarization. Herein we describe the algorithms developed to enable complex queries and fast retrieval of search results, as well as the intuitive aspects of the user interface and summarization viewer.

A simulation-approximation approach to sample size planning for high-dimensional classification studies.

Classification studies with high-dimensional measurements and relatively small sample sizes are increasingly common. Prospective analysis of the role of sample sizes in the performance of such studies is important for study design and interpretation of results, but the complexity of typical pattern discovery methods makes this problem challenging. The approach developed here combines Monte Carlo methods and new approximations for linear discriminant analysis, assuming multivariate normal distributions. Monte Carlo methods are used to sample the distribution of which features are selected for a classifier and the mean and variance of features given that they are selected. Given selected features, the linear discriminant problem involves different distributions of training data and generalization data, for which 2 approximations are compared: one based on Taylor series approximation of the generalization error and the other on approximating the discriminant scores as normally distributed. Combining the Monte Carlo and approximation approaches to different aspects of the problem allows efficient estimation of expected generalization error without full simulations of the entire sampling and analysis process. To evaluate the method and investigate realistic study design questions, full simulations are used to ask how validation error rate depends on the strength and number of informative features, the number of noninformative features, the sample size, and the number of features allowed into the pattern. Both approximation methods perform well for most cases but only the normal discriminant score approximation performs well for cases of very many weakly informative or uninformative dimensions. The simulated cases show that many realistic study designs will typically estimate substantially suboptimal patterns and may have low probability of statistically significant validation results.

Solid-state NMR spectroscopic methods in chemistry.

Over the last decades, NMR spectroscopy has grown into an indispensable tool for chemical analysis, structure determination, and the study of dynamics in organic, inorganic, and biological systems. It is commonly used for a wide range of applications from the characterization of synthetic products to the study of molecular structures of systems such as catalysts, polymers, and proteins. Although most NMR experiments are performed on liquid-state samples, solid-state NMR is rapidly emerging as a powerful method for the study of solid samples and materials. This Review outlines some of the developments of solid-state NMR spectroscopy, including techniques such as cross-polarization, magic-angle spinning, multiple-pulse sequences, homo- and heteronuclear decoupling and recoupling techniques, multiple-quantum spectroscopy, and dynamic angle spinning, as well as their applications to structure determination. Modern solid-state NMR spectroscopic techniques not only produce spectra with a resolution close to that of liquid-state spectra, but also capitalize on anisotropic interactions, which are often unavailable for liquid samples. With this background, the future of solid-state NMR spectroscopy in chemistry appears to be promising, indeed.

Laser-polarized (129)Xe NMR and MRI at ultralow magnetic fields.

Laser-polarized (129)Xe and a high-T(c)superconducting quantum interference device (SQUID) are used to obtain magnetic resonance images in porous materials at a magnetic field of 2.3 mT, corresponding to a Larmor frequency of 27 kHz. Image resolution of 1 mm is obtained with gradients of only 1 mT/m. The resolution of xenon chemical shifts in different physicochemical environments at ultralow fields is also demonstrated. Details of the circulating flow optical pumping apparatus and the SQUID spectrometer are presented.

Characterization of a novel CRAC inhibitor that potently blocks human T cell activation and effector functions.

Store operated calcium entry (SOCE) downstream of T cell receptor (TCR) activation in T lymphocytes has been shown to be mediated mainly through the Calcium Release Activated Calcium (CRAC) channel. Here, we compared the effects of a novel, potent and selective CRAC current inhibitor, 2,6-Difluoro-N-{5-[4-methyl-1-(5-methyl-thiazol-2-yl)-1,2,5,6-tetrahydro-pyridin-3-yl]-pyrazin-2-yl}-benzamide (RO2959), on T cell effector functions with that of a previously reported CRAC channel inhibitor, YM-58483, and a calcineurin inhibitor Cyclosporin A (CsA). Using both electrophysiological and calcium-based fluorescence measurements, we showed that RO2959 is a potent SOCE inhibitor that blocked an IP3-dependent current in CRAC-expressing RBL-2H3 cells and CHO cells stably expressing human Orai1 and Stim1, as well as SOCE in human primary CD4(+) T cells triggered by either TCR stimulation or thapsigargin treatment. Furthermore, we demonstrated that RO2959 completely inhibited cytokine production as well as T cell proliferation mediated by TCR stimulation or MLR (mixed lymphocyte reaction). Lastly, we showed by gene expression array analysis that RO2959 potently blocked TCR triggered gene expression and T cell functional pathways similar to CsA and another calcineurin inhibitor FK506. Thus, both from a functional and transcriptional level, our data provide evidence that RO2959 is a novel and selective CRAC current inhibitor that potently inhibits human T cell functions.

Missing Value Estimation for Compound-Target Activity Data.

Relationships between drug targets and associated diseases have traditionally been investigated by means of sequence similarity, comparative protein modeling, and pathway analysis. Recently, a complementary paradigm has emerged to link targets and drugs via biological responses within activity data and visualize findings in networks. It has been indicated that one of the obstacles towards the identification of novel interactions is the sparsity of available data. In this article, we provide a survey of estimation methods that address the challenge of data sparsity. Each method is described in terms of its advantages and limitations, and an exemplary application on compound-target activity data is demonstrated. With such imputation methods in-hand, the opportunity to combine efforts in molecular informatics can be realized, yielding novel insights into ligand-target space.

An automated, sheathless capillary electrophoresis-mass spectrometry platform for discovery of biomarkers in human serum.

A capillary electrophoresis-mass spectrometry (CE-MS) method has been developed to perform routine, automated analysis of low-molecular-weight peptides in human serum. The method incorporates transient isotachophoresis for in-line preconcentration and a sheathless electrospray interface. To evaluate the performance of the method and demonstrate the utility of the approach, an experiment was designed in which peptides were added to sera from individuals at each of two different concentrations, artificially creating two groups of samples. The CE-MS data from the serum samples were divided into separate training and test sets. A pattern-recognition/feature-selection algorithm based on support vector machines was used to select the mass-to-charge (m/z) values from the training set data that distinguished the two groups of samples from each other. The added peptides were identified correctly as the distinguishing features, and pattern recognition based on these peptides was used to assign each sample in the independent test set to its respective group. A twofold difference in peptide concentration could be detected with statistical significance (p-value < 0.0001). The accuracy of the assignment was 95%, demonstrating the utility of this technique for the discovery of patterns of biomarkers in serum.