Functional and structural characterization of Hyp730, a highly conserved and dormancy-specific hypothetical membrane protein.

Membrane proteins represent major drug targets, and the ability to determine their functions, structures, and conformational changes will significantly advance mechanistic approaches to both biotechnology and bioremediation, as well as the fight against pathogenic bacteria. A pertinent example is Mycobacterium tuberculosis (H37Rv), which contains ~4000 protein-coding genes, with almost a thousand having been categorized as 'membrane protein', and a few of which (~1%) have been functionally characterized and structurally modeled. However, the functions and structures of most membrane proteins that are sparsely, or only transiently, expressed, but essential in small phenotypic subpopulations or under stress conditions such as persistence or dormancy, remain unknown. Our deep quantitative proteomics profiles revealed that the hypothetical membrane protein 730 (Hyp730) WP_010079730 (protein ID Mlut_RS11895) from M. luteus is upregulated in dormancy despite a ~5-fold reduction in overall protein diversity. Its H37Rv paralog, Rv1234, showed a similar proteomic signature, but the function of Hyp730-like proteins has never been characterized. Here, we present an extensive proteomic and transcriptomic analysis of Hyp730 and have also characterized its in vitro recombinant expression, purification, refolding, and essentiality as well as its tertiary fold. Our biophysical studies, circular dichroism, and tryptophan fluorescence are in immediate agreement with in-depth in silico 3D-structure prediction, suggesting that Hyp730 is a double-pass membrane-spanning protein. Ablation of Hyp730-expression did not alter M. luteus growth, indicating that Hyp730 is not essential. Structural homology comparisons showed that Hyp730 is highly conserved and non-redundant in G+C rich Actinobacteria and might be involved, under stress conditions, in an energy-saving role in respiration during dormancy.

Screening of Focused Compound Library Targeting Liver X Receptors in Pancreatic Cancer Identified Ligands with Inverse Agonist and Degrader Activity.

Pancreatic ductal adenocarcinoma (PDAC) is the predominant form of pancreatic cancer. PDACs harbor oncogenic mutations in the KRAS gene, and ongoing efforts to directly target its mutant protein product to inhibit tumor growth are a priority not only in pancreatic cancer but in other malignancies such as lung and colorectal cancers where KRAS is also commonly mutated. An alternative strategy to directly targeting KRAS is to identify and target druggable receptors involved in dysregulated cancer hallmarks downstream of KRAS dysregulation. Liver X receptors (LXRs) are members of the nuclear receptor family of ligand-modulated transcription factors and are involved in the regulation of genes which function in key cancer-related processes, including cholesterol transport, lipid and glucose metabolism, and inflammatory and immune responses. Modulation of LXRs via small molecule ligands has emerged as a promising approach for directly targeting tumor cells or the stromal and immune cells within the tumor microenvironment. We have previously shown that only one of the two LXR subtypes (LXRß) is expressed in pancreatic cancer cells, and targeting LXR with available synthetic ligands blocked the proliferation of PDAC cells and tumor formation. In a screen of a focused library of drug-like small molecules predicted to dock in the ligand-binding pocket of LXRß, we identified two novel LXR ligands with more potent antitumor activity than current LXR agonists used in our published studies. Characterization of the two lead compounds (GAC0001E5 and GAC0003A4) indicates that they function as LXR inverse agonists which inhibit their transcriptional activity. Prolonged treatments with novel ligands further revealed their function as LXR "degraders" which significantly reduced LXR protein levels in all three PDAC cell lines tested. These findings support the utility of these novel inhibitors in basic research on ligand design, allosteric mechanisms, and LXR functions and their potential application as treatments for advanced pancreatic cancer and other recalcitrant malignancies.

Insights into the substrate binding specificity of quorum-quenching acylase PvdQ.

Quorum sensing is a cell to cell signaling mechanism that enables them to coordinate their behaviors in a density-dependent manner mediated by small diffusible signaling molecules, which can control the virulence and biofilm gene expression in many Gram-negative and positive bacteria. N-acyl homoserine lactone acylase PvdQ from human opportunistic pathogen Pseudomonas aeruginosa is a quorum-quenching enzyme that can hydrolyze the amide bond of the quorum signaling N-acyl homoserine lactones (AHLs) thereby degrading the signaling molecules, turning off the biofilm phenotype and resulting in a reduction of bacterial virulence. Previous studies demonstrated that PvdQ has different preferences for N-acyl substrates with different acyl chain lengths and substituents. However, the substrate binding specificity determinants of the quorum-quenching enzyme PvdQ with the different bacterial ligands are unknown and unintuitive. Further, elucidation of these determinants can lead to mutants with efficiency and broader substrate promiscuity. To investigate this question, a computational study was carried out combining multiple molecular docking methods, molecular dynamics simulations, residue interaction network analysis, and binding free energy calculations. The main findings are: firstly, the results from pKa predictions support that the pKa of the N-terminus of Serß1 was depressed due to the surrounding residues. Multiple molecular docking studies provide useful information about the detailed binding modes and binding affinities. Secondly, 300 ns molecular dynamics simulations were carried out to analyze the overall molecular motions of substrate-bound and substrate-free PvdQ. The specific interactions between the active site of PvdQ and different ligands revealed the determinants for the preference among the ligands. A systematic comparison and analysis of the protein dynamic fingerprint of each complex demonstrated that binding of the most favorable ligand, C12-homoserine lactone (C12-HSL), reduced the global motions of the complex and maintained the correct arrangement of the catalytic site. Further, the residue interaction network analysis of each system illustrated that there are more communication contacts and pathways between the residues in the C12-HSL complex as compared to complexes with the other ligands. The binding of the C12-HSL ligand facilitates structural communication between the two knobs and the active site. While the binding of the other ligands tend to impair specific communication pathways between the two knobs and the active site, and lead to a catalytically inefficient state. Finally, simulation results from free energy landscape and binding free energy analysis revealed that the C12-HSL ligand has the lowest binding free energy and greater stability than the less favored ligands. Each of the following residues: Serß1, Hisß23, Pheß24, Metß30, Pheß32, Leuß50, Asnß57, Thrß69, Valß70, Trpß162, Trpß186, Asnß269, Argß297 and Leuα146, play different roles in substrate binding specificity. This is the first computational study that provides molecular information for structure-dynamic-function relationships of PvdQ with different ligands and demonstrates determinants of bacterial substrate binding specificity.

Receptor-based pharmacophore and pharmacophore key descriptors for virtual screening and QSAR modeling.

The intuitive nature of the pharmacophore concept has made it widely accepted by the medicinal chemistry community, evidenced by the past 3 decades of development and application of computerized pharmacophore modeling tools. On the other hand, shape complementarity has been recognized as a critical factor in molecular recognition between drugs and their receptors. Recent development of fast and accurate shape comparison tools has facilitated the wide spread use of shape matching technologies in drug discovery. However, pharmacophore and shape technologies, if used separately, often lead to high false positive rate. Thus, integrating pharmacophore matching and shape matching technologies into one program has the potential to reduce the false positive rates in virtual screening. Other issues of current pharmacophore technologies include sometimes high false negative rate and non-quantitative prediction. In this article, we first focus on a recently implemented method (Shape4) that combines receptor based shape matching and pharmacophore comparison in a single algorithm to create shape pharmacophore models for virtual screening. We also examine a recent example that utilizes multi-complex information to develop receptor-based pharmacophore models that promises to reduce false negative rate. Finally, we review several methods that employ receptor-based pharmacophore map and pharmacophore key descriptors for QSAR modeling. We conclude by emphasizing the concept of receptor-based shape pharmacophore and its roles in future drug discovery.

Informatics approach to the rational design of siRNA libraries.

This chapter surveys the literature for state-of-the-art methods for the rational design of siRNA libraries. It identifies and presents major milestones in the field of computational modeling of siRNA's gene silencing efficacy. Commonly used features of siRNAs are summarized along with major machine learning techniques employed to build the predictive models. It has also outlined several web-enabled siRNA design tools. To face the challenge of modeling and rational design of chemically modified siRNAs, it also proposes a new cheminformatics approach for the representation and characterization of siRNA molecules. Some preliminary results with this new approach are presented to demonstrate the promising potential of this method for the modeling of siRNA's efficacy. Together with novel delivery technologies and chemical modification techniques, rational siRNA design algorithms will ultimately contribute to chemical biology research and the efficient development of siRNA therapeutics.

Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design.

Optimization of chemical library composition affords more efficient identification of hits from biological screening experiments. The optimization could be achieved through rational selection of reagents used in combinatorial library synthesis. However, with a rapid advent of parallel synthesis methods and availability of millions of compounds synthesized by many vendors, it may be more efficient to design targeted libraries by means of virtual screening of commercial compound collections. This chapter reviews the application of advanced cheminformatics approaches such as quantitative structure-activity relationships (QSAR) and pharmacophore modeling (both ligand and structure based) for virtual screening. Both approaches rely on empirical SAR data to build models; thus, the emphasis is placed on achieving models of the highest rigor and external predictive power. We present several examples of successful applications of both approaches for virtual screening to illustrate their utility. We suggest that the expert use of both QSAR and pharmacophore models, either independently or in combination, enables users to achieve targeted libraries enriched with experimentally confirmed hit compounds.

Molecular shape technologies in drug discovery: methods and applications.

Shape complementarity is a critically important factor in molecular recognition among drugs and their biological receptors. The notion that molecules with similar 3D shapes tend to have similar biological activity has been recognized and implemented in computational drug discovery tools for decades. But the low computational efficiency and the lack of widely accessible software tools limited the use of early shape-matching algorithms. However, recent development of fast and accurate shape comparison tools has changed the landscape, and facilitated the wide spread use of both the ligand-based and receptor-based shape-matching technologies in drug discovery. In this article, we summarize some of the well-known shape algorithms. We first describe the computational principles for both the superposition-based and the superposition-free shape-matching methods. These include ROCS (Rapid Overlay of Compound Structures), SQ, and the CatShape method in the former category; and the shape signatures algorithm and USR (Ultrafast Shape Recognition) that belong to the latter category. We then highlight some recent validation studies and practical applications of various shape technologies. Because of the rapid development of modern shape-matching algorithms, and the increasingly affordable computational resources and software tools, we anticipate much broader use of the molecular shape technologies in future drug discovery. They will be especially useful in chemogenomics research, where large scale associations between small molecules and protein targets are studied. Thus, molecular shape technologies, together with well-defined pharmacophore constraints, can afford both efficient and effective means for drug discovery and chemical genomics research.

A novel structure-based multimode QSAR method affords predictive models for phosphodiesterase inhibitors.

Quantitative structure-activity relationship (QSAR) methods aim to build quantitatively predictive models for the discovery of new molecules. It has been widely used in medicinal chemistry for drug discovery. Many QSAR techniques have been developed since Hansch's seminal work, and more are still being developed. Motivated by Hopfinger's receptor-dependent QSAR (RD-QSAR) formalism and the Lukacova-Balaz scheme to treat multimode issues, we have initiated studies that focus on a structure-based multimode QSAR (SBMM QSAR) method, where the structure of the target protein is used in characterizing the ligand, and the multimode issue of ligand binding is systematically treated with a modified Lukacova-Balaz scheme. All ligand molecules are first docked to the target binding pocket to obtain a set of aligned ligand poses. A structure-based pharmacophore concept is adopted to characterize the binding pocket. Specifically, we represent the binding pocket as a geometric grid labeled by pharmacophoric features. Each pose of the ligand is also represented as a labeled grid, where each grid point is labeled according to the atom types of nearby ligand atoms. These labeled grids or three-dimensional (3D) maps (both the receptor map (R-map) and the ligand map (L-map)) are compared to each other to derive descriptors for each pose of the ligand, resulting in a multimode structure-activity relationship (SAR) table. Iterative partial least-squares (PLS) is employed to build the QSAR models. When we applied this method to analyze PDE-4 inhibitors, predictive models have been developed, obtaining models with excellent training correlation (r(2) = 0.65-0.66), as well as test correlation (R(2) = 0.64-0.65). A comparative analysis with 4 other QSAR techniques demonstrates that this new method affords better models, in terms of the prediction power for the test set.

Cheminformatics Approach to Gene Silencing: Z Descriptors of Nucleotides and SVM Regression Afford Predictive Models for siRNA Potency.

Short interfering RNA mediated gene silencing technology has been through tremendous development over the past decade, and has found broad applications in both basic biomedical research and pharmaceutical development. Critical to the effective use of this technology is the development of reliable algorithms to predict the potency and selectivity of siRNAs under study. Existing algorithms are mostly built upon sequence information of siRNAs and then employ statistical pattern recognition or machine learning techniques to derive rules or models. However, sequence-based features have limited ability to characterize siRNAs, especially chemically modified ones. In this study, we proposed a cheminformatics approach to describe siRNAs. Principal component scores (z1, z2, z3, z4) have been derived for each of the 5 nucleotides (A, U, G, C, T) from the descriptor matrix computed by the MOE program. Descriptors of a given siRNA sequence are simply the concatenation of the z values of its composing nucleotides. Thus, for each of the 2431 siRNA sequences in the Huesken dataset, 76 descriptors were generated for the 19-NT representation, and 84 descriptors were generated for the 21-NT representation of siRNAs. Support Vector Machine regression (SVMR) was employed to develop predictive models. In all cases, the models achieved Pearson correlation coefficient r and R about 0.84 and 0.65 for the training sets and test sets, respectively. A minimum of 25 % of the whole dataset was needed to obtain predictive models that could accurately predict 75 % of the remaining siRNAs. Thus, for the first time, a cheminformatics approach has been developed to successfully model the structure-potency relationship in siRNA-based gene silencing data, which has laid a solid foundation for quantitative modeling of chemically modified siRNAs.

Structure-based shape pharmacophore modeling for the discovery of novel anesthetic compounds.

Current anesthetics, especially the inhaled ones, have troublesome side effects and may be associated with durable changes in cognition. It is therefore highly desirable to develop novel chemical entities that reduce these effects while preserving or enhancing anesthetic potency. In spite of progress toward identifying protein targets involved in anesthesia, we still do not have the necessary atomic level structural information to delineate their interactions with anesthetic molecules. Recently, we have described a protein target, apoferritin, to which several anesthetics bind specifically and in a pharmacodynamically relevant manner. Further, we have reported the high resolution X-ray structure of two anesthetic/apoferritin complexes (Liu, R.; Loll, P. J.; Eckenhoff, R. G. FASEB J. 2005, 19, 567). Thus, we describe in this paper a structure-based approach to establish validated shape pharmacophore models for future application to virtual and high throughput screening of anesthetic compounds. We use the 3D structure of apoferritin as the basis for the development of several shape pharmacophore models. To validate these models, we demonstrate that (1) they can be used to effectively recover known anesthetic agents from a diverse database of compounds; (2) the shape pharmacophore scores afford a significant linear correlation with the measured binding energetics of several known anesthetic compounds to the apoferritin site; and (3) the computed scores based on the shape pharmacophore models also predict the trend of the EC(50) values of a set of anesthetics. Therefore, we have now obtained a set of structure-based shape pharmacophore models, using ferritin as the surrogate target, which may afford a new way to rationally discover novel anesthetic agents in the future.

Hybrid quantum mechanical/molecular mechanical molecular dynamics simulations of HIV-1 integrase/inhibitor complexes.

Human immunodeficiency virus (HIV)-1 integrase (IN) is an attractive target for development of acquired immunodeficiency syndrome chemotherapy. In this study, conventional and coupled quantum mechanical and molecular mechanical (QM/MM) molecular dynamics (MD) simulations of HIV-1 IN complexed with 5CITEP (IN-5CITEP) were carried out. In addition to differences in the bound position of 5CITEP, significant differences at the two levels of theory were observed in the metal coordination geometry and the areas involving residues 116-119 and 140-166. In the conventional MD simulation, the coordination of Mg(2+) was found to be a near-perfect octahedral geometry whereas a distorted octahedral complex was observed in QM/MM. All of the above reasons lead to a different pattern of protein-ligand salt link formation that was not observed in the classical MD simulation. Furthermore to provide a theoretical understanding of inhibition mechanisms of 5CITEP and its derivative (DKA), hybrid QM/MM MD simulations of the two complexes (IN-5CITEP and IN-DKA) have been performed. The results reveal that areas involving residues 60-68, 116-119, and 140-149 were substantially different among the two systems. The two systems show similar pattern of metal coordination geometry, i.e., a distorted octahedron. In IN-DKA, both OD1 and OD2 of Asp-64 coordinate the Mg(2+) in a monodentate fashion whereas only OD1 is chelated to the metal as observed in IN-5CITEP. The high potency of DKA as compared to 5CITEP is supported by a strong salt link formed between its carboxylate moiety and the ammonium group of Lys-159. Detailed comparisons between HIV-1 IN complexed with DKA and with 5CITEP provide information about ligand structure effects on protein-ligand interactions in particular with the Lys-159. This is useful for the design of new selective HIV-1 IN inhibitors.

Electrostatic steering at acetylcholine binding sites.

The electrostatic environments near the acetylcholine binding sites on the nicotinic acetylcholine receptor (nAChR) and acetylcholinesterase were measured by diffusion-enhanced fluorescence energy transfer (DEFET) to determine the influence of long-range electrostatic interactions on ligand binding kinetics and net binding energy. Changes in DEFET from variously charged Tb3+ -chelates revealed net potentials of -20 mV at the nAChR agonist sites and -14 mV at the entrance to the AChE active site, in physiological ionic strength conditions. The potential at the alphadelta-binding site of the nAChR was determined independently in the presence of d-tubocurarine to be -14 mV; the calculated potential at the alphagamma-site was approximately threefold stronger than at the alphadelta-site. By determining the local potential in increasing ionic strength, Debye-Hückel theory predicted that the potentials near the nAChR agonist binding sites are constituted by one to three charges in close proximity to the binding site. Examination of the binding kinetics of the fluorescent acetylcholine analog dansyl-C6-choline at ionic strengths from 12.5 to 400 mM revealed a twofold decrease in association rate. Debye-Hückel analysis of the kinetics revealed a similar charge distribution as seen by changes in the potentials. To determine whether the experimentally determined potentials are reflected by continuum electrostatics calculations, solutions to the nonlinear Poisson-Boltzmann equation were used to compute the potentials expected from DEFET measurements from high-resolution models of the nAChR and AChE. These calculations are in good agreement with the DEFET measurements for AChE and for the alphagamma-site of the nAChR. We conclude that long-range electrostatic interactions contribute -0.3 and -1 kcal/mol to the binding energy at the nAChR alphadelta- and alphagamma-sites due to an increase in association rates.

Computed pore potentials of the nicotinic acetylcholine receptor.

Electrostatic surface potentials in the vestibule of the nicotinic acetylcholine receptor (nAChR) were computed from structural models using the University of Houston Brownian Dynamics program to determine their effect on ion conduction and ionic selectivity. To further determine whether computed potentials accurately reflect the electrostatic environment of the channel, the potentials were used to predict the rate constants for diffusion-enhanced fluorescence energy transfer; the calculated energy transfer rates are directly comparable with those determined experimentally (see companion article by Meltzer et al. in this issue). To include any effects on the local potentials by the bound acceptor fluorophore crystal violet, its binding site was first localized within the pore by fluorescence energy transfer measurements from dansyl-C6-choline bound to the agonist sites and also by simulations of binding using Autodock. To compare the computed potentials with those determined experimentally, we used the predicted energy transfer rates from Tb3+ chelates of varying charge to calculate an expected potential using the Boltzmann relationship. This expected potential (from -20 to -40 mV) overestimates the values determined experimentally (from -10 to -25 mV) by two- to fourfold at similar conditions of ionic strength. Although the results indicate a basic discrepancy between experimental and computed surface potentials, both methods demonstrate that the vestibular potential has a relatively small effect on conduction and selectivity.