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Find video protocols related to scientific articles indexed in Pubmed.
A Reaction Path Study of the Catalysis and Inhibition of the Bacillus anthracis CapD ?-Glutamyl Transpeptidase.
Biochemistry
PUBLISHED: 10-31-2014
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The CapD enzyme of Bacillus anthracis is a ?-glutamyl transpeptidase from the N-terminal nucleophile hydrolase superfamily that covalently anchors the poly-?-d-glutamic acid (pDGA) capsule to the peptidoglycan. The capsule hinders phagocytosis of B. anthracis by host cells and is essential for virulence. The role CapD plays in capsule anchoring and remodeling makes the enzyme a promising target for anthrax medical countermeasures. Although the structure of CapD is known, and a covalent inhibitor, capsidin, has been identified, the mechanisms of CapD catalysis and inhibition are poorly understood. Here, we used a computational approach to map out the reaction steps involved in CapD catalysis and inhibition. We found that the rate-limiting step of either CapD catalysis or inhibition was a concerted asynchronous formation of the tetrahedral intermediate with a barrier of 22-23 kcal/mol. However, the mechanisms of these reactions differed for the two amides. The formation of the tetrahedral intermediate with pDGA was substrate-assisted with two proton transfers. In contrast, capsidin formed the tetrahedral intermediate in a conventional way with one proton transfer. Interestingly, capsidin coupled a conformational change in the catalytic residue of the tetrahedral intermediate to stretching of the scissile amide bond. Furthermore, capsidin took advantage of iminol-amide tautomerism of its diacetamide moiety to convert the tetrahedral intermediate to the acetylated CapD. As evidence of the promiscuous nature of CapD, the enzyme cleaved the amide bond of capsidin by attacking it on the opposite side compared to pDGA.
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Simulation of B cell affinity maturation explains enhanced antibody cross-reactivity induced by the polyvalent malaria vaccine AMA1.
J. Immunol.
PUBLISHED: 07-30-2014
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Polyvalent vaccines use a mixture of Ags representing distinct pathogen strains to induce an immune response that is cross-reactive and protective. However, such approaches often have mixed results, and it is unclear how polyvalency alters the fine specificity of the Ab response and what those consequences might be for protection. In this article, we present a coarse-grain theoretical model of B cell affinity maturation during monovalent and polyvalent vaccinations that predicts the fine specificity and cross-reactivity of the Ab response. We stochastically simulate affinity maturation using a population dynamics approach in which the host B cell repertoire is represented explicitly, and individual B cell subpopulations undergo rounds of stimulation, mutation, and differentiation. Ags contain multiple epitopes and are present in subpopulations of distinct pathogen strains, each with varying degrees of cross-reactivity at the epitope level. This epitope- and strain-specific model of affinity maturation enables us to study the composition of the polyclonal response in granular detail and identify the mechanisms driving serum specificity and cross-reactivity. We applied this approach to predict the Ab response to a polyvalent vaccine based on the highly polymorphic malaria Ag apical membrane antigen-1. Our simulations show how polyvalent apical membrane Ag-1 vaccination alters the selection pressure during affinity maturation to favor cross-reactive B cells to both conserved and strain-specific epitopes and demonstrate how a polyvalent vaccine with a small number of strains and only moderate allelic coverage may be broadly neutralizing. Our findings suggest that altered fine specificity and enhanced cross-reactivity may be a universal feature of polyvalent vaccines.
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Modeling metabolism and stage-specific growth of Plasmodium falciparum HB3 during the intraerythrocytic developmental cycle.
Mol Biosyst
PUBLISHED: 07-09-2014
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The human malaria parasite Plasmodium falciparum goes through a complex life cycle, including a roughly 48-hour-long intraerythrocytic developmental cycle (IDC) in human red blood cells. A better understanding of the metabolic processes required during the asexual blood-stage reproduction will enhance our basic knowledge of P. falciparum and help identify critical metabolic reactions and pathways associated with blood-stage malaria. We developed a metabolic network model that mechanistically links time-dependent gene expression, metabolism, and stage-specific growth, allowing us to predict the metabolic fluxes, the biomass production rates, and the timing of production of the different biomass components during the IDC. We predicted time- and stage-specific production of precursors and macromolecules for P. falciparum (strain HB3), allowing us to link specific metabolites to specific physiological functions. For example, we hypothesized that coenzyme A might be involved in late-IDC DNA replication and cell division. Moreover, the predicted ATP metabolism indicated that energy was mainly produced from glycolysis and utilized for non-metabolic processes. Finally, we used the model to classify the entire tricarboxylic acid cycle into segments, each with a distinct function, such as superoxide detoxification, glutamate/glutamine processing, and metabolism of fumarate as a byproduct of purine biosynthesis. By capturing the normal metabolic and growth progression in P. falciparum during the IDC, our model provides a starting point for further elucidation of strain-specific metabolic activity, host-parasite interactions, stress-induced metabolic responses, and metabolic responses to antimalarial drugs and drug candidates.
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A systems biology strategy to identify molecular mechanisms of action and protein indicators of traumatic brain injury.
J. Neurosci. Res.
PUBLISHED: 06-02-2014
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The multifactorial nature of traumatic brain injury (TBI), especially the complex secondary tissue injury involving intertwined networks of molecular pathways that mediate cellular behavior, has confounded attempts to elucidate the pathology underlying the progression of TBI. Here, systems biology strategies are exploited to identify novel molecular mechanisms and protein indicators of brain injury. To this end, we performed a meta-analysis of four distinct high-throughput gene expression studies involving different animal models of TBI. By using canonical pathways and a large human protein-interaction network as a scaffold, we separately overlaid the gene expression data from each study to identify molecular signatures that were conserved across the different studies. At 24 hr after injury, the significantly activated molecular signatures were nonspecific to TBI, whereas the significantly suppressed molecular signatures were specific to the nervous system. In particular, we identified a suppressed subnetwork consisting of 58 highly interacting, coregulated proteins associated with synaptic function. We selected three proteins from this subnetwork, postsynaptic density protein 95, nitric oxide synthase 1, and disrupted in schizophrenia 1, and hypothesized that their abundance would be significantly reduced after TBI. In a penetrating ballistic-like brain injury rat model of severe TBI, Western blot analysis confirmed our hypothesis. In addition, our analysis recovered 12 previously identified protein biomarkers of TBI. The results suggest that systems biology may provide an efficient, high-yield approach to generate testable hypotheses that can be experimentally validated to identify novel mechanisms of action and molecular indicators of TBI. © 2014 Wiley Periodicals, Inc.
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A computational study of the respiratory airflow characteristics in normal and obstructed human airways.
Comput. Biol. Med.
PUBLISHED: 04-23-2014
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Obstructive lung diseases in the lower airways are a leading health concern worldwide. To improve our understanding of the pathophysiology of lower airways, we studied airflow characteristics in the lung between the 8th and the 14th generations using a three-dimensional computational fluid dynamics model, where we compared normal and obstructed airways for a range of breathing conditions. We employed a novel technique based on computing the Pearson?s correlation coefficient to quantitatively characterize the differences in airflow patterns between the normal and obstructed airways. We found that the airflow patterns demonstrated clear differences between normal and diseased conditions for high expiratory flow rates (>2300ml/s), but not for inspiratory flow rates. Moreover, airflow patterns subjected to filtering demonstrated higher sensitivity than airway resistance for differentiating normal and diseased conditions. Further, we showed that wall shear stresses were not only dependent on breathing rates, but also on the distribution of the obstructed sites in the lung: for the same degree of obstruction and breathing rate, we observed as much as two-fold differences in shear stresses. In contrast to previous studies that suggest increased wall shear stress due to obstructions as a possible damage mechanism for small airways, our model demonstrated that for flow rates corresponding to heavy activities, the wall shear stress in both normal and obstructed airways was <0.3Pa, which is within the physiological limit needed to promote respiratory defense mechanisms. In summary, our model enables the study of airflow characteristics that may be impractical to assess experimentally.
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DBSecSys: a database of Burkholderia mallei secretion systems.
BMC Bioinformatics
PUBLISHED: 04-07-2014
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Bacterial pathogenicity represents a major public health concern worldwide. Secretion systems are a key component of bacterial pathogenicity, as they provide the means for bacterial proteins to penetrate host-cell membranes and insert themselves directly into the host cells' cytosol. Burkholderia mallei is a Gram-negative bacterium that uses multiple secretion systems during its host infection life cycle. To date, the identities of secretion system proteins for B. mallei are not well known, and their pathogenic mechanisms of action and host factors are largely uncharacterized.
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Merging applicability domains for in silico assessment of chemical mutagenicity.
J Chem Inf Model
PUBLISHED: 02-14-2014
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Using a benchmark Ames mutagenicity data set, we evaluated the performance of molecular fingerprints as descriptors for developing quantitative structure-activity relationship (QSAR) models and defining applicability domains with two machine-learning methods: random forest (RF) and variable nearest neighbor (v-NN). The two methods focus on complementary aspects of chemical mutagenicity and use different characteristics of the molecular fingerprints to achieve high levels of prediction accuracies. Thus, while RF flags mutagenic compounds using the presence or absence of small molecular fragments akin to structural alerts, the v-NN method uses molecular structural similarity as measured by fingerprint-based Tanimoto distances between molecules. We showed that the extended connectivity fingerprints could intuitively be used to define and quantify an applicability domain for either method. The importance of using applicability domains in QSAR modeling cannot be understated; compounds that are outside the applicability domain do not have any close representative in the training set, and therefore, we cannot make reliable predictions. Using either approach, we developed highly robust models that rival the performance of a state-of-the-art proprietary software package. Importantly, based on the complementary approach used by the methods, we showed that by combining the model predictions we raised the applicability domain from roughly 80% to 90%. These results indicated that the proposed QSAR protocol constituted a highly robust chemical mutagenicity prediction model.
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Computational approach to characterize causative factors and molecular indicators of chronic wound inflammation.
J. Immunol.
PUBLISHED: 01-22-2014
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Chronic inflammation is rapidly becoming recognized as a key contributor to numerous pathologies. Despite detailed investigations, understanding of the molecular mechanisms regulating inflammation is incomplete. Knowledge of such critical regulatory processes and informative indicators of chronic inflammation is necessary for efficacious therapeutic interventions and diagnostic support to clinicians. We used a computational modeling approach to elucidate the critical factors responsible for chronic inflammation and to identify robust molecular indicators of chronic inflammatory conditions. Our kinetic model successfully captured experimentally observed cell and cytokine dynamics for both acute and chronic inflammatory responses. Using sensitivity analysis, we identified macrophage influx and efflux rate modulation as the strongest inducing factor of chronic inflammation for a wide range of scenarios. Moreover, our model predicted that, among all major inflammatory mediators, IL-6, TGF-?, and PDGF may generally be considered the most sensitive and robust indicators of chronic inflammation, which is supported by existing, but limited, experimental evidence.
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Exploiting large-scale drug-protein interaction information for computational drug repurposing.
BMC Bioinformatics
PUBLISHED: 01-17-2014
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Despite increased investment in pharmaceutical research and development, fewer and fewer new drugs are entering the marketplace. This has prompted studies in repurposing existing drugs for use against diseases with unmet medical needs. A popular approach is to develop a classification model based on drugs with and without a desired therapeutic effect. For this approach to be statistically sound, it requires a large number of drugs in both classes. However, given few or no approved drugs for the diseases of highest medical urgency and interest, different strategies need to be investigated.
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Synthesis and evaluation of Strychnos alkaloids as MDR reversal agents for cancer cell eradication.
Bioorg. Med. Chem.
PUBLISHED: 01-11-2014
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Natural products represent the fourth generation of multidrug resistance (MDR) reversal agents that resensitize MDR cancer cells overexpressing P-glycoprotein (Pgp) to cytotoxic agents. We have developed an effective synthetic route to prepare various Strychnos alkaloids and their derivatives. Molecular modeling of these alkaloids docked to a homology model of Pgp was employed to optimize ligand-protein interactions and design analogues with increased affinity to Pgp. Moreover, the compounds were evaluated for their (1) binding affinity to Pgp by fluorescence quenching, and (2) MDR reversal activity using a panel of in vitro and cell-based assays and compared to verapamil, a known inhibitor of Pgp activity. Compound 7 revealed the highest affinity to Pgp of all Strychnos congeners (Kd=4.4?M), the strongest inhibition of Pgp ATPase activity, and the strongest MDR reversal effect in two Pgp-expressing cell lines. Altogether, our findings suggest the clinical potential of these synthesized compounds as viable Pgp modulators justifies further investigation.
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Prediction of metabolic flux distribution from gene expression data based on the flux minimization principle.
PLoS ONE
PUBLISHED: 01-01-2014
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Prediction of possible flux distributions in a metabolic network provides detailed phenotypic information that links metabolism to cellular physiology. To estimate metabolic steady-state fluxes, the most common approach is to solve a set of macroscopic mass balance equations subjected to stoichiometric constraints while attempting to optimize an assumed optimal objective function. This assumption is justifiable in specific cases but may be invalid when tested across different conditions, cell populations, or other organisms. With an aim to providing a more consistent and reliable prediction of flux distributions over a wide range of conditions, in this article we propose a framework that uses the flux minimization principle to predict active metabolic pathways from mRNA expression data. The proposed algorithm minimizes a weighted sum of flux magnitudes, while biomass production can be bounded to fit an ample range from very low to very high values according to the analyzed context. We have formulated the flux weights as a function of the corresponding enzyme reaction's gene expression value, enabling the creation of context-specific fluxes based on a generic metabolic network. In case studies of wild-type Saccharomyces cerevisiae, and wild-type and mutant Escherichia coli strains, our method achieved high prediction accuracy, as gauged by correlation coefficients and sums of squared error, with respect to the experimentally measured values. In contrast to other approaches, our method was able to provide quantitative predictions for both model organisms under a variety of conditions. Our approach requires no prior knowledge or assumption of a context-specific metabolic functionality and does not require trial-and-error parameter adjustments. Thus, our framework is of general applicability for modeling the transcription-dependent metabolism of bacteria and yeasts.
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Systems level analysis and identification of pathways and networks associated with liver fibrosis.
PLoS ONE
PUBLISHED: 01-01-2014
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Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.
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Characterization of chemically induced liver injuries using gene co-expression modules.
PLoS ONE
PUBLISHED: 01-01-2014
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Liver injuries due to ingestion or exposure to chemicals and industrial toxicants pose a serious health risk that may be hard to assess due to a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific damage and clinical outcomes via biomarkers or biomarker panels will provide the foundation for highly specific and robust diagnostic tests. Here, we have used DrugMatrix, a toxicogenomics database containing organ-specific gene expression data matched to dose-dependent chemical exposures and adverse clinical pathology assessments in Sprague Dawley rats, to identify groups of co-expressed genes (modules) specific to injury endpoints in the liver. We identified 78 such gene co-expression modules associated with 25 diverse injury endpoints categorized from clinical pathology, organ weight changes, and histopathology. Using gene expression data associated with an injury condition, we showed that these modules exhibited different patterns of activation characteristic of each injury. We further showed that specific module genes mapped to 1) known biochemical pathways associated with liver injuries and 2) clinically used diagnostic tests for liver fibrosis. As such, the gene modules have characteristics of both generalized and specific toxic response pathways. Using these results, we proposed three gene signature sets characteristic of liver fibrosis, steatosis, and general liver injury based on genes from the co-expression modules. Out of all 92 identified genes, 18 (20%) genes have well-documented relationships with liver disease, whereas the rest are novel and have not previously been associated with liver disease. In conclusion, identifying gene co-expression modules associated with chemically induced liver injuries aids in generating testable hypotheses and has the potential to identify putative biomarkers of adverse health effects.
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Exploring chemical reaction mechanisms through harmonic Fourier beads path optimization.
J Chem Phys
PUBLISHED: 11-05-2013
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Here, we apply the harmonic Fourier beads (HFB) path optimization method to study chemical reactions involving covalent bond breaking and forming on quantum mechanical (QM) and hybrid QM?molecular mechanical (QM?MM) potential energy surfaces. To improve efficiency of the path optimization on such computationally demanding potentials, we combined HFB with conjugate gradient (CG) optimization. The combined CG-HFB method was used to study two biologically relevant reactions, namely, L- to D-alanine amino acid inversion and alcohol acylation by amides. The optimized paths revealed several unexpected reaction steps in the gas phase. For example, on the B3LYP?6-31G(d,p) potential, we found that alanine inversion proceeded via previously unknown intermediates, 2-iminopropane-1,1-diol and 3-amino-3-methyloxiran-2-ol. The CG-HFB method accurately located transition states, aiding in the interpretation of complex reaction mechanisms. Thus, on the B3LYP?6-31G(d,p) potential, the gas phase activation barriers for the inversion and acylation reactions were 50.5 and 39.9 kcal?mol, respectively. These barriers determine the spontaneous loss of amino acid chirality and cleavage of peptide bonds in proteins. We conclude that the combined CG-HFB method further advances QM and QM?MM studies of reaction mechanisms.
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A fusion-inhibiting peptide against Rift Valley fever virus inhibits multiple, diverse viruses.
PLoS Negl Trop Dis
PUBLISHED: 09-01-2013
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For enveloped viruses, fusion of the viral envelope with a cellular membrane is critical for a productive infection to occur. This fusion process is mediated by at least three classes of fusion proteins (Class I, II, and III) based on the protein sequence and structure. For Rift Valley fever virus (RVFV), the glycoprotein Gc (Class II fusion protein) mediates this fusion event following entry into the endocytic pathway, allowing the viral genome access to the cell cytoplasm. Here, we show that peptides analogous to the RVFV Gc stem region inhibited RVFV infectivity in cell culture by inhibiting the fusion process. Further, we show that infectivity can be inhibited for diverse, unrelated RNA viruses that have Class I (Ebola virus), Class II (Andes virus), or Class III (vesicular stomatitis virus) fusion proteins using this single peptide. Our findings are consistent with an inhibition mechanism similar to that proposed for stem peptide fusion inhibitors of dengue virus in which the RVFV inhibitory peptide first binds to both the virion and cell membranes, allowing it to traffic with the virus into the endocytic pathway. Upon acidification and rearrangement of Gc, the peptide is then able to specifically bind to Gc and prevent fusion of the viral and endocytic membranes, thus inhibiting viral infection. These results could provide novel insights into conserved features among the three classes of viral fusion proteins and offer direction for the future development of broadly active fusion inhibitors.
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3-Substituted Indole Inhibitors Against Francisella tularensis FabI Identified by Structure-Based Virtual Screening.
J. Med. Chem.
PUBLISHED: 07-01-2013
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In this study, we describe novel inhibitors against Francisella tularensis SchuS4 FabI identified from structure-based in silico screening with integrated molecular dynamics simulations to account for induced fit of a flexible loop crucial for inhibitor binding. Two 3-substituted indoles, 54 and 57, preferentially bound the NAD(+) form of the enzyme and inhibited growth of F. tularensis SchuS4 at concentrations near that of their measured Ki. While 57 was species-specific, 54 showed a broader spectrum of growth inhibition against F. tularensis , Bacillus anthracis , and Staphylococcus aureus . Binding interaction analysis in conjunction with site-directed mutagenesis revealed key residues and elements that contribute to inhibitor binding and species specificity. Mutation of Arg-96, a poorly conserved residue opposite the loop, was unexpectedly found to enhance inhibitor binding in the R96G and R96M variants. This residue may affect the stability and closure of the flexible loop to enhance inhibitor (or substrate) binding.
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Bridging the gap between gene expression and metabolic phenotype via kinetic models.
BMC Syst Biol
PUBLISHED: 06-27-2013
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Despite the close association between gene expression and metabolism, experimental evidence shows that gene expression levels alone cannot predict metabolic phenotypes, indicating a knowledge gap in our understanding of how these processes are connected. Here, we present a method that integrates transcriptome, fluxome, and metabolome data using kinetic models to create a mechanistic link between gene expression and metabolism.
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Novel Burkholderia mallei virulence factors linked to specific host-pathogen protein interactions.
Mol. Cell Proteomics
PUBLISHED: 06-24-2013
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Burkholderia mallei is an infectious intracellular pathogen whose virulence and resistance to antibiotics makes it a potential bioterrorism agent. Given its genetic origin as a commensal soil organism, it is equipped with an extensive and varied set of adapted mechanisms to cope with and modulate host-cell environments. One essential virulence mechanism constitutes the specialized secretion systems that are designed to penetrate host-cell membranes and insert pathogen proteins directly into the host cells cytosol. However, the secretion systems proteins and, in particular, their host targets are largely uncharacterized. Here, we used a combined in silico, in vitro, and in vivo approach to identify B. mallei proteins required for pathogenicity. We used bioinformatics tools, including orthology detection and ab initio predictions of secretion system proteins, as well as published experimental Burkholderia data to initially select a small number of proteins as putative virulence factors. We then used yeast two-hybrid assays against normalized whole human and whole murine proteome libraries to detect and identify interactions among each of these bacterial proteins and host proteins. Analysis of such interactions provided both verification of known virulence factors and identification of three new putative virulence proteins. We successfully created insertion mutants for each of these three proteins using the virulent B. mallei ATCC 23344 strain. We exposed BALB/c mice to mutant strains and the wild-type strain in an aerosol challenge model using lethal B. mallei doses. In each set of experiments, mice exposed to mutant strains survived for the 21-day duration of the experiment, whereas mice exposed to the wild-type strain rapidly died. Given their in vivo role in pathogenicity, and based on the yeast two-hybrid interaction data, these results point to the importance of these pathogen proteins in modulating host ubiquitination pathways, phagosomal escape, and actin-cytoskeleton rearrangement processes.
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Nonyloxytryptamine mimics polysialic acid and modulates neuronal and glial functions in cell culture.
J. Neurochem.
PUBLISHED: 05-27-2013
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Polysialic acid (PSA) is a major regulator of cell-cell interactions in the developing nervous system and in neural plasticity in the adult. As a polyanionic molecule with high water-binding capacity, PSA increases the intercellular space generating permissive conditions for cell motility. PSA enhances stem cell migration and axon path finding and promotes repair in the lesioned peripheral and central nervous systems, thus contributing to regeneration. As a next step in developing an improved PSA-based approach to treat nervous system injuries, we searched for small organic compounds that mimic PSA and identified as a PSA mimetic 5-nonyloxytryptamine oxalate, described as a selective 5-hydroxytryptamine receptor 1B (5-HT1B ) agonist. Similar to PSA, 5-nonyloxytryptamine binds to the PSA-specific monoclonal antibody 735, enhances neurite outgrowth of cultured primary neurons and process formation of Schwann cells, protects neurons from oxidative stress, reduces migration of astrocytes and enhances myelination in vitro. Furthermore, nonyloxytryptamine treatment enhances expression of the neural cell adhesion molecule (NCAM) and its polysialylated form PSA-NCAM and reduces expression of the microtubule-associated protein MAP2 in cultured neuroblastoma cells. These results demonstrate that 5-nonyloxytryptamine mimics PSA and triggers PSA-mediated functions, thus contributing to the repertoire of molecules with the potential to improve recovery in acute and chronic injuries of the mammalian peripheral and central nervous systems. Polysialic acid (PSA) plays important roles in nervous system development, as well as synaptic plasticity and regeneration in the adult. 5-Nonyloxytryptamine oxalate (5-NOT) mimics PSA and triggers PSA-mediated functions in neurons and glial cells. 5-NOT stimulates neuritogenesis, myelination and Schwann cell migration. This study sets the basis to develop a PSA-mediated therapy of acute and chronic nervous system diseases.
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Reconstituting protein interaction networks using parameter-dependent domain-domain interactions.
BMC Bioinformatics
PUBLISHED: 04-05-2013
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We can describe protein-protein interactions (PPIs) as sets of distinct domain-domain interactions (DDIs) that mediate the physical interactions between proteins. Experimental data confirm that DDIs are more consistent than their corresponding PPIs, lending support to the notion that analyses of DDIs may improve our understanding of PPIs and lead to further insights into cellular function, disease, and evolution. However, currently available experimental DDI data cover only a small fraction of all existing PPIs and, in the absence of structural data, determining which particular DDI mediates any given PPI is a challenge.
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Identifying cytochrome p450 functional networks and their allosteric regulatory elements.
PLoS ONE
PUBLISHED: 01-01-2013
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Cytochrome P450 (CYP) enzymes play key roles in drug metabolism and adverse drug-drug interactions. Despite tremendous efforts in the past decades, essential questions regarding the function and activity of CYPs remain unanswered. Here, we used a combination of sequence-based co-evolutionary analysis and structure-based anisotropic thermal diffusion (ATD) molecular dynamics simulations to detect allosteric networks of amino acid residues and characterize their biological and molecular functions. We investigated four CYP subfamilies (CYP1A, CYP2D, CYP2C, and CYP3A) that are involved in 90% of all metabolic drug transformations and identified four amino acid interaction networks associated with specific CYP functionalities, i.e., membrane binding, heme binding, catalytic activity, and dimerization. Interestingly, we did not detect any co-evolved substrate-binding network, suggesting that substrate recognition is specific for each subfamily. Analysis of the membrane binding networks revealed that different CYP proteins adopt different membrane-bound orientations, consistent with the differing substrate preference for each isoform. The catalytic networks were associated with conservation of catalytic function among CYP isoforms, whereas the dimerization network was specific to different CYP isoforms. We further applied low-temperature ATD simulations to verify proposed allosteric sites associated with the heme-binding network and their role in regulating metabolic fate. Our approach allowed for a broad characterization of CYP properties, such as membrane interactions, catalytic mechanisms, dimerization, and linking these to groups of residues that can serve as allosteric regulators. The presented combined co-evolutionary analysis and ATD simulation approach is also generally applicable to other biological systems where allostery plays a role.
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Computational and experimental validation of B and T-cell epitopes of the in vivo immune response to a novel malarial antigen.
PLoS ONE
PUBLISHED: 01-01-2013
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Vaccine development efforts will be guided by algorithms that predict immunogenic epitopes. Such prediction methods rely on classification-based algorithms that are trained against curated data sets of known B and T cell epitopes. It is unclear whether this empirical approach can be applied prospectively to predict epitopes associated with protective immunity for novel antigens. We present a comprehensive comparison of in silico B and T cell epitope predictions with in vivo validation using an previously uncharacterized malaria antigen, CelTOS. CelTOS has no known conserved structural elements with any known proteins, and thus is not represented in any epitope databases used to train prediction algorithms. This analysis represents a blind assessment of this approach in the context of a novel, immunologically relevant antigen. The limited accuracy of the tested algorithms to predict the in vivo immune responses emphasizes the need to improve their predictive capabilities for use as tools in vaccine design.
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Rapid countermeasure discovery against Francisella tularensis based on a metabolic network reconstruction.
PLoS ONE
PUBLISHED: 01-01-2013
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In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small-molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 µM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of active versus tested compounds over an elapsed time period of 32 weeks, from pathogen strain identification to selection and validation of novel antimicrobial compounds.
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Separation of Betti Reaction Product Enantiomers: Absolute Configuration and Inhibition of Botulinum Neurotoxin A.
ACS Med Chem Lett
PUBLISHED: 11-22-2011
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The racemic product of the Betti reaction of 5-chloro-8-hydroxyquinoline, benzaldehyde and 2-aminopyridine was separated by chiral HPLC to determine which enantiomer inhibited botulinum neurotoxin serotype A. When the enantiomers unexpectedly proved to have comparable activity, the absolute structures of (+)-(R)-1 and (-)-(S)-1 were determined by comparison of calculated and observed circular dichroism spectra. Molecular modeling studies were undertaken in an effort to understand the observed bioactivity and revealed different ensembles of binding modes, with roughly equal binding energies, for the two enantiomers.
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Improved Binding Free Energy Predictions from Single-Reference Thermodynamic Integration Augmented with Hamiltonian Replica Exchange.
J Chem Theory Comput
PUBLISHED: 11-03-2011
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Reliable predictions of relative binding free energies are essential in drug discovery, where chemists modify promising compounds with the aim of increasing binding affinity. Conventional Thermodynamic Integration (TI) approaches can estimate corresponding changes in binding free energies, but suffer from inadequate sampling due to ruggedness of the molecular energy surfaces. Here, we present an improved TI strategy for computing relative binding free energies of congeneric ligands. This strategy employs a specific, unphysical single-reference (SR) state and Hamiltonian replica exchange (HREX) to locally enhance sampling. We then apply this strategy to compute relative binding free energies of twelve ligands in the L99A mutant of T4 Lysozyme. Besides the ligands, our approach enhances hindered rotations of the important V111, as well as V87 and L118 sidechains. Concurrently, we devise practical strategies to monitor and improve HREX-SRTI efficiency. Overall, the HREX-SRTI results agree well (R(2) = 0.76, RMSE = 0.3 kcal/mol) with available experimental data. When optimized for efficiency, the HREX-SRTI precision matches that of experimental measurements.
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Categorizing biases in high-confidence high-throughput protein-protein interaction data sets.
Mol. Cell Proteomics
PUBLISHED: 08-29-2011
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We characterized and evaluated the functional attributes of three yeast high-confidence protein-protein interaction data sets derived from affinity purification/mass spectrometry, protein-fragment complementation assay, and yeast two-hybrid experiments. The interacting proteins retrieved from these data sets formed distinct, partially overlapping sets with different protein-protein interaction characteristics. These differences were primarily a function of the deployed experimental technologies used to recover these interactions. This affected the total coverage of interactions and was especially evident in the recovery of interactions among different functional classes of proteins. We found that the interaction data obtained by the yeast two-hybrid method was the least biased toward any particular functional characterization. In contrast, interacting proteins in the affinity purification/mass spectrometry and protein-fragment complementation assay data sets were over- and under-represented among distinct and different functional categories. We delineated how these differences affected protein complex organization in the network of interactions, in particular for strongly interacting complexes (e.g. RNA and protein synthesis) versus weak and transient interacting complexes (e.g. protein transport). We quantified methodological differences in detecting protein interactions from larger protein complexes, in the correlation of protein abundance among interacting proteins, and in their connectivity of essential proteins. In the latter case, we showed that minimizing inherent methodology biases removed many of the ambiguous conclusions about protein essentiality and protein connectivity. We used these findings to rationalize how biological insights obtained by analyzing data sets originating from different sources sometimes do not agree or may even contradict each other. An important corollary of this work was that discrepancies in biological insights did not necessarily imply that one detection methodology was better or worse, but rather that, to a large extent, the insights reflected the methodological biases themselves. Consequently, interpreting the protein interaction data within their experimental or cellular context provided the best avenue for overcoming biases and inferring biological knowledge.
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Novel plant-derived recombinant human interferons with broad spectrum antiviral activity.
Antiviral Res.
PUBLISHED: 08-12-2011
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Type I interferons (IFNs) are potent mediators of the innate immune response to viral infection. IFNs released from infected cells bind to a receptor (IFNAR) on neighboring cells, triggering signaling cascades that limit further infection. Subtle variations in amino acids can alter IFNAR binding and signaling outcomes. We used a new gene crossbreeding method to generate hybrid, type I human IFNs with enhanced antiviral activity against four dissimilar, highly pathogenic viruses. Approximately 1400 novel IFN genes were expressed in plants, and the resultant IFN proteins were screened for antiviral activity. Comparing the gene sequences of a final set of 12 potent IFNs to those of parent genes revealed strong selection pressures at numerous amino acids. Using three-dimensional models based on a recently solved experimental structure of IFN bound to IFNAR, we show that many but not all of the amino acids that were highly selected for are predicted to improve receptor binding.
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Modeling synergistic drug inhibition of Mycobacterium tuberculosis growth in murine macrophages.
Mol Biosyst
PUBLISHED: 06-29-2011
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We developed a metabolism-based systems biology framework to model drug-induced growth inhibition of Mycobacterium tuberculosis in murine macrophage cells. We used it to simulate ex vivo bacterial growth inhibition due to 3-nitropropionate (3-NP) and calculated the corresponding time- and drug concentration-dependent dose-response curves. 3-NP targets the isocitrate lyase 1 (ICL1) and ICL2 enzymes in the glyoxylate shunt, an essential component in carbon metabolism of many important prokaryotic organisms. We used the framework to in silico mimic drugging additional enzymes in combination with 3-NP to understand how synergy can arise among metabolic enzyme targets. In particular, we focused on exploring additional targets among the central carbon metabolism pathways and ascertaining the impact of jointly inhibiting these targets and the ICL1/ICL2 enzymes. Thus, additionally inhibiting the malate synthase (MS) enzyme in the glyoxylate shunt did not produce synergistic effects, whereas additional inhibition of the glycerol-3-phosphate dehydrogenase (G3PD) enzyme showed a reduction in bacterial growth beyond what each single inhibition could achieve. Whereas the ICL1/ICL2-MS pair essentially works on the same branch of the metabolic pathway processing lipids as carbon sources (the glyoxylate shunt), the ICL1/ICL2-G3PD pair inhibition targets different branches among the lipid utilization pathways. This allowed the ICL1/ICL2-G3PD drug combination to synergistically inhibit carbon processing and ultimately affect cellular growth. Our previously developed model for in vitro conditions failed to capture these effects, highlighting the importance of constructing accurate representations of the experimental ex vivo macrophage system.
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Classification of scaffold-hopping approaches.
Drug Discov. Today
PUBLISHED: 06-16-2011
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The general goal of drug discovery is to identify novel compounds that are active against a preselected biological target with acceptable pharmacological properties defined by marketed drugs. Scaffold hopping has been widely applied by medicinal chemists to discover equipotent compounds with novel backbones that have improved properties. In this article we classify scaffold hopping into four major categories, namely heterocycle replacements, ring opening or closure, peptidomimetics and topology-based hopping. We review the structural diversity of original and final scaffolds with respect to each category. We discuss the advantages and limitations of small, medium and large-step scaffold hopping. Finally, we summarize software that is frequently used to facilitate different kinds of scaffold-hopping methods.
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Spontaneous buckling of lipid bilayer and vesicle budding induced by antimicrobial peptide magainin 2: a coarse-grained simulation study.
J Phys Chem B
PUBLISHED: 06-09-2011
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Molecular mechanisms of the action of antimicrobial peptides on bacterial membranes were studied by large scale coarse-grained simulations of magainin 2-dipalmitoylphosphatidylcholine/palmitoyloleoylphosphatidylglycerol (DPPC/POPG) mixed bilayer systems with spatial extents up to 0.1 ?m containing up to 1600 peptides. Equilibrium simulations exhibit disordered toroidal pores stabilized by peptides. However, when a layer of peptides is placed near the lipid head groups on one side of the bilayer only, their incorporation leads to a spontaneous buckling of the bilayer. This buckling is followed by the formation of a quasi-spherical vesicular bud connected to the bilayer by a narrow neck. The mean curvature of the budding region is consistent with what is expected based on the dependence of the area per lipid on the peptide-to-lipid ratio in equilibrium simulations. Our simulations suggest that the incorporation of antimicrobial peptides on the exterior surface of a vesicle or a bacterial cell leads to buckling and vesicle budding, presumably accompanied by nucleations of giant transient pores of sizes that are much larger than indicated by equilibrium measurements and simulations.
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On the proper calculation of electrostatic interactions in solid-supported bilayer systems.
J Chem Phys
PUBLISHED: 02-10-2011
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Modeling systems that are not inherently isotropic, e.g., extended bilayers, using molecular simulation techniques poses a potential problem. Since these methods rely on a finite number of atoms and molecules to describe the system, periodic boundary conditions are implemented to avoid edge effects and capture long-range electrostatic interactions. Systems consisting of a solvated bilayer adsorbed on a solid surface and exposed to an air/vacuum interface occur in many experimental settings and present some unique challenges in this respect. Here, we investigated the effects of implementing different electrostatic boundary conditions on the structural and electrostatic properties of a quartz/water/vacuum interface and a similar quartz-supported hydrated lipid bilayer exposed to vacuum. Since these interfacial systems have a net polarization, implementing the standard Ewald summation with the conducting boundary condition for the electrostatic long-range interactions introduced an artificial periodicity in the out-of-plane dimension. In particular, abnormal orientational polarizations of water were observed with the conducting boundary condition. Implementing the Ewald summation technique with the planar vacuum boundary condition and calculating electrostatic properties compatible with the implemented electrostatic boundary condition removed these inconsistencies. This formulation is generally applicable to similar interfacial systems in bulk solution.
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Nonequilibrium phase transitions associated with DNA replication.
Phys. Rev. Lett.
PUBLISHED: 02-09-2011
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Thermodynamics governing the synthesis of DNA and RNA strands under a template is considered analytically and applied to the population dynamics of competing replicators. We find a nonequilibrium phase transition for high values of polymerase fidelity in a single replicator, where the two phases correspond to stationary states with higher elongation velocity and lower error rate than the other. At the critical point, the susceptibility linking velocity to thermodynamic force diverges. The overall behavior closely resembles the liquid-vapor phase transition in equilibrium. For a population of self-replicating macromolecules, Eigens error catastrophe transition precedes this thermodynamic phase transition during starvation. For a given thermodynamic force, the fitness of replicators increases with increasing polymerase fidelity above a threshold.
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Can computationally designed protein sequences improve secondary structure prediction?
Protein Eng. Des. Sel.
PUBLISHED: 01-31-2011
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Computational sequence design methods are used to engineer proteins with desired properties such as increased thermal stability and novel function. In addition, these algorithms can be used to identify an envelope of sequences that may be compatible with a particular protein fold topology. In this regard, we hypothesized that sequence-property prediction, specifically secondary structure, could be significantly enhanced by using a large database of computationally designed sequences. We performed a large-scale test of this hypothesis with 6511 diverse protein domains and 50 designed sequences per domain. After analysis of the inherent accuracy of the designed sequences database, we realized that it was necessary to put constraints on what fraction of the native sequence should be allowed to change. With mutational constraints, accuracy was improved vs. no constraints, but the diversity of designed sequences, and hence effective size of the database, was moderately reduced. Overall, the best three-state prediction accuracy (Q(3)) that we achieved was nearly a percentage point improved over using a natural sequence database alone, well below the theoretical possibility for improvement of 8-10 percentage points. Furthermore, our nascent method was used to augment the state-of-the-art PSIPRED program by a percentage point.
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Computing Relative Free Energies of Solvation using Single Reference Thermodynamic Integration Augmented with Hamiltonian Replica Exchange.
J Chem Theory Comput
PUBLISHED: 12-15-2010
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This paper introduces an efficient single-topology variant of Thermodynamic Integration (TI) for computing relative transformation free energies in a series of molecules with respect to a single reference state. The presented TI variant that we refer to as Single-Reference TI (SR-TI) combines well-established molecular simulation methodologies into a practical computational tool. Augmented with Hamiltonian Replica Exchange (HREX), the SR-TI variant can deliver enhanced sampling in select degrees of freedom. The utility of the SR-TI variant is demonstrated in calculations of relative solvation free energies for a series of benzene derivatives with increasing complexity. Noteworthy, the SR-TI variant with the HREX option provides converged results in a challenging case of an amide molecule with a high (13-15 kcal/mol) barrier for internal cis/trans interconversion using simulation times of only 1 to 4 ns.
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Unraveling the conundrum of seemingly discordant protein-protein interaction datasets.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 11-25-2010
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Most high-throughput experimental results of protein-protein interactions (PPIs) are seemingly inconsistent with each other. In this article, we re-evaluated these contradictions within the context of the underlying domain-domain interactions (DDIs) for two Escherichia coli and four Saccharomyces cerevisiae PPI datasets derived from high-throughput (yeast two-hybrid and tandem affinity purification) experimental platforms. For shared DDIs across pairs of compared datasets, we observed a remarkably high pair-wise correlation (Pearson correlation coefficient between 0.80 and 0.84) between datasets of the same organism derived from the same experimental platform. To a lesser degree, this concordance also held true for more general inter-platform and intra-species comparisons (Pearson correlation coefficient between 0.52 and 0.89). Thus, although varying experimental conditions can influence the ability of individual proteins to interact and, therefore, create apparent differences among PPIs, the physical nature of the underlying interactions, captured by DDIs, is the same and can be used to model and predict PPIs.
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Development and analysis of an in vivo-compatible metabolic network of Mycobacterium tuberculosis.
BMC Syst Biol
PUBLISHED: 03-12-2010
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During infection, Mycobacterium tuberculosis confronts a generally hostile and nutrient-poor in vivo host environment. Existing models and analyses of M. tuberculosis metabolic networks are able to reproduce experimentally measured cellular growth rates and identify genes required for growth in a range of different in vitro media. However, these models, under in vitro conditions, do not provide an adequate description of the metabolic processes required by the pathogen to infect and persist in a host.
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Interaction of the disordered Yersinia effector protein YopE with its cognate chaperone SycE.
Biochemistry
PUBLISHED: 11-03-2009
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We describe an efficient approach to model the binding interaction of the disordered effector protein to its cognate chaperone in the type III secretion system (T3SS). Starting from de novo models, we generated ensembles of unfolded conformations of the Yersinia effector YopE using REMD simulations and docked them to the chaperone SycE using a multistep protein docking strategy. The predicted YopE/SycE complex was in good agreement with the experimental structure. The ability of our computational protocol to mimic the structural transition upon chaperone binding opens up the possibility of studying the underlying specificity of chaperone/effector interactions and devising strategies for interfering with T3SS transport.
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Structure and dynamics of end-to-end loop formation of the penta-peptide Cys-Ala-Gly-Gln-Trp in implicit solvents.
J Phys Chem B
PUBLISHED: 08-19-2009
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To investigate the effects of implicit solvents on peptide structure and dynamics, we performed extensive molecular dynamics simulations on the penta-peptide Cys-Ala-Gly-Gln-Trp. Two different implicit solvent models based on the CHARMM22 all-atom force field were used. Structural properties of the peptide such as distributions of end-to-end distances and dihedral angles obtained from molecular dynamics simulations with implicit solvent models were in a good agreement with those obtained from a previous explicit solvent simulation using the same force field. Representative structures observed in explicit solvent were sampled by implicit solvent models but with different relative probabilities. However, we observed significant differences in dynamical properties in explicit and implicit solvent models when we used traditional methods for the temperature control, such as Nose-Hoover or Berendsen thermostats. The explicitly solvated peptide displayed the slowest dynamics in both end-to-end contact formation and intrinsic diffusive motion of end-to-end distances. A closer agreement between implicit and explicit solvated peptide dynamics was observed when Langevin dynamics with a friction coefficient of 10 ps(-1) was used to maintain the temperature of the systems.
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A novel scoring approach for protein co-purification data reveals high interaction specificity.
PLoS Comput. Biol.
PUBLISHED: 06-04-2009
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Large-scale protein interaction networks (PINs) have typically been discerned using affinity purification followed by mass spectrometry (AP/MS) and yeast two-hybrid (Y2H) techniques. It is generally recognized that Y2H screens detect direct binary interactions while the AP/MS method captures co-complex associations; however, the latter technique is known to yield prevalent false positives arising from a number of effects, including abundance. We describe a novel approach to compute the propensity for two proteins to co-purify in an AP/MS data set, thereby allowing us to assess the detected level of interaction specificity by analyzing the corresponding distribution of interaction scores. We find that two recent AP/MS data sets of yeast contain enrichments of specific, or high-scoring, associations as compared to commensurate random profiles, and that curated, direct physical interactions in two prominent data bases have consistently high scores. Our scored interaction data sets are generally more comprehensive than those of previous studies when compared against four diverse, high-quality reference sets. Furthermore, we find that our scored data sets are more enriched with curated, direct physical associations than Y2H sets. A high-confidence protein interaction network (PIN) derived from the AP/MS data is revealed to be highly modular, and we show that this topology is not the result of misrepresenting indirect associations as direct interactions. In fact, we propose that the modularity in Y2H data sets may be underrepresented, as they contain indirect associations that are significantly enriched with false negatives. The AP/MS PIN is also found to contain significant assortative mixing; however, in line with a previous study we confirm that Y2H interaction data show weak disassortativeness, thus revealing more clearly the distinctive natures of the interaction detection methods. We expect that our scored yeast data sets are ideal for further biological discovery and that our scoring system will prove useful for other AP/MS data sets.
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In silico analyses of substrate interactions with human serum paraoxonase 1.
Proteins
PUBLISHED: 05-30-2009
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Human paraoxonase (HuPON1) is a serum enzyme that exhibits a broad spectrum of hydrolytic activities, including the hydrolysis of various organophosphates, esters, and recently identified lactone substrates. Despite intensive site-directed mutagenesis and other biological studies, the structural basis for the specificity of substrate interactions of HuPON1 remains elusive. In this study, we apply homology modeling, docking, and molecular dynamic (MD) simulations to probe the binding interactions of HuPON1 with representative substrates. The results suggest that the active site of HuPON1 is characterized by two distinct binding regions: the hydrophobic binding site for arylesters/lactones, and the paraoxon binding site for phosphotriesters. The unique binding modes proposed for each type of substrate reveal a number of key residues governing substrate specificity. The polymorphic residue R/Q192 interacts with the leaving group of paraoxon, suggesting it plays an important role in the proper positioning of this substrate in the active site. MD simulations of the optimal binding complexes show that residue Y71 undergoes an "open-closed" conformational change upon ligand binding, and forms strong interactions with substrates. Further binding free energy calculations and residual decomposition give a more refined molecular view of the energetics and origin of HuPON1/substrate interactions. These studies provide a theoretical model of substrate binding and specificity associated with wild type and mutant forms of HuPON1, which can be applied in the rational design of HuPON1 variants as bioscavengers with enhanced catalytic activity.
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A systems biology framework for modeling metabolic enzyme inhibition of Mycobacterium tuberculosis.
BMC Syst Biol
PUBLISHED: 04-05-2009
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Because metabolism is fundamental in sustaining microbial life, drugs that target pathogen-specific metabolic enzymes and pathways can be very effective. In particular, the metabolic challenges faced by intracellular pathogens, such as Mycobacterium tuberculosis, residing in the infected host provide novel opportunities for therapeutic intervention.
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PSPP: a protein structure prediction pipeline for computing clusters.
PLoS ONE
PUBLISHED: 03-24-2009
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Protein structures are critical for understanding the mechanisms of biological systems and, subsequently, for drug and vaccine design. Unfortunately, protein sequence data exceed structural data by a factor of more than 200 to 1. This gap can be partially filled by using computational protein structure prediction. While structure prediction Web servers are a notable option, they often restrict the number of sequence queries and/or provide a limited set of prediction methodologies. Therefore, we present a standalone protein structure prediction software package suitable for high-throughput structural genomic applications that performs all three classes of prediction methodologies: comparative modeling, fold recognition, and ab initio. This software can be deployed on a users own high-performance computing cluster.
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FIEFDom: a transparent domain boundary recognition system using a fuzzy mean operator.
Nucleic Acids Res.
PUBLISHED: 02-28-2009
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Protein domain prediction is often the preliminary step in both experimental and computational protein research. Here we present a new method to predict the domain boundaries of a multidomain protein from its amino acid sequence using a fuzzy mean operator. Using the nr-sequence database together with a reference protein set (RPS) containing known domain boundaries, the operator is used to assign a likelihood value for each residue of the query sequence as belonging to a domain boundary. This procedure robustly identifies contiguous boundary regions. For a dataset with a maximum sequence identity of 30%, the average domain prediction accuracy of our method is 97% for one domain proteins and 58% for multidomain proteins. The presented model is capable of using new sequence/structure information without re-parameterization after each RPS update. When tested on a current database using a four year old RPS and on a database that contains different domain definitions than those used to train the models, our method consistently yielded the same accuracy while two other published methods did not. A comparison with other domain prediction methods used in the CASP7 competition indicates that our method performs better than existing sequence-based methods.
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Influence of protein abundance on high-throughput protein-protein interaction detection.
PLoS ONE
PUBLISHED: 02-27-2009
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Experimental protein-protein interaction (PPI) networks are increasingly being exploited in diverse ways for biological discovery. Accordingly, it is vital to discern their underlying natures by identifying and classifying the various types of deterministic (specific) and probabilistic (nonspecific) interactions detected. To this end, we have analyzed PPI networks determined using a range of high-throughput experimental techniques with the aim of systematically quantifying any biases that arise from the varying cellular abundances of the proteins. We confirm that PPI networks determined using affinity purification methods for yeast and Escherichia coli incorporate a correlation between protein degree, or number of interactions, and cellular abundance. The observed correlations are small but statistically significant and occur in both unprocessed (raw) and processed (high-confidence) data sets. In contrast, the yeast two-hybrid system yields networks that contain no such relationship. While previously commented based on mRNA abundance, our more extensive analysis based on protein abundance confirms a systematic difference between PPI networks determined from the two technologies. We additionally demonstrate that the centrality-lethality rule, which implies that higher-degree proteins are more likely to be essential, may be misleading, as protein abundance measurements identify essential proteins to be more prevalent than nonessential proteins. In fact, we generally find that when there is a degree/abundance correlation, the degree distributions of nonessential and essential proteins are also disparate. Conversely, when there is no degree/abundance correlation, the degree distributions of nonessential and essential proteins are not different. However, we show that essentiality manifests itself as a biological property in all of the yeast PPI networks investigated here via enrichments of interactions between essential proteins. These findings provide valuable insights into the underlying natures of the various high-throughput technologies utilized to detect PPIs and should lead to more effective strategies for the inference and analysis of high-quality PPI data sets.
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Bioinformatic analysis of patient-derived ASPS gene expressions and ASPL-TFE3 fusion transcript levels identify potential therapeutic targets.
PLoS ONE
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Gene expression data, collected from ASPS tumors of seven different patients and from one immortalized ASPS cell line (ASPS-1), was analyzed jointly with patient ASPL-TFE3 (t(X;17)(p11;q25)) fusion transcript data to identify disease-specific pathways and their component genes. Data analysis of the pooled patient and ASPS-1 gene expression data, using conventional clustering methods, revealed a relatively small set of pathways and genes characterizing the biology of ASPS. These results could be largely recapitulated using only the gene expression data collected from patient tumor samples. The concordance between expression measures derived from ASPS-1 and both pooled and individual patient tumor data provided a rationale for extending the analysis to include patient ASPL-TFE3 fusion transcript data. A novel linear model was exploited to link gene expressions to fusion transcript data and used to identify a small set of ASPS-specific pathways and their gene expression. Cellular pathways that appear aberrantly regulated in response to the t(X;17)(p11;q25) translocation include the cell cycle and cell adhesion. The identification of pathways and gene subsets characteristic of ASPS support current therapeutic strategies that target the FLT1 and MET, while also proposing additional targeting of genes found in pathways involved in the cell cycle (CHK1), cell adhesion (ARHGD1A), cell division (CDC6), control of meiosis (RAD51L3) and mitosis (BIRC5), and chemokine-related protein tyrosine kinase activity (CCL4).
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Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software.
Future Med Chem
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Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.
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Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia.
PLoS Comput. Biol.
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The ability to adapt to different conditions is key for Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), to successfully infect human hosts. Adaptations allow the organism to evade the host immune responses during acute infections and persist for an extended period of time during the latent infectious stage. In latently infected individuals, estimated to include one-third of the human population, the organism exists in a variety of metabolic states, which impedes the development of a simple strategy for controlling or eradicating this disease. Direct knowledge of the metabolic states of M. tuberculosis in patients would aid in the management of the disease as well as in forming the basis for developing new drugs and designing more efficacious drug cocktails. Here, we propose an in silico approach to create state-specific models based on readily available gene expression data. The coupling of differential gene expression data with a metabolic network model allowed us to characterize the metabolic adaptations of M. tuberculosis H37Rv to hypoxia. Given the microarray data for the alterations in gene expression, our model predicted reduced oxygen uptake, ATP production changes, and a global change from an oxidative to a reductive tricarboxylic acid (TCA) program. Alterations in the biomass composition indicated an increase in the cell wall metabolites required for cell-wall growth, as well as heightened accumulation of triacylglycerol in preparation for a low-nutrient, low metabolic activity life style. In contrast, the gene expression program in the deletion mutant of dosR, which encodes the immediate hypoxic response regulator, failed to adapt to low-oxygen stress. Our predictions were compatible with recent experimental observations of M. tuberculosis activity under hypoxic and anaerobic conditions. Importantly, alterations in the flow and accumulation of a particular metabolite were not necessarily directly linked to differential gene expression of the enzymes catalyzing the related metabolic reactions.
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QSAR classification model for antibacterial compounds and its use in virtual screening.
J Chem Inf Model
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As novel and drug-resistant bacterial strains continue to present an emerging health threat, the development of new antibacterial agents is critical. This includes making improvements to existing antibacterial scaffolds as well as identifying novel ones. The aim of this study is to apply a Bayesian classification QSAR approach to rapidly screen chemical libraries for compounds predicted to have antibacterial activity. Toward this end we assembled a data set of 317 known antibacterial compounds as well as a second data set of diverse, well-validated, non-antibacterial compounds from 215 PubChem Bioassays against various bacterial species. We constructed a Bayesian classification model using structural fingerprints and physicochemical property descriptors and achieved an accuracy of 84% and precision of 86% on an independent test set in identifying antibacterial compounds. To demonstrate the practical applicability of the model in virtual screening, we screened an independent data set of ~200k compounds. The results show that the model can screen top hits of PubChem Bioassay actives with accuracy up to ~76%, representing a 1.5-2-fold enrichment. The top screened hits represented a mixture of both known antibacterial scaffolds as well as novel scaffolds. Our study suggests that a well-validated Bayesian classification QSAR approach could compliment other screening approaches in identifying novel and promising hits. The data sets used in constructing and validating this model have been made publicly available.
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Locally weighted learning methods for predicting dose-dependent toxicity with application to the human maximum recommended daily dose.
Chem. Res. Toxicol.
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Toxicological experiments in animals are carried out to determine the type and severity of any potential toxic effect associated with a new lead compound. The collected data are then used to extrapolate the effects on humans and determine initial dose regimens for clinical trials. The underlying assumption is that the severity of the toxic effects in animals is correlated with that in humans. However, there is a general lack of toxic correlations across species. Thus, it is more advantageous to predict the toxicological effects of a compound on humans directly from the human toxicological data of related compounds. However, many popular quantitative structure-activity relationship (QSAR) methods that build a single global model by fitting all training data appear inappropriate for predicting toxicological effects of structurally diverse compounds because the observed toxicological effects may originate from very different and mostly unknown molecular mechanisms. In this article, we demonstrate, via application to the human maximum recommended daily dose data that locally weighted learning methods, such as k-nearest neighbors, are well suited for predicting toxicological effects of structurally diverse compounds. We also show that a significant flaw of the k-nearest neighbor method is that it always uses a constant number of nearest neighbors in making prediction for a target compound, irrespective of whether the nearest neighbors are structurally similar enough to the target compound to ensure that they share the same mechanism of action. To remedy this flaw, we proposed and implemented a variable number nearest neighbor method. The advantages of the variable number nearest neighbor method over other QSAR methods include (1) allowing more reliable predictions to be achieved by applying a tighter molecular distance threshold and (2) automatic detection for when a prediction should not be made because the compound is outside the applicable domain.
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Quantitative predictions of binding free energy changes in drug-resistant influenza neuraminidase.
PLoS Comput. Biol.
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Quantitatively predicting changes in drug sensitivity associated with residue mutations is a major challenge in structural biology. By expanding the limits of free energy calculations, we successfully identified mutations in influenza neuraminidase (NA) that confer drug resistance to two antiviral drugs, zanamivir and oseltamivir. We augmented molecular dynamics (MD) with Hamiltonian Replica Exchange and calculated binding free energy changes for H274Y, N294S, and Y252H mutants. Based on experimental data, our calculations achieved high accuracy and precision compared with results from established computational methods. Analysis of 15 micros of aggregated MD trajectories provided insights into the molecular mechanisms underlying drug resistance that are at odds with current interpretations of the crystallographic data. Contrary to the notion that resistance is caused by mutant-induced changes in hydrophobicity of the binding pocket, our simulations showed that drug resistance mutations in NA led to subtle rearrangements in the protein structure and its dynamics that together alter the active-site electrostatic environment and modulate inhibitor binding. Importantly, different mutations confer resistance through different conformational changes, suggesting that a generalized mechanism for NA drug resistance is unlikely.
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2D SMARTCyp reactivity-based site of metabolism prediction for major drug-metabolizing cytochrome P450 enzymes.
J Chem Inf Model
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Cytochrome P450 (CYP) 3A4, 2D6, 2C9, 2C19, and 1A2 are the most important drug-metabolizing enzymes in the human liver. Knowledge of which parts of a drug molecule are subject to metabolic reactions catalyzed by these enzymes is crucial for rational drug design to mitigate ADME/toxicity issues. SMARTCyp, a recently developed 2D ligand structure-based method, is able to predict site-specific metabolic reactivity of CYP3A4 and CYP2D6 substrates with an accuracy that rivals the best and more computationally demanding 3D structure-based methods. In this article, the SMARTCyp approach was extended to predict the metabolic hotspots for CYP2C9, CYP2C19, and CYP1A2 substrates. This was accomplished by taking into account the impact of a key substrate-receptor recognition feature of each enzyme as a correction term to the SMARTCyp reactivity. The corrected reactivity was then used to rank order the likely sites of CYP-mediated metabolic reactions. For 60 CYP1A2 substrates, the observed major sites of CYP1A2 catalyzed metabolic reactions were among the top-ranked 1, 2, and 3 positions in 67%, 80%, and 83% of the cases, respectively. The results were similar to those obtained by MetaSite and the reactivity + docking approach. For 70 CYP2C9 substrates, the observed sites of CYP2C9 metabolism were among the top-ranked 1, 2, and 3 positions in 66%, 86%, and 87% of the cases, respectively. These results were better than the corresponding results of StarDrop version 5.0, which were 61%, 73%, and 77%, respectively. For 36 compounds metabolized by CYP2C19, the observed sites of metabolism were found to be among the top-ranked 1, 2, and 3 sites in 78%, 89%, and 94% of the cases, respectively. The computational procedure was implemented as an extension to the program SMARTCyp 2.0. With the extension, the program can now predict the site of metabolism for all five major drug-metabolizing enzymes with an accuracy similar to or better than that achieved by the best 3D structure-based methods. Both the Java source code and the binary executable of the program are freely available to interested users.
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Inferring high-confidence human protein-protein interactions.
BMC Bioinformatics
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As numerous experimental factors drive the acquisition, identification, and interpretation of protein-protein interactions (PPIs), aggregated assemblies of human PPI data invariably contain experiment-dependent noise. Ascertaining the reliability of PPIs collected from these diverse studies and scoring them to infer high-confidence networks is a non-trivial task. Moreover, a large number of PPIs share the same number of reported occurrences, making it impossible to distinguish the reliability of these PPIs and rank-order them. For example, for the data analyzed here, we found that the majority (>83%) of currently available human PPIs have been reported only once.
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Free energy difference in indolicidin attraction to eukaryotic and prokaryotic model cell membranes.
J Phys Chem B
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We analyzed the thermodynamic and structural determinants of indolicidin interactions with eukaryotic and prokaryotic cell membranes using a series of atomistically detailed molecular dynamics simulations. We used quartz-supported bilayers with two different compositions of zwitterionic and anionic phospholipids as model eukaryotic and prokaryotic cell membranes. Indolicidin was preferentially attracted to the model prokaryotic cell membrane in contrast to the weak adsorption on the eukaryotic membrane. The nature of the indolicidin surface adsorption depended on an electrostatic guiding component, an attractive enthalpic component derived from van der Waals interactions, and a balance between entropic factors related to peptide confinement at the interface and counterion release from the bilayer surface. Thus, whereas we attributed the specificity of the indolicidin/membrane interaction to electrostatics, these interactions were not the sole contributors to the free energy of adsorption. Instead, a balance between an attractive van der Waals enthalpic component and a repulsive entropic component determined the overall strength of indolicidin adsorption.
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A physicochemical descriptor-based scoring scheme for effective and rapid filtering of kinase-like chemical space.
J Cheminform
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The current chemical space of known small molecules is estimated to exceed 1060 structures. Though the largest physical compound repositories contain only a few tens of millions of unique compounds, virtual screening of databases of this size is still difficult. In recent years, the application of physicochemical descriptor-based profiling, such as Lipinskis rule-of-five for drug-likeness and Opreas criteria of lead-likeness, as early stage filters in drug discovery has gained widespread acceptance. In the current study, we outline a kinase-likeness scoring function based on known kinase inhibitors.
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Probing the donor and acceptor substrate specificity of the ?-glutamyl transpeptidase.
Biochemistry
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?-Glutamyl transpeptidase (GGT) is a two-substrate enzyme that plays a central role in glutathione metabolism and is a potential target for drug design. GGT catalyzes the cleavage of ?-glutamyl donor substrates and the transfer of the ?-glutamyl moiety to an amine of an acceptor substrate or water. Although structures of bacterial GGT have revealed details of the protein-ligand interactions at the donor site, the acceptor substrate site is relatively undefined. The recent identification of a species-specific acceptor site inhibitor, OU749, suggests that these inhibitors may be less toxic than glutamine analogues. Here we investigated the donor and acceptor substrate preferences of Bacillus anthracis GGT (CapD) and applied computational approaches in combination with kinetics to probe the structural basis of the enzymes substrate and inhibitor binding specificities and compare them with human GGT. Site-directed mutagenesis studies showed that the R432A and R520S variants exhibited 6- and 95-fold decreases in hydrolase activity, respectively, and that their activity was not stimulated by the addition of the l-Cys acceptor substrate, suggesting an additional role in acceptor binding and/or catalysis of transpeptidation. Rat GGT (and presumably HuGGT) has strict stereospecificity for L-amino acid acceptor substrates, while CapD can utilize both L- and D-acceptor substrates comparably. Modeling and kinetic analysis suggest that R520 and R432 allow two alternate acceptor substrate binding modes for L- and D-acceptors. R432 is conserved in Francisella tularensis, Yersinia pestis, Burkholderia mallei, Helicobacter pylori and Escherichia coli, but not in human GGT. Docking and MD simulations point toward key residues that contribute to inhibitor and acceptor substrate binding, providing a guide to designing novel and specific GGT inhibitors.
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