A Refined Model for the Structure of Acireductone Dioxygenase from Klebsiella ATCC 8724 Incorporating Residual Dipolar Couplings

Acireductone dioxygenase (ARD) from Klebsiella ATCC 8724 is a metalloenzyme that is capable of catalyzing different reactions with the same substrates (acireductone and O₂) depending upon the metal bound in the active site. A model for the solution structure of the paramagnetic Ni²⁺-containing ARD has been refined using residual dipolar couplings (RDCs) measured in two media. Additional dihedral restraints based on chemical shift (TALOS) were included in the refinement, and backbone structure in the vicinity of the active site was modeled from a crystallographic structure of the mouse homolog of ARD. The incorporation of residual dipolar couplings into the structural refinement alters the relative orientations of several structural features significantly, and improves local secondary structure determination. Comparisons between the solution structures obtained with and without RDCs are made, and structural similarities and differences between mouse and bacterial enzymes are described. Finally, the biological significance of these differences is considered.     

Using homology modeling to interrogate binding affinity in neutralization of ricin toxin by a family of single domain antibodies

In this report we investigated, within a group of closely related single domain camelid antibodies (V_HHs), the relationship between binding affinity and neutralizing activity as it pertains to ricin, a fast‐acting toxin and biothreat agent. The V1C7‐like V_HHs (V1C7, V2B9, V2E8, and V5C1) are similar in amino acid sequence, but differ in their binding affinities and toxin‐neutralizing activities. Using the X‐ray crystal structure of V1C7 in complex with ricin's enzymatic subunit (RTA) as a template, Rosetta‐based homology modeling coupled with energetic decomposition led us to predict that a single pairwise interaction between Arg29 on V5C1 and Glu67 on RTA was responsible for the difference in ricin toxin binding affinity between V1C7, a weak neutralizer, and V5C1, a moderate neutralizer. This prediction was borne out experimentally: substitution of Arg for Gly at position 29 enhanced V1C7's binding affinity for ricin, whereas the reverse (ie, Gly for Arg at position 29) diminished V5C1's binding affinity by >10 fold. As expected, the V5C1_R29G mutant was largely devoid of toxin‐neutralizing activity (TNA). However, the TNA of the V1C7_G29R mutant was not correspondingly improved, indicating that in the V1C7 family binding affinity alone does not account for differences in antibody function. V1C7 and V5C1, as well as their respective point mutants, recognized indistinguishable epitopes on RTA, at least at the level of sensitivity afforded by hydrogen‐deuterium mass spectrometry. The results of this study have implications for engineering therapeutic antibodies because they demonstrate that even subtle differences in epitope specificity can account for important differences in antibody function.     

Barred owl occupancy surveys within the range of the northern spotted owl

The range expansion by barred owls (Strix varia) into western North America has raised considerable concern regarding their potential effects on declining northern spotted owl (Strix occidentalis caurina) populations, yet most information on the occurrence of barred owls in the region is limited to incidental detections during surveys for spotted owls. To address this shortcoming we investigated response behavior, detection probabilities, and landscape occupancy patterns of barred owls in western Oregon, USA, during conspecific versus spotted owl call-broadcast surveys. Subtle differences in barred owl response behavior to conspecific versus spotted owl vocalizations combined with minor procedural differences between species-specific survey protocols led to a sizeable difference in estimated detection probabilities during conspecific (0.66, 95% CI = 0.61–0.71) versus spotted owl (0.48, 95% CI = 0.39–0.56) surveys. We identified 61 territorial pairs of barred owls during repeated surveys of a multi-ownership study area with the probability of occupancy being highest in the structurally diverse mixture of mature and old forests that occurred almost entirely on public lands. Our findings suggest that research and management strategies to address potential competitive interactions between spotted owls and barred owls will require carefully designed, species-specific survey methods that account for erratic response behaviors and imperfect detection of both species. Our sampling methods can be used by forest managers to determine the occurrence and distribution of barred owls with high confidence. © 2011 The Wildlife Society.     

Conformational control in structure-based drug design

In structure-based drug design, the basic goal is to design molecules that fit complementarily to a given binding pocket. Since such computationally modeled molecules may not adopt the intended bound conformation outside the binding pocket, one challenge is to ensure that the designed ligands adopt similar low energy conformations both inside and outside of the binding pocket. Computational chemistry methods and conformational preferences of small molecules from PDB and Cambridge Structural Database (CSD) can be used to predict the bound structures of the designed molecules. Herein, we review applications of conformational control in structure-based drug design using selected examples from the recent medicinal chemistry literature. The main purpose is to highlight some intriguing conformational features that can be applied to other drug discovery programs.     

Modelling and simulation of mutant alleles of breast cancer metastasis suppressor 1 (BRMS1) gene

Computational tools occupy the prime position in the analysis of large volume of post-genomic data. These tools have advantage over the wet lab experiments in terms of high coverage, cost and time. Breast cancer is the most common cancer in females worldwide. It is a genetically heterogeneous disorder and many genes are involved in the pathway of the disease. Mutations in metastasis suppressor gene are the major cause of the disease. In this study, the effects of mutations in breast cancer metastasis suppressor 1gene upon protein structure and function were examined by means of computational tools and information from databases.This study can be useful to predict the potential effect of every allelic variant, devise new biological experiments and to interpret and predict the patho-physiological impact of new mutations or non-synonymous polymorphisms.     

Production and characterization of recombinant P1 adhesin essential for adhesion,gliding, and antigenic variation in the human pathogenic bacterium,Mycoplasma pneumoniae

Mycoplasma pneumoniae forms an attachment organelle at one cell pole, binds to the host cell surface, and glides via a unique mechanism. A 170-kDa protein, P1 adhesin, present on the organelle surface plays a critical role in the binding and gliding process. In this study, we obtained a recombinant P1 adhesin comprising 1476 amino acid residues, excluding the C-terminal domain of 109 amino acids that carried the transmembrane segment, that were fused to additional 17 amino acid residues carrying a hexa-histidine (6?×?His) tag using an Escherichia coli expression system. The recombinant protein showed solubility, and chirality in circular dichroism (CD). The results of analytical gel filtration, ultracentrifugation, negative-staining electron microscopy, and small-angle X-ray scattering (SAXS) showed that the recombinant protein exists in a monomeric form with a uniformly folded structure. SAXS analysis suggested the presence of a compact and ellipsoidal structure rather than random or molten globule-like conformation. Structure model based on SAXS results fitted well with the corresponding structure obtained with cryo-electron tomography from a closely related species, M. genitalium. This recombinant protein may be useful for structural and functional studies as well as for the preparation of antibodies for medical applications.     

Fast and accurate modeling of protein-protein interactions by combining template-interface-based docking with flexible refinement

The similarity between folding and binding led us to posit the concept that the number of protein-protein interface motifs in nature is limited, and interacting protein pairs can use similar interface architectures repeatedly, even if their global folds completely vary. Thus, known protein-protein interface architectures can be used to model the complexes between two target proteins on the proteome scale, even if their global structures differ. This powerful concept is combined with a flexible refinement and global energy assessment tool. The accuracy of the method is highly dependent on the structural diversity of the interface architectures in the template dataset. Here, we validate this knowledge-based combinatorial method on the Docking Benchmark and show that it efficiently finds high-quality models for benchmark complexes and their binding regions even in the absence of template interfaces having sequence similarity to the targets. Compared to "classical" docking, it is computationally faster; as the number of target proteins increases, the difference becomes more dramatic. Further, it is able to distinguish binders from nonbinders. These features allow performing large-scale network modeling. The results on an independent target set (proteins in the p53 molecular interaction map) show that current method can be used to predict whether a given protein pair interacts. Overall, while constrained by the diversity of the template set, this approach efficiently produces high-quality models of protein-protein complexes. We expect that with the growing number of known interface architectures, this type of knowledge-based methods will be increasingly used by the broad proteomics community.     

Downscaling Aggregate Urban Metabolism Accounts to Local Districts

Urban metabolism accounts of total annual energy, water, and other resource flows are increasingly available for a variety of world cities. For local decision makers, however, it may be important to understand the variations of resource consumption within the city. Given the difficulty of gathering suburban resource consumption data for many cities, this article investigates the potential of statistical downscaling methods to estimate local resource consumption using socioeconomic or other data sources. We evaluate six classes of downscaling methods: ratio‐based normalization; linear regression (both internally and externally calibrated); linear regression with spatial autocorrelation; multilevel linear regression; and a basic Bayesian analysis. The methods were applied to domestic energy consumption in London, UK, and our results show that it is possible to downscale aggregate resource consumption to smaller geographies with an average absolute prediction error of around 20%; however, performance varies widely by method, geography size, and fuel type. We also show how mapping these results can quickly identify districts with noteworthy resource consumption profiles. Further work should explore the design of local data collection strategies to enhance these methods and apply the techniques to other urban resources such as water or waste.     

Nonparametric Analysis of Thermal Proteome Profiles Reveals Novel Drug-binding Proteins*

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Highlights

• •Method for the analysis of response curves from thermal proteome profiling (TPP).
• •NPARC uses nonparametric statistics and provides false discovery-rate (FDR) control.
• •Increased proteome coverage and sensitivity to identify drug-binding proteins.