Analysis of Human Dopamine D3 Receptor Quaternary Structure

The dopamine D₃ receptor is a class A, rhodopsin-like G protein-coupled receptor that can form dimers and/or higher order oligomers. However, the molecular basis for production of these complexes is not well defined. Using combinations of molecular modeling, site-directed mutagenesis, and homogenous time-resolved FRET, the interfaces that allow dopamine D₃ receptor monomers to interact were defined and used to describe likely quaternary arrangements of the receptor. These were then compared with published crystal structures of dimeric β₁-adrenoreceptor, μ-opioid, and CXCR4 receptors. The data indicate important contributions of residues from within each of transmembrane domains I, II, IV, V, VI, and VII as well as the intracellular helix VIII in the formation of D₃-D₃ receptor interfaces within homo-oligomers and are consistent with the D₃ receptor adopting a β₁-adrenoreceptor-like quaternary arrangement. Specifically, results suggest that D₃ protomers can interact with each other via at least two distinct interfaces: the first one comprising residues from transmembrane domains I and II along with those from helix VIII and a second one involving transmembrane domains IV and V. Moreover, rather than existing only as distinct dimeric species, the results are consistent with the D₃ receptor also assuming a quaternary structure in which two transmembrane domain I-II-helix VIII dimers interact to form a ”rhombic” tetramer via an interface involving residues from transmembrane domains VI and VII. In addition, the results also provide insights into the potential contribution of molecules of cholesterol to the overall organization and potential stability of the D₃ receptor and possibly other GPCR quaternary structures.     

A Toolbox for Representational Similarity Analysis

Neuronal population codes are increasingly being investigated with multivariate pattern-information analyses. A key challenge is to use measured brain-activity patterns to test computational models of brain information processing. One approach to this problem is representational similarity analysis (RSA), which characterizes a representation in a brain or computational model by the distance matrix of the response patterns elicited by a set of stimuli. The representational distance matrix encapsulates what distinctions between stimuli are emphasized and what distinctions are de-emphasized in the representation. A model is tested by comparing the representational distance matrix it predicts to that of a measured brain region. RSA also enables us to compare representations between stages of processing within a given brain or model, between brain and behavioral data, and between individuals and species. Here, we introduce a Matlab toolbox for RSA. The toolbox supports an analysis approach that is simultaneously data- and hypothesis-driven. It is designed to help integrate a wide range of computational models into the analysis of multichannel brain-activity measurements as provided by modern functional imaging and neuronal recording techniques. Tools for visualization and inference enable the user to relate sets of models to sets of brain regions and to statistically test and compare the models using nonparametric inference methods. The toolbox supports searchlight-based RSA, to continuously map a measured brain volume in search of a neuronal population code with a specific geometry. Finally, we introduce the linear-discriminant t value as a measure of representational discriminability that bridges the gap between linear decoding analyses and RSA. In order to demonstrate the capabilities of the toolbox, we apply it to both simulated and real fMRI data. The key functions are equally applicable to other modalities of brain-activity measurement. The toolbox is freely available to the community under an open-source license agreement (http://www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/).

This is a PLOS Computational Biology Software Article

     

PathVisio 3: An Extendable Pathway Analysis Toolbox

PathVisio is a commonly used pathway editor, visualization and analysis software. Biological pathways have been used by biologists for many years to describe the detailed steps in biological processes. Those powerful, visual representations help researchers to better understand, share and discuss knowledge. Since the first publication of PathVisio in 2008, the original paper was cited more than 170 times and PathVisio was used in many different biological studies. As an online editor PathVisio is also integrated in the community curated pathway database WikiPathways.Here we present the third version of PathVisio with the newest additions and improvements of the application. The core features of PathVisio are pathway drawing, advanced data visualization and pathway statistics. Additionally, PathVisio 3 introduces a new powerful extension systems that allows other developers to contribute additional functionality in form of plugins without changing the core application.PathVisio can be downloaded from http://www.pathvisio.org and in 2014 PathVisio 3 has been downloaded over 5,500 times. There are already more than 15 plugins available in the central plugin repository. PathVisio is a freely available, open-source tool published under the Apache 2.0 license (http://www.apache.org/licenses/LICENSE-2.0). It is implemented in Java and thus runs on all major operating systems. The code repository is available at http://svn.bigcat.unimaas.nl/pathvisio. The support mailing list for users is available on https://groups.google.com/forum/#!forum/wikipathways-discuss and for developers on https://groups.google.com/forum/#!forum/wikipathways-devel.

This is a PLOS Computational Biology software article.

     

PATBox: A Toolbox for Classification and Analysis of P-Type ATPases

P-Type ATPases are part of the regulatory system of the cell where they are responsible for transporting ions and lipids through the cell membrane. These pumps are found in all eukaryotes and their malfunction has been found to cause several severe diseases. Knowing which substrate is pumped by a certain P-Type ATPase is therefore vital. The P-Type ATPases can be divided into 11 subtypes based on their specificity, that is, the substrate that they pump. Determining the subtype experimentally is time-consuming. Thus it is of great interest to be able to accurately predict the subtype based on the amino acid sequence only. We present an approach to P-Type ATPase sequence classification based on the k-nearest neighbors, similar to a homology search, and show that this method provides performs very well and, to the best of our knowledge, better than any existing method despite its simplicity. The classifier is made available as a web service at http://services.birc.au.dk/patbox/ which also provides access to a database of potential P-Type ATPases and their predicted subtypes.     

SAMA: A Method for 3D Morphological Analysis

Three-dimensional (3D) culture models are critical tools for understanding tissue morphogenesis. A key requirement for their analysis is the ability to reconstruct the tissue into computational models that allow quantitative evaluation of the formed structures. Here, we present Software for Automated Morphological Analysis (SAMA), a method by which epithelial structures grown in 3D cultures can be imaged, reconstructed and analyzed with minimum human intervention. SAMA allows quantitative analysis of key features of epithelial morphogenesis such as ductal elongation, branching and lumen formation that distinguish different hormonal treatments. SAMA is a user-friendly set of customized macros operated via FIJI (http://fiji.sc/Fiji), an open-source image analysis platform in combination with a set of functions in R (http://www.r-project.org/), an open-source program for statistical analysis. SAMA enables a rapid, exhaustive and quantitative 3D analysis of the shape of a population of structures in a 3D image. SAMA is cross-platform, licensed under the GPLv3 and available at http://montevil.theobio.org/content/sama.     

Phytochrome: A Re-examination of the Quaternary Structure

Highly purified phytochrome samples from rye (Secale Cereale cv. Cougar) were fractionated by ultracentrifugation in isokinetic sucrose density gradients. Three protein species were separated with estimated sedimentation coefficients of 6.5S, 8.0S, and 11.5S. The 6.5S and 8.0S forms contained photoreversible phytochrome and produced a single subunit of 120,000 molecular weight upon reduction and electrophoresis in the presence of sodium dodecyl sulfate. The 11.5S species contained no detectable phytochrome. Reduction and electrophoresis of the 11.5S species in the presence of sodium dodecyl sulfate produced a major polypeptide of 32,000 molecular weight and a minor polypeptide of 48,000 molecular weight. The square tetrameric structures, observed by electron microscopy and previously thought to be phytochrome molecules, were found to be due to the presence of this 11.5S species in phytochrome preparations.     

aBEAT: A Toolbox for Consistent Analysis of Longitudinal Adult Brain MRI

Longitudinal brain image analysis is critical for revealing subtle but complex structural and functional changes of brain during aging or in neurodevelopmental disease. However, even with the rapid increase of clinical research and trials, a software toolbox dedicated for longitudinal image analysis is still lacking publicly. To cater for this increasing need, we have developed a dedicated 4D Adult Brain Extraction and Analysis Toolbox (aBEAT) to provide robust and accurate analysis of the longitudinal adult brain MR images. Specially, a group of image processing tools were integrated into aBEAT, including 4D brain extraction, 4D tissue segmentation, and 4D brain labeling. First, a 4D deformable-surface-based brain extraction algorithm, which can deform serial brain surfaces simultaneously under temporal smoothness constraint, was developed for consistent brain extraction. Second, a level-sets-based 4D tissue segmentation algorithm that incorporates local intensity distribution, spatial cortical-thickness constraint, and temporal cortical-thickness consistency was also included in aBEAT for consistent brain tissue segmentation. Third, a longitudinal groupwise image registration framework was further integrated into aBEAT for consistent ROI labeling by simultaneously warping a pre-labeled brain atlas to the longitudinal brain images. The performance of aBEAT has been extensively evaluated on a large number of longitudinal MR T1 images which include normal and dementia subjects, achieving very promising results. A Linux-based standalone package of aBEAT is now freely available at http://www.nitrc.org/projects/abeat.     

3D Modeling for TM Receptors: Algorithms and Validations

Summary This paper outlines the basic strategy to build 3D models of transmembrane G-protein coupled receptors (GPCRs) starting from their amino acid sequences in a ‘block-by-block’ manner: (i) locate possible TM helical fragments in the GPCR sequence; (ii) build 3D structures for these helices; (iii) arrange isolated helices across the membrane; (iv) calculate all pairwise helix-helix interactions; (v) assemble helical bundle(s); (vi) restore interhelical loops and N- and C-termini; and (vii) refine the entire 3D structure(s). Computer algorithms and preliminary results for most of the steps are discussed.     

3D Modeling for TM Receptors: Algorithms and Validations

This paper outlines the basic strategy to build 3D models of transmembrane G-protein coupled receptors (GPCRs) starting from their amino acid sequences in a block-by-block manner: (i) locate possible TM helical fragments in the GPCR sequence; (ii) build 3D structures for these helices; (iii) arrange isolated helices across the membrane; (iv) calculate all pairwise helix-helix interactions; (v) assemble helical bundle(s); (vi) restore interhelical loops and N- and C-termini; and (vii) refine the entire 3D structure(s). Computer algorithms and preliminary results for most of the steps are discussed.     

CARd-3D: Carbon Distribution in 3D Structure Program for Globular Proteins

Spatial arrangement of carbon in protein structure is analyzed here. Particularly, the carbon fractions around individual atoms arecompared. It is hoped that it follows the principle of 31.45% carbon around individual atoms. The results reveal that globularprotein''s atoms follow this principle. A comparative study on monomer versus dimer reveal that carbon is better distributed indimeric form than in its monomeric form. Similar study on solid versus liquid structures reveals that the liquid (NMR) structurehas better carbon distribution over the corresponding solid (X-Ray) structure. The carbon fraction distributions in fiber and toxinprotein are compared. Fiber proteins follow the principle of carbon fraction distribution. At the same time it has another broadspectrum of carbon distribution than in globular proteins. The toxin protein follows an abnormal carbon fraction distribution. Thecarbon fraction distribution plays an important role in deciding the structure and shape of proteins. It is hoped to help inunderstanding the protein folding and function.     

Automated Microarray Image Analysis Toolbox for MATLAB

Summary: The Automated Microarray Image Analysis (AMIA) Toolboxfor MATLAB is a flexible, open-source, microarray image analysistool that allows the user to customize analyses of microarrayimage sets. This tool provides several methods to identify andquantify spot statistics, as well as extensive diagnostic statisticsand images to evaluate data quality and array processing. Theopen, modular nature of AMIA provides access to implementationdetails and encourages modification and extension of AMIA'scapabilities. Availability: The AMIA Toolbox is freely available at http://www.pnl.gov/statistics/amia.The AMIA Toolbox requires MATLAB 6.5 (R13) (MathWorks, Inc.Natick, MA), as well as the Statistics Toolbox 4.1 and ImageProcessing Toolbox 4.1 for MATLAB or more recentversions. Contact: amanda.white{at}pnl.gov     

Structure of FGFR3 Transmembrane Domain Dimer: Implications for Signaling and Human Pathologies

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t-LSE: A Novel Robust Geometric Approach for Modeling Protein-Protein Interaction Networks

Protein-protein interaction (PPI) networks provide insights into understanding of biological processes, function and the underlying complex evolutionary mechanisms of the cell. Modeling PPI network is an important and fundamental problem in system biology, where it is still of major concern to find a better fitting model that requires less structural assumptions and is more robust against the large fraction of noisy PPIs. In this paper, we propose a new approach called t-logistic semantic embedding (t-LSE) to model PPI networks. t-LSE tries to adaptively learn a metric embedding under the simple geometric assumption of PPI networks, and a non-convex cost function was adopted to deal with the noise in PPI networks. The experimental results show the superiority of the fit of t-LSE over other network models to PPI data. Furthermore, the robust loss function adopted here leads to big improvements for dealing with the noise in PPI network. The proposed model could thus facilitate further graph-based studies of PPIs and may help infer the hidden underlying biological knowledge. The Matlab code implementing the proposed method is freely available from the web site: http://home.ustc.edu.cn/~yzh33108/PPIModel.htm.     

The Use of 3-D Culture in Peptide Hydrogel for Analysis of Discoidin Domain Receptor 1-Collagen Interaction

The aim of this study is to examine a novel drop culture model using a biologically inspired self-assembling peptide: hydrogel (RAD16-I, also called PuraMatrix), which produces a nanoscale environment similar to native extracellular matrix (ECM) for a cell line weakly adherent to a plastic surface during cell culture. Our work investigates quantitatively analyzing discoidin domain receptor (DDR) 1-mediated protein interactions between collagen type I and matrix metalloproteinase (MMP)-2 or -9, as well as cell invasion, using, as a scaffold, PuraMatrix, a novel peptide hydrogel. Results demonstrate that the dynamic cell culture technique produced a highly stable reharvesting of cells throughout the constructs with HP-75, human pituitary adenoma cell line when compared to the traditional seeding methods. Secretion of MMP via collagen type I was observed quantitatively in the supernatant (EC50; MMP-2, 50.4 ng/ml: MMP-9, 57.6 ng/ml). In PuraMatrix gel impregnated with 50 ng/ml of collagen type I, transfection of the vector encoding full-length DDR1 or siRNA targeting DDR1 up- or down-regulated respectively secretion of MMP-2 and -9, and cell invasion. Our results show that incorporation of this peptide with each ECM component provides a more permissive environment to elucidate ECM to cell signal interaction.     

The Use of 3-D Culture in Peptide Hydrogel for Analysis of Discoidin Domain Receptor 1-Collagen Interaction

The aim of this study is to examine a novel drop culture model using a biologically inspired self-assembling peptide: hydrogel (RAD16-I, also called PuraMatrix), which produces a nanoscale environment similar to native extracellular matrix (ECM) for a cell line weakly adherent to a plastic surface during cell culture. Our work investigates quantitatively analyzing discoidin domain receptor (DDR) 1-mediated protein interactions between collagen type I and matrix metalloproteinase (MMP)-2 or -9, as well as cell invasion, using, as a scaffold, PuraMatrix, a novel peptide hydrogel. Results demonstrate that the dynamic cell culture technique produced a highly stable reharvesting of cells throughout the constructs with HP-75, human pituitary adenoma cell line when compared to the traditional seeding methods. Secretion of MMP via collagen type I was observed quantitatively in the supernatant (EC50; MMP-2, 50.4 ng/ml: MMP-9, 57.6 ng/ml). In PuraMatrix gel impregnated with 50 ng/ml of collagen type I, transfection of the vector encoding full-length DDR1 or siRNA targeting DDR1 up- or downregulated respectively secretion of MMP-2 and -9, and cell invasion. Our results show that incorporation of this peptide with each ECM component provides a more permissive environment to elucidate ECM to cell signal interaction.Key Words: biological scaffolds, cell invasion, ionic self-complementary peptides, nano-fiber hydrogels, pituitary adenoma     

QUAFIT: A Novel Method for the Quaternary Structure Determination from Small-Angle Scattering Data

The new QUAFIT method for determining the quaternary structure of biological macromolecular assemblies by analyzing x-ray or neutron small-angle scattering data is presented. The method is based on the idea that asymmetric monomers, formed by rigid domains of known atomic structure possibly connected by flexible linkers of known sequence, are assembled according to a point-group symmetry combined with a screw axis. Scattering amplitudes of domains and linkers are determined by means of a spherical harmonics expansion and combined to get the form factor of the assembly. To avoid any overlap among domains, the contact distance between two asymmetric domains is determined as a function of their orientation by a new algorithm, based on Stone's Invariants expansion. To account for continuity and compactness of the whole assembly, an anisotropic Lennard-Jones potential among domains, written in terms of the contact distances, is included in the merit function. QUAFIT allows for the simultaneous presence of oligomerization intermediates as well as of monomers distributed over multiple conformations. QUAFIT has been tested by studying the structure of a high molecular weight protein, the hemocyanin from Octopus vulgaris, under solution conditions that stabilize the decameric form or induce dissociation into monomers, respectively. Results are in very good agreement with the structural model derived from electron microscopy observations.     

A Generalized Approach to the Modeling and Analysis of 3D Surface Morphology in Organisms

The surface geometry of an organism represents the boundary of its three-dimensional (3D) form and can be used as a proxy for the phenotype. A mathematical approach is presented that describes surface morphology using parametric 3D equations with variables expressed as x, y, z in terms of parameters u, v. Partial differentiation of variables with respect to parameters yields elements of the Jacobian representing tangent lines and planes of every point on the surface. Jacobian elements provide a compact size-free summary of the entire surface, and can be used as variables in principal components analysis to produce a morphospace. Mollusk and echinoid models are generated to demonstrate that whole organisms can be represented in a common morphospace, regardless of differences in size, geometry, and taxonomic affinity. Models can be used to simulate theoretical forms, novel morphologies, and patterns of phenotypic variation, and can also be empirically-based by designing them with reference to actual forms using reverse engineering principles. Although this study uses the Jacobian to summarize models, they can also be analyzed with 3D methods such as eigensurface, spherical harmonics, wavelet analysis, and geometric morphometrics. This general approach should prove useful for exploring broad questions regarding morphological evolution and variation.