Enabling dynamic network analysis through visualization in TVNViewer

ABSTRACT: BACKGROUND: Many biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development of TVNViewer (http://sailing.cs.cmu.edu/tvnviewer), a visualization tool for dynamic network analysis. RESULTS: In this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets. CONCLUSIONS: TVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space.     

Investigation of silent information regulator 1 (Sirt1) agonists from Traditional Chinese Medicine

Silent information regulator 1 (Sirt1), a class III nicotinamide adenine dinucleotide dependent histone deacetylases, is important in cardioprotection, neuroprotection, metabolic disease, calorie restriction, and diseases associated with aging. Traditional Chinese Medicine (TCM) compounds from TCM Database@Taiwan (http://tcm.cmu.edu.tw/) were employed for screening potent Sirt1 agonists, and molecular dynamics (MD) simulation was implemented to simulate ligand optimum docking poses and protein structure under dynamic conditions. TCM compounds such as (S)-tryptophan-betaxanthin, 5-O-feruloylquinic acid, and RosA exhibited good binding affinity across different computational methods, and their drug-like potential were validated by MD simulation. Docking poses indicate that the carboxylic group of the three candidates generated H-bonds with residues in the protein chain from Ser441 to Lys444 and formed H-bond, π–cation interactions, or hydrophobic contacts with Phe297 and key active residue, His363. During MD, stable π–cation interactions with residues Phe273 or Arg274 were formed by (S)-tryptophan-betaxanthin and RosA. All candidates were anchored to His363 by stable π- or H-bonds. Hence, we propose (S)-tryptophan-betaxanthin, 5-O-feruloylquinic acid, and RosA as potential lead compounds that can be further tested in drug development process for diseases associated with aging

An animated interactive 3D complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:JBSD:28     

Three-in-one agonists for PPAR-α, PPAR-γ, and PPAR-δ from traditional Chinese medicine

Nowadays, the occurrence of metabolic syndrome, which is characterized by obesity and clinical disorders, has been increasing rapidly over the world. It induces several serious chronic diseases such as cardiovascular disease, dyslipidemia, gall bladder disease, hypertension, osteoarthritis, sleep apnea, stroke, and type 2 diabetes mellitus. Peroxisome proliferator-activated receptors (PPARs), which have three isoforms: PPAR-α, PPAR-γ, and PPAR-δ, are key regulators of adipogenesis, lipid and carbohydrate metabolism, and are potential drug targets for treating metabolic syndrome. The traditional Chinese medicine (TCM) compounds from TCM Database@Taiwan (http://tcm.cmu.edu.tw/) were employed to virtually screen for potential PPAR agonists, and structure-based pharmacophore models were generated to identify the key interactions for each PPAR protein. In addition, molecular dynamics (MD) simulation was performed to evaluate the stability of the PPAR–ligand complexes in a dynamic state. (S)-Tryptophan-betaxanthin and berberrubine, which have higher Dock Score than controls, form stable interactions during MD, and are further supported by the structure-based pharmacophore models in each PPAR protein. Key features include stable H-bonds with Thr279 and Ala333 of PPAR-α, with Thr252, Thr253 and Lys331 of PPAR-δ, and with Arg316 and Glu371 of PPAR-γ. Hence, we propose the top two TCM candidates as potential lead compounds in developing agonists targeting PPARs protein for treating metabolic syndrome.     

Interactive visualization software for exploring phylogenetic trees and clades

Summary: The Summary Tree Explorer (STE) is a Java applicationfor interactively exploring sets of phylogenetic trees usingtwo coupled representations: a node-and-link diagram and a textuallist of common clades. Selection, pruning, filtering or re-rootingin one representation is immediately reflected in the other.While summary trees are more effective at showing the relationshipamong clades, they can only show a consistent subset of thosethat appear in the textual list. Working with both representationsmitigates the disadvantages of having to choose just one. Availability: STE, along with several sample datasets, is availableat http://cityscape.inf.cs.cmu.edu/phylogeny/ Contact: mad{at}cs.cmu.edu Associate Editor: Martin Bishop     

DIP: The Database of Interacting Proteins: 2001 update

The Database of Interacting Proteins (DIP; http://dip.doe-mbi.ucla. edu) is a database that documents experimentally determined protein-protein interactions. Since January 2000 the number of protein-protein interactions in DIP has nearly tripled to 3472 and the number of proteins to 2659. New interactive tools have been developed to aid in the visualization, navigation and study of networks of protein interactions.     

A possible strategy against head and neck cancer: in silico investigation of three-in-one inhibitors

Overexpression of epidermal growth factor receptor (EGFR), Her2, and uroporphyrinogen decarboxylase (UROD) occurs in a variety of malignant tumor tissues. UROD has potential to modulate tumor response of radiotherapy for head and neck cancer, and EGFR and Her2 are common drug targets for the treatment of head and neck cancer. This study attempts to find a possible lead compound backbone from TCM Database@Taiwan (http://tcm.cmu.edu.tw/) for EGFR, Her2, and UROD proteins against head and neck cancer using computational techniques. Possible traditional Chinese medicine (TCM) lead compounds had potential binding affinities with EGFR, Her2, and UROD proteins. The candidates formed stable interactions with residues Arg803, Thr854 in EGFR, residues Thr862, Asp863 in Her2 protein, and residues Arg37, Arg41 in UROD protein, which are key residues in the binding or catalytic domain of EGFR, Her2, and UROD proteins. Thus, the TCM candidates indicated a possible molecule backbone for evolving potential inhibitors for three drug target proteins against head and neck cancer.

An animated interactive 3D complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:JBSD:35     

TangleSolve: topological analysis of site-specific recombination

TangleSolve is a program for analysing site-specific recombination using the tangle model. The program offers an easy-to-use graphical user interface and a visualization tool. Biologists working in topological enzymology can use this program to compute and visualize site-specific recombination mechanisms that accommodate their experimental data. TangleSolve can also prove useful as a teaching aid for mathematical biology and computational molecular biology courses. AVAILABILITY: http://bio.math.berkeley.edu/TangleSolve/     

BLMT

Statistical analysis of amino acid and nucleotide sequences, especially sequence alignment, is one of the most commonly performed tasks in modern molecular biology. However, for many tasks in bioinformatics, the requirement for the features in an alignment to be consecutive is restrictive and "n-grams" (aka k-tuples) have been used as features instead. N-grams are usually short nucleotide or amino acid sequences of length n, but the unit for a gram may be chosen arbitrarily. The n-gram concept is borrowed from language technologies where n-grams of words form the fundamental units in statistical language models. Despite the demonstrated utility of n-gram statistics for the biology domain, there is currently no publicly accessible generic tool for the efficient calculation of such statistics. Most sequence analysis tools will disregard matches because of the lack of statistical significance in finding short sequences. This article presents the integrated Biological Language Modeling Toolkit (BLMT) that allows efficient calculation of n-gram statistics for arbitrary sequence datasets. AVAILABILITY: BLMT can be downloaded from http://www.cs.cmu.edu/~blmt/source and installed for standalone use on any Unix platform or Unix shell emulation such as Cygwin on the Windows platform. Specific tools and usage details are described in a "readme" file. The n-gram computations carried out by the BLMT are part of a broader set of tools borrowed from language technologies and modified for statistical analysis of biological sequences; these are available at http://flan.blm.cs.cmu.edu/.     

Molecular dynamics analysis of potent inhibitors of M2 proton channel against H1N1 swine influenza virus

The recent H1N1 (swine) influenza pandemic highlighted the urgent need of having effective anti‐viral strategies. In addition to neuraminidase inhibitors, there is another class of anti-viral drug known as M2 inhibitors that were, in the past, effective in treating seasonal influenza. However, due to the emergence of M2 inhibitor‐resistant influenza viruses, this class of drugs was not recommended for clinical usage in the latest influenza pandemic. In order to identify novel M2 inhibitors, we have performed molecular docking using a traditional Chinese medicine database (http://tcm.cmu.edu.tw/index.php). Docking and subsequent de novo designs gave 10 derivatives that have much better docking results than the control. Of these 10 derivatives, the top three, methyl isoferulate_1, genipin_1 and genipin_2, were selected for molecular dynamics simulation. During the simulation run, the top three derivatives all had stable interactions with M2 residues, Ser31 and Ala30. Methyl isoferulate_1 also has stable interaction to His37. Therefore, we recommend these three derivatives for further biomolecular experiments and clinical studies.     

Improved recognition of figures containing fluorescence microscope images in online journal articles using graphical models

MOTIVATION: There is extensive interest in automating the collection, organization and analysis of biological data. Data in the form of images in online literature present special challenges for such efforts. The first steps in understanding the contents of a figure are decomposing it into panels and determining the type of each panel. In biological literature, panel types include many kinds of images collected by different techniques, such as photographs of gels or images from microscopes. We have previously described the SLIF system (http://slif.cbi.cmu.edu) that identifies panels containing fluorescence microscope images among figures in online journal articles as a prelude to further analysis of the subcellular patterns in such images. This system contains a pretrained classifier that uses image features to assign a type (class) to each separate panel. However, the types of panels in a figure are often correlated, so that we can consider the class of a panel to be dependent not only on its own features but also on the types of the other panels in a figure. RESULTS: In this article, we introduce the use of a type of probabilistic graphical model, a factor graph, to represent the structured information about the images in a figure, and permit more robust and accurate inference about their types. We obtain significant improvement over results for considering panels separately. AVAILABILITY: The code and data used for the experiments described here are available from http://murphylab.web.cmu.edu/software.     

TMpro web server and web service: transmembrane helix prediction through amino acid property analysis

TMpro is a transmembrane (TM) helix prediction algorithm that uses language processing methodology for TM segment identification. It is primarily based on the analysis of statistical distributions of properties of amino acids in transmembrane segments. This article describes the availability of TMpro on the internet via a web interface. The key features of the interface are: (i) output is generated in multiple formats including a user-interactive graphical chart which allows comparison of TMpro predicted segment locations with other labeled segments input by the user, such as predictions from other methods. (ii) Up to 5000 sequences can be submitted at a time for prediction. (iii) TMpro is available as a web server and is published as a web service so that the method can be accessed by users as well as other services depending on the need for data integration. Availability: http://linzer.blm.cs.cmu.edu/tmpro/ (web server and help), http://blm.sis.pitt.edu:8080/axis/services/TMProFetcherService (web service).     

Structure-based and ligand-based drug design for microsomal prostaglandin E synthase-1 inhibitors

Microsomal prostaglandin E synthase-1 (mPGES-1) has been regarded as an attractive drug for inflammation-related diseases. In search of new mPGES-1 inhibitors, we performed virtual screening using our traditional Chinese medicine and natural products database (http://tcm.cmu.edu.tw/) and constructed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) using a training set of 30 experimentally tested mPGES-1 inhibitors. The CoMFA and CoMSIA models derived were statistically significant with cross-validated coefficient values of 0.808 for CoMFA and 0.829 for CoMSIA and non-cross-validated coefficient values of 0.829 for CoMFA and 0.980 for CoMSIA. Docking and de novo evolution design gave three top derivatives, 2-O-caffeoyl tartaric acid-Evo_2, glucogallin-Evo_1 and 3-O-feruloylquinic acid-Evo_7 that have higher binding affinities than the control, glutathione. These three derivatives have interactions with Arg70, Arg73, Arg110, Arg126 and Arg38, which all are mPGES-1 key active site residues. In addition, these derivatives fit well into the CoMFA and CoMSIA models, with hydrophobic, hydrophilic and electropositive substructures mapped onto corresponding contour plots. Hence, we suggest that these three de novo compounds could be a starting basis for new mPGES-1 inhibitors.     

AVIS: AJAX viewer of interactive signaling networks

MOTIVATION: Increasing complexity of cell signaling network maps requires sophisticated visualization technologies. Simple web-based visualization tools can allow for improved data presentation and collaboration. Researchers studying cell signaling would benefit from having the ability to embed dynamic cell signaling maps in web pages. SUMMARY: AVIS is a Google gadget compatible web-based viewer of interactive cell signaling networks. AVIS is an implementation of AJAX (Asynchronous JavaScript with XML) with the usage of the libraries GraphViz, ImageMagic (PerlMagic) and overLib. AVIS provides web-based visualization of text-based signaling networks with dynamical zooming, panning and linking capabilities. AVIS is a cross-platform web-based tool that can be used to visualize network maps as embedded objects in any web page. AVIS was implemented for visualization of PathwayGenerator, a tool that displays over 4000 automatically generated mammalian cell signaling maps; NodeNeighborhood a tool to visualize first and second interacting neighbors of yeast and mammalian proteins; and for Genes2Networks, a tool to connect lists of genes and protein using background protein interaction networks. AVAILABILITY: A demo page of AVIS and links to applications and distributions can be found at http://actin.pharm.mssm.edu/AVIS2. Detailed instructions for using and configuring AVIS can be found in the user manual at http://actin.pharm.mssm.edu/AVIS2/manual.pdf.     

Uroporphyrinogen Decarboxylase as a Potential Target for Specific Components of Traditional Chinese Medicine: A Virtual Screening and Molecular Dynamics Study

Uroporphyrinogen decarboxylase (UROD) has been suggested as a protectant against radiation for head and neck cancer (HNC). In this study, we employed traditional Chinese medicine (TCM) compounds from TCM nawiaT@esabataD (http://tcm.cmu.edu.tw/) to screen for drug-like candidates with potential UROD inhibition characteristics using virtual screening techniques. Isopraeroside IV, scopolin, and nodakenin exhibited the highest Dock Scores, and were predicted to have good Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties. Two common moieties, 2H-chromen-2-one and glucoside, were observed among the top TCM candidates. Cross comparison of the docking poses indicated that candidates formed stable interactions with key binding and catalytic residues of UROD through these two moieties. The 2H-chromen-2-one moiety enabled pi-cation interactions with Arg37 and H-bonds with Tyr164. The glucoside moiety was involved in forming H-bonds with Arg37 and Asp86. From our computational results, we propose isopraeroside IV, scopolin, and nodakenin as ligands that might exhibit drug-like inhibitory effects on UROD. The glucoside and 2H-chromen-2-one moieties may potentially be used for designing inhibitors of UROD.     

Key Features for Designing Phosphodiesterase-5 Inhibitors

Abstract

Phosphodiesterase superfamily is the key regulator of 3′,5′-cyclic guanosine monophosphate (cGMP) decomposition in human body. Phosphodiesterase-5 (PDE-5) inhibitors, sildenafil, vardenafil and tadalafil, are well known oral treatment for males with erectile dysfunction. To investigate the inhibitory effects of traditional Chinese medicine (TCM) compounds to PDE-5, we performed both ligand-based and structure-based studies on this topic. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies were conducted to construct three dimensional quantitative structure-activity relationship (3D-QSAR) models of series of known PDE-5 inhibitors. The predictive models had cross-validated, q², and non cross-validated coefficient, r², values of 0.791 and 0.948 for CoMFA and 0.724 and 0.908 for CoMSIA. These two 3D-QSAR models were used to predict activity of TCM compounds. Docking simulations were performed to further analyze the binding mode of training set and TCM compounds. A putative binding model was proposed based on CoMFA and CoMSIA contour maps and docking simulations; formation of pi-stacking, water bridge and specific hydrogen bonding were deemed important interactions between ligands and PDE-5. Of our TCM compounds, engeletin, satisfied our binding model, and hence, emerged as PDE-5 inhibitor candidate.

Using this study as an example, we demonstrated that docking should be conducted for qualitative purposes, such as identifying protein characteristics, rather than for quantitative analyses that rank compound efficacy based on results of scoring functions. Prediction of compound activity should be reserved for QSAR analyses, and scoring functions and docking scores should be used for preliminary screening of TCM database (http://tcm.cmu.edu.tw/index.php).     

XdomView: protein domain and exon position visualization

SUMMARY: The relationship between intron distribution in the eukaryotic gene and protein structural elements is essential for understanding the origin and evolution of genes. XdomView is a web-based viewer mapping protein structural domains and intron positions in eukaryotic homologues to its tertiary structure. The association of sequence signals to 3D structure in XdomView provides a valuable visualization environment for eukaryotic gene organization, gene evolution, protein folding and protein structure classification. AVAILABILITY: Freely available from http://surya.bic.nus.edu.sg/xdom.     

Internet-based image analysis quantifies contractile behavior of individual fibroblasts inside model tissue

In a cell-populated collagen gel, intrinsic fiber structure visible in differential interference contrast images can provide markers for an in situ strain gauge to quantify cell-gel mechanics, while optical sections of fluorescent protein distribution capture cytoskeletal kinematics. Mechanics quantification can be derived automatically from timelapse differential interference contrast images using a Deformation Quantification and Analysis software package accessible online at http://dqa.web.cmu.edu. In our studies, fibroblast contractile machinery was observed to function entirely within pseudopods, while GFP-alpha-actinin concentrated in pseudopod tips and cortex. Complex strain patterns around individual cells showed instances of both elastic and inelastic strain transmission, suggesting a role in observed long-range alignment of cells.