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L1pred: a sequence-based prediction tool for catalytic residues in enzymes with the L1-logreg classifier.
To understand enzyme functions, identifying the catalytic residues is a usual first step. Moreover, knowledge about catalytic residues is also useful for protein engineering and drug-design. However, to experimentally identify catalytic residues remains challenging for reasons of time and cost. Therefore, computational methods have been explored to predict catalytic residues. Here, we developed a new algorithm, L1pred, for catalytic residue prediction, by using the L1-logreg classifier to integrate eight sequence-based scoring functions. We tested L1pred and compared it against several existing sequence-based methods on carefully designed datasets Data604 and Data63. With ten-fold cross-validation, L1pred showed the area under precision-recall curve (AUPR) and the area under ROC curve (AUC) of 0.2198 and 0.9494 on the training dataset, Data604, respectively. In addition, on the independent test dataset, Data63, it showed the AUPR and AUC values of 0.2636 and 0.9375, respectively. Compared with other sequence-based methods, L1pred showed the best performance on both datasets. We also analyzed the importance of each attribute in the algorithm, and found that all the scores contributed more or less equally to the L1pred performance.
Authors: Ambrish Roy, Dong Xu, Jonathan Poisson, Yang Zhang.
Published: 11-03-2011
Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently >20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.
22 Related JoVE Articles!
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Authors: James Smadbeck, Meghan B. Peterson, George A. Khoury, Martin S. Taylor, Christodoulos A. Floudas.
Institutions: Princeton University.
The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity. To disseminate these methods for broader use we present Protein WISDOM (, a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.
Genetics, Issue 77, Molecular Biology, Bioengineering, Biochemistry, Biomedical Engineering, Chemical Engineering, Computational Biology, Genomics, Proteomics, Protein, Protein Binding, Computational Biology, Drug Design, optimization (mathematics), Amino Acids, Peptides, and Proteins, De novo protein and peptide design, Drug design, In silico sequence selection, Optimization, Fold specificity, Binding affinity, sequencing
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Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays
Authors: Srikanth Kudithipudi, Denis Kusevic, Sara Weirich, Albert Jeltsch.
Institutions: Stuttgart University.
Lysine methylation is an emerging post-translation modification and it has been identified on several histone and non-histone proteins, where it plays crucial roles in cell development and many diseases. Approximately 5,000 lysine methylation sites were identified on different proteins, which are set by few dozens of protein lysine methyltransferases. This suggests that each PKMT methylates multiple proteins, however till now only one or two substrates have been identified for several of these enzymes. To approach this problem, we have introduced peptide array based substrate specificity analyses of PKMTs. Peptide arrays are powerful tools to characterize the specificity of PKMTs because methylation of several substrates with different sequences can be tested on one array. We synthesized peptide arrays on cellulose membrane using an Intavis SPOT synthesizer and analyzed the specificity of various PKMTs. Based on the results, for several of these enzymes, novel substrates could be identified. For example, for NSD1 by employing peptide arrays, we showed that it methylates K44 of H4 instead of the reported H4K20 and in addition H1.5K168 is the highly preferred substrate over the previously known H3K36. Hence, peptide arrays are powerful tools to biochemically characterize the PKMTs.
Biochemistry, Issue 93, Peptide arrays, solid phase peptide synthesis, SPOT synthesis, protein lysine methyltransferases, substrate specificity profile analysis, lysine methylation
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Retropinacol/Cross-pinacol Coupling Reactions - A Catalytic Access to 1,2-Unsymmetrical Diols
Authors: Ulf Scheffler, Rainer Mahrwald.
Institutions: Humboldt University of Berlin.
Unsymmetrical 1,2-diols are hardly accessible by reductive pinacol coupling processes. A successful execution of such a transformation is bound to a clear recognition and strict differentiation of two similar carbonyl compounds (aldehydes → secondary 1,2-diols or ketones → tertiary 1,2-diols). This fine-tuning is still a challenge and an unsolved problem for an organic chemist. There exist several reports on successful execution of this transformation but they cannot be generalized. Herein we describe a catalytic direct pinacol coupling process which proceeds via a retropinacol/cross-pinacol coupling sequence. Thus, unsymmetrical substituted 1,2-diols can be accessed with almost quantitative yields by means of an operationally simple performance under very mild conditions. Artificial techniques, such as syringe-pump techniques or delayed additions of reactants are not necessary. The procedure we describe provides a very rapid access to cross-pinacol products (1,2-diols, vicinal diols). A further extension of this new process, e.g. an enantioselective performance could provide a very useful tool for the synthesis of unsymmetrical chiral 1,2-diols.
Chemistry, Issue 86, cross-pinacol coupling reactions, unsymmetrical 1,2-diols, catalysis, titanium(IV) alkoxides, mechanism, aldehydes, ketones
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The ITS2 Database
Authors: Benjamin Merget, Christian Koetschan, Thomas Hackl, Frank Förster, Thomas Dandekar, Tobias Müller, Jörg Schultz, Matthias Wolf.
Institutions: University of Würzburg, University of Würzburg.
The internal transcribed spacer 2 (ITS2) has been used as a phylogenetic marker for more than two decades. As ITS2 research mainly focused on the very variable ITS2 sequence, it confined this marker to low-level phylogenetics only. However, the combination of the ITS2 sequence and its highly conserved secondary structure improves the phylogenetic resolution1 and allows phylogenetic inference at multiple taxonomic ranks, including species delimitation2-8. The ITS2 Database9 presents an exhaustive dataset of internal transcribed spacer 2 sequences from NCBI GenBank11 accurately reannotated10. Following an annotation by profile Hidden Markov Models (HMMs), the secondary structure of each sequence is predicted. First, it is tested whether a minimum energy based fold12 (direct fold) results in a correct, four helix conformation. If this is not the case, the structure is predicted by homology modeling13. In homology modeling, an already known secondary structure is transferred to another ITS2 sequence, whose secondary structure was not able to fold correctly in a direct fold. The ITS2 Database is not only a database for storage and retrieval of ITS2 sequence-structures. It also provides several tools to process your own ITS2 sequences, including annotation, structural prediction, motif detection and BLAST14 search on the combined sequence-structure information. Moreover, it integrates trimmed versions of 4SALE15,16 and ProfDistS17 for multiple sequence-structure alignment calculation and Neighbor Joining18 tree reconstruction. Together they form a coherent analysis pipeline from an initial set of sequences to a phylogeny based on sequence and secondary structure. In a nutshell, this workbench simplifies first phylogenetic analyses to only a few mouse-clicks, while additionally providing tools and data for comprehensive large-scale analyses.
Genetics, Issue 61, alignment, internal transcribed spacer 2, molecular systematics, secondary structure, ribosomal RNA, phylogenetic tree, homology modeling, phylogeny
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Mouse Genome Engineering Using Designer Nucleases
Authors: Mario Hermann, Tomas Cermak, Daniel F. Voytas, Pawel Pelczar.
Institutions: University of Zurich, University of Minnesota.
Transgenic mice carrying site-specific genome modifications (knockout, knock-in) are of vital importance for dissecting complex biological systems as well as for modeling human diseases and testing therapeutic strategies. Recent advances in the use of designer nucleases such as zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) 9 system for site-specific genome engineering open the possibility to perform rapid targeted genome modification in virtually any laboratory species without the need to rely on embryonic stem (ES) cell technology. A genome editing experiment typically starts with identification of designer nuclease target sites within a gene of interest followed by construction of custom DNA-binding domains to direct nuclease activity to the investigator-defined genomic locus. Designer nuclease plasmids are in vitro transcribed to generate mRNA for microinjection of fertilized mouse oocytes. Here, we provide a protocol for achieving targeted genome modification by direct injection of TALEN mRNA into fertilized mouse oocytes.
Genetics, Issue 86, Oocyte microinjection, Designer nucleases, ZFN, TALEN, Genome Engineering
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Steady-state, Pre-steady-state, and Single-turnover Kinetic Measurement for DNA Glycosylase Activity
Authors: Akira Sassa, William A. Beard, David D. Shock, Samuel H. Wilson.
Institutions: NIEHS, National Institutes of Health.
Human 8-oxoguanine DNA glycosylase (OGG1) excises the mutagenic oxidative DNA lesion 8-oxo-7,8-dihydroguanine (8-oxoG) from DNA. Kinetic characterization of OGG1 is undertaken to measure the rates of 8-oxoG excision and product release. When the OGG1 concentration is lower than substrate DNA, time courses of product formation are biphasic; a rapid exponential phase (i.e. burst) of product formation is followed by a linear steady-state phase. The initial burst of product formation corresponds to the concentration of enzyme properly engaged on the substrate, and the burst amplitude depends on the concentration of enzyme. The first-order rate constant of the burst corresponds to the intrinsic rate of 8-oxoG excision and the slower steady-state rate measures the rate of product release (product DNA dissociation rate constant, koff). Here, we describe steady-state, pre-steady-state, and single-turnover approaches to isolate and measure specific steps during OGG1 catalytic cycling. A fluorescent labeled lesion-containing oligonucleotide and purified OGG1 are used to facilitate precise kinetic measurements. Since low enzyme concentrations are used to make steady-state measurements, manual mixing of reagents and quenching of the reaction can be performed to ascertain the steady-state rate (koff). Additionally, extrapolation of the steady-state rate to a point on the ordinate at zero time indicates that a burst of product formation occurred during the first turnover (i.e. y-intercept is positive). The first-order rate constant of the exponential burst phase can be measured using a rapid mixing and quenching technique that examines the amount of product formed at short time intervals (<1 sec) before the steady-state phase and corresponds to the rate of 8-oxoG excision (i.e. chemistry). The chemical step can also be measured using a single-turnover approach where catalytic cycling is prevented by saturating substrate DNA with enzyme (E>S). These approaches can measure elementary rate constants that influence the efficiency of removal of a DNA lesion.
Chemistry, Issue 78, Biochemistry, Genetics, Molecular Biology, Microbiology, Structural Biology, Chemical Biology, Eukaryota, Amino Acids, Peptides, and Proteins, Nucleic Acids, Nucleotides, and Nucleosides, Enzymes and Coenzymes, Life Sciences (General), enzymology, rapid quench-flow, active site titration, steady-state, pre-steady-state, single-turnover, kinetics, base excision repair, DNA glycosylase, 8-oxo-7,8-dihydroguanine, 8-oxoG, sequencing
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RNA Secondary Structure Prediction Using High-throughput SHAPE
Authors: Sabrina Lusvarghi, Joanna Sztuba-Solinska, Katarzyna J. Purzycka, Jason W. Rausch, Stuart F.J. Le Grice.
Institutions: Frederick National Laboratory for Cancer Research.
Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed "high-throughput selective 2' hydroxyl acylation analyzed by primer extension", or SHAPE, allows prediction of RNA secondary structure with single nucleotide resolution. This approach utilizes chemical probing agents that preferentially acylate single stranded or flexible regions of RNA in aqueous solution. Sites of chemical modification are detected by reverse transcription of the modified RNA, and the products of this reaction are fractionated by automated capillary electrophoresis (CE). Since reverse transcriptase pauses at those RNA nucleotides modified by the SHAPE reagents, the resulting cDNA library indirectly maps those ribonucleotides that are single stranded in the context of the folded RNA. Using ShapeFinder software, the electropherograms produced by automated CE are processed and converted into nucleotide reactivity tables that are themselves converted into pseudo-energy constraints used in the RNAStructure (v5.3) prediction algorithm. The two-dimensional RNA structures obtained by combining SHAPE probing with in silico RNA secondary structure prediction have been found to be far more accurate than structures obtained using either method alone.
Genetics, Issue 75, Molecular Biology, Biochemistry, Virology, Cancer Biology, Medicine, Genomics, Nucleic Acid Probes, RNA Probes, RNA, High-throughput SHAPE, Capillary electrophoresis, RNA structure, RNA probing, RNA folding, secondary structure, DNA, nucleic acids, electropherogram, synthesis, transcription, high throughput, sequencing
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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
Authors: Regina Salvat, Leonard Moise, Chris Bailey-Kellogg, Karl E. Griswold.
Institutions: Dartmouth College, University of Rhode Island, Dartmouth College.
Biochemical assays with recombinant human MHC II molecules can provide rapid, quantitative insights into immunogenic epitope identification, deletion, or design1,2. Here, a peptide-MHC II binding assay is scaled to 384-well format. The scaled down protocol reduces reagent costs by 75% and is higher throughput than previously described 96-well protocols1,3-5. Specifically, the experimental design permits robust and reproducible analysis of up to 15 peptides against one MHC II allele per 384-well ELISA plate. Using a single liquid handling robot, this method allows one researcher to analyze approximately ninety test peptides in triplicate over a range of eight concentrations and four MHC II allele types in less than 48 hr. Others working in the fields of protein deimmunization or vaccine design and development may find the protocol to be useful in facilitating their own work. In particular, the step-by-step instructions and the visual format of JoVE should allow other users to quickly and easily establish this methodology in their own labs.
Biochemistry, Issue 85, Immunoassay, Protein Immunogenicity, MHC II, T cell epitope, High Throughput Screen, Deimmunization, Vaccine Design
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Expression of Recombinant Cellulase Cel5A from Trichoderma reesei in Tobacco Plants
Authors: Megan Garvey, Johannes Klinger, Holger Klose, Rainer Fischer, Ulrich Commandeur.
Institutions: RWTH Aachen University, Fraunhofer Institute for Molecular Biology and Applied Ecology.
Cellulose degrading enzymes, cellulases, are targets of both research and industrial interests. The preponderance of these enzymes in difficult-to-culture organisms, such as hyphae-building fungi and anaerobic bacteria, has hastened the use of recombinant technologies in this field. Plant expression methods are a desirable system for large-scale production of enzymes and other industrially useful proteins. Herein, methods for the transient expression of a fungal endoglucanase, Trichoderma reesei Cel5A, in Nicotiana tabacum are demonstrated. Successful protein expression is shown, monitored by fluorescence using an mCherry-enzyme fusion protein. Additionally, a set of basic tests are used to examine the activity of transiently expressed T. reesei Cel5A, including SDS-PAGE, Western blotting, zymography, as well as fluorescence and dye-based substrate degradation assays. The system described here can be used to produce an active cellulase in a short time period, so as to assess the potential for further production in plants through constitutive or inducible expression systems.
Environmental Sciences, Issue 88, heterologous expression, endoplasmic reticulum, endoglucanase, cellulose, glycosyl-hydrolase, fluorescence, cellulase, Trichoderma reesei, tobacco plants
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High Throughput Screening of Fungal Endoglucanase Activity in Escherichia coli
Authors: Mary F. Farrow, Frances H. Arnold.
Institutions: California Institute of Technology, California Institute of Technology.
Cellulase enzymes (endoglucanases, cellobiohydrolases, and β-glucosidases) hydrolyze cellulose into component sugars, which in turn can be converted into fuel alcohols1. The potential for enzymatic hydrolysis of cellulosic biomass to provide renewable energy has intensified efforts to engineer cellulases for economical fuel production2. Of particular interest are fungal cellulases3-8, which are already being used industrially for foods and textiles processing. Identifying active variants among a library of mutant cellulases is critical to the engineering process; active mutants can be further tested for improved properties and/or subjected to additional mutagenesis. Efficient engineering of fungal cellulases has been hampered by a lack of genetic tools for native organisms and by difficulties in expressing the enzymes in heterologous hosts. Recently, Morikawa and coworkers developed a method for expressing in E. coli the catalytic domains of endoglucanases from H. jecorina3,9, an important industrial fungus with the capacity to secrete cellulases in large quantities. Functional E. coli expression has also been reported for cellulases from other fungi, including Macrophomina phaseolina10 and Phanerochaete chrysosporium11-12. We present a method for high throughput screening of fungal endoglucanase activity in E. coli. (Fig 1) This method uses the common microbial dye Congo Red (CR) to visualize enzymatic degradation of carboxymethyl cellulose (CMC) by cells growing on solid medium. The activity assay requires inexpensive reagents, minimal manipulation, and gives unambiguous results as zones of degradation (“halos”) at the colony site. Although a quantitative measure of enzymatic activity cannot be determined by this method, we have found that halo size correlates with total enzymatic activity in the cell. Further characterization of individual positive clones will determine , relative protein fitness. Traditional bacterial whole cell CMC/CR activity assays13 involve pouring agar containing CMC onto colonies, which is subject to cross-contamination, or incubating cultures in CMC agar wells, which is less amenable to large-scale experimentation. Here we report an improved protocol that modifies existing wash methods14 for cellulase activity: cells grown on CMC agar plates are removed prior to CR staining. Our protocol significantly reduces cross-contamination and is highly scalable, allowing the rapid screening of thousands of clones. In addition to H. jecorina enzymes, we have expressed and screened endoglucanase variants from the Thermoascus aurantiacus and Penicillium decumbens (shown in Figure 2), suggesting that this protocol is applicable to enzymes from a range of organisms.
Molecular Biology, Issue 54, cellulase, endoglucanase, CMC, Congo Red
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Sequence-specific Labeling of Nucleic Acids and Proteins with Methyltransferases and Cofactor Analogues
Authors: Gisela Maria Hanz, Britta Jung, Anna Giesbertz, Matyas Juhasz, Elmar Weinhold.
Institutions: RWTH Aachen University.
S-Adenosyl-l-methionine (AdoMet or SAM)-dependent methyltransferases (MTase) catalyze the transfer of the activated methyl group from AdoMet to specific positions in DNA, RNA, proteins and small biomolecules. This natural methylation reaction can be expanded to a wide variety of alkylation reactions using synthetic cofactor analogues. Replacement of the reactive sulfonium center of AdoMet with an aziridine ring leads to cofactors which can be coupled with DNA by various DNA MTases. These aziridine cofactors can be equipped with reporter groups at different positions of the adenine moiety and used for Sequence-specific Methyltransferase-Induced Labeling of DNA (SMILing DNA). As a typical example we give a protocol for biotinylation of pBR322 plasmid DNA at the 5’-ATCGAT-3’ sequence with the DNA MTase M.BseCI and the aziridine cofactor 6BAz in one step. Extension of the activated methyl group with unsaturated alkyl groups results in another class of AdoMet analogues which are used for methyltransferase-directed Transfer of Activated Groups (mTAG). Since the extended side chains are activated by the sulfonium center and the unsaturated bond, these cofactors are called double-activated AdoMet analogues. These analogues not only function as cofactors for DNA MTases, like the aziridine cofactors, but also for RNA, protein and small molecule MTases. They are typically used for enzymatic modification of MTase substrates with unique functional groups which are labeled with reporter groups in a second chemical step. This is exemplified in a protocol for fluorescence labeling of histone H3 protein. A small propargyl group is transferred from the cofactor analogue SeAdoYn to the protein by the histone H3 lysine 4 (H3K4) MTase Set7/9 followed by click labeling of the alkynylated histone H3 with TAMRA azide. MTase-mediated labeling with cofactor analogues is an enabling technology for many exciting applications including identification and functional study of MTase substrates as well as DNA genotyping and methylation detection.
Biochemistry, Issue 93, S-adenosyl-l-methionine, AdoMet, SAM, aziridine cofactor, double activated cofactor, methyltransferase, DNA methylation, protein methylation, biotin labeling, fluorescence labeling, SMILing, mTAG
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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
Authors: Wen-Ting Tsai, Ahmed Hassan, Purbasha Sarkar, Joaquin Correa, Zoltan Metlagel, Danielle M. Jorgens, Manfred Auer.
Institutions: Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory.
Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, enabling the visualization of large macromolecular machines, such as adhesion complexes, as well as higher-order structures, such as the cytoskeleton and cellular organelles in their respective cell and tissue context. Given the inherent complexity of cellular volumes, it is essential to first extract the features of interest in order to allow visualization, quantification, and therefore comprehension of their 3D organization. Each data set is defined by distinct characteristics, e.g., signal-to-noise ratio, crispness (sharpness) of the data, heterogeneity of its features, crowdedness of features, presence or absence of characteristic shapes that allow for easy identification, and the percentage of the entire volume that a specific region of interest occupies. All these characteristics need to be considered when deciding on which approach to take for segmentation. The six different 3D ultrastructural data sets presented were obtained by three different imaging approaches: resin embedded stained electron tomography, focused ion beam- and serial block face- scanning electron microscopy (FIB-SEM, SBF-SEM) of mildly stained and heavily stained samples, respectively. For these data sets, four different segmentation approaches have been applied: (1) fully manual model building followed solely by visualization of the model, (2) manual tracing segmentation of the data followed by surface rendering, (3) semi-automated approaches followed by surface rendering, or (4) automated custom-designed segmentation algorithms followed by surface rendering and quantitative analysis. Depending on the combination of data set characteristics, it was found that typically one of these four categorical approaches outperforms the others, but depending on the exact sequence of criteria, more than one approach may be successful. Based on these data, we propose a triage scheme that categorizes both objective data set characteristics and subjective personal criteria for the analysis of the different data sets.
Bioengineering, Issue 90, 3D electron microscopy, feature extraction, segmentation, image analysis, reconstruction, manual tracing, thresholding
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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
Authors: Johannes Felix Buyel, Rainer Fischer.
Institutions: RWTH Aachen University, Fraunhofer Gesellschaft.
Plants provide multiple benefits for the production of biopharmaceuticals including low costs, scalability, and safety. Transient expression offers the additional advantage of short development and production times, but expression levels can vary significantly between batches thus giving rise to regulatory concerns in the context of good manufacturing practice. We used a design of experiments (DoE) approach to determine the impact of major factors such as regulatory elements in the expression construct, plant growth and development parameters, and the incubation conditions during expression, on the variability of expression between batches. We tested plants expressing a model anti-HIV monoclonal antibody (2G12) and a fluorescent marker protein (DsRed). We discuss the rationale for selecting certain properties of the model and identify its potential limitations. The general approach can easily be transferred to other problems because the principles of the model are broadly applicable: knowledge-based parameter selection, complexity reduction by splitting the initial problem into smaller modules, software-guided setup of optimal experiment combinations and step-wise design augmentation. Therefore, the methodology is not only useful for characterizing protein expression in plants but also for the investigation of other complex systems lacking a mechanistic description. The predictive equations describing the interconnectivity between parameters can be used to establish mechanistic models for other complex systems.
Bioengineering, Issue 83, design of experiments (DoE), transient protein expression, plant-derived biopharmaceuticals, promoter, 5'UTR, fluorescent reporter protein, model building, incubation conditions, monoclonal antibody
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A Practical Guide to Phylogenetics for Nonexperts
Authors: Damien O'Halloran.
Institutions: The George Washington University.
Many researchers, across incredibly diverse foci, are applying phylogenetics to their research question(s). However, many researchers are new to this topic and so it presents inherent problems. Here we compile a practical introduction to phylogenetics for nonexperts. We outline in a step-by-step manner, a pipeline for generating reliable phylogenies from gene sequence datasets. We begin with a user-guide for similarity search tools via online interfaces as well as local executables. Next, we explore programs for generating multiple sequence alignments followed by protocols for using software to determine best-fit models of evolution. We then outline protocols for reconstructing phylogenetic relationships via maximum likelihood and Bayesian criteria and finally describe tools for visualizing phylogenetic trees. While this is not by any means an exhaustive description of phylogenetic approaches, it does provide the reader with practical starting information on key software applications commonly utilized by phylogeneticists. The vision for this article would be that it could serve as a practical training tool for researchers embarking on phylogenetic studies and also serve as an educational resource that could be incorporated into a classroom or teaching-lab.
Basic Protocol, Issue 84, phylogenetics, multiple sequence alignments, phylogenetic tree, BLAST executables, basic local alignment search tool, Bayesian models
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Authors: Rangaraj M. Rangayyan, Shantanu Banik, J.E. Leo Desautels.
Institutions: University of Calgary , University of Calgary .
We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion. Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.
Medicine, Issue 78, Anatomy, Physiology, Cancer Biology, angular spread, architectural distortion, breast cancer, Computer-Assisted Diagnosis, computer-aided diagnosis (CAD), entropy, fractional Brownian motion, fractal dimension, Gabor filters, Image Processing, Medical Informatics, node map, oriented texture, Pattern Recognition, phase portraits, prior mammograms, spectral analysis
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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
Authors: Marcus Cheetham, Lutz Jancke.
Institutions: University of Zurich.
Mori's Uncanny Valley Hypothesis1,2 proposes that the perception of humanlike characters such as robots and, by extension, avatars (computer-generated characters) can evoke negative or positive affect (valence) depending on the object's degree of visual and behavioral realism along a dimension of human likeness (DHL) (Figure 1). But studies of affective valence of subjective responses to variously realistic non-human characters have produced inconsistent findings 3, 4, 5, 6. One of a number of reasons for this is that human likeness is not perceived as the hypothesis assumes. While the DHL can be defined following Mori's description as a smooth linear change in the degree of physical humanlike similarity, subjective perception of objects along the DHL can be understood in terms of the psychological effects of categorical perception (CP) 7. Further behavioral and neuroimaging investigations of category processing and CP along the DHL and of the potential influence of the dimension's underlying category structure on affective experience are needed. This protocol therefore focuses on the DHL and allows examination of CP. Based on the protocol presented in the video as an example, issues surrounding the methodology in the protocol and the use in "uncanny" research of stimuli drawn from morph continua to represent the DHL are discussed in the article that accompanies the video. The use of neuroimaging and morph stimuli to represent the DHL in order to disentangle brain regions neurally responsive to physical human-like similarity from those responsive to category change and category processing is briefly illustrated.
Behavior, Issue 76, Neuroscience, Neurobiology, Molecular Biology, Psychology, Neuropsychology, uncanny valley, functional magnetic resonance imaging, fMRI, categorical perception, virtual reality, avatar, human likeness, Mori, uncanny valley hypothesis, perception, magnetic resonance imaging, MRI, imaging, clinical techniques
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Authors: Phoebe Spetsieris, Yilong Ma, Shichun Peng, Ji Hyun Ko, Vijay Dhawan, Chris C. Tang, David Eidelberg.
Institutions: The Feinstein Institute for Medical Research.
The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.
Medicine, Issue 76, Neurobiology, Neuroscience, Anatomy, Physiology, Molecular Biology, Basal Ganglia Diseases, Parkinsonian Disorders, Parkinson Disease, Movement Disorders, Neurodegenerative Diseases, PCA, SSM, PET, imaging biomarkers, functional brain imaging, multivariate spatial covariance analysis, global normalization, differential diagnosis, PD, brain, imaging, clinical techniques
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A Restriction Enzyme Based Cloning Method to Assess the In vitro Replication Capacity of HIV-1 Subtype C Gag-MJ4 Chimeric Viruses
Authors: Daniel T. Claiborne, Jessica L. Prince, Eric Hunter.
Institutions: Emory University, Emory University.
The protective effect of many HLA class I alleles on HIV-1 pathogenesis and disease progression is, in part, attributed to their ability to target conserved portions of the HIV-1 genome that escape with difficulty. Sequence changes attributed to cellular immune pressure arise across the genome during infection, and if found within conserved regions of the genome such as Gag, can affect the ability of the virus to replicate in vitro. Transmission of HLA-linked polymorphisms in Gag to HLA-mismatched recipients has been associated with reduced set point viral loads. We hypothesized this may be due to a reduced replication capacity of the virus. Here we present a novel method for assessing the in vitro replication of HIV-1 as influenced by the gag gene isolated from acute time points from subtype C infected Zambians. This method uses restriction enzyme based cloning to insert the gag gene into a common subtype C HIV-1 proviral backbone, MJ4. This makes it more appropriate to the study of subtype C sequences than previous recombination based methods that have assessed the in vitro replication of chronically derived gag-pro sequences. Nevertheless, the protocol could be readily modified for studies of viruses from other subtypes. Moreover, this protocol details a robust and reproducible method for assessing the replication capacity of the Gag-MJ4 chimeric viruses on a CEM-based T cell line. This method was utilized for the study of Gag-MJ4 chimeric viruses derived from 149 subtype C acutely infected Zambians, and has allowed for the identification of residues in Gag that affect replication. More importantly, the implementation of this technique has facilitated a deeper understanding of how viral replication defines parameters of early HIV-1 pathogenesis such as set point viral load and longitudinal CD4+ T cell decline.
Infectious Diseases, Issue 90, HIV-1, Gag, viral replication, replication capacity, viral fitness, MJ4, CEM, GXR25
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Basics of Multivariate Analysis in Neuroimaging Data
Authors: Christian Georg Habeck.
Institutions: Columbia University.
Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques1,5,6,7,8,9. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic data set from the Alzheimer s Disease Neuroimaging Initiative (ADNI), clearly demonstrating the superior performance of the multivariate approach.
JoVE Neuroscience, Issue 41, fMRI, PET, multivariate analysis, cognitive neuroscience, clinical neuroscience
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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
Authors: Viktor Martyanov, Robert H. Gross.
Institutions: Dartmouth College.
SCOPE is an ensemble motif finder that uses three component algorithms in parallel to identify potential regulatory motifs by over-representation and motif position preference1. Each component algorithm is optimized to find a different kind of motif. By taking the best of these three approaches, SCOPE performs better than any single algorithm, even in the presence of noisy data1. In this article, we utilize a web version of SCOPE2 to examine genes that are involved in telomere maintenance. SCOPE has been incorporated into at least two other motif finding programs3,4 and has been used in other studies5-8. The three algorithms that comprise SCOPE are BEAM9, which finds non-degenerate motifs (ACCGGT), PRISM10, which finds degenerate motifs (ASCGWT), and SPACER11, which finds longer bipartite motifs (ACCnnnnnnnnGGT). These three algorithms have been optimized to find their corresponding type of motif. Together, they allow SCOPE to perform extremely well. Once a gene set has been analyzed and candidate motifs identified, SCOPE can look for other genes that contain the motif which, when added to the original set, will improve the motif score. This can occur through over-representation or motif position preference. Working with partial gene sets that have biologically verified transcription factor binding sites, SCOPE was able to identify most of the rest of the genes also regulated by the given transcription factor. Output from SCOPE shows candidate motifs, their significance, and other information both as a table and as a graphical motif map. FAQs and video tutorials are available at the SCOPE web site which also includes a "Sample Search" button that allows the user to perform a trial run. Scope has a very friendly user interface that enables novice users to access the algorithm's full power without having to become an expert in the bioinformatics of motif finding. As input, SCOPE can take a list of genes, or FASTA sequences. These can be entered in browser text fields, or read from a file. The output from SCOPE contains a list of all identified motifs with their scores, number of occurrences, fraction of genes containing the motif, and the algorithm used to identify the motif. For each motif, result details include a consensus representation of the motif, a sequence logo, a position weight matrix, and a list of instances for every motif occurrence (with exact positions and "strand" indicated). Results are returned in a browser window and also optionally by email. Previous papers describe the SCOPE algorithms in detail1,2,9-11.
Genetics, Issue 51, gene regulation, computational biology, algorithm, promoter sequence motif
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Cross-Modal Multivariate Pattern Analysis
Authors: Kaspar Meyer, Jonas T. Kaplan.
Institutions: University of Southern California.
Multivariate pattern analysis (MVPA) is an increasingly popular method of analyzing functional magnetic resonance imaging (fMRI) data1-4. Typically, the method is used to identify a subject's perceptual experience from neural activity in certain regions of the brain. For instance, it has been employed to predict the orientation of visual gratings a subject perceives from activity in early visual cortices5 or, analogously, the content of speech from activity in early auditory cortices6. Here, we present an extension of the classical MVPA paradigm, according to which perceptual stimuli are not predicted within, but across sensory systems. Specifically, the method we describe addresses the question of whether stimuli that evoke memory associations in modalities other than the one through which they are presented induce content-specific activity patterns in the sensory cortices of those other modalities. For instance, seeing a muted video clip of a glass vase shattering on the ground automatically triggers in most observers an auditory image of the associated sound; is the experience of this image in the "mind's ear" correlated with a specific neural activity pattern in early auditory cortices? Furthermore, is this activity pattern distinct from the pattern that could be observed if the subject were, instead, watching a video clip of a howling dog? In two previous studies7,8, we were able to predict sound- and touch-implying video clips based on neural activity in early auditory and somatosensory cortices, respectively. Our results are in line with a neuroarchitectural framework proposed by Damasio9,10, according to which the experience of mental images that are based on memories - such as hearing the shattering sound of a vase in the "mind's ear" upon seeing the corresponding video clip - is supported by the re-construction of content-specific neural activity patterns in early sensory cortices.
Neuroscience, Issue 57, perception, sensory, cross-modal, top-down, mental imagery, fMRI, MRI, neuroimaging, multivariate pattern analysis, MVPA
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
Authors: Wenan Chen, Ashwin Belle, Charles Cockrell, Kevin R. Ward, Kayvan Najarian.
Institutions: Virginia Commonwealth University, Virginia Commonwealth University Reanimation Engineering Science (VCURES) Center, Virginia Commonwealth University, Virginia Commonwealth University, Virginia Commonwealth University.
In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift estimation and intracranial pressure (ICP) pre-screening system. To estimate the midline shift, first an estimation of the ideal midline is performed based on the symmetry of the skull and anatomical features in the brain CT scan. Then, segmentation of the ventricles from the CT scan is performed and used as a guide for the identification of the actual midline through shape matching. These processes mimic the measuring process by physicians and have shown promising results in the evaluation. In the second component, more features are extracted related to ICP, such as the texture information, blood amount from CT scans and other recorded features, such as age, injury severity score to estimate the ICP are also incorporated. Machine learning techniques including feature selection and classification, such as Support Vector Machines (SVMs), are employed to build the prediction model using RapidMiner. The evaluation of the prediction shows potential usefulness of the model. The estimated ideal midline shift and predicted ICP levels may be used as a fast pre-screening step for physicians to make decisions, so as to recommend for or against invasive ICP monitoring.
Medicine, Issue 74, Biomedical Engineering, Molecular Biology, Neurobiology, Biophysics, Physiology, Anatomy, Brain CT Image Processing, CT, Midline Shift, Intracranial Pressure Pre-screening, Gaussian Mixture Model, Shape Matching, Machine Learning, traumatic brain injury, TBI, imaging, clinical techniques
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

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We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.