Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The iterative threading assembly refinement (I-TASSER) server is an integrated platform for automated protein structure and function prediction based on the sequence-to-structure-to-function paradigm. Starting from an amino acid sequence, I-TASSER first generates three-dimensional (3D) atomic models from multiple threading alignments and iterative structural assembly simulations. The function of the protein is then inferred by structurally matching the 3D models with other known proteins. The output from a typical server run contains full-length secondary and tertiary structure predictions, and functional annotations on ligand-binding sites, Enzyme Commission numbers and Gene Ontology terms. An estimate of accuracy of the predictions is provided based on the confidence score of the modeling. This protocol provides new insights and guidelines for designing of online server systems for the state-of-the-art protein structure and function predictions. The server is available at http://zhanglab.ccmb.med.umich.edu/I-TASSER.

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I-TASSER: a unified platform for automated protein structure and function prediction

Gram-negative bacteria characteristically are surrounded by an additional membrane layer, the outer membrane. Although outer membrane components often play important roles in the interaction of symbiotic or pathogenic bacteria with their host organisms, the major role of this membrane must usually be to serve as a permeability barrier to prevent the entry of noxious compounds and at the same time to allow the influx of nutrient molecules. This review summarizes the development in the field since our previous review (H. Nikaido and M. Vaara, Microbiol. Rev. 49:1-32, 1985) was published. With the discovery of protein channels, structural knowledge enables us to understand in molecular detail how porins, specific channels, TonB-linked receptors, and other proteins function. We are now beginning to see how the export of large proteins occurs across the outer membrane. With our knowledge of the lipopolysaccharide-phospholipid asymmetric bilayer of the outer membrane, we are finally beginning to understand how this bilayer can retard the entry of lipophilic compounds, owing to our increasing knowledge about the chemistry of lipopolysaccharide from diverse organisms and the way in which lipopolysaccharide structure is modified by environmental conditions.

Molecular Basis of Bacterial Outer Membrane Permeability Revisited

Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

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Comparative Protein Structure Modeling Using MODELLER

The accuracy and scalability of multiple sequence alignment (MSA) of DNAs and proteins have long been and are still important issues in bioinformatics. To rapidly construct a reasonable MSA, we developed the initial version of the MAFFT program in 2002. MSA software is now facing greater challenges in both scalability and accuracy than those of 5 years ago. As increasing amounts of sequence data are being generated by large-scale sequencing projects, scalability is now critical in many situations. The requirement of accuracy has also entered a new stage since the discovery of functional noncoding RNAs (ncRNAs); the secondary structure should be considered for constructing a high-quality alignment of distantly related ncRNAs. To deal with these problems, in 2007, we updated MAFFT to Version 6 with two new techniques: the PartTree algorithm and the Four-way consistency objective function. The former improved the scalability of progressive alignment and the latter improved the accuracy of ncRNA alignment. We review these and other techniques that MAFFTuses and suggest possible future directions of MSA software as a basis of comparative analyses. MAFFT is available at http://align.bmr.kyushu-u.ac.jp/mafft/software/.

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Recent developments in the MAFFT multiple sequence alignment program

FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties.

There remains a great challenge in the sensitive detection of microRNA because of the short length and low abundance of microRNAs in cells. Here, we have demonstrated an ultrasensitive detection platform for microRNA by combining the tetrahedral DNA nanostructure probes and hybridization chain reaction (HCR) amplification. The detection limits for DNA and microRNA are 100 aM and 10 aM (corresponding to 600 microRNAs in a 100 μL sample), respectively. Compared to the widely used supersandwich amplification, the detection limits are improved by 3 orders of magnitude. The uncontrolled surface immobilization and consumption of target molecules that limit the amplification efficiency of supersandwich are eliminated in our platform. Taking advantage of DNA nanotechnology, we employed three-dimensional tetrahedral DNA nanostructure as the scaffold to immobilize DNA recognition probes to increase the reactivity and accessibility, while DNA nanowire tentacles are used for efficient signal amplification by capturin...

Hybridization chain reaction amplification of microRNA detection with a tetrahedral DNA nanostructure-based electrochemical biosensor.

DNA is typically impermeable to the plasma membrane due to its polyanionic nature. Interestingly, several different DNA nanostructures can be readily taken up by cells in the absence of transfection agents, which suggests new opportunities for constructing intelligent cargo delivery systems from these biocompatible, nonviral DNA nanocarriers. However, the underlying mechanism of entry of the DNA nanostructures into the cells remains unknown. Herein, we investigated the endocytotic internalization and subsequent transport of tetrahedral DNA nanostructures (TDNs) by mammalian cells through single-particle tracking. We found that the TDNs were rapidly internalized by a caveolin-dependent pathway. After endocytosis, the TDNs were transported to the lysosomes in a highly ordered, microtubule-dependent manner. Although the TDNs retained their structural integrity within cells over long time periods, their localization in the lysosomes precludes their use as effective delivery agents. To modulate the cellular fate of the TDNs, we functionalized them with nuclear localization signals that directed their escape from the lysosomes and entry into the cellular nuclei. This study improves our understanding of the entry into cells and transport pathways of DNA nanostructures, and the results can be used as a basis for designing DNA-nanostructure-based drug delivery nanocarriers for targeted therapy.

Single-Particle Tracking and Modulation of Cell Entry Pathways of a Tetrahedral DNA Nanostructure in Live Cells

Self-assembled DNA nanostructures have emerged as a type of nano-biomaterials with precise structures, versatile functions and numerous applications One particularly promising application of these DNA nanostructures is to develop universal nanocarriers for smart and targeted drug delivery DNA is the genetic material in nature, and inherently biocompatible Nevertheless, cell membranes are barely permeable to naked DNA molecules, either single- or double- stranded; transport across the cell membrane is only possible with the assistance of transfection agents Interestingly, recent studies revealed that many DNA nanostructures could readily go into cells with high cell uptake efficiency In this Progress Report, we will review recent advances on using various DNA nanostructures, eg, DNA nanotubes, DNA tetrahedra, and DNA origami nanorobot, as drug delivery nanocarriers, and demonstrate several examples aiming at therapeutic applications with CpG-based immunostimulatory and siRNA-based gene silencing oligonucleotides

Smart drug delivery nanocarriers with self-assembled DNA nanostructures.

Structural antibody database (SAbDab; http://opig.stats.ox.ac.uk/webapps/sabdab) is an online resource containing all the publicly available antibody structures annotated and presented in a consistent fashion. The data are annotated with several properties including experimental information, gene details, correct heavy and light chain pairings, antigen details and, where available, antibody–antigen binding affinity. The user can select structures, according to these attributes as well as structural properties such as complementarity determining region loop conformation and variable domain orientation. Individual structures, datasets and the complete database can be downloaded.

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Jiye Shi

Papers

FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties.

Hybridization chain reaction amplification of microRNA detection with a tetrahedral DNA nanostructure-based electrochemical biosensor.

Single-Particle Tracking and Modulation of Cell Entry Pathways of a Tetrahedral DNA Nanostructure in Live Cells

Smart drug delivery nanocarriers with self-assembled DNA nanostructures.

SAbDab: the structural antibody database