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CSIRO Develops Statistical Tools for Cancer Diagnosis.

Childhood Stress Linked to Early Puberty in Girls.

Sonography More Accurate than Mammography.

New Genes Related to Parkinson’s Disease Discovered.

China to Use Microbial Genome Technology for Oil Extraction.

Curcumin May Prevent Alcohol-Related Liver Disease.

Reliance Life Starts Trials on Bio-Engineered Skin Products.

Japanese Scientists Use Mouth Cells for Treatment of Damaged Eyes.

New Anti-Mad Cow Disease Enzyme Discovered.

Breakthrough Study in Congenital Birth Abnormalities.

Faster Healing with New Drug.

Bioremediation to Keep Atrazine from Waterways.

Fifth Brain Disorder Gene Located.

Moving towards Emerging Field of Mechanobiology.

Singapore and Israel Join Forces in Research and Innovation.

Phylogica to Discover Peptide-based Vaccines with Pfizer.

TGR BioSciences Partners with Cedarlane.

China Sky One Medical Cultivates TCM Herbs in Northeast China.

Chindex and Fosun Pharma Ink Joint Venture.

China SHESAYS Introduces Sirona's CEREC AC Dental Restoration Robot.

Novozymes Opens New R&D Center in India.

Avila, Sanofi-aventis Partner to Develop Targeted Covalent Oncology Drugs.

Biogen Idec Launches Tysabri for Multiple Sclerosis Treatment in India.

GSK, Impax in $11.5 mn Deal for Parkinson's Drug.

Eisai Enters Local Oncology Market with Treakisym.

RaQualia Announces Collaboration and License Agreement With Lilly for Pain Treatments.

Bristol-Myers Squibb and Oncolys BioPharma Enter Global Licensing Agreement for Investigational HIV Compound.

A*STAR and Cytos Extend Influenza Vaccine Collaboration to Supply of Clinical Trial Material.

AUSTRALIA – Less frequent dosing in hemophilia B patients shows improved pharmacokinetic profile.

AUSTRALIA – Novogen identifies highly active drug candidates against prostate cancer.

SINGAPORE – Gold nanoprobes used to unlock genetic profiles.

SINGAPORE – Clinical checks to detect relapses for patients with peripheral T-cell lymphoma just as effective as imaging.

SINGAPORE – QT Vascular initiates chocolate cases in Singapore.

THE PHILIPPINES – Trial set for world's first leprosy vaccine.

THE PHILIPPINES – Nickel-eating Philippine plant may have wide uses.

EUROPE – New study to investigate dabigatran etexilate for prevention of recurrent stroke in high-risk patients.

EUROPE – Researchers use DNA to build tool that may literally shine light on cancer.

UNITED STATES – Scientists slow brain tumor growth in mice.

UNITED STATES – Researchers pinpoint new role for enzyme in DNA repair, kidney cancer.

UNITED STATES – Progress on multiple sclerosis treatment with embryonic stem cells.

UNITED STATES – Researchers develop vaccine candidate using genetically engineered malaria parasite.

Characterizing enzyme sequences and identifying their active sites is a very important task. The current experimental methods are too expensive and labor intensive to handle the rapidly accumulating protein sequences and structure data. Thus accurate, high-throughput in silico methods for identifying catalytic residues and enzyme function prediction are much needed. In this paper, we propose a novel sequence-based catalytic domain prediction method using a sequence clustering and an information-theoretic approaches. The first step is to perform the sequence clustering analysis of enzyme sequences from the same functional category (those with the same EC label). The clustering analysis is used to handle the problem of widely varying sequence similarity levels in enzyme sequences. The clustering analysis constructs a sequence graph where nodes are enzyme sequences and edges are a pair of sequences with a certain degree of sequence similarity, and uses graph properties, such as biconnected components and articulation points, to generate sequence segments common to the enzyme sequences. Then amino acid subsequences in the common shared regions are aligned and then an information theoretic approach called aggregated column related scoring scheme is performed to highlight potential active sites in enzyme sequences. The aggregated information content scoring scheme is shown to be effective to highlight residues of active sites effectively. The proposed method of combining the clustering and the aggregated information content scoring methods was successful in highlighting known catalytic sites in enzymes of Escherichia coli K12 in terms of the Catalytic Site Atlas database. Our method is shown to be not only accurate in predicting potential active sites in the enzyme sequences but also computationally efficient since the clustering approach utilizes two graph properties that can be computed in linear to the number of edges in the sequence graph and computation of mutual information does not require much time. We believe that the proposed method can be useful for identifying active sites of enzyme sequences from many genome projects.

Genomics is faced with the issue of many partially annotated putative enzyme-encoding genes for which activities have not yet been verified, while metabolomics is faced with the issue of many putative enzyme reactions for which full equations have not been verified. Knowledge of enzymes has been collected by IUBMB, and has been made public as the Enzyme List. To date, however, the terminology of the Enzyme List has not been assessed comprehensively by bioinformatics studies. Instead, most of the bioinformatics studies simply use the identifiers of the enzymes, i.e. the Enzyme Commission (EC) numbers. We investigated the actual usage of terminology throughout the Enzyme List, and demonstrated that the partial characteristics of reactions cannot be retrieved by simply using EC numbers. Thus, we developed a novel ontology, named PIERO, for annotating biochemical transformations as follows. First, the terminology describing enzymatic reactions was retrieved from the Enzyme List, and was grouped into those related to overall reactions and biochemical transformations. Consequently, these terms were mapped onto the actual transformations taken from enzymatic reaction equations. This ontology was linked to Gene Ontology (GO) and EC numbers, allowing the extraction of common partial reaction characteristics from given sets of orthologous genes and the elucidation of possible enzymes from the given transformations. Further future development of the PIERO ontology should enhance the Enzyme List to promote the integration of genomics and metabolomics.

Visualizing large-scale data produced by the high throughput experiments as a biological graph leads to better understanding and analysis. This study describes a customized force-directed layout algorithm, EClerize, for biological graphs that represent pathways in which the nodes are associated with Enzyme Commission (EC) attributes. The nodes with the same EC class numbers are treated as members of the same cluster. Positions of nodes are then determined based on both the biological similarity and the connection structure. EClerize minimizes the intra-cluster distance, that is the distance between the nodes of the same EC cluster and maximizes the inter-cluster distance, that is the distance between two distinct EC clusters. EClerize is tested on a number of biological pathways and the improvement brought in is presented with respect to the original algorithm. EClerize is available as a plug-in to Cytoscape (http://apps.cytoscape.org/apps/eclerize).

Enzymes catalyze diverse biochemical reactions and are building blocks of cellular and metabolic pathways. Data and metadata of enzymes are distributed across databases and are archived in various formats. The enzyme databases provide utilities for efficient searches and downloading enzyme records in batch mode but do not support organism-specific extraction of subsets of data. Users are required to write scripts for parsing entries for customized data extraction prior to downstream analysis. Integrated Customized Extraction of Enzyme Data (iCEED) has been developed to provide organism-specific customized data extraction utilities for seven commonly used enzyme databases and brings these resources under an integrated portal. iCEED provides dropdown menus and search boxes using typehead utility for submission of queries as well as enzyme class-based browsing utility. A utility to facilitate mapping and visualization of functionally important features on the three-dimensional (3D) structures of enzymes is integrated. The customized data extraction utilities provided in iCEED are expected to be useful for biochemists, biotechnologists, computational biologists, and life science researchers to build curated datasets of their choice through an easy to navigate web-based interface. The integrated feature visualization system is useful for a fine-grained understanding of the enzyme structure–function relationship. Desired subsets of data, extracted and curated using iCEED can be subsequently used for downstream processing, analyses, and knowledge discovery. iCEED can also be used for training and teaching purposes.