Narrow Results

An enzyme system organized in a flow device with three parallel channels was used to mimic a reversible Double Feynman Gate (DFG) with three input and three output signals. Reversible conversion of NAD⁺ and NADH cofactors was used to perform XOR logic operations, while biocatalytic oxidation of NADH resulted in Identity operation working in parallel. The first biomolecular realization of a DFG gate is promising for integrating into complex biomolecular networks operating in future unconventional biocomputing systems, as well as for novel biosensor applications.

The distribution of the kinetic parameters of a reversible enzymic reaction with an ordered mechanism is theoretically studied under the assumption that during evolution the increase in reaction rate was an important target of natural selection. The optimal individual rate constants in the steady state for fixed reactant concentrations are determined from optimization principles. The reaction rate is a homogeneous function of first degree of the elementary rate constants and the determination of states of maximal activity is only possible if constraints for the rate constants are taken into account. Besides a fixed thermodynamical equilibrium constant, this concerns upper limits for the values of the individual rate constants. In extension of previous work on the optimization of enzyme kinetic parameters the influence of constraints concerning upper limits of the rate constants is analyzed. Two different models are introduced: the separate limit model and the overall limit model. The concept of “evolutionary effort” is applied to derive an expression for the cost function leading to an overall upper limit for the values of the rate constants. The resulting optimization problem is solved for ordered mechanisms involving different numbers of elementary steps depending on the reactant concentrations and on the thermodynamical equilibrium constant.

Fiber modifications by environmentally friendly processing are essential in order to simplify the preparation and finishing processes, in addition to minimizing the chemical waste and associated disposal problem. In this regard, enzymes have been used extensively because it can remove the small fiber ends from yarn surface to create a smooth fabric surface appearance and introduce a degree of softness without using traditional chemical treatment. However, a significant strength reduction and slow reaction rate of the enzymatic reaction limit its industrial application. In this paper, the potential of using low-temperature plasma (LTP) as a surface pre-treatment prior to enzyme treatment on flax fiber has been studied. By means of the LTP pre-treatment, the effectiveness of enzyme treatment can be enhanced.

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.

In applicative biosensor technology, mathematical modeling plays an indispensable role in explaining the transport of electrical signals by analyzing the binding behavior of the biochemical enzyme inhibitors to the target molecule. The biosensors are extensively used in clinical diagnostics, drug detention, food analysis and environment monitoring because they are highly sensitive, reliable and relatively cheap. Dynamic mathematical models used for biological investigation serve the purpose efficiently with very reasonable outcomes. In this study, a time-independent mathematical model for biosensor enzyme–substrate–inhibitor system under uncompetitive inhibition based on the nonlinear diffusion equations taking into consideration the kinetic rate constants and the initial concentrations of enzyme, substrate and inhibitor has been formulated and solved analytically using variational iteration method (VIM). The reliability and accuracy has been proved by comparing our results with the solution obtained by standard VIM. Chosen biosensors showed desirable sensitivity, selectivity and potential for application on real samples. They are frequently made to prevent interference from undesirable components that are present in the monitored system. The VIM is effectively and easily used to obtain solution of nonlinear equations accurately. Further, the solution has been discussed exhaustively for different values of reaction parameters avoiding linearization and unrealistic assumptions and the results obtained significantly agree with existing literature.

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.

Ferrochelatase (also known as PPIX ferrochelatase; Enzyme Commission number 4.9.9.1.1) catalyzes the insertion of ferrous iron into PPIX to form heme. This reaction unites the biochemically synchronized pathways of porphyrin synthesis and iron transport in nearly all living organisms. The ferrochelatases are an evolutionarily diverse family of enzymes with no more than six active site residues known to be perfectly conserved. The availability of over thirty different crystal structures, including many with bound metal ions or porphyrins, has added tremendously to our understanding of ferrochelatase structure and function. It is generally believed that ferrous iron is directly channeled to ferrochelatase in vivo, but the identity of the suspected chaperone remains uncertain despite much recent progress in this area. Identification of a conserved metal ion binding site at the base of the active site cleft may be an important clue as to how ferrochelatases acquire iron, and catalyze desolvation during transport to the catalytic site to complete heme synthesis.

Ferrochelatase, the terminal enzyme of the heme biosynthetic pathway, catalyzes the insertion of ferrous iron into protoporphyrin IX to give heme. Resonance Raman spectroscopy has been instrumental in defining the distortion (mode and extent) of the porphyrin substrate, which is a critical step in the catalytic mechanism of ferrochelatase. Saddling is the predominant porphyrin out-of-plane deformation induced upon binding to ferrochelatase. Our analysis demonstrated that the intensity of the γ₁₅ line, which is assigned to an out-of-plane porphyrin vibration, in resonance Raman spectra obtained for wild-type- and variant ferrochelatase-bound porphyrin, correlates with the saddling deformation undergone by the porphyrin substrate. Further analysis of the three dimensional X-ray structures of bacterial, human and yeast ferrochelatases and the type and extent of distortion of the protein-bound porphyrin substrate and inhibitors using normal structure decomposition, support the view that ferrochelatase catalysis involves binding of a distorted porphyrin substrate and releasing of a flatter, metalated porphyrin.

Ferrochelatase catalyzes the insertion of ferrous iron into protoporphyrin IX to generate heme. Despite recent research on the reaction mechanism of ferrochelatase, the precise roles and localization of individual active site residues in catalysis, particularly those involved in the insertion of the ferrous iron into the protoporphyrin IX substrate, remain controversial. One outstanding question is from which side of the macrocycle of the bound porphyin substrate is the ferrous iron substrate inserted. Pre-steady state kinetic experiments done under single-turnover conditions conclusively demonstrate that metal ion insertion is pH-dependent, and that the conserved active site His-Glu pair coordinately catalyzes the metal ion insertion reaction. Further, p$K_a$ calculations and molecular dynamic simulations indicate that the active site His is deprotonated and the protonation state of the Glu relates to the conformational state of ferrochelatase. Specifically, the conserved Glu in the open conformation of ferrochelatase is deprotonated, while it remains protonated in the closed conformation. These findings support not only the role of the His-Glu pair in catalyzing metal ion insertion, as these residues need to be deprotonated to bind the incoming metal ion, but also the importance of the relationship between the protonation state of the Glu residue and the conformation of ferrochelatase. Finally, the results of this study are consistent with our previous proposal that the unwinding of the $\pi$-helix, the major structural determinant of the closed to open conformational transition in ferrochelatase, is associated with the Glu residue binding the Fe${}^{2+}$ substrate from a mitochondrial Fe${}^{2+}$ donor.

This short review highlights the author’s group research on modified vitamin B${}_{12}$ derivatives with a peptide backbone as (1) inhibitors of B${}_{12}$-dependent enzymes and as (2) models of cofactor B${}_{12}$-protein complexes.

Recent development and challenges in DNA biosensing technology for the detection of DNA hybridization are reviewed with respect to their abilities to achieve lower detection limit and higher selectivity. Researchers exploit a range of different chemistries for the development of DNA hybridization biosensors, however all the designs take advantage of heterogenous hybridization between the surface-bound DNA (the probe) and the DNA sample (target) in the solution. The detection protocols include using optical, microgravimetry, and electrochemical-based device to transduce DNA hybridization by observing changes in light, mass/frequency, and current/charge, respectively, upon exposure to the sample. The pros and cons of these biosensor designs are discussed with illustrative examples.

miRNA-21 (miR-21) is a potential biomarker for the monitoring of diseases through its expression levels. Simple, portable and sensitive miR-21 detection of is advantageous for health monitoring in Point of Care Testing (POCT). Gold nanoparticles (AuNP) as excellent colorimetric sensors are widely used in the POCT. However, their low sensitivity is a limitation of their clinical use. Herein, we developed an AuNPs-based miR-21 assay with enzyme-assisted amplification reaction to construct the colorimetric platform capable of detecting as low as 0.1nM. In this platform, template ssDNA as a signal molecule could hybridize with ssDNA-modified AuNPs to generate the color reaction. The target miR-21 specifically hybridized to the template ssDNA, which was then cleaved by exonuclease III (Exo III) to release the target miR-21. As a trigger, miR-21 catalyzed the degradation of the template ssDNA to amplify the signal by Exo III. By hybridizing miR-21 and template ssDNA in the presence of Exo III, R-21 induced a significant decrease in the level of template ssDNA to reduce the aggregation of AuNPs. There is a clear color difference in the presence/absence of miR-21 in the assay. In this assay, the optimal concentration of templated ssDNA and Exo III were 100nM and 0.06U/$\mu$L in a 100$\mu$L detection system. The LoD for UV–Vis spectrum and colorimetric reaction were 0.1nM and 0.5nM, respectively. The detection system has good selectivity and can be used to detect miR-21 in the simulated saliva. It has great potential for application in biomedical research as well as in clinical diagnostics.

Metal-oxide nanoparticles with high surface area, controllable functionality and thermal and mechanical stability provide high affinity for enzymes when the next generation of biosensor applications are being considered. We report on the synthesis of metal-oxide-based nanoparticles (with different physical and chemical properties) using hydrothermal processing, photo-deposition and silane functionalization. Physical and chemical properties of the user-synthesized nanoparticles were investigated using scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), and Raman scattering, respectively. Thus, characterized metal-oxide-based nanoparticles served as nanosupports for the immobilization of soybean peroxidase enzyme (a model enzyme) through physical binding. The enzyme–nanosupport interface was evaluated to assess the optimum nanosupport characteristics that preserve enzyme functionality and its catalytic behavior. Our results showed that both the nanosupport geometry and its charge influence the functionality and catalytic behavior of the bio-metal-oxide hybrid system.

Enzymes are protein molecules that play a crucial role in various biological processes in living organisms. They function as catalysts in biological reactions such as digestion, metabolism, DNA replication and other physiological processes. Furthermore, enzymes are widely used in food production, pharmaceuticals and biofuel production. In these industries, they accelerate desired chemical reactions as biocatalysts. Therefore, applying computational methods and data-driven algorithms to predict enzyme properties is essential. Over the past decade, deep learning has made remarkable advancements in science and technology. Deep learning is a subset of machine learning algorithms that rely on artificial neural networks. These algorithms can be employed for supervised, semi-supervised and unsupervised learning. Here, to provide an update on the current literature, we provide an overview of various deep learning algorithms and recent advancements in their application to enzyme science. These applications can generally be categorized into diverse subjects: function prediction, enzyme kinetic parameters prediction, enzyme-substrate identification, condition optimization, thermophilic property prediction, enzyme catalytic site prediction and enzyme design. In conclusion, we discuss the convergence of enzyme science and deep learning, highlighting the potential opportunities and challenges.