Genomic, Biochemical, and Modeling Analyses of Asparagine Synthetases from Wheat.

Asparagine synthetase activity in cereals has become an important issue with the discovery that free asparagine concentration determines the potential for formation of acrylamide, a probably carcinogenic processing contaminant, in baked cereal products. Asparagine synthetase catalyses the ATP-dependent transfer of the amino group of glutamine to a molecule of aspartate to generate glutamate and asparagine. Here, asparagine synthetase-encoding polymerase chain reaction (PCR) products were amplified from wheat (Triticum aestivum) cv. Spark cDNA. The encoded proteins were assigned the names TaASN1, TaASN2, and TaASN3 on the basis of comparisons with other wheat and cereal asparagine synthetases. Although very similar to each other they differed slightly in size, with molecular masses of 65.49, 65.06, and 66.24 kDa, respectively. Chromosomal positions and scaffold references were established for TaASN1, TaASN2, and TaASN3, and a fourth, more recently identified gene, TaASN4. TaASN1, TaASN2, and TaASN4 were all found to be single copy genes, located on chromosomes 5, 3, and 4, respectively, of each genome (A, B, and D), although variety Chinese Spring lacked a TaASN2 gene in the B genome. Two copies of TaASN3 were found on chromosome 1 of each genome, and these were given the names TaASN3.1 and TaASN3.2. The TaASN1, TaASN2, and TaASN3 PCR products were heterologously expressed in Escherichia coli (TaASN4 was not investigated in this part of the study). Western blot analysis identified two monoclonal antibodies that recognized the three proteins, but did not distinguish between them, despite being raised to epitopes SKKPRMIEVAAP and GGSNKPGVMNTV in the variable C-terminal regions of the proteins. The heterologously expressed TaASN1 and TaASN2 proteins were found to be active asparagine synthetases, producing asparagine and glutamate from glutamine and aspartate. The asparagine synthetase reaction was modeled using SNOOPY® software and information from the BRENDA database to generate differential equations to describe the reaction stages, based on mass action kinetics. Experimental data from the reactions catalyzed by TaASN1 and TaASN2 were entered into the model using Copasi, enabling values to be determined for kinetic parameters. Both the reaction data and the modeling showed that the enzymes continued to produce glutamate even when the synthesis of asparagine had ceased due to a lack of aspartate.

Computational modelling of cancerous mutations in the EGFR/ERK signalling pathway.

BACKGROUND: The Epidermal Growth Factor Receptor (EGFR) activated Extracellular-signal Regulated Kinase (ERK) pathway is a critical cell signalling pathway that relays the signal for a cell to proliferate from the plasma membrane to the nucleus. Deregulation of the EGFR/ERK pathway due to alterations affecting the expression or function of a number of pathway components has long been associated with numerous forms of cancer. Under normal conditions, Epidermal Growth Factor (EGF) stimulates a rapid but transient activation of ERK as the signal is rapidly shutdown. Whereas, under cancerous mutation conditions the ERK signal cannot be shutdown and is sustained resulting in the constitutive activation of ERK and continual cell proliferation. In this study, we have used computational modelling techniques to investigate what effects various cancerous alterations have on the signalling flow through the ERK pathway. RESULTS: We have generated a new model of the EGFR activated ERK pathway, which was verified by our own experimental data. We then altered our model to represent various cancerous situations such as Ras, B-Raf and EGFR mutations, as well as EGFR overexpression. Analysis of the models showed that different cancerous situations resulted in different signalling patterns through the ERK pathway, especially when compared to the normal EGF signal pattern. Our model predicts that cancerous EGFR mutation and overexpression signals almost exclusively via the Rap1 pathway, predicting that this pathway is the best target for drugs. Furthermore, our model also highlights the importance of receptor degradation in normal and cancerous EGFR signalling, and suggests that receptor degradation is a key difference between the signalling from the EGF and Nerve Growth Factor (NGF) receptors. CONCLUSION: Our results suggest that different routes to ERK activation are being utilised in different cancerous situations which therefore has interesting implications for drug selection strategies. We also conducted a comparison of the critical differences between signalling from different growth factor receptors (namely EGFR, mutated EGFR, NGF, and Insulin) with our results suggesting the difference between the systems are large scale and can be attributed to the presence/absence of entire pathways rather than subtle difference in individual rate constants between the systems.

Prediction of protein-protein interaction types using association rule based classification.

BACKGROUND: Protein-protein interactions (PPI) can be classified according to their characteristics into, for example obligate or transient interactions. The identification and characterization of these PPI types may help in the functional annotation of new protein complexes and in the prediction of protein interaction partners by knowledge driven approaches. RESULTS: This work addresses pattern discovery of the interaction sites for four different interaction types to characterize and uses them for the prediction of PPI types employing Association Rule Based Classification (ARBC) which includes association rule generation and posterior classification. We incorporated domain information from protein complexes in SCOP proteins and identified 354 domain-interaction sites. 14 interface properties were calculated from amino acid and secondary structure composition and then used to generate a set of association rules characterizing these domain-interaction sites employing the APRIORI algorithm. Our results regarding the classification of PPI types based on a set of discovered association rules shows that the discriminative ability of association rules can significantly impact on the prediction power of classification models. We also showed that the accuracy of the classification can be improved through the use of structural domain information and also the use of secondary structure content. CONCLUSION: The advantage of our approach is that we can extract biologically significant information from the interpretation of the discovered association rules in terms of understandability and interpretability of rules. A web application based on our method can be found at http://bioinfo.ssu.ac.kr/~shpark/picasso/

A novel main chain motif in proteins bridged by cationic groups: the niche.

We have surveyed the bridging of pairs of main chain carbonyl oxygens by cations or by delta(+) hydrogens within hydrogen bonding groups. A three to four residue motif, which we call the niche, with characteristic phi,psi angles, is by far the commonest feature with this property. The niche accommodates atoms or groups that offer delta(+) charges, including water molecules or metal ions, as well as amines, guanidines, and other NH(2) groups. Seven percent of all residues in an average soluble protein belong to a niche; another 7% have the niche conformation but no obvious bridging delta(+) group. Fifty-five percent of niches occur either following a type 1 beta-turn or at the C-termini of alpha-helices, and niches turn out to be the most common C-terminal features of alpha-helices: 39% of alpha-helical C-termini are niches, whereas 34% are Schellman loops. 3(10) helices also frequently terminate in niches. Niches that bind K(+), Na(+) or Ca(2+) occur in some functional contexts: in the cyclic peptides valinomycin and antamanide; in several enzymes that are allosterically activated by Na(+) or K(+); and in the calcium pump, where a niche is integrally involved in the ion transport.

Magnesium dietary manipulation and recovery of function following controlled cortical damage in the rat.

Previous research has shown that dietary magnesium (Mg2+) deficiency prior to injury worsens recovery of function and that systemic administration of Mg2+ pre or post-injury significantly improves functional recovery. The purpose of the present study was to determine if manipulations in dietary Mg2+ would alter functional recovery following unilateral cortical injuries. Two weeks prior to injury, rats were placed on a customized diet enriched with Mg2+, deficient in Mg2+, or on a standard Mg2+ diet. Rats were then prepared with unilateral cortical contusion injuries (CCI) of the sensorimotor cortex. Two days following CCI, rats were tested on a battery of sensorimotor (vibrissae-forelimb placing and bilateral tactile adhesive removal tests), as well as the acquisition of reference memory in the Morris water maze. Serum analysis for Mg2+ prior to injury showed a diet-dependent modulation in levels. The Mg(2+)-enriched diet showed significantly higher levels of serum Mg2+ compared to the normal diet and the Mg(2+)-deficient diet showed significantly lower levels compared to the Mg(2+)-normal diet. On the placing and tactile removal tests Mg2+ deficiency significantly worsened recovery compared to the Mg(2+)-enriched and Mg(2+-)normal diet conditions. There were no statistically significant differences between the Mg(2+)-normal and Mg(2+)-enriched diets on the sensorimotor tests. On the acquisition of reference memory there were no significant difference between diet conditions; however, the Mg(2+)-deficient diet showed a trend toward impaired performance compared to the other diet conditions. The Mg(2+)-deficient diet resulted in a larger lesion cavity compared to the other diet conditions. These findings suggest that dietary Mg2+ modulates recovery of function.

Automatic generation of 3D motifs for classification of protein binding sites.

BACKGROUND: Since many of the new protein structures delivered by high-throughput processes do not have any known function, there is a need for structure-based prediction of protein function. Protein 3D structures can be clustered according to their fold or secondary structures to produce classes of some functional significance. A recent alternative has been to detect specific 3D motifs which are often associated to active sites. Unfortunately, there are very few known 3D motifs, which are usually the result of a manual process, compared to the number of sequential motifs already known. In this paper, we report a method to automatically generate 3D motifs of protein structure binding sites based on consensus atom positions and evaluate it on a set of adenine based ligands. RESULTS: Our new approach was validated by generating automatically 3D patterns for the main adenine based ligands, i.e. AMP, ADP and ATP. Out of the 18 detected patterns, only one, the ADP4 pattern, is not associated with well defined structural patterns. Moreover, most of the patterns could be classified as binding site 3D motifs. Literature research revealed that the ADP4 pattern actually corresponds to structural features which show complex evolutionary links between ligases and transferases. Therefore, all of the generated patterns prove to be meaningful. Each pattern was used to query all PDB proteins which bind either purine based or guanine based ligands, in order to evaluate the classification and annotation properties of the pattern. Overall, our 3D patterns matched 31% of proteins with adenine based ligands and 95.5% of them were classified correctly. CONCLUSION: A new metric has been introduced allowing the classification of proteins according to the similarity of atomic environment of binding sites, and a methodology has been developed to automatically produce 3D patterns from that classification. A study of proteins binding adenine based ligands showed that these 3D patterns are not only biochemically meaningful, but can be used for protein classification and annotation.

Predicting protein function by machine learning on amino acid sequences--a critical evaluation.

BACKGROUND: Predicting the function of newly discovered proteins by simply inspecting their amino acid sequence is one of the major challenges of post-genomic computational biology, especially when done without recourse to experimentation or homology information. Machine learning classifiers are able to discriminate between proteins belonging to different functional classes. Until now, however, it has been unclear if this ability would be transferable to proteins of unknown function, which may show distinct biases compared to experimentally more tractable proteins. RESULTS: Here we show that proteins with known and unknown function do indeed differ significantly. We then show that proteins from different bacterial species also differ to an even larger and very surprising extent, but that functional classifiers nonetheless generalize successfully across species boundaries. We also show that in the case of highly specialized proteomes classifiers from a different, but more conventional, species may in fact outperform the endogenous species-specific classifier. CONCLUSION: We conclude that there is very good prospect of successfully predicting the function of yet uncharacterized proteins using machine learning classifiers trained on proteins of known function.

Nicotinamide reduces acute cortical neuronal death and edema in the traumatically injured brain.

Previous studies have shown that administration of nicotinamide (Vitamin B(3)) in animal models of traumatic brain injury (TBI) and ischemia significantly reduced the size of infarction or injury and improved functional recovery. The present study evaluated the ability of nicotinamide to provide acute neuroprotection and edema reduction following TBI. Groups of rats were assigned to nicotinamide (500mg/kg) or saline (1.0ml/kg) treatment conditions and received contusion injuries or sham surgeries. Drug treatment was administered 15min following injury. Brains were harvested 24h later and either processed for histology or water content. Frozen sections were stained with the degenerating neuron stain (Fluoro-Jade B) (FJ) and cell counts were performed at the site of injury. Additional brains were processed for water content (a measure of injury-induced edema). Results of this study showed that administration of nicotinamide following TBI significantly reduced the number of FJ(+) neurons in the injured cortex compared to saline-treated animals. Examination of the water content of the brains also revealed that administration of nicotinamide significantly attenuated the amount of water compared to saline-treated animals in the injured cortex. These results indicate that nicotinamide administration significantly reduced neuronal death and attenuated cerebral edema following injury. The current findings suggest that nicotinamide significantly modulates acute pathophysiological processes following injury and that this may account for its beneficial effects on recovery of function following injury.

A lock-and-key model for protein-protein interactions.

MOTIVATION: Protein-protein interaction networks are one of the major post-genomic data sources available to molecular biologists. They provide a comprehensive view of the global interaction structure of an organism's proteome, as well as detailed information on specific interactions. Here we suggest a physical model of protein interactions that can be used to extract additional information at an intermediate level: It enables us to identify proteins which share biological interaction motifs, and also to identify potentially missing or spurious interactions. RESULTS: Our new graph model explains observed interactions between proteins by an underlying interaction of complementary binding domains (lock-and-key model). This leads to a novel graph-theoretical algorithm to identify bipartite subgraphs within protein-protein interaction networks where the underlying data are taken from yeast two-hybrid experimental results. By testing on synthetic data, we demonstrate that under certain modelling assumptions, the algorithm will return correct domain information about each protein in the network. Tests on data from various model organisms show that the local and global patterns predicted by the model are indeed found in experimental data. Using functional and protein structure annotations, we show that bipartite subnetworks can be identified that correspond to biologically relevant interaction motifs. Some of these are novel and we discuss an example involving SH3 domains from the Saccharomyces cerevisiae interactome. AVAILABILITY: The algorithm (in Matlab format) is available (see http://www.maths.strath.ac.uk/~aas96106/lock_key.html).

Computational modelling of the receptor-tyrosine-kinase-activated MAPK pathway.

The MAPK (mitogen-activated protein kinase) pathway is one of the most important and intensively studied signalling pathways. It is at the heart of a molecular-signalling network that governs the growth, proliferation, differentiation and survival of many, if not all, cell types. It is de-regulated in various diseases, ranging from cancer to immunological, inflammatory and degenerative syndromes, and thus represents an important drug target. Over recent years, the computational or mathematical modelling of biological systems has become increasingly valuable, and there is now a wide variety of mathematical models of the MAPK pathway which have led to some novel insights and predictions as to how this system functions. In the present review we give an overview of the processes involved in modelling a biological system using the popular approach of ordinary differential equations. Focusing on the MAPK pathway, we introduce the features and functions of the pathway itself before comparing the available models and describing what new biological insights they have led to.

GeneRank: using search engine technology for the analysis of microarray experiments.

BACKGROUND: Interpretation of simple microarray experiments is usually based on the fold-change of gene expression between a reference and a "treated" sample where the treatment can be of many types from drug exposure to genetic variation. Interpretation of the results usually combines lists of differentially expressed genes with previous knowledge about their biological function. Here we evaluate a method--based on the PageRank algorithm employed by the popular search engine Google--that tries to automate some of this procedure to generate prioritized gene lists by exploiting biological background information. RESULTS: GeneRank is an intuitive modification of PageRank that maintains many of its mathematical properties. It combines gene expression information with a network structure derived from gene annotations (gene ontologies) or expression profile correlations. Using both simulated and real data we find that the algorithm offers an improved ranking of genes compared to pure expression change rankings. CONCLUSION: Our modification of the PageRank algorithm provides an alternative method of evaluating microarray experimental results which combines prior knowledge about the underlying network. GeneRank offers an improvement compared to assessing the importance of a gene based on its experimentally observed fold-change alone and may be used as a basis for further analytical developments.

TOPS: an enhanced database of protein structural topology.

The TOPS database holds topological descriptions of protein structures. These compact and highly abstract descriptions reduce the protein fold to a sequence of Secondary Structure Elements (SSEs) and three sets of pairwise relationships between them, hydrogen bonds relating parallel and anti- parallel beta strands, spatial adjacencies relating neighbouring SSEs, and the chiralities of selected supersecondary structures, including connections in betaalphabeta units and between parallel alpha helices. The database is used as a resource for visualizing folding topologies, fast topological pattern searching and structure comparison. Here, significant enhancements to the TOPS database are described. The topological description has been enhanced to include packing relationships between helices, which significantly improves the description of protein folds with little beta strand content. Further, the topological description has been annotated with sequence information. The query interfaces to the database have been improved and the new version can be found at http://www.tops.leeds.ac.uk/.