In Silico Biology - Volume 2, issue 4

Authors: Olsen, Addie Nina | Mundy, John | Skriver, Karen

Article Type: Research Article

Abstract: Few plant peptides involved in intercellular communication have been experimentally isolated. Sequence analysis of the Arabidopsis thaliana genome has revealed numerous transmembrane receptors predicted to bind proteinacious ligands, emphasizing the importance of identifying peptides with signaling function. Annotation of the Arabidopsis genome sequence has made it possible to identify peptide-encoding genes. However, such annotational identification is impeded because small genes are poorly predicted by gene-prediction algorithms, thus prompting the alternative approaches described …here. We initially performed a systematic analysis of short polypeptides encoded by annotated genes on two Arabidopsis chromosomes using SignalP to identify potentially secreted peptides. Subsequent homology searches with selected, putatively secreted peptides, led to the identification of a potential, large Arabidopsis family of 34 genes. The predicted peptides are characterized by a conserved C-terminal sequence motif and additional primary structure conservation in a core region. The majority of these genes had not previously been annotated. A subset of the predicted peptides show high overall sequence similarity to Rapid Alkalinization Factor (RALF), a peptide isolated from tobacco. We therefore refer to this peptide family as RALFL for RALF-Like. RT-PCR analysis confirmed that several of the Arabidopsis genes are expressed and that their expression patterns vary. The identification of a large gene family in the genome of the model organism Arabidopsis thaliana demonstrates that a combination of systematic analysis and homology searching can contribute to peptide discovery. Show more

Keywords: In silico peptide discovery, plant peptide, peptide family, peptide signaling, secreted peptide, cationic peptide, intercellular communication, Arabidopsis thaliana, model organism, rapid alkalinization factor (RALF), phytosulfokine (PSK), genome annotation, small open reading frame, homology search, proregions, RT-PCR,

Citation: In Silico Biology, vol. 2, no. 4, pp. 441-451, 2002

Authors: Cárdenas-García, Maura | Lagunez Otero, Jaime | Korneev, Nikolai A.

Article Type: Research Article

Abstract: Ras is a protein related to cancer development. It is a convergence point for different signal transduction pathways that allow the cell to respond to external stimuli with different cell functions like growth, division, death, etc. In this paper, we analyze the signal pathways generated by different Ras effectors (Raf, RalGDS and PI3K), and the pathway relating Ras to the cell cycle control. We show that the interaction among different elements of these pathways induces a …topologic structure in the set of elements. We discuss properties of this topology and give an algorithm to build it. The application of topological concepts makes easier the interaction analysis. Using a computational algorithm, we can create isolated, independently manageable sub-groups. Then we construct their hierarchical structure. The procedure allows us to visualize groups of elements related to the Ras effectors involved in cell growth, the elements involved in the cytoskeleton regulation, and the elements related to the cell cycle control. Thus the division in sub-groups does not only make easier the analysis, but it also provides a biologically meaningful subdivision. Show more

Keywords: cell signal transduction pathways, abstract topology, Ras protein

Citation: In Silico Biology, vol. 2, no. 4, pp. 453-460, 2002

Authors: Chong, Allen | Zhang, Guanglan | Bajic, Vladimir B.

Article Type: Short Communication

Abstract: FIE (5'-end Information Extraction) is a web-based program designed primarily to extract the sequence of the regions around the 5'-end and around the translation initiation sites for a particular gene, based on information provided by LocusLink.

Keywords: gene start region, promoter region, translation start region, sequence extraction, LocusLink, RefSeq

Citation: In Silico Biology, vol. 2, no. 4, pp. 461-465, 2002

Authors: Hurlebaus, Jochen | Buchholz, Arne | Alt, Wolfgang | Wiechert, Wolfgang | Takors, Ralf

Article Type: Research Article

Abstract: The identification of metabolic regulation is a major concern in metabolic engineering. Metabolic regulation phenomena depend on intracellular compounds such as enzymes, metabolites and cofactors. A complete understanding of metabolic regulation requires quantitative information about these compounds under in vivo conditions. This quantitative knowledge in combination with the known network of metabolic pathways allows the construction of mathematical models that describe the dynamic changes in metabolite concentrations over time. Rapid sampling combined …with pulse experiments is a useful tool for the identification of metabolic regulation owing to the transient data they provide. Enzymatic tests in combination with ESI-LC-MS (Electrospray Ionization Liquid Chromatographic Tandem Mass Spectrometry) and HPLC measurements have been used to identify up to 30 metabolites and nucleotides from rapid sampling experiments. A metabolic modeling tool (MMT) that is built on a relational database was developed specifically for analysis of rapid sampling experiments. The tool allows to construct complex pathway models with information stored in the relational database. Parameter fitting and simulation algorithms for the resulting system of Ordinary Differential Equations (ODEs) are part of MMT. Additionally explicit sensitivity functions are calculated. The integration of all necessary algorithms in one tool allows fast model analysis and comparison. Complex models have been developed to describe the central metabolic pathways of Escherichia coli during a glucose pulse experiment. Show more

Keywords: metabolism modeling, rapid sampling, E. coli

Citation: In Silico Biology, vol. 2, no. 4, pp. 467-484, 2002

Authors: Lao, Demelo Madrazo | Okuno, Toshikatsu | Shimizu, Toshio

Article Type: Research Article

Abstract: Reported performance of existing transmembrane (TM) topology prediction methods were often based on evaluations which neglected the risk of signal peptides (SP) being predicted as putative TM as well. Here, we evaluated 12 selected TM topology prediction methods (TMpred, TopPred II, DAS, TMAP, MEMSAT 2, SOSUI, PRED-TMR2, TMHMM 2.0, HMMTOP 2.0, SPLIT 3.5, TM Finder, and MPEx) for the effect of SP in prediction performance considering three SP treatments, namely: remain (untreated), removed …first, and removed later. The results showed that the presence of SP significantly affected the prediction performance of the 12 selected TM topology prediction methods for all three predicted attributes (the number of transmembrane segments (TMSs), the number of TMSs plus position, and the N-tail location) and for the predicted topology (combined predictions of three attributes) by causing a reduction in prediction accuracy. In particular, lower prediction accuracies were obtained if SP is left untreated (remain) while significant increases were observed if SP is removed either first or later. However, between removed first and removed later SP treatments, the difference was statistically insignificant. In addition, we found that machine learning-based prediction methods were less affected by the presence of SP than hydropathy-based methods, but still the potential risk of degrading the prediction performance is there however to a lesser degree. Thus, when performing genome-wide analysis, the SP issue should be addressed during TM topology prediction. Show more

Keywords: signal peptide, transmembrane, evaluation, transmembrane topology prediction,

Citation: In Silico Biology, vol. 2, no. 4, pp. 485-494, 2002

Authors: Nudelman, German | Tennenbaum, Tamar | Mehr, Ramit | Unger, Ron

Article Type: Research Article

Abstract: We present a system for predicting protein-protein modifications, and demonstrate its usefulness in the field of signal transduction research. Signal transduction is one of the most important areas of investigation in biological research. One of the major mechanisms frequently employed by cells to regulate signal transduction processes involves protein phosphorylation by various kinases. As many as 1000 protein kinases and 500 protein phosphatases in the human genome are thought to be involved in phosphorylation processes …which regulate all aspects of cell function. The complexity of such interactions stems from the enormous number of factors and interactions, which makes the identification of putative substrates for any given enzyme by straightforward experimentation increasingly difficult. We present here a data mining algorithm, based on the similarity between the modifier proteins and between the modified proteins, and on experimental constraints. The application presented here (PESI) focuses on substrate phosphorylation by various enzymes. This algorithm reduces the number of substrate candidates for experimental study by about two orders of magnitude. Moreover, this algorithm has already yielded predictions for previously unknown substrates of the enzymes PKC ä and PKC ç, which we have confirmed experimentally. Show more

Keywords: protein-protein interactions, automated systems, data mining, similarity search, phosphorylation, kinases, biological pathways

Citation: In Silico Biology, vol. 2, no. 4, pp. 495-505, 2002

Authors: Sivakumar, Ashwin

Article Type: Other

Abstract: 2-D Gel Technology has had profound impact on proteomic research over the years. Informatics support brought a new dimension to 2D gels and associated technologies. But with advent of new and emerging technologies, it will be interesting to observe the trends of 2D gel technology in the years to come. Here we review 2D gel technology and its applications besides looking at the future scope of 2D gels in the post genome era.

Keywords: proteomics, 2D gel databases, 2D gel technology, bioinformatics, protein chips

Citation: In Silico Biology, vol. 2, no. 4, pp. 507-510, 2002

Authors: Clare, Amanda | King, Ross D.

Article Type: Research Article

Abstract: We wished to quantify the state-of-the-art of our understanding of clusters in microarray data. To do this we systematically compared the clusters produced on sets of microarray data using a representative set of clustering algorithms (hierarchical, k-means, and a modified version of QT_CLUST) with the annotation schemes MIPS, GeneOntology and GenProtEC. We assumed that if a cluster reflected known biology its members would share related ontological annotations. This assumption is the basis of …guilt-by-association and is commonly used to assign the putative function of proteins. To statistically measure the relationship between cluster and annotation we developed a new predictive discriminatory measure. We found that the clusters found in microarray data do not in general agree with functional annotation classes. Although many statistically significant relationships can be found, the majority of clusters are not related to known biology (as described in annotation ontologies). This implies that use of guilt-by-association is not supported by annotation ontologies. Depending on the estimate of the amount of noise in the data, our results suggest that bioinformatics has only codified a small proportion of the biological knowledge required to understand microarray data. The annotated clusters can be found at http://www.aber.ac.uk/compsci/Research/bio/dss/gba/. Show more

Keywords: ontology, machine-learning, transcriptome, microarray data, statistics

Citation: In Silico Biology, vol. 2, no. 4, pp. 511-522, 2002

Authors: Wingender, Edgar

Article Type: Other

Citation: In Silico Biology, vol. 2, no. 4, pp. 523-524, 2002

Authors: Zorzet, Anna | Gustafsson, Mats | Hammerling, Ulf

Article Type: Research Article

Abstract: Food hypersensitivity is constantly increasing in Western societies with a prevalence of about 1-2% in Europe and in the USA. Among children, the incidence is even higher. Because of the introduction of foods derived from genetically modified crops on the marketplace, the scientific community, regulatory bodies and international associations have intensified discussions on risk assessment procedures to identify potential food allergenicity of the newly introduced proteins. In this work, we present a novel biocomputational …methodology for the classification of amino acid sequences with regard to food allergenicity and non-allergenicity. This method relies on a computerised learning system trained using selected excerpts of amino acid sequences. One example of such a successful learning system is presented which consists of feature extraction from sequence alignments performed with the FASTA3 algorithm (employing the BLOSUM50 substitution matrix) combined with the k-Nearest-Neighbour (kNN) classification algorithm. Briefly, the two features extracted are the alignment score and the alignment length and the kNN algorithm assigns the pair of extracted features from an unknown sequence to the prevalent class among its k nearest neighbours in the training (prototype) set available. 91 food allergens from several specialised public repositories of food allergy and the SWALL database were identified, pre-processed, and stored, yielding one of the most extensively characterised repositories of allergenic sequences known today. All allergenic sequences were classified using a standard one-leave-out cross validation procedure yielding about 81% correctly classified allergens and the classification of 367 non-allergens in an independent test set resulted in about 98% correct classifications. The biocomputational approach presented should be regarded as a significant extension and refinement of earlier attempts suggested for in silico food safety assessment. Our results show that the framework described here is powerful enough to become useful as part of a multiple-procedure test scheme that also depicts other evaluation approaches such as solid phase immunoassay and tests for stability to digestions. Show more

Keywords: food allergy, risk assessment, computational toxicology

Citation: In Silico Biology, vol. 2, no. 4, pp. 525-534, 2002