GraBCas: a bioinformatics tool for score-based prediction of Caspase- and Granzyme B-cleavage sites in protein sequences

Caspases and granzyme B are proteases that share the primary specificity to cleave at the carboxyl terminal of aspartate residues in their substrates. Both, caspases and granzyme B are enzymes that are involved in fundamental cellular processes and play a central role in apoptotic cell death. Although various targets are described, many substrates still await identification and many cleavage sites of known substrates are not identified or experimentally verified. A more comprehensive knowledge of caspase and granzyme B substrates is essential to understand the biological roles of these enzymes in more detail. The relatively high variability in cleavage site recognition sequence often complicates the identification of cleavage sites. As of yet there is no software available that allows identification of caspase and/or granzyme with cleavage sites differing from the consensus sequence. Here, we present a bioinformatics tool ‘GraBCas’ that provides score-based prediction of potential cleavage sites for the caspases 1–9 and granzyme B including an estimation of the fragment size. We tested GraBCas on already known substrates and showed its usefulness for protein sequence analysis. GraBCas is available at

GeneTrail—advanced gene set enrichment analysis

We present a comprehensive and efficient gene set analysis tool, called ‘GeneTrail’ that offers a rich functionality and is easy to use. Our web-based application facilitates the statistical evaluation of high-throughput genomic or proteomic data sets with respect to enrichment of functional categories. GeneTrail covers a wide variety of biological categories and pathways, among others KEGG, TRANSPATH, TRANSFAC, and GO. Our web server provides two common statistical approaches, ‘Over-Representation Analysis’ (ORA) comparing a reference set of genes to a test set, and ‘Gene Set Enrichment Analysis’ (GSEA) scoring sorted lists of genes. Besides other newly developed features, GeneTrail's statistics module includes a novel dynamic-programming algorithm that improves the P-value computation of GSEA methods considerably. GeneTrail is freely accessible at http://genetrail.bioinf.uni-sb.d

Structure-Based Prediction of Asparagine and Aspartate Degradation Sites in Antibody Variable Regions

Monoclonal antibodies (mAbs) and proteins containing antibody domains are the most prevalent class of biotherapeutics in diverse indication areas. Today, established techniques such as immunization or phage display allow for an efficient generation of new mAbs. Besides functional properties, the stability of future therapeutic mAbs is a key selection criterion which is essential for the development of a drug candidate into a marketed product. Therapeutic proteins may degrade via asparagine (Asn) deamidation and aspartate (Asp) isomerization, but the factors responsible for such degradation remain poorly understood. We studied the structural properties of a large, uniform dataset of Asn and Asp residues in the variable domains of antibodies. Their structural parameters were correlated with the degradation propensities measured by mass spectrometry. We show that degradation hotspots can be characterized by their conformational flexibility, the size of the C-terminally flanking amino acid residue, and secondary structural parameters. From these results we derive an accurate in silico prediction method for the degradation propensity of both Asn and Asp residues in the complementarity-determining regions (CDRs) of mAbs

An integer linear programming approach for finding deregulated subgraphs in regulatory networks

Deregulation of cell signaling pathways plays a crucial role in the development of tumors. The identification of such pathways requires effective analysis tools that facilitate the interpretation of expression differences. Here, we present a novel and highly efficient method for identifying deregulated subnetworks in a regulatory network. Given a score for each node that measures the degree of deregulation of the corresponding gene or protein, the algorithm computes the heaviest connected subnetwork of a specified size reachable from a designated root node. This root node can be interpreted as a molecular key player responsible for the observed deregulation. To demonstrate the potential of our approach, we analyzed three gene expression data sets. In one scenario, we compared expression profiles of non-malignant primary mammary epithelial cells derived from BRCA1 mutation carriers and of epithelial cells without BRCA1 mutation. Our results suggest that oxidative stress plays an important role in epithelial cells of BRCA1 mutation carriers and that the activation of stress proteins may result in avoidance of apoptosis leading to an increased overall survival of cells with genetic alterations. In summary, our approach opens new avenues for the elucidation of pathogenic mechanisms and for the detection of molecular key players

prediction of Caspase- and Granzyme B-cleavage

sites in protein sequence

ROC plot for comparison of different pruning levels of decision trees.

<p>Decision trees were pruned automatically as implemented in Pipeline Pilot. Average numbers of false-positive and false-negative Asn/Asp residues are results of 40 rounds of Monte Carlo cross validation. TPR (true positive rate)  =  number of true positives divided by number of positives. FPR (false positive rate)  =  number of false positives divided by number of negatives. Trees 1-3 and 5-6 are shown as spheres, tree 4 as a black triangle. Tree 1 is the un-pruned tree model. Tree 4 was selected for prediction.</p

ROC plots for comparison of 3D classifiers to sequence-based prediction shows significant decrease of false-positive rates.

<p>Evaluation of different statistical methods is compared with only sequence-based prediction. For statistical classification methods, average numbers of false-positive and false-negative Asn/Asp residues are results of 40 rounds of Monte Carlo cross validation. TPR (true positive rate)  =  number of true positives divided by number of positives. FPR (false positive rate)  =  number of false positives divided by number of negatives. Tree, rpart, PP (Pipeline Pilot) tree, and RandomForest are recursive partitioning algorithms; svm, ksvm are support vector machine algorithms; rda is a regularized discriminant analysis algorithm; nnet is a neural network; sequence-based corresponds to prediction based on sequence motifs NG, NS, NT, and DG, DS, DT, DD, DH. The Pipeline Pilot tree, shown as a yellow circle, was selected as prediction algorithm, at pruning level 4. <b>A</b>: Asp model; <b>B</b>: Asn model. Panels <b>C</b> and <b>D</b> show a zoom view of the panels A and B, respectively. The numerical values shown in these graphs can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100736#pone.0100736.s005" target="_blank">Table S3</a>.</p

Experimental Asn and Asp hotspot collection.

<p>*only Asp as deamidation species.</p>‡<p>excluded from hotspot data set because of interaction with a CDR glycosylation site which is not represented by the homology models.</p>#<p>proof of modification site impossible with available methods (tryptic peptide, AspN peptide, CID fragmentation, HCD fragmentation), thus excluded from the hotspot data set.</p><p>Main modifications are written in bold. iD = isomerization, suc = succinimide, dea = deamidation, n.a.: not assessed.</p