MADNet: microarray database network web server

MADNet is a user-friendly data mining and visualization tool for rapid analysis of diverse high-throughput biological data such as microarray, phage display or even metagenome experiments. It presents biological information in the context of metabolic and signalling pathways, transcription factors and drug targets through minimal user input, consisting only of the file with the experimental data. These data are integrated with information stored in various biological databases such as NCBI nucleotide and protein databases, metabolic and signalling pathway databases (KEGG), transcription regulation (TRANSFAC©) and drug target database (DrugBank). MADNet is freely available for academic use at http://www.bioinfo.hr/madnet

Demosponge EST Sequencing Reveals a Complex Genetic Toolkit of the Simplest Metazoans

Sponges (Porifera) are among the simplest living and the earliest branching metazoans. They hold a pivotal role for studying genome evolution of the entire metazoan branch, both as an outgroup to Eumetazoa and as the closest branching phylum to the common ancestor of all multicellular animals (Urmetazoa). In order to assess the transcription inventory of sponges, we sequenced expressed sequence tag libraries of two demosponge species, Suberites domuncula and Lubomirskia baicalensis, and systematically analyzed the assembled sponge transcripts against their homologs from complete proteomes of six well-characterized metazoans—Nematostella vectensis, Caenorhabditis elegans, Drosophila melanogaster, Strongylocentrotus purpuratus, Ciona intestinalis, and Homo sapiens. We show that even the earliest metazoan species already have strikingly complex genomes in terms of gene content and functional repertoire and that the rich gene repertoire existed even before the emergence of true tissues, therefore further emphasizing the importance of gene loss and spatio-temporal changes in regulation of gene expression in shaping the metazoan genomes. Our findings further indicate that sponge and human genes generally show similarity levels higher than expected from their respective positions in metazoan phylogeny, providing direct evidence for slow rate of evolution in both “basal” and “apical” metazoan genome lineages. We propose that the ancestor of all metazoans had already had an unusually complex genome, thereby shifting the origins of genome complexity from Urbilateria to Urmetazoa

CyclinPred: A SVM-Based Method for Predicting Cyclin Protein Sequences

Functional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity making discovery of novel cyclins and establishing orthologous relationships amongst the cyclins, a difficult task. The currently identified cyclin motifs and cyclin associated domains do not represent all of the identified and characterized cyclin sequences. We describe a Support Vector Machine (SVM) based classifier, CyclinPred, which can predict cyclin sequences with high efficiency. The SVM classifier was trained with features of selected cyclin and non cyclin protein sequences. The training features of the protein sequences include amino acid composition, dipeptide composition, secondary structure composition and PSI-BLAST generated Position Specific Scoring Matrix (PSSM) profiles. Results obtained from Leave-One-Out cross validation or jackknife test, self consistency and holdout tests prove that the SVM classifier trained with features of PSSM profile was more accurate than the classifiers based on either of the other features alone or hybrids of these features. A cyclin prediction server- CyclinPred has been setup based on SVM model trained with PSSM profiles. CyclinPred prediction results prove that the method may be used as a cyclin prediction tool, complementing conventional cyclin prediction methods

Stability of domain structures in multi-domain proteins

Multi-domain proteins have many advantages with respect to stability and folding inside cells. Here we attempt to understand the intricate relationship between the domain-domain interactions and the stability of domains in isolation. We provide quantitative treatment and proof for prevailing intuitive ideas on the strategies employed by nature to stabilize otherwise unstable domains. We find that domains incapable of independent stability are stabilized by favourable interactions with tethered domains in the multi-domain context. Stability of such folds to exist independently is optimized by evolution. Specific residue mutations in the sites equivalent to inter-domain interface enhance the overall solvation, thereby stabilizing these domain folds independently. A few naturally occurring variants at these sites alter communication between domains and affect stability leading to disease manifestation. Our analysis provides safe guidelines for mutagenesis which have attractive applications in obtaining stable fragments and domain constructs essential for structural studies by crystallography and NMR

Prodepth: Predict Residue Depth by Support Vector Regression Approach from Protein Sequences Only

Residue depth (RD) is a solvent exposure measure that complements the information provided by conventional accessible surface area (ASA) and describes to what extent a residue is buried in the protein structure space. Previous studies have established that RD is correlated with several protein properties, such as protein stability, residue conservation and amino acid types. Accurate prediction of RD has many potentially important applications in the field of structural bioinformatics, for example, facilitating the identification of functionally important residues, or residues in the folding nucleus, or enzyme active sites from sequence information. In this work, we introduce an efficient approach that uses support vector regression to quantify the relationship between RD and protein sequence. We systematically investigated eight different sequence encoding schemes including both local and global sequence characteristics and examined their respective prediction performances. For the objective evaluation of our approach, we used 5-fold cross-validation to assess the prediction accuracies and showed that the overall best performance could be achieved with a correlation coefficient (CC) of 0.71 between the observed and predicted RD values and a root mean square error (RMSE) of 1.74, after incorporating the relevant multiple sequence features. The results suggest that residue depth could be reliably predicted solely from protein primary sequences: local sequence environments are the major determinants, while global sequence features could influence the prediction performance marginally. We highlight two examples as a comparison in order to illustrate the applicability of this approach. We also discuss the potential implications of this new structural parameter in the field of protein structure prediction and homology modeling. This method might prove to be a powerful tool for sequence analysis