DIANA-microT web server: elucidating microRNA functions through target prediction

Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at www.microrna.gr/microT

A method to improve protein subcellular localization prediction by integrating various biological data sources

<p>Abstract</p> <p>Background</p> <p>Protein subcellular localization is crucial information to elucidate protein functions. Owing to the need for large-scale genome analysis, computational method for efficiently predicting protein subcellular localization is highly required. Although many previous works have been done for this task, the problem is still challenging due to several reasons: the number of subcellular locations in practice is large; distribution of protein in locations is imbalanced, that is the number of protein in each location remarkably different; and there are many proteins located in multiple locations. Thus it is necessary to explore new features and appropriate classification methods to improve the prediction performance.</p> <p>Results</p> <p>In this paper we propose a new predicting method which combines two key ideas: 1) Information of neighbour proteins in a probabilistic gene network is integrated to enrich the prediction features. 2) Fuzzy k-NN, a classification method based on fuzzy set theory is applied to predict protein locating in multiple sites. Experiment was conducted on a dataset consisting of 22 locations from Budding yeast proteins and significant improvement was observed.</p> <p>Conclusion</p> <p>Our results suggest that the neighbourhood information from functional gene networks is predictive to subcellular localization. The proposed method thus can be integrated and complementary to other available prediction methods.</p

Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques

<p>Abstract</p> <p>Background</p> <p>Protein kinases play crucial roles in cell growth, differentiation, and apoptosis. Abnormal function of protein kinases can lead to many serious diseases, such as cancer. Kinase inhibitors have potential for treatment of these diseases. However, current inhibitors interact with a broad variety of kinases and interfere with multiple vital cellular processes, which causes toxic effects. Bioinformatics approaches that can predict inhibitor-kinase interactions from the chemical properties of the inhibitors and the kinase macromolecules might aid in design of more selective therapeutic agents, that show better efficacy and lower toxicity.</p> <p>Results</p> <p>We applied proteochemometric modelling to correlate the properties of 317 wild-type and mutated kinases and 38 inhibitors (12,046 inhibitor-kinase combinations) to the respective combination's interaction dissociation constant (K_d). We compared six approaches for description of protein kinases and several linear and non-linear correlation methods. The best performing models encoded kinase sequences with amino acid physico-chemical z-scale descriptors and used support vector machines or partial least- squares projections to latent structures for the correlations. Modelling performance was estimated by double cross-validation. The best models showed high predictive ability; the squared correlation coefficient for new kinase-inhibitor pairs ranging P²= 0.67-0.73; for new kinases it ranged P²_kin= 0.65-0.70. Models could also separate interacting from non-interacting inhibitor-kinase pairs with high sensitivity and specificity; the areas under the ROC curves ranging AUC = 0.92-0.93. We also investigated the relationship between the number of protein kinases in the dataset and the modelling results. Using only 10% of all data still a valid model was obtained with P²= 0.47, P²_kin= 0.42 and AUC = 0.83.</p> <p>Conclusions</p> <p>Our results strongly support the applicability of proteochemometrics for kinome-wide interaction modelling. Proteochemometrics might be used to speed-up identification and optimization of protein kinase targeted and multi-targeted inhibitors.</p

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

The whole genome sequence of the Mediterranean fruit fly, Ceratitis capitata (Wiedemann), reveals insights into the biology and adaptive evolution of a highly invasive pest species

The Mediterranean fruit fly (medfly), Ceratitis capitata, is a major destructive insect pest due to its broad host range, which includes hundreds of fruits and vegetables. It exhibits a unique ability to invade and adapt to ecological niches throughout tropical and subtropical regions of the world, though medfly infestations have been prevented and controlled by the sterile insect technique (SIT) as part of integrated pest management programs (IPMs). The genetic analysis and manipulation of medfly has been subject to intensive study in an effort to improve SIT efficacy and other aspects of IPM control

Use of Mutagenesis, Genetic Mapping and Next Generation Transcriptomics to Investigate Insecticide Resistance Mechanisms

Insecticide resistance is a worldwide problem with major impact on agriculture and human health. Understanding the underlying molecular mechanisms is crucial for the management of the phenomenon; however, this information often comes late with respect to the implementation of efficient counter-measures, particularly in the case of metabolism-based resistance mechanisms. We employed a genome-wide insertional mutagenesis screen to Drosophila melanogaster, using a Minos-based construct, and retrieved a line (MiT[w−]3R2) resistant to the neonicotinoid insecticide Imidacloprid. Biochemical and bioassay data indicated that resistance was due to increased P450 detoxification. Deep sequencing transcriptomic analysis revealed substantial over- and under-representation of 357 transcripts in the resistant line, including statistically significant changes in mixed function oxidases, peptidases and cuticular proteins. Three P450 genes (Cyp4p2, Cyp6a2 and Cyp6g1) located on the 2R chromosome, are highly up-regulated in mutant flies compared to susceptible Drosophila. One of them (Cyp6g1) has been already described as a major factor for Imidacloprid resistance, which validated the approach. Elevated expression of the Cyp4p2 was not previously documented in Drosophila lines resistant to neonicotinoids. In silico analysis using the Drosophila reference genome failed to detect transcription binding factors or microRNAs associated with the over-expressed Cyp genes. The resistant line did not contain a Minos insertion in its chromosomes, suggesting a hit-and-run event, i.e. an insertion of the transposable element, followed by an excision which caused the mutation. Genetic mapping placed the resistance locus to the right arm of the second chromosome, within a ∼1 Mb region, where the highly up-regulated Cyp6g1 gene is located. The nature of the unknown mutation that causes resistance is discussed on the basis of these results

Encoding of Spatio-Temporal Input Characteristics by a CA1 Pyramidal Neuron Model

The in vivo activity of CA1 pyramidal neurons alternates between regular spiking and bursting, but how these changes affect information processing remains unclear. Using a detailed CA1 pyramidal neuron model, we investigate how timing and spatial arrangement variations in synaptic inputs to the distal and proximal dendritic layers influence the information content of model responses. We find that the temporal delay between activation of the two layers acts as a switch between excitability modes: short delays induce bursting while long delays decrease firing. For long delays, the average firing frequency of the model response discriminates spatially clustered from diffused inputs to the distal dendritic tree. For short delays, the onset latency and inter-spike-interval succession of model responses can accurately classify input signals as temporally close or distant and spatially clustered or diffused across different stimulation protocols. These findings suggest that a CA1 pyramidal neuron may be capable of encoding and transmitting presynaptic spatiotemporal information about the activity of the entorhinal cortex-hippocampal network to higher brain regions via the selective use of either a temporal or a rate code

Possible import routes of proteins into the cyanobacterial endosymbionts/plastids of Paulinella chromatophora

The rhizarian amoeba Paulinella chromatophora harbors two photosynthetically active and deeply integrated cyanobacterial endosymbionts acquired ~60 million years ago. Recent genomic analyses of P. chromatophora have revealed the loss of many essential genes from the endosymbiont’s genome, and have identified more than 30 genes that have been transferred to the host cell’s nucleus through endosymbiotic gene transfer (EGT). This indicates that, similar to classical primary plastids, Paulinella endosymbionts have evolved a transport system to import their nuclear-encoded proteins. To deduce how these proteins are transported, we searched for potential targeting signals in genes for 10 EGT-derived proteins. Our analyses indicate that five proteins carry potential signal peptides, implying they are targeted via the host endomembrane system. One sequence encodes a mitochondrial-like transit peptide, which suggests an import pathway involving a channel protein residing in the outer membrane of the endosymbiont. No N-terminal targeting signals were identified in the four other genes, but their encoded proteins could utilize non-classical targeting signals contained internally or in C-terminal regions. Several amino acids more often found in the Paulinella EGT-derived proteins than in their ancestral set (proteins still encoded in the endosymbiont genome) could constitute such signals. Characteristic features of the EGT-derived proteins are low molecular weight and nearly neutral charge, which both could be adaptations to enhance passage through the peptidoglycan wall present in the intermembrane space of the endosymbiont’s envelope. Our results suggest that Paulinella endosymbionts/plastids have evolved several different import routes, as has been shown in classical primary plastids