Assigning roles to DNA regulatory motifs using comparative genomics

Motivation: Transcription factors (TFs) are crucial during the lifetime of the cell. Their functional roles are defined by the genes they regulate. Uncovering these roles not only sheds light on the TF at hand but puts it into the context of the complete regulatory network

Dual-functioning transcription factors in the developmental gene network of Drosophila melanogaster

Quantitative models for transcriptional regulation have shown great promise for advancing our understanding of the biological mechanisms underlying gene regulation. However, all of the models to date assume a transcription factor (TF) to have either activating or repressing function towards all the genes it is regulating.In this paper we demonstrate, on the example of the developmental gene network in D. melanogaster, that the data-fit can be improved by up to 40% if the model is allowing certain TFs to have dual function, that is, acting as activator for some genes and as repressor for others. We demonstrate that the improvement is not due to additional flexibility in the model but rather derived from the data itself. We also found no evidence for the involvement of other known site-specific TFs in regulating this network. Finally, we propose SUMOylation as a candidate biological mechanism allowing TFs to switch their role when a small ubiquitin-like modifier (SUMO) is covalently attached to the TF. We strengthen this hypothesis by demonstrating that the TFs predicted to have dual function also contain the known SUMO consensus motif, while TFs predicted to have only one role lack this motif.We argue that a SUMOylation-dependent mechanism allowing TFs to have dual function represents a promising area for further research and might be another step towards uncovering the biological mechanisms underlying transcriptional regulation

It's about time: Signal recognition in staged models of protein translocation

During their synthesis, a large fraction of proteins are directed to the secretory pathway. There are several models that aim to distinguish between different destinations along this pathway; however, they rarely distinguish between known stages of this translocation process. This paper presents a translocation probability function which models the protein SRP-recruitment process—the first stage of the secretory pathway. It unifies groups of proteins with distinct final destinations, allowing more specific sorting to be done in due course, mirroring the hierarchical nature of secretory translocation. We apply conditional random fields to evaluate the prediction accuracy of a full sequence model. Introducing the translocation function improves substantially compared to a model based on properties that are relevant to the subsequent stages and final destinations only. For the discrimination of secretory, signal peptide (SP)-equipped proteins and non-secretory proteins a correlation coefficient of 0.98 is achieved—a level of performance that is only met by specialized SP predictors. Transmembrane proteins cause considerable confusion in signal peptide predictors, but fit naturally into our transparent design and reduce the performance of the translocation function only slightly. The proposed function and model assist efforts to uncover localization and function for the growing numbers of protein sequence data. Applying our model we estimate with high confidence that about 27% of the human and 29% of the mouse proteins are associated with the secretory pathway

Predicting SUMOylation sites

Recent evidence suggests that SUMOylation of proteins plays a key regulatory role in the assembly and dis-assembly of nuclear sub-compartments, and may repress transcription by modifying chromatin. Determining whether a protein contains a, SUMOylation site or not thus provides essential clues about a substrate's intra-nuclear spatial association and function. Previous SUMOylation predictors are largely based on a degenerate and functionally unreliable consensus motif description, not rendering satisfactory accuracy to confidently map the extent of this essential class of regulatory modifications. This paper embarks on an exploration of predictive dependencies among SUMOylation site amino acids, non-local and structural properties (including secondary structure, solvent accessibility and evolutionary profiles). An extensive examination of two main machine learning paradigms, Support-Vector-Machine and Bidirectional Recurrent Neural Networks, demonstrates that (I) with careful attention to generalization issues both methods achieve comparable performance and, that (2) local features enable best generalization, with structural features having little to no impact. The predictive model for SUMOylation sites based on the primary protein sequence achieves an area tinder the ROC of 0.92 using 5-fold cross-validation, and 96% accuracy on an independent hold-out test set. However, similar to other predictors, the new predictor is unable to generalize beyond the simple consensus motif

Triplex-Inspector: an analysis tool for triplex-mediated targeting of genomic loci

At the heart of many modern biotechnological and therapeutic applications lies the need to target specific genomic loci with pinpoint accuracy. Although landmark experiments demonstrate technological maturity in manufacturing and delivering genetic material, the genomic sequence analysis to find suitable targets lags behind. We provide a computational aid for the sophisticated design of sequence-specific ligands and selection of appropriate targets, taking gene location and genomic architecture into account.Availability: Source code and binaries are downloadable from www.bioinformatics.org.au/triplexator/inspector. Contact: Supplementary information: Supplementary data are available at Bioinformatics online

Predicting SUMOylation sites in developmental transcription factors of Drosophila melanogaster

Recent evidence suggests that SUMOylation of proteins plays a keys role in the assembly and disassembly of nuclear sub-compartments, as well as gene regulation by reversing the functional role of transcription factors. Determining whether a protein contains a SUMOylation site or not thus provides essential clues about its intra-nuclear spatial association and function