Most of the position weight matrix (PWM) based bioinformatics methods developed to predict transcription factor binding sites (TFBS) assume each nucleotide in the sequence motif contributes independently to the interaction between protein and DNA sequence, usually producing high false positive predictions. The increasing availability of TF enrichment profiles from recent ChIP-Seq methodology facilitates the investigation of dependent structure and accurate prediction of TFBSs. We develop a novel Tree-based PWM (TPWM) approach to accurately model the interaction between TF and its binding site. The whole tree-structured PWM could be considered as a mixture of different conditional-PWMs. We propose a discriminative approach, called TPD (TPWM based Discriminative Approach), to construct the TPWM from the ChIP-Seq data with a pre-existing PWM. To achieve the maximum discriminative power between the positive and negative datasets, the cutoff value is determined based on the Matthew Correlation Coefficient (MCC). The resulting TPWMs are evaluated with respect to accuracy on extensive synthetic datasets. We then apply our TPWM discriminative approach on several real ChIP-Seq datasets to refine the current TFBS models stored in the TRANSFAC database. Experiments on both the simulated and real ChIP-Seq data show that the proposed method starting from existing PWM has consistently better performance than existing tools in detecting the TFBSs. The improved accuracy is the result of modelling the complete dependent structure of the motifs and better prediction of true positive rate. The findings could lead to better understanding of the mechanisms of TF-DNA interactions.

The virulence of Pseudomonas aeruginosa is multifactorial and under the control of quorum sensing signals, such as acyl homoserine lactones (AHLs). The importance of these molecules in the establishment of infection has been previously reported. These molecules either improve the virulence potential of P. aeruginosa or modulate the host immune response. To establish the immune modulating potential of quorum sensing signal molecules, previous studies have only used synthetic AHLs. However, there can be differences in the biological properties of synthetic and natural AHLs. The use of naturally extracted AHLs from the culture supernatant of P. aeruginosa is likely to simulate natural conditions more than the use of synthetic AHLs. Therefore, in the present study, the immune modulating potential of synthetic and naturally extracted AHLs was compared using a thymidine uptake assay, immunophenotyping and sandwich ELISA in order to assess mouse T-cell proliferation and production of Th1 and Th2 cytokines. Natural AHLs were able to suppress T-cell proliferation, even at low concentrations, compared to synthetic AHLs. The majority of cells undergoing proliferation were CD4+, as revealed by immunophenotyping. The inhibition of T-cells was stronger with natural AHLs compared to synthetic AHLs. Moreover, the natural AHLs were also able to shift immune responses away from host protective Th1 responses to pathogen protective Th2 responses.