Synthesis and evaluation of novel caged DNA alkylating agents bearing 3,4-epoxypiperidine structure.

Previously, we reported that the 3,4-epoxypiperidine structure, whose design was based on the active site of DNA alkylating antitumor antibiotics, azinomycins A and B, possesses prominent DNA cleavage activity. In this report, novel caged DNA alkylating agents, which were designed to be activated by UV irradiation, were synthesized by the introduction of four photo-labile protecting groups to a 3,4-epoxypiperidine derivative. The DNA cleavage activity and cytotoxicity of the caged DNA alkylating agents were examined under UV irradiation. Four caged DNA alkylating agents showed various degrees of bioactivity depending on the photosensitivity of the protecting groups.

Identifying cooperative transcriptional regulations using protein-protein interactions.

Cooperative transcriptional activations among multiple transcription factors (TFs) are important to understand the mechanisms of complex transcriptional regulations in eukaryotes. Previous studies have attempted to find cooperative TFs based on gene expression data with gene expression profiles as a measure of similarity of gene regulations. In this paper, we use protein-protein interaction data to infer synergistic binding of cooperative TFs. Our fundamental idea is based on the assumption that genes contributing to a similar biological process are regulated under the same control mechanism. First, the protein-protein interaction networks are used to calculate the similarity of biological processes among genes. Second, we integrate this similarity and the chromatin immuno-precipitation data to identify cooperative TFs. Our computational experiments in yeast show that predictions made by our method have successfully identified eight pairs of cooperative TFs that have literature evidences but could not be identified by the previous method. Further, 12 new possible pairs have been inferred and we have examined the biological relevances for them. However, since a typical problem using protein-protein interaction data is that many false-positive data are contained, we propose a method combining various biological data to increase the prediction accuracy.

Discriminative discovery of transcription factor binding sites from location data.

MOTIVATION: The availability of genome-wide location analyses based on chromatin immunoprecipitation (ChIP) data gives a new insight for in silico analysis of transcriptional regulations. RESULTS: We propose a novel discriminative discovery framework for precisely identifying transcriptional regulatory motifs from both positive and negative samples (sets of upstream sequences of both bound and unbound genes by a transcription factor (TF)) based on the genome-wide location data. In this framework, our goal is to find such discriminative motifs that best explain the location data in the sense that the motifs precisely discriminate the positive samples from the negative ones. First, in order to discover an initial set of discriminative substrings between positive and negative samples, we apply a decision tree learning method which produces a text-classification tree. We extract several clusters consisting of similar substrings from the internal nodes of the learned tree. Second, we start with initial profile-HMMs constructed from each cluster for representing putative motifs and iteratively refine the profile-HMMs to improve the discrimination accuracies. Our genome-wide experimental results on yeast show that our method successfully identifies the consensus sequences for known TFs in the literature and further presents significant performances for discriminating between positive and negative samples in all the TFs, while most other motif detecting methods show very poor performances on the problem of discriminations. Our learned profile-HMMs also improve false negative predictions of ChIP data.