Transformation potency of ErbB heterodimer signaling is determined by B-Raf kinase.

Cellular transformation occurs only in cells that express both ErbB1 and ErbB4 receptors, but not in cells expressing only one or the other of these receptors. However, when both receptors are coexpressed and ligand-stimulated, they interact with virtually the same adaptor/effector proteins as when expressed singly. To reveal the underlying regulatory mechanism of the kinase/phosphatase network in ErbB homo- and heterodimer receptor signaling, extracellular signal-regulated kinase (ERK) and Akt activities were evaluated in the presence of several enzyme inhibitors in ligand-induced cells expressing ErbB1 (E1), ErbB4 (E4), and ErbB1/ErbB4 (E1/4) receptor. The PP2A inhibitor okadaic acid showed receptor-specific inhibitory profiles for ERK and Akt activities. Moreover, B-Raf isolated only from E1/4 cells could induce in vitro phosphorylation for MEK; this B-Raf kinase activity was abolished by pretreatment of the cells with okadaic acid. Our study further showed that the E1/4 cell-specific B-Raf activity was stimulated by PLC gamma and subsequent Rap1 activation. The present study suggests that B-Raf kinase, which was specifically activated in the cells coexpressing ErbB1 and ErbB4 receptors, elevates total ERK activity within the cell and, therefore, can induce cellular transformation.

A Generic Criterion for Gene Recognitions in Genomic Sequences.

In this paper, we evaluated the complexity and accuracy of dicodon model for gene finding using Hidden Markov Model with Self-Identification Learning. We used five different models as competitors with smaller parametric space than the dicodon model. Our evaluation result shows that the dicodon model outperforms other competitors in terms of sensitivity as well as specificity. This result indicates that the dicodon model can not be represented by a combination of the pair amino-acid, the codon usage, and the G+C content.

Automated Processing of 2-D Gel Electrophoretograms of Genomic DNA for Hunting Pathogenic DNA Molecular Changes.

We have developed the automated processing algorithms for 2-dimensional (2-D) electrophoretograms of genomic DNA based on RLGS (Restriction Landmark Genomic Scanning) method, which scans the restriction enzyme recognition sites as the landmark and maps them onto a 2-D electrophoresis gel. Our powerful processing algorithms realize the automated spot recognition from RLGS electrophoretograms and the automated comparison of a huge number of such images. In the final stage of the automated processing, a master spot pattern, on which all the spots in the RLGS images are mapped at once, can be obtained. The spot pattern variations which seemed to be specific to the pathogenic DNA molecular changes can be easily detected by simply looking over the master spot pattern. When we applied our algorithms to the analysis of 33 RLGS images derived from human colon tissues, we successfully detected several colon tumor specific spot pattern changes.

Fully-Automated Spot Recognition and Matching Algorithms for 2-D Gel Electrophoretogram of Genomic DNA.

We have developed the fully-automated algorithms for processing 2-D gel electrophoretograms based on RLGS (restriction landmark genomic scanning) method; one for fully-automated spot recognition from RLGS electrophoretogram and another for fully-automated pairwise matching of the spots found on such 2-D electrophoretograms. Without any human interaction, several thousands of spots on a 2-D electrophoretogram, including hidden spots found at the shoulder of large spots, can be identified correctly by applying our spot recognition algorithm, except for only a few true-negative and false-positive spots. Once the locations and intensities of the landmark spots are correctly recognized automatically, our pairwise spot matching algorithm reliably and rapidly identifies equivalent pairs of spots found on the nonlinearly distorted RLGS electrophoretograms in the fully-automatic way, i.e., the boring and annoying spot landmarking process is unnecessary. At the beginning of the spot matching process, most suitable pair of corresponding spots is searched automatically, then the other equivalent pairs of spots are identified. With our powerful image processing algorithms, it is possible to detect DNA molecular changes such as deletions, additions, amplifications or DNA methylations occurring at or near to the restriction enzyme cleavage sites by means of comparing large amount of RLGS electrophoretograms, without any visual inspection and human interaction.

Stochastic motif extraction using hidden Markov model.

In this paper, we study the application of an HMM (hidden Markov model) to the problem of representing protein sequences by a stochastic motif. A stochastic protein motif represents the small segments of protein sequences that have a certain function or structure. The stochastic motif, represented by an HMM, has conditional probabilities to deal with the stochastic nature of the motif. This HMM directly reflects the characteristics of the motif, such as a protein periodical structure or grouping. In order to obtain the optimal HMM, we developed the "ilerative duplication method" for HMM topology learning. It starts from a small fully-connected network and iterates the network generation and parameter optimization until it achieves sufficient discrimination accuracy. Using this method, we obtained an HMM for a leucine zipper motif. Compared to the accuracy of a symbolic pattern representation with accuracy of 14.8 percent, an HMM achieved 79.3 percent in prediction. Additionally, the method can obtain an HMM for various types of zinc finger motifs, and it might separate the mixed data. We demonstrated that this approach is applicable to the validation of the protein database; a constructed HMM has indicated that one protein sequence annotated as "leucine-zipper like sequence" in the database is quite different from other leucine-zipper sequences in terms of likelihood, and we found this discrimination is plausible.

Hidden Markov models and iterative aligners: study of their equivalence and possibilities.

There are many shared attributes between existing iterative aligners and Hidden Markov Model (HMM). A learning algorithm of HMM called Viterbi is the same as the iteration of DP-matching of iterative aligners. HMM aligners can use the result of an iterative aligner initially, incorporate the similarity score of amino acids, and apply the detailed gap cost systems to improve the matching accuracy. On the other hand, the iterative aligner can inherit the modeling capability of HMM, and provide the better representation of the proteins than motifs. In this paper, we present an overview of several iterative aligners which include the parallel iterative aligner of ICOT and the HMM aligner of Haussler's group. We compare the merits and shortcomings of these aligners. This comparison enables us to formulate a better, more advanced aligner through proper integration of the iterative technique and HMM technique.