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Nishi-Tokyo-shi, Japan

Gotoh O.,Computational Biology Research Center | Gotoh O.,Japan National Institute of Advanced Industrial Science and Technology
Methods in Molecular Biology | Year: 2014

Computation of multiple sequence alignment (MSA) is usually formulated as a combinatory optimization problem of an objective function. Solving the problem for virtually all sensible objective functions is known to be NP-complete implying that some heuristics must be adopted. Several general strategies have been proven effective to obtain accurate MSAs in reasonable computational costs. This chapter is devoted to a brief summary of most successful heuristic approaches. © 2014 Springer Science+Business Media, LLC.

Fang C.,Waseda University | Fang C.,Computational Biology Research Center | Noguchi T.,Computational Biology Research Center | Noguchi T.,Meiji Pharmaceutical University | Yamana H.,Waseda University
International Journal of Data Mining and Bioinformatics | Year: 2015

Position-specific scoring matrix (PSSM) has been widely used for identifying protein functional sites. However, it is 20-dimentional and contains many redundant features. The Kidera factors were reported to contain information relating almost all physical properties of amino acids, but it requires appropriate weighting coefficients to express their properties. We developed a novel method, named as KSPSSMpred, which integrated PSSM and the Kidera Factors into a 10-dimensional matrix (KSPSSM) for ligandbinding site prediction. Flavin adenine dinucleotide (FAD) was chosen as a representative ligand for this study. When compared with five other featurebased methods on a benchmark dataset, KSPSSMpred performed the best. This study demonstrates that, KSPSSM is an effective feature extraction method which can enrich PSSM with information relating 188 physical properties of residues, and reduce 50% feature dimensions without losing information included in the PSSM. Copyright © 2015 Inderscience Enterprises Ltd.

Chen L.-C.,I - Shou University | Chen J.-C.,National Kaohsiung Normal University | Shu J.-C.,Chang Gung University | Chen C.-Y.,National Chung Cheng University | And 5 more authors.
Environmental Microbiology | Year: 2012

The alternative transcription factor σB of Bacillus cereus controls the expression of a number of genes that respond to environmental stress. Four proteins encoded in the sigB gene cluster, including RsbV, RsbW, RsbY (RsbU) and RsbK, are known to be essential in the σB-mediated stress response. In the context of stress, the hybrid sensor kinase RsbK is thought to phosphorylate the response regulator RsbY, a PP2C serine phosphatase, leading to the dephosphorylation of the phosphorylated RsbV. The unphosphorylated RsbV then sequesters the σB antagonist, RsbW, ultimately liberating σB. The gene arrangement reveals an open reading frame, bc1007, flanked immediately downstream by rsbK within the sigB gene cluster. However, little is known about the function of bc1007. In this study, the deletion of bc1007 resulted in high constitutive σB expression independent of environmental stimuli, indicating that bc1007 plays a role in σB regulation. A bacterial two-hybrid analysis demonstrated that BC1007 interacts directly with RsbK, and autoradiographic studies revealed a specific C14-methyl transfer from the radiolabelled S-adenosylmethionine to RsbK when RsbK was incubated with purified BC1007. Our data suggest that BC1007 (RsbM) negatively regulates σB activity by methylating RsbK. Additionally, mutagenic substitution was employed to modify 12 predicted methylation residues in RsbK. Certain RsbK mutants were able to rescue σB activation in a rsbK-deleted bacterial strain, but RsbKE439A failed to activate σB, and RsbKE446A only moderately induced σB. These results suggest that Glu439 is the preferred methylation site and that Glu446 is potentially a minor methylation site. Gene arrays of the rsbK orthologues and the neighbouring rsbM orthologues are found in a wide range of bacteria. The regulation of sigma factors through metylation of RsbK-like sensor kinases appears to be widespread in the microbial world. © 2012 Society for Applied Microbiology and Blackwell Publishing Ltd.

Fang C.,Waseda University | Fang C.,Computational Biology Research Center | Noguchi T.,Meiji Pharmaceutical University | Noguchi T.,Computational Biology Research Center | Yamana H.,Waseda University
IPSJ Transactions on Bioinformatics | Year: 2013

In this paper, we propose a novel method, named SCPSSMpred (Smoothed and Condensed PSSM based prediction), which uses a simplified position-specific scoring matrix (PSSM) for predicting ligand-binding sites. Although the simplified PSSM has only ten dimensions, it combines abundant features, such as amino acid arrangement, information of neighboring residues, physicochemical properties, and evolutionary information. Our method employs no predicted results from other classifiers as input, i.e., all features used in this method are extracted from the sequences only. Three ligands (FAD, NAD and ATP) were used to verify the versatility of our method, and three alternative traditional methods were also analyzed for comparison. All the methods were tested at both the residue level and the protein sequence level. Experimental results showed that the SCPSSMpred method achieved the best performance besides reducing 50% of redundant features in PSSM. In addition, it showed a remarkable adaptability in dealing with unbalanced data compared to other methods when tested on the protein sequence level. This study not only demonstrates the importance of reducing redundant features in PSSM, but also identifies sequence-derived hallmarks of ligand-binding sites, such that both the arrangements and physicochemical properties of neighboring residues significantly impact ligand-binding behavior. ©2013 Information Processing Society of Japan.

Kielbasa S.M.,Max Planck Institute for Molecular Genetics | Wan R.,Computational Biology Research Center | Sato K.,University of Tokyo | Horton P.,Computational Biology Research Center | Frith M.C.,Computational Biology Research Center
Genome Research | Year: 2011

The main way of analyzing biological sequences is by comparing and aligning them to each other. It remains difficult, however, to compare modern multi-billionbase DNA data sets. The difficulty is caused by the nonuniform (oligo)nucleotide composition of these sequences, rather than their size per se. To solve this problem, we modified the standard seed-and-extend approach (e.g., BLAST) to use adaptive seeds. Adaptive seeds are matches that are chosen based on their rareness, instead of using fixed-length matches. This method guarantees that the number of matches, and thus the running time, increases linearly, instead of quadratically, with sequence length. LAST, our open source implementation of adaptive seeds, enables fast and sensitive comparison of large sequences with arbitrarily nonuniform composition. © 2011 by Cold Spring Harbor Laboratory Press.

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