Kyushu University Academic Staff Educational and Research Activities Database
List of Presentations
Osamu Maruyama Last modified date:2020.06.25

Associate Professor / Modeling and Optimization / Department of Human Science / Faculty of Design

1. Osamu Maruyama, Fumiko Matsuzaki, DegSampler3: Pairwise Dependency Model in Degradation Motif Site Prediction of Substrate Protein Sequences, 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019, 2019.10, [URL].
2. Osamu Maruyama, Fumiko Matsuzaki, DegSampler: Collapsed Gibbs Sampler for Detecting E3 Binding Sites, 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE), 2018.12, In this paper, we address the problem of finding sequence motifs in substrate proteins specific to E3 ubiquitin ligases (E3s). We formulated a posterior probability distribution of sites by designing a likelihood function based on amino acid indexing and a prior distribution based on the disorderness of protein sequences. These designs are derived from known characteristics of E3 binding sites in substrate proteins. Then, we devise a collapsed Gibbs sampling algorithm for the posterior probability distribution called DegSampler. We performed computational experiments using 36 sets of substrate proteins specific to E3s and compared the performance of DegSampler with those of popular motif finders, MEME and GLAM2. The results showed that DegSampler was superior to the others in finding E3 binding motifs. Thus, DegSampler is a promising tool for finding E3 motifs in substrate proteins..
3. Osamu Maruyama, Limsoon Wong, Regularizing predicted complexes by mutually exclusive protein-protein interactions, International Symposium on Network Enabled Health Informatics, Biomedicine and Bioinformatics, HI-BI-BI 2015, 2015.08, Protein complexes are key entities in the cell responsible for various cellular mechanisms and biological processes. We
propose here a method for predicting protein complexes from
a protein-protein interaction (PPI) network, using information
on mutually exclusive PPIs. If two interactions are mutually
exclusive, they are not allowed to exist simultaneously in the
same predicted complex. We introduce a new regularization term
which checks whether predicted complexes are connected by mu-
tually exclusive PPIs. This regularization term is added into the
scoring function of our earlier protein complex prediction tool,
PPSampler2. We show that PPSampler2 with mutually exclusive
PPIs outperforms the original one. Furthermore, the performance
is superior to well-known representative conventional protein
complex prediction methods. Thus, it is is effective to use mutual
exclusiveness of PPIs in protein complex prediction..
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