DOI： 10.1101/2023.12.25.573282

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Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

DOI： 10.1021/acsomega.3c01314

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Language：English   Publishing type：Research paper (scientific journal)  

Recently, cyclic peptides have been considered breakthrough drugs because they can interact with "undruggable" targets such as intracellular protein-protein interactions. Membrane permeability is an essential indicator of oral bioavailability and intracellular targeting, and the development of membrane-permeable peptides is a bottleneck in cyclic peptide drug discovery. Although many experimental data on membrane permeability of cyclic peptides have been reported, a comprehensive database is not yet available. A comprehensive membrane permeability database is essential for developing computational methods for cyclic peptide drug design. In this study, we constructed CycPeptMPDB, the first web-accessible database of cyclic peptide membrane permeability. We collected information on a total of 7334 cyclic peptides, including the structure and experimentally measured membrane permeability, from 45 published papers and 2 patents from pharmaceutical companies. To unambiguously represent cyclic peptides larger than small molecules, we used the hierarchical editing language for macromolecules notation to generate a uniform sequence representation of peptides. In addition to data storage, CycPeptMPDB provides several supporting functions such as online data visualization, data analysis, and downloading. CycPeptMPDB is expected to be a valuable platform to support membrane permeability research on cyclic peptides. CycPeptMPDB can be freely accessed at http://cycpeptmpdb.com.

DOI： 10.1021/acs.jcim.2c01573

PubMed

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Language：English   Publishing type：Research paper (scientific journal)  

Naturally occurring peptides with high membrane permeability often have ester bonds on their backbones. However, the impact of amide-to-ester substitutions on the membrane permeability of peptides has not been directly evaluated. Here we report the effect of amide-to-ester substitutions on the membrane permeability and conformational ensemble of cyclic peptides related to membrane permeation. Amide-to-ester substitutions are shown to improve the membrane permeability of dipeptides and a model cyclic hexapeptide. NMR-based conformational analysis and enhanced sampling molecular dynamics simulations suggest that the conformational transition of the cyclic hexapeptide upon membrane permeation is differently influenced by an amide-to-ester substitution and an amide N-methylation. The effect of amide-to-ester substitution on membrane permeability of other cyclic hexapeptides, cyclic octapeptides, and a cyclic nonapeptide is also investigated to examine the scope of the substitution. Appropriate utilization of amide-to-ester substitution based on our results will facilitate the development of membrane-permeable peptides.

DOI： 10.1038/s41467-023-36978-z

PubMed

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Language：English   Publishing type：Research paper (scientific journal)  

Cyclic peptides have attracted attention as a promising pharmaceutical modality due to their potential to selectively inhibit previously undruggable targets, such as intracellular protein-protein interactions. Poor membrane permeability is the biggest bottleneck hindering successful drug discovery based on cyclic peptides. Therefore, the development of computational methods that can predict membrane permeability and support elucidation of the membrane permeation mechanism of drug candidate peptides is much sought after. In this study, we developed a protocol to simulate the behavior in membrane permeation steps and estimate the membrane permeability of large cyclic peptides with more than or equal to 10 residues. This protocol requires the use of a more realistic membrane model than a single-lipid phospholipid bilayer. To select a membrane model, we first analyzed the effect of cholesterol concentration in the model membrane on the potential of mean force and hydrogen bonding networks along the direction perpendicular to the membrane surface as predicted by molecular dynamics simulations using cyclosporine A. These results suggest that a membrane model with 40 or 50 mol % cholesterol was suitable for predicting the permeation process. Subsequently, two types of membrane models containing 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine and 40 and 50 mol % cholesterol were used. To validate the efficiency of our protocol, the membrane permeability of 18 ten-residue peptides was predicted. Correlation coefficients of R > 0.8 between the experimental and calculated permeability values were obtained with both model membranes. The results of this study demonstrate that the lipid membrane is not just a medium but also among the main factors determining the membrane permeability of molecules. The computational protocol proposed in this study and the findings obtained on the effect of membrane model composition will contribute to building a schematic view of the membrane permeation process. Furthermore, the results of this study will eventually aid the elucidation of design rules for peptide drugs with high membrane permeability.

DOI： 10.1021/acs.jcim.2c00931

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society ({ACS})  

Virtual screening is a commonly used process to search for feasible drug candidates from a huge number of compounds during the early stages of drug design. As the compound database continues to expand to billions of entries or more, there remains an urgent need to accelerate the process of docking calculations. Reuse of calculation results is a possible way to accelerate the process. In this study, we first propose yet another virtual screening-oriented docking strategy by combining three factors, namely, compound decomposition, simplified fragment grid storing k-best scores, and flexibility consideration with pregenerated conformers. Candidate compounds contain many common fragments (chemical substructures). Thus, the calculation results of these common fragments can be reused among them. As a proof-of-concept of the aforementioned strategies, we also conducted the development of REstretto, a tool that implements the three factors to enable the reuse of calculation results. We demonstrated that the speed and accuracy of REstretto were comparable to those of AutoDock Vina, a well-known free docking tool. The implementation of REstretto has much room for further performance improvement, and therefore, the results show the feasibility of the strategy. The code is available under an MIT license at https://github.com/akiyamalab/restretto.

DOI： 10.1021/acsomega.2c03470

PubMed

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Publishing type：Research paper (scientific journal)   Publisher：{MDPI} {AG}  

DOI： 10.3390/e24030354

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Language：English   Publishing type：Research paper (scientific journal)  

MOTIVATION: In recent years, cyclic peptide drugs have been receiving increasing attention because they can target proteins that are difficult to be tackled by conventional small-molecule drugs or antibody drugs. Plasma protein binding rate (%PPB) is a significant pharmacokinetic property of a compound in drug discovery and design. However, due to structural differences, previous computational prediction methods developed for small-molecule compounds cannot be successfully applied to cyclic peptides, and methods for predicting the PPB rate of cyclic peptides with high accuracy are not yet available. RESULTS: Cyclic peptides are larger than small molecules, and their local structures have a considerable impact on PPB; thus, molecular descriptors expressing residue-level local features of cyclic peptides, instead of those expressing the entire molecule, as well as the circularity of the cyclic peptides should be considered. Therefore, we developed a prediction method named CycPeptPPB using deep learning that considers both factors. First, the macrocycle ring of cyclic peptides was decomposed residue by residue. The residue-based descriptors were arranged according to the sequence information of the cyclic peptide. Furthermore, the circular data augmentation method was used, and the circular convolution method CyclicConv was devised to express the cyclic structure. CycPeptPPB exhibited excellent performance, with mean absolute error (MAE) of 4.79% and correlation coefficient (R) of 0.92 for the public drug dataset, compared to the prediction performance of the existing PPB rate prediction software (MAE=15.08%, R=0.63). AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in the online supplementary material. The source code of CycPeptPPB is available at https://github.com/akiyamalab/cycpeptppb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI： 10.1093/bioinformatics/btab726

PubMed

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/genes12091455

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

Membrane permeability is a significant obstacle facing the development of cyclic peptide drugs. However, membrane permeation mechanisms are poorly understood. To investigate common features of permeable (and nonpermeable) designs, it is necessary to reproduce the membrane permeation process of cyclic peptides through the lipid bilayer. We simulated the membrane permeation process of 100 six-residue cyclic peptides across the lipid bilayer based on steered molecular dynamics (MD) and replica-exchange umbrella sampling simulations and predicted membrane permeability using the inhomogeneous solubility-diffusion model and a modified version of it. Furthermore, we confirmed the effectiveness of this protocol by predicting the membrane permeability of 56 eight-residue cyclic peptides with diverse chemical structures, including some confidential designs from a pharmaceutical company. As a result, a reasonable correlation between experimentally assessed and calculated membrane permeability of cyclic peptides was observed for the peptide libraries, except for strongly hydrophobic peptides. Our analysis of the MD trajectory demonstrated that most peptides were stabilized in the boundary region between bulk water and membrane and that for most peptides, the process of crossing the center of the membrane is the main obstacle to membrane permeation. The height of this barrier is well correlated with the electrostatic interaction between the peptide and the surrounding media. The structural and energetic features of the representative peptide at each vertical position within the membrane were also analyzed, revealing that peptides permeate the membrane by changing their orientation and conformation according to the surrounding environment.

DOI： 10.1021/acs.jcim.1c00380

PubMed

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

Information on protein-protein interactions (PPIs) not only advances our
understanding of molecular biology but also provides important clues for target
selection in drug discovery and the design of PPI inhibitors. One of the
techniques used for computational prediction of PPIs is protein-protein docking
calculations, and a variety of software has been developed. However, a friendly
interface for users who are not sufficiently familiar with the command line
interface has not been developed so far. In this study, we have developed a
graphical user interface, MEGADOCK-GUI, which enables users to easily predict
PPIs and protein complex structures. In addition to the original 3-D molecular
viewer and input file preparation functions, MEGADOCK-GUI is software that can
automatically perform complete cross-docking of $M$ vs. $N$ proteins. With
MEGADOCK-GUI, various applications related to the prediction of PPIs, such as
ensemble docking that handles multiple conformations of proteins and screening
of binding partner proteins that bind to specific proteins, can now be easily
performed.

arXiv

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Other Link： http://arxiv.org/pdf/2105.03617v1

Language：English   Publishing type：Research paper (scientific journal)   Publisher：{MDPI} {AG}  

Periodontitis is an inflammation of tooth-supporting tissues, which is caused by bacteria in the subgingival plaque (biofilm) and the host immune response. Traditionally, subgingival pathogens have been investigated using methods such as culturing, DNA probes, or PCR. The development of next-generation sequencing made it possible to investigate the whole microbiome in the subgingival plaque. Previous studies have implicated dysbiosis of the subgingival microbiome in the etiology of periodontitis. However, details are still lacking. In this study, we conducted a metagenomic analysis of subgingival plaque samples from a group of Japanese individuals with and without periodontitis. In the taxonomic composition analysis, genus Bacteroides and Mycobacterium demonstrated significantly different compositions between healthy sites and sites with periodontal pockets. The results from the relative abundance of functional gene categories, carbohydrate metabolism, glycan biosynthesis and metabolism, amino acid metabolism, replication and repair showed significant differences between healthy sites and sites with periodontal pockets. These results provide important insights into the shift in the taxonomic and functional gene category abundance caused by dysbiosis, which occurs during the progression of periodontal disease.

DOI： 10.3390/ijms22105298

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1053/j.gastro.2021.02.013

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J-GLOBAL

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Other Link： https://lens.org/078-876-781-729-191

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.chom.2020.06.005

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Other Link： https://lens.org/017-865-169-917-413

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1080/03610926.2019.1594295

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Other Link： https://lens.org/016-051-677-137-65X

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/ijerph17165842

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Public cloud computing environments, such as Amazon AWS, Microsoft Azure, and
the Google Cloud Platform, have achieved remarkable improvements in
computational performance in recent years, and are also expected to be able to
perform massively parallel computing. As the cloud enables users to use
thousands of CPU cores and GPU accelerators casually, and various software
types can be used very easily by cloud images, the cloud is beginning to be
used in the field of bioinformatics. In this study, we ported the original
protein-protein interaction prediction (protein-protein docking) software,
MEGADOCK, into Microsoft Azure as an example of an HPC cloud environment. A
cloud parallel computing environment with up to 1,600 CPU cores and 960 GPUs
was constructed using four CPU instance types and two GPU instance types, and
the parallel computing performance was evaluated. Our MEGADOCK on Azure system
showed a strong scaling value of 0.93 for the CPU instance when H16 instance
with 100 instances were used compared to 50, and a strong scaling value of 0.89
for the GPU instance when NC24 instance with 20 were used compared to 5.
Moreover, the results of the usage fee and total computation time supported
that using a GPU instance reduced the computation time of MEGADOCK and the
cloud usage fee required for the computation. The developed environment
deployed on the cloud is highly portable, making it suitable for applications
in which an on-demand and large-scale HPC environment is desirable.

DOI： 10.1007/978-3-030-69984-0_53

arXiv

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Other Link： http://arxiv.org/pdf/2006.08905v1

Abstract In this manuscript, we showed a statistically significant difference of the doubling times of the death toll between the group of countries with national universal Bacillus Calmette-Guerin (BCG) vaccination and the group without it for recent years. Based on a statistical test, the distributions of the doubling time of these two groups were significantly different (p=0.007). Miller et al. reported the relationship between BCG vaccination and mortality for COVID-19 based on deaths per million inhabitants. However, they did not take into account the differences in COVID-19 detection rates among the countries and the epidemic stages of the countries. Therefore we used a doubling time of the death toll as a more stable indicator instead. We also investigated the dependency of the BCG strains. Among the 42 BCG-vaccinated countries, the median doubling time of the eight countries using “Tokyo 172-1” strain at least partially (Japan, Iraq, Malaysia, South Korea, Philippines, Saudi Arabia, Pakistan, and Bangladesh) was 7.2 days, and that of the other 34 vaccinated countries was 5.5 days. Their distributions were also significantly different (p=0.026).

DOI： 10.1101/2020.04.06.20055251

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Other Link： https://lens.org/034-288-684-460-007

Publishing type：Research paper (scientific journal)  

DOI： 10.14529/js200102

Scopus

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Publishing type：Research paper (international conference proceedings)   Publisher：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

DOI： 10.1007/978-3-030-48842-0_2

Scopus

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Other Link： https://dblp.uni-trier.de/db/conf/scfa/scfa2020.html#AoyamaWOA20

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.2142/biophysico.BSJ-2019050

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Other Link： https://lens.org/012-380-392-395-489

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1038/s41598-019-55069-y

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Other Link： https://lens.org/045-492-132-391-725

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.jmgm.2019.07.009

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Other Link： https://lens.org/022-411-045-076-979

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/BIBE.2019.00035

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Other Link： https://dblp.uni-trier.de/db/conf/bibe/bibe2019.html#OhueYIA19

Machine learning is often used in virtual screening to find compounds that
are pharmacologically active on a target protein. The weave module is a type of
graph convolutional deep neural network that uses not only features focusing on
atoms alone (atom features) but also features focusing on atom pairs (pair
features); thus, it can consider information of nonadjacent atoms. However, the
correlation between the distance on the graph and the three-dimensional
coordinate distance is uncertain. In this paper, we propose three improvements
for modifying the weave module. First, the distances between ring atoms on the
graph were modified to bring the distances on the graph closer to the
coordinate distance. Second, different weight matrices were used depending on
the distance on the graph in the convolution layers of the pair features.
Finally, a weighted sum, by distance, was used when converting pair features to
atom features. The experimental results show that the performance of the
proposed method is slightly better than that of the weave module, and the
improvement in the distance representation might be useful for compound
activity prediction.

arXiv

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Other Link： http://arxiv.org/pdf/1907.01103v2

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.bbrep.2019.01.008

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Other Link： https://lens.org/066-027-314-913-940

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s11030-018-9842-3

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.bpj.2018.11.3026

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Other Link： https://dblp.uni-trier.de/db/journals/bmcbi/bmcbi19S.html#HayashiMYOA18

Publisher：ACM  

DOI： 10.1145/3365610.3368409

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J-GLOBAL

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Other Link： https://dblp.uni-trier.de/db/conf/mum/mum2019.html#JiangZIKNSOA19

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1186/s12859-018-2529-z

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Other Link： https://dblp.uni-trier.de/db/journals/bmcbi/bmcbi19S.html#TajimiWYYOA18

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s11095-018-2479-1

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s10015-017-0416-8

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Other Link： https://dblp.uni-trier.de/db/journals/alr/alr23.html#SuzukiOA18

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.compbiolchem.2018.03.013

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Other Link： https://dblp.uni-trier.de/db/journals/candc/candc74.html#YanagisawaKKOA18

Language：English   Publishing type：Research paper (scientific journal)  

AIMS: We report the autopsy findings of a 58-year-old man with malignant mesothelioma in the left pleural cavity. METHODS AND RESULTS: The patient had a history of asbestos exposure, and the chest computed tomography scan on initial admission demonstrated an extrapleural sign, suggesting a nodular lesion in the chest wall. However, no nodular lesions were detectable in either of his lungs. In spite of chemotherapy, he died 4 months after the initial admission. An autopsy revealed markedly thickened pleura in a large section of the left pleural cavity without visible intrapulmonary primary tumour lesions. Histological examination of a biopsy specimen obtained prior to chemotherapy and that of an autopsy specimen showed that the pleural tumour was composed of a mixture of mesothelioma and tumour cells with squamous differentiation mimicking squamous cell carcinoma. CONCLUSIONS: To the best of our knowledge, this is the first case report of mesothelioma with extensive squamous differentiation in the English-language literature. The extensive squamous differentiation reminiscent of squamous cell carcinoma can be a pitfall in the pathological diagnosis of pleural cytology and that of biopsy specimens from patients with mesothelioma. Here, we report autopsy findings of a case of malignant mesothelioma with portions of extensive squamous differentiation, mimicking a squamous cell carcinoma.

DOI： 10.1111/his.13482

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1186/s12859-018-2073-x

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.compbiolchem.2018.02.008

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Other Link： https://dblp.uni-trier.de/db/journals/candc/candc73.html#BanOA18

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1080/07391102.2017.1329098

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Other Link： https://lens.org/023-796-028-741-654

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1038/s41598-017-06411-9

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Other Link： https://lens.org/011-525-842-841-14X

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1093/bioinformatics/btx178

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Other Link： https://dblp.uni-trier.de/db/journals/bioinformatics/bioinformatics33.html#YanagisawaKSOIA17

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1038/s41598-017-10275-4

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/ICCABS.2017.8114299

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Other Link： https://dblp.uni-trier.de/db/conf/iccabs/iccabs2017.html#BanOA17

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/ijms18102124

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Other Link： https://lens.org/066-477-973-497-707

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.bpj.2016.11.2421

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Other Link： https://lens.org/077-365-459-201-945

Publishing type：Research paper (scientific journal)  

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Other Link： https://dblp.uni-trier.de/db/journals/corr/corr1705.html#OhueYBA17

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-319-63312-1_48

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arXiv

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Other Link： http://arxiv.org/pdf/1705.01667v2

Language：English   Publishing type：Part of collection (book)  

DOI： 10.1007/10_2016_41

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Other Link： https://lens.org/000-339-409-344-558

Language：English   Publishing type：Part of collection (book)  

DOI： 10.1007/978-1-4939-7015-5_2

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Other Link： https://lens.org/002-771-571-070-220

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1371/journal.pone.0157338

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Other Link： https://lens.org/129-521-194-668-895

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.xphs.2016.04.023

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Other Link： https://lens.org/006-564-709-886-315

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.bpj.2015.11.1759

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Other Link： https://lens.org/027-248-641-360-663

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.2142/biophysico.13.0_209

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Other Link： https://lens.org/063-995-691-298-25X

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.2142/biophysico.13.0_105

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Other Link： https://lens.org/180-482-575-404-56X

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1038/srep17209

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1093/bioinformatics/btu780

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Other Link： https://dblp.uni-trier.de/db/journals/bioinformatics/bioinformatics31.html#SuzukiKIA15

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1186/1752-0509-9-S1-S6

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Other Link： https://dblp.uni-trier.de/db/journals/bmcsb/bmcsb9S.html#ShimodaSOIA15

Publishing type：Research paper (scientific journal)  

DOI： 10.2197/ipsjtbio.8.21

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Other Link： https://lens.org/050-647-253-987-220

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1186/s12859-014-0406-y

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Other Link： https://dblp.uni-trier.de/db/journals/bmcbi/bmcbi15.html#YanoMAYK14

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1124/dmd.114.057893

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1371/journal.pone.0103833

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Other Link： https://lens.org/140-131-994-210-377

Publishing type：Research paper (scientific journal)  

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Other Link： https://lens.org/010-430-082-021-57X

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Other Link： https://lens.org/072-809-988-052-060

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.2174/09298665113209990066

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Other Link： https://lens.org/003-694-090-161-64X

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.2174/09298665113209990050

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1093/bioinformatics/btu532

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Other Link： https://dblp.uni-trier.de/db/journals/bioinformatics/bioinformatics30.html#OhueSSMIA14

Publishing type：Research paper (scientific journal)  

DOI： 10.14529/jsfi140206

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：The Biophysical Society of Japan General Incorporated Association  

To understand mechanisms of protein-protein interactions, we have used rigid-body docking algorithm generating many protein complexes, called decoys. This docking method is popular and useful but with some serious cases of no near-native decoys (NN-decoys). We developed Re-docking scheme, iterating rigid-body docking for generating NN-decoys using Interaction FingerPrints (IFPs). By this scheme, after re-docking process in each interfaces, we could obtain more NN-decoys. To predict interaction surfaces with NN-decoys, we investigated several parameters of interaction surfaces. In this work, we analyzed interaction surfaces of decoys by IFPs, and will discuss interaction surfaces generating more near-native decoys by re-docking process.

DOI： 10.2142/biophys.54.S240_1

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：The Biophysical Society of Japan General Incorporated Association  

γS-crystallin is a protein which exists in crystalline lens. γS-crystallin maintains transparency of the crystalline lens and increases the refraction index. The aggregation of this protein enhances the development of cataract. In addition, frequency of aggregation of mutant γS-crystallin is higher than that of wild-type γS-crystallin (γS-WT). We focus on γS-G18V which is a mutant γS-crystallin where 18th glycine is replaced by valine. This protein is related to childhood-onset cortical cataract. In the present study, we calculated the root mean square deviation (RMSD), radius of gyration (Rg), and obtained Ramachandran plot of the protein structure and compared the structures of γS-WT and γS-G18V by performing molecular dynamics simulation.

DOI： 10.2142/biophys.54.S197_2

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-319-09330-7_32

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Other Link： https://dblp.uni-trier.de/db/conf/icic/icic2014-3.html#KomatsuIA14

Publishing type：Research paper (scientific journal)  

DOI： 10.1080/1062936X.2013.864997

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Language：English   Publishing type：Research paper (scientific journal)  

BACKGROUND: Elucidation of protein-protein interaction (PPI) networks is important for understanding disease mechanisms and for drug discovery. Tertiary-structure-based in silico PPI prediction methods have been developed with two typical approaches: a method based on template matching with known protein structures and a method based on de novo protein docking. However, the template-based method has a narrow applicable range because of its use of template information, and the de novo docking based method does not have good prediction performance. In addition, both of these in silico prediction methods have insufficient precision, and require validation of the predicted PPIs by biological experiments, leading to considerable expenditure; therefore, PPI prediction methods with greater precision are needed. RESULTS: We have proposed a new structure-based PPI prediction method by combining template-based prediction and de novo docking prediction. When we applied the method to the human apoptosis signaling pathway, we obtained a precision value of 0.333, which is higher than that achieved using conventional methods (0.231 for PRISM, a template-based method, and 0.145 for MEGADOCK, a non-template-based method), while maintaining an F-measure value (0.285) comparable to that obtained using conventional methods (0.296 for PRISM, and 0.220 for MEGADOCK). CONCLUSIONS: Our consensus method successfully predicted a PPI network with greater precision than conventional template/non-template methods, which may thus reduce the cost of validation by laboratory experiments for confirming novel PPIs from predicted PPIs. Therefore, our method may serve as an aid for promoting interactome analysis.

DOI： 10.1186/1753-6561-7-S7-S6

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：1  

BACKGROUND: Protein-protein interaction (PPI) plays a core role in cellular functions. Massively parallel supercomputing systems have been actively developed over the past few years, which enable large-scale biological problems to be solved, such as PPI network prediction based on tertiary structures. RESULTS: We have developed a high throughput and ultra-fast PPI prediction system based on rigid docking, "MEGADOCK", by employing a hybrid parallelization (MPI/OpenMP) technique assuming usages on massively parallel supercomputing systems. MEGADOCK displays significantly faster processing speed in the rigid-body docking process that leads to full utilization of protein tertiary structural data for large-scale and network-level problems in systems biology. Moreover, the system was scalable as shown by measurements carried out on two supercomputing environments. We then conducted prediction of biological PPI networks using the post-docking analysis. CONCLUSIONS: We present a new protein-protein docking engine aimed at exhaustive docking of mega-order numbers of protein pairs. The system was shown to be scalable by running on thousands of nodes. The software package is available at: http://www.bi.cs.titech.ac.jp/megadock/k/.

DOI： 10.1186/1751-0473-8-18

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Other Link： https://dblp.uni-trier.de/db/journals/scfbm/scfbm8.html#MatsuzakiUOSSIA13

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DOI： 10.2174/15734099113099990031

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Other Link： https://lens.org/020-682-811-247-063

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DOI： 10.1371/journal.pone.0069365

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DOI： 10.1186/1471-2105-14-S8-S7

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Other Link： https://dblp.uni-trier.de/db/journals/bmcbi/bmcbi14S.html#NamikiIA13

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DOI： 10.1016/j.ympev.2013.02.011

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/2506583.2506669

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：The Biophysical Society of Japan General Incorporated Association  

Recent progress in molecular biology and genome science leads to finding many functional RNAs which control gene expression and replication. These functions are achieved through the protein-RNA and RNA-RNA interactions. Deriving the tertiary structures of these complex is very helpful to clarify the mechanism of those functions in detail. We developed the new method based on fragment assembly algorithm to predict RNA-RNA complex structures from nucleotide sequence and secondary structure information. We applied our method to predict several kinds of RNA-RNA complex structures which include kissing-loops, hammerhead ribozymes and other difficult targets, and derived successful results especially in the prediction of kissing-loop targets.

DOI： 10.2142/biophys.53.S203_2

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DOI： 10.1145/2506583.2506693

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Other Link： https://dblp.uni-trier.de/db/conf/bcb/bcb2013.html#ShimodaISOA13

Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/2506583.2506670

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DOI： 10.1007/978-1-4614-6418-1_714-1

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Other Link： https://lens.org/028-010-092-267-007

Language：Japanese  

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DOI： 10.1371/journal.pone.0036060

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DOI： 10.1007/978-3-642-34123-6_16

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Other Link： https://dblp.uni-trier.de/db/conf/prib/prib2012.html#OhueMIA12

Language：English   Publishing type：Research paper (scientific journal)   Publisher：The Biophysical Society of Japan General Incorporated Association  

A huge amoullt molecuLar interaction data are accumu]ated to elucidate entire tandscape ef cellu]ar activities. 3D comp]ex structures of interacting pToteins can add their physical mechanisms, such as interacting residues and dtiving binding forces. The goal of this study is to gather all the reported molecutar interactions, and to predict their 3D structures using hornology modeling as many as poss{b]e, Another goa] is to propose biologically important interacting protein sets to be so]ved their 3D complex structures in the future. However, current experimental molecular interaction data arc not comprehensive and re]iab]e. To overcome these prob]ems, wc eempare interactions of severa] different organisms, and censider conserved interaetions to be highly reliable,

DOI： 10.2142/biophys.52.S122_1

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Other Link： https://lens.org/040-996-385-786-175

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DOI： 10.1109/SC.Companion.2012.320

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Other Link： https://dblp.uni-trier.de/db/conf/sc/sc2012c.html#SuzukiIA12

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DOI： 10.1007/978-3-642-31837-5_66

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DOI： 10.1016/j.jmb.2011.09.031

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/3DIC.2012.6263017

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DOI： 10.2197/ipsjtbio.4.21

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DOI： 10.1124/dmd.110.032789

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Other Link： https://lens.org/179-495-449-693-302

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DOI： 10.1093/nar/gkm999

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Language：English   Publisher：Information and Media Technologies Editorial Board  

We created a Free Energy Landscape Analysis System based on a parallelized molecular dynamics (MD) simulation adapted for the IBM Blue Gene/L supercomputer. We begin with an outline of our Free Energy Landscape Analysis system. Next we discuss how Parallel MD was tuned for Blue Gene/L. We then show the results for some test targets run on Blue Gene/L, including their efficiency. Finally, we mention some future directions for extension of this project.

DOI： 10.11185/imt.3.201

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In many cases of biological observations such as cell array, DNA micro-array or tissue microscopy, primary data are obtained as photographs. Specialized processing methods are needed for each kind of photographs because they have very wide variety, and often needed automated systems for modern high-throughput observations. We developed a fully-automated image processing system for cell array, high-throughput time series observation system for living cells, to evaluate gene expression levels and phenotype changes in time of each cell.

DOI： 10.2197/ipsjdc.3.728

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Other Link： https://dblp.uni-trier.de/db/conf/pdcn/pdcn2007.html#SekijimaDNAS07

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DOI： 10.1007/978-3-540-74742-0_84

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DOI： 10.1007/BF02697264

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Language：English   Publisher：The Biophysical Society of Japan General Incorporated Association  

DOI： 10.2142/biophys.45.S230_4

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DOI： 10.1107/S0108767305088574

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DOI： 10.1107/S090744490402428X

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society for Bioinformatics  

Figure 1: Screen shot of the PLOC and the example of result. To understand the functions of various proteins, it would be helpful to obtain information about their subcellular location. This is because the subcellular location of proteins is closely related to their function. Thus, it would be worthwhile to develop a fast computational prediction method to identify protein’s subcellular location. In this research, we have developed a prediction tool named PLOC (Protein LOCalization prediction) using the compositions of amino acids and (gapped) amino acid pairs by support vector machines [2]. This prediction method is available at http://www.genome.ad.jp/SIT/ploc.html (Figure 1).

DOI： 10.11234/gi1990.14.559

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society for Bioinformatics  

Recent progress in determination of human draft sequences [6, 8] and tertiary structure of seven-transmembrane-helix receptors (7-TMR) accelerates the comprehensive sequence analysis of 7-TMRin whole human genome. Despite the enormous amount of available data on genome sequences [6, 8],theoverviewedfamiliesof7-TMRhavenotyetbeenwellcharacterized. OurSEVENSdatabaseintendsto cover all \7-TMR universe" with not only the known sequences but also to use newly discoveredsequence by computational gene nding program.

DOI： 10.11234/gi1990.13.511

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DOI： 10.2142/biophys.41.S79_4

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This paper reports on our study of the feasible conformations and the potential energy distributions of a β-hairpin called a “tyrosine corner”. Tyrosine corners are a feature of 5 or 6 amino acid residues found in most Greek key β-barrel proteins [2]. According to earlier research results [2], the tyrosine corners appear to contribute to the stability of a Greek key connection over a hairpin connection, and may aid in the process of folding up Greek key structures.

DOI： 10.11234/gi1990.12.362

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society for Bioinformatics  

The discovery and high-throughput structure modeling of specific membrane protein families, such as G-protein coupled receptors (GPCRs) and channel proteins from complete genome sequences, are important issues in pharmacogenomics and structure-based drug design. Membrane protein families from other protein sequences have been detected and classified with high accuracy by means of homology search, hidden Markov model, physicochemical profiles, and neural networks [5]. Structure modeling of membrane proteins, by (1) comparative modeling of GPCRs using the structural template of bovine rhodopsin and (2) several non-statistical approaches has also been proposed [2, 3, 8, 7]. Transmembrane (TM) regions in membrane proteins play a major role in structure modeling due to the functional insights they provide and their simple architecture, which consists of a membrane spanning a-helix composed of approximately a 20-30 amino acids. On the other hand, inter-helical (IH) loop segments that link TM helices other than the large loop segments formed in a compact soluble domain, have been omitted from the structure modeling process. However, a couple of recent crystallography and diffraction studies of membrane proteins, such as potassium and water channel proteins, have revealed that the specific medium loop segments between TM helices fold back onto membrane positions and play an important role in membrane protein folding and biological function [1, 6]. Previous methods of TM helix prediction have not considered the contribution of these IH loop groups. Consequently, specific medium loop segment is often predicted as a TM helix. This false-positive prediction causes serious problems in TM topology prediction and structure modeling. Here, we analyzed the IH loop segments in proteins having multi-TM helices for classification and detection of the specific medium loop segment from amino acid sequences. The results show that the specific loop length and extra-cellular environment are dominant factor for the classification of IH loop types.

DOI： 10.11234/gi1990.12.332

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society for Bioinformatics  

1 Computational Biology Research Center (CBRC), National Institute of Advanced IndustrialScience and Technology (AIST), 2-41-6 Aomi, Koto-ku, Tokyo 135-0064, Japan 2 Center for Computational Science and Engineering, Fuji Research Institute Corporation, 2-3 Kandanishiki-cho, Chiyoda-ku, Tokyo 101-8443, Japan 3 Genome Science Division, Research Center for Advanced Science and Technology (RCAST), University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan

DOI： 10.11234/gi1990.12.425

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DOI： 10.1109/HPC.2000.843608

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1 Electrotechnical Laboratory (ETL), Agency of Industrial Science and Technology, Ministry of International Trade and Industry, 1-1-4 Umezono, Tsukuba, Ibaraki 3058568, Japan 2 Center for Computational Science and Engineering, Fuji Research Institute Corporation, 2-3, Kandanishiki-cho, Chiyoda-ku, Tokyo 101-8443, Japan 3 Information and Mathematical Science Laboratory Inc., Ikebukuro, Toshima-ku, Tokyo 171-0014, Japan 4 Genome Science Division, RCAST, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan

DOI： 10.11234/gi1990.11.410

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Publishing type：Research paper (international conference proceedings)   Publisher：CSREA Press  

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Other Link： https://dblp.uni-trier.de/db/conf/pdpta/pdpta1999-3.html#AndoAON99

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society for Bioinformatics  

We have successfully ported our parallel application programs, the PAPIA system [1] and the MolTreC no cut-off molecular dynamics simulator [2] onto a new compact 8-node Linux PC cluster. The PAPIA (Parallel Protein Information Analysis) system [1] is an integrated parallel computing system dedicated to typical search/analysis problems required in protein research, such as structure similarity search, sequence homology search, and multiple sequence alignment. The system was implemented on a 64-node PC cluster (PAPIA Cluster) and we have been operating a WWW-based free service since April 1998 (http://www.rwcp.or.jp/papia/). Now the PAPIA system also runs on a compact and inexpensive 8-node cluster which we will demonstrate at GIW99 (Fig. 1, Table 1).

DOI： 10.11234/gi1990.10.202

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Computer Society  

DOI： 10.1109/IWIA.1998.779070

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Japanese Society for Bioinformatics  

Protein information analysis is widely regarded as a key technology in drug design, macromolecular engineering, and understanding genome sequences. Because vast amount of calculations are required, further speed-up for protein information analysis is very much in demand. We have implemented the PAPIA (PArallel Protein Information Analysis) system on the RWC PC cluster IIa ("PAPIA cluster") which consists of 64 Pentium Pro 200MHz microprocessors. The PAPIA system performs fast parallel processing for typical calculations in protein analysis, such as structure similarity search, sequence homology search and multiple sequence alignment, nearly 60 times faster th an a single processor. We have started a WWW service (http://www.rwcp.or.jp/papia/), allowing any biologist to easily submit jobs to the PAPIA system through a WWW browser. The user can experience the power of current parallel processing technology.

DOI： 10.11234/gi1990.9.131

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Information Processing Society of Japan (IPSJ)  

DOI： 10.1007/BFb0024223

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Other Link： https://dblp.uni-trier.de/db/conf/ishpc/ishpc1997.html#AkiyamaMOMSNOA97

Publishing type：Research paper (international conference proceedings)   Publisher：AAAI  

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Other Link： https://dblp.uni-trier.de/conf/ismb/1997

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Other Link： https://lens.org/033-189-255-167-793

Publishing type：Doctoral thesis  

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Other Link： https://lens.org/139-314-701-273-144

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Computer Society  

DOI： 10.1109/TAI.1989.65368

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Mitochondrial diseases are largely caused by dysfunction in mitochondrial
proteins. However, annotations of human mitochondrial proteins are scattered
across various public databases and individual studies. To facilitate research
aimed at elucidating mitochondrial functions, we constructed the
MEGADOCK-Web-Mito database as a protein-protein interaction (PPI) prediction
data archive, including prediction results for exhaustive protein pairs of 654
mitochondria-related human proteins. MEGADOCK-Web-Mito enables users to search
for all PPI prediction results efficiently and comprehensively. In particular,
we linked functional annotations to each human mitochondrial protein. The
comprehensive and specialized human mitochondrial PPI prediction results and
searching function of MEGADOCK-Web-Mito will support further research on
mitochondria and mitochondrial diseases.

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タンパク質の複合体構造を予測するドッキングシミュレーションは創薬において重要な役割を果たす.タンパク質は複合体を形成する際に立体構造の一部が変化することが知られているが,ドッキングシミュレーションの際にそのような分子の柔軟性を考慮すると,候補構造の探索空間が膨大になる問題があった.そこで本研究ではゲーミフィケーションにより,探索の効率化を実現する方法を提案する.具体的には,生物物理学の知識を持たないユーザーであっても,直感的に分子表面の側鎖を動かしてより良いドッキング状態を形成可能な仕組みを備えたゲームソフトを実装し,多数のプレイヤーを競わせることで高い精度で複合体構造を予測する.本研究の利用により,計算機が自動で探索を行う旧来の手法と比較して,高精度の予測を短時間で達成できることが期待できる.

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DOI： 10.1016/j.bpj.2016.11.2418

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Other Link： https://lens.org/070-770-531-566-127

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DOI： 10.1016/j.bpj.2016.11.1574

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Recently, development of efficient biological data analysis systems on a supercomputer has been highly required in order to tackle the vast amount of biological data generated by the latest experimental techniques such as a next-generation DNA sequencer. Exome analysis, which analyzes the regions in a genome that will remain in a matured RNA, is useful because it targets only exonic sequences in a genome and enables effective search for important mutations throughout the genome. On the other hand, to meet the demands of current medical researches such as application to personalized genome analysis, we need to deal with the situation in which hundreds to thousand exome sequences are needed to be analyzed in realistic time. It is of significant importance to develop a high-performance large-scale sequence analysis environment. In this study, we developed an exome analysis pipeline on the K computer. We not only developed a pipeline useful for biologists on a supercomputer but also improved the parallel performance of the pipeline using a master-worker model task distribution framework implemented by MPI and efficient task partitioning strategy.

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DOI： 10.1016/j.bpj.2015.11.1758

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It is necessary to confirm that a new drug can be appropriately cleared from the human body. However, checking the clearance pathway of a drug in the human body requires clinical trials, and therefore requires large cost. Thus, computational methods for drug clearance pathway prediction have been studied. The proposed prediction methods developed previously were based on a supervised learning algorithm, which requires clearance pathway information for all drugs in a training set as input labels. However, these data are often insufficient because of the high cost of their acquisition. In this paper, we propose a new drug clearance pathway prediction method based on semi-supervised learning, which can use not only labeled data but also unlabeled data. We evaluated the effectiveness of our method, focusing on the cytochrome P450 2C19 enzyme, which is involved in one of the major clearance pathways.

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DOI： 10.1016/j.bpj.2014.11.2579

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

Protein-protein interaction (PPI) is important for understanding biological functions. Currently, various in silico PPI prediction methods have been developed and they are generally classified into two typical approaches: a method based on protein docking and a method based on known PPI information. The result of de novo docking based method contains many false-positive predictions, and the known PPI information based method is difficult to predict a novel PPI. In this research, we have proposed new hybrid PPI prediction methods by combining de novo docking based prediction and known PPI information based prediction. For evaluating proposed method, we applied the methods to human apoptosis signaling pathway. As results, proposed method achieved an improvement in F-measure (0.21 → 0.26).

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Many proteins include flexible regions generally called disordered region. Some methods for accurate prediction of disorder regions are proposed. We focus on deep learning because deep networks have already been successful predictors in the fields of image recognition and speech recognition. DNdisorder, which is the disorder predictor using deep learning with deep belief networks, was proposed, but its performance was not the best in the CASP9 evaluation. Thus, we tried to apply stacked denoising autoencoder, which is another method of deep learning, to the prediction of disordered protein regions. As a result, the proposed method is achieved performance of over DNdisorder in the CASP9 evaluation.

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Glide protein-ligand docking algorithm often fails to find a correct binding mode because the search easily falls into local minima, when the search target area widely distributes on a protein surface and a large search grid is used. In this research, we propose a novel method to improve search efficiency in the case by dividing a large search grid into multiple small search grids. In addition, we propose a method to minimize the number of these grids by converting the problem into a set cover problem.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

Nowadays, drug development requires too much time and budget, and it is necessary to reduce them. In order to accept a compound as a new drug, it must be confirmed that it is metabolized and excreted. In this respect, one of the computational methods used for selecting compounds is drug clearance pathway prediction. This prediction method uses well-known drug's clearance pathway data as a training set. However data is expensive to get, and thus there are too few data. For this reason, we evaluated the usefulness of semi-supervised learning in this prediction problem, and tried to improve accuracy of this clearance pathway prediction. We also tried to add some features of compounds which are selected from 802 features by greedy algorithm to improve accuracy and evaluated their effect.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

Exome analysis, which is a method to analyze only protein coding region, has been used in various research fields such as a cancer genome research. Because of the improvement of a high-speed sequencer, demands of effective sequence analysis on large computational environment have been increased. Genomon-exome is a useful pipeline software for analyzing exome data but executable on only general PC clusters. In this study, We attempted to implement the Genomon-exome on the K-computer using a Master-Worker model task distribution framework implemented MPI. We also evaluated the scalability of the pipeline on K-computer.

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

Exome analysis, which is a method to analyze only protein coding region, has been used in various research fields such as a cancer genome research. Because of the improvement of a high-speed sequencer, demands of effective sequence analysis on large computational environment have been increased. Genomon-exome is a useful pipeline software for analyzing exome data but executable on only general PC clusters. In this study, We attempted to implement the Genomon-exome on the K-computer using a Master-Worker model task distribution framework implemented MPI. We also evaluated the scalability of the pipeline on K-computer.

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

The application of protein-protein docking to the large-scale interactome analysis, the treatment of protein flexibility or multiple protein-protein docking problem are current challenges in structural bioinformatics that require huge computing resource. In this work we present MEGADOCK 4.0, an FFT-based docking software which makes extensive use of recent GPU supercomputers and show the powerful scalable performance of over 97% strong scaling with TSUBAME 2.5 supercomputing system. In addition, a million protein-protein docking jobs can be calculated about a half day by using 420 nodes of TSUBAME 2.5.

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

Nowadays, drug development requires too much time and budget, and it is necessary to reduce them. In order to accept a compound as a new drug, it must be confirmed that it is metabolized and excreted. In this respect, one of the computational methods used for selecting compounds is drug clearance pathway prediction. This prediction method uses well-known drug's clearance pathway data as a training set. However data is expensive to get, and thus there are too few data. For this reason, we evaluated the usefulness of semi-supervised learning in this prediction problem, and tried to improve accuracy of this clearance pathway prediction. We also tried to add some features of compounds which are selected from 802 features by greedy algorithm to improve accuracy and evaluated their effect.

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

In structure based-drug design, selecting a drug target protein is very important. For the target protein selection, several database systems integrating various related information, such as genomic information of pathogens and phenotypic information, have been proposed. However, biological pathway information, which may facilitate understanding the importance of proteins, has not been integrated. In this research, we integrated the biological pathway information about Trypanosomatidae family, protozoans parasites into a database system iNTRODB, which has been developed for selecting drug target protein of neglected tropical diseases. We also developed an interface to display pathway information with genome and protein information to improve the search process of drug target proteins.

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現在、ゲノムの解析には次世代シーケンサと呼ばれるハイスループットなゲノム読み取り装置が用いられている。しかしこのシーケンサはゲノムを読み取る際に元のゲノム配列を短く切断してしまうため、読み取った短いゲノム配列を元の長いゲノム配列に再構築（アセンブリ）する必要が有るが、ゲノムアセンブリには膨大な量のメモリが必要となることが知られている。本研究では、代表的なショートリード向けのアセンブラの1つであるVelvetについて、ハッシュテーブルやde Bruijn Graphなどのデータ構造を分割する手法を提案した。これによりハッシュテーブルやde Bruijn Graph1つあたりの消費メモリ量を削減し、少量のメモリしか搭載していない計算機でも大規模なゲノムのアセンブリを可能にした。

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Language：English  

DOI： 10.1016/j.bpj.2013.11.2310

Web of Science

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Other Link： https://lens.org/053-035-316-219-838

J-GLOBAL

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

In structure based-drug design, selecting a drug target protein is very important. For the target protein selection, several database systems integrating various related information, such as genomic information of pathogens and phenotypic information, have been proposed. However, biological pathway information, which may facilitate understanding the importance of proteins, has not been integrated. In this research, we integrated the biological pathway information about Trypanosomatidae family, protozoans parasites into a database system iNTRODB, which has been developed for selecting drug target protein of neglected tropical diseases. We also developed an interface to display pathway information with genome and protein information to improve the search process of drug target proteins.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

The application of protein-protein docking to the large-scale interactome analysis, the treatment of protein flexibility or multiple protein-protein docking problem are current challenges in structural bioinformatics that require huge computing resource. In this work we present MEGADOCK 4.0, an FFT-based docking software which makes extensive use of recent GPU supercomputers and show the powerful scalable performance of over 97% strong scaling with TSUBAME 2.5 supercomputing system. In addition, a million protein-protein docking jobs can be calculated about a half day by using 420 nodes of TSUBAME 2.5.

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Language：Japanese   Publisher：一般社団法人情報処理学会  

蛋白質-化合物ドッキングソフトウェア Glide では,蛋白質表面を広範囲にわたって探索する必要がある場合,標準では対象となる領域を包含する 1 つの大きなグリッドを配置するため探索に偏りが生じてしまう事がある.そこで本研究では,対象となる探索範囲を全て包含するように複数の小さなグリッドを分散させて配置する手法を提案し探索効率を向上させることに成功した.

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

Metagenomic analysis is a research to analyze genes obtained from an environment, and often requires large amount of sensitive homology searches which require immense amount of time. NCBI BLAST has been the most widely-used for this purpose, but its calculation speed is insufficient for a large amount of DNA sequence data obtained from current sequencers. In previous study, we developed a fast GPU-based homology search tool for metagenomic analysis. However, it is required further speed-up in accordance with improvement of the sequencers. Here, we propose a new and more efficient GPU-based homology search tool by using reduced amino acid alphabet. Additionally compared with several conventional tools, we establish the usefulness of the proposed tool.

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

It is a well-known fact that the memory consumption of Velvet, which is one of the representative de novo assembler based on de Bruijn Graph, is too large. Velvet is composed of two steps, and several methods have been already proposed for decreasing the memory consumption of the first step by dividing the hash table. Here we proposed a graph dividing method. By using this method, we have succeeded to decrease the memory consumption of the latter step of Velvet.

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

In metagenomic analysis, phylogenetic classification of DNA fragments from a variety of microorganisms is often performed to understand the taxonomic composition of microbial communities. In recent years, with the improvement of DNA sequencer's throughput, a faster classifier for metagenomic reads is required. In this research, we focused on naive Bayes which can classify in short time with sufficient accuracy, and aimed to develop a faster and accurate classification method by using improved naive Bayes, naive Bayes with a hidden class (NBH) and hidden naive Bayes (HNB). In addition, we tried to improve algorithm of HNB for a faster classification, and checked the performance and computing time by using UCI reoisitory dataset.

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Language：Japanese   Publisher：Ritsumeikan University  

DOI： 10.34382/00001082

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Other Link： http://hdl.handle.net/10367/5157

Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

Evaluating the reliability of estimated phylogenetic trees is of critical importance in the field of molecular phylogenetics. The bootstrap method is a well known computational approach to assessing phylogenetic trees, and more generally for assessing the reliability of statistical models. However, it is known to be biased under certain circumstances, calling into question the accuracy of the method. Therefore, several advanced bootstrap methods have been developed to achieve higher accuracy, one of which is the speedy double bootstrap approach (sDBP-method). In the phylogenetic tree selection problem, it has been shown that the sDBP-method has comparable accuracy to the double bootstrap approach and is much more computationally efficient. In this study, we thus develop an R package named SDBP, which is an implementation of our sDBP-method on a statistical software R to assesse the reliability of phylogenetic trees. And in this paper we briefly introduce the mathematical theory of the sDBP-method and its algorithm for assessing the reliability of phylogenetic trees. Then, we describe the basic usage of our package.

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Tandem mass spectrometry, a method involving multiple steps of mass spectral selection, is widely used in various biological fields. In recent years, steady improvements have been made with respect to speed, and the number of protein databases available for analysis has rapidly increased. Consequently, computational analysis has become the bottleneck in tandem mass spectrometry. To overcome this problem, we attempted to improve the tandem mass spectrometry analysis software CoCoozo. To accelerate the program, we improved the algorithm and also incorporated utilization of multi-core CPU and GPGPU. As a result, when all mass spectral data files had precursor data, we achieved 8.9-fold speedups compared with the original software. In addition, in the case of no precursor data, by using a 12-core CPU and a GPU card we achieved 18.1-fold speedups compared with the original software.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

In metagenome analysis, DNA sequences are transformed into amino acid sequences before homology search. However, next generation sequencers became to produce larger data than previous sequencers. And then it takes more time to analyze data from next generation sequencers. Thus, we have made clusters of subsequences from database before homology search and search queries from the representations in clusters to search efficiently.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

Recently, drug development needs longer term and larger budget than before, so it is needed to decrease of them. From such a reason, the computer simulation-based techniques to design drugs which inhibit a target-protein is in the spotlight now. On the other hand, the drugs must be metabolized and excreted. And, one of the techniques to search the chemical compound which satisfy this condition is the drug clearance pathway prediction. Then, we tried to use binding free energy calculation of protein-ligand complex with the docking calculation, one of the computer simulation, for the drug clearance pathway prediction, we improved precision of the system for drug clearance pathway prediction previously developed in our lab.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

Development of Next Generation Sequencing technologies has provided an unprecedented increase in DNA sequencing throuput while this technology produces shorter reads than previous technology. These two factors make a number of new short read de novo assemblers. In this report, by evaluation of Sparse Assembler, we clarified that this asssembler is useful at the point of memory usage and time, while producing less quality of contigs than other assemblers already developed. This study was made to assembly with less computer memory and in shorter time than previous study by implement Sparse k-mer graph algorithm on Velvet, which is the most widely used assembler.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

Tandem mass spectrometry, a method involving multiple steps of mass spectral selection, is widely used in various biological fields. In recent years, steady improvements have been made with respect to speed, and the number of protein databases available for analysis has rapidly increased. Consequently, computational analysis has become the bottleneck in tandem mass spectrometry. To overcome this problem, we attempted to improve the tandem mass spectrometry analysis software CoCoozo. To accelerate the program, we improved the algorithm and also incorporated utilization of multi-core CPU and GPGPU. As a result, when all mass spectral data files had precursor data, we achieved 8.9-fold speedups compared with the original software. In addition, in the case of no precursor data, by using a 12-core CPU and a GPU card we achieved 18.1-fold speedups compared with the original software.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

Velvet is one of the most representative de novo assembler. However it has a problem that its memory consumption is too large for large scale assembling. Here, we propose a method to decrease the memory consumption of velveth which is the first half of Velvet and requires generally larger memory than the remaining half part. We propose a novel hash dividing method by dividing reads. By using this method, we have succeeded to decrease the elapsed time compared to the existing method, which divides a hash table corresponding the k-mer value.

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We developed the system for fast homology search using suffix array. However, next generation sequencers are improving gradually and become to produce larger data than previous sequencers. Thus, we have developed a new faster system. To accelerate search using suffix array, we store the results of searching patterns whose length is less than L_<hash> and use them as caches. In addition, we enhanced our system to map bisulfite reads for epigenomics.

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In this study, we proposed a new hydrophobic interaction model which applied Atomic Contact Energy for our protein-protein docking software called MEGADOCK in which we previously used only two score terms, namely, shape complementarity and electrostatic interaction. Using the proposed score function, MEGADOCK can calculate three phisico-chemical effects with only one correlation function. Therefore we succeeded improvement of accuracy without loosing speed.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

In protein-protein docking, a fast calculation method using fast Fourier transform (FFT) is well known, but the form of the evaluation function is limited to the sum of convolution. In this study, we developed an efficient docking calculation method without using FFT in order to use various evaluation functions. Against the increase of computational cost, we proposed the heuristic method that hierarchically searches only high-score complex structures using the locality of high-score complex structures.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

The protein-protein docking software "MEGADOCK" uses the two terms in its target function; shape complementarity and electrostatic. However, the optimal balance between those two terms is defferent for each protein. Thus, dynamic adjustment of the weight of the electrostatic term based on the surface charge of a protein was proposed in a previous work. In this work, we improved the method by using support vector regression and additional characteristics of a protein. By using our new method, we achieved the better prediction performance for the data used in the previous study. We also applied the method to new data set.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

We have been developing prediction systems of the clearance pathway of drugs. The performance of the system is insufficient because of the shortage of the training data, and the additional training data is required for the improvement of the system. However, in vivo experiments of human clearance pathway for new compounds require huge cost and time, and thus it is difficult to gather much training data. In this study, we propose a method which suggests high-priority compounds to be examined by using active learning.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

We had developed a homology search tool for metagenomics as GHOSTM. However, the tool is designed for a search for short query sequences, and unsuitable to search for long query sequences. Also, the tool uses the same number of CPU cores and GPUs. However a node has more CPU cores than GPUs on most of computer system. Therefore, GHOSTM does not utilize the all resources of a node. To solve these problems, we improved the previously developed system, which uses suffix array for the seed search, and accelerated the seed extension part based on Smith Waterman algorithm by using GPUs calculation. As results, the tool can use all calculation resources of a computation node and efficiently works even for long query sequences.

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Other Link： http://id.nii.ac.jp/1001/00073258/

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近年，次世代シーケンサーの登場により一度のシーケンシングで大量のDNA配列データが得られるようになった．これに伴い，読み取られた配列データの解析に要する時間が急激に増大したため， 大量の配列データを高速処理できる新しい解析アルゴリズムが必要になりつつある．本研究では次世代シーケンサーから得られたDNA配列のクラスタリングを高速化することを目的とし，配列クラスタリングにおける類似配列ペアの高速フィルタリング手法として「LCS filtering」を開発した．これを既存手法と組み合わせることで，精度を維持しつつ大規模DNA配列データのクラスタリング処理を大幅に高速化することに成功した．

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タンパク質-RNA間相互作用は遺伝子発現調節などのシステム的理解に重要であり，その予測は生命情報科学の大きな課題である．我々はタンパク質の立体構造情報を用いたタンパク質間相互作用ネットワークの予測に取り組んできたが，本研究ではRNA結合タンパク質に着目し，従来タンパク質同士の計算に用いられていた高速ドッキング計算手法をRNAも扱えるように拡張した．また，本手法によって大量の複合体候補構造を生成し，それらの結合エネルギーに基づくリランキングを用いたタンパク質-RNA間相互作用予測手法を提案する．Protein Data Bankに含まれるRNA結合タンパク質78例を用いた78×78通りの網羅的予測に本手法を適用した結果，F値0.52での予測に成功した．

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近年、次世代シーケンサーと呼ばれるハイスループットなゲノム解読装置が登場し、大量のDNA断片を短時間で得ることが可能になったが、データの情報解析が追いつかないという深刻な問題が発生している。これに対して先行研究ではGPUを用いて処理することで高速化を実現させた。しかし更なる高速化にはマルチGPU環境への対応が期待される。また最新型のシーケンサーから得られる配列断片が長くなりつつあることで、GPUのメモリ容量の制限内で処理するには設計変更が必要である。そこで本研究では、先行研究で開発された高速なマッピングツールを更に高速化するために、マルチGPU環境向けの最適化と長断片への対応に取り組んだ。

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

To predict all-to-all the protein-protein interactions (PPIs) from protein monomeric structures, we have been developing a PPI prediction method based on a protein docking calculation. In this study, we developed two methods which visualize complex candidates generated by protein docking calculations. The first method visualizes 3D distribution of the complex candidates in space. And the second one visualizes PPI tendency of each residue by coloring them according to the interaction frequency. By using these visualization methods, we also developed a new score to identify false positives and improved the accuracy of aPPI prediction.

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Language：English   Publisher：The Biophysical Society of Japan General Incorporated Association  

Japan General IncorporatedAssociation our previous study vve found that gn en correct seLenddry structure information thc native gtmLture ot a protein can be predicted vciy aLLuiately [Fuiuta et ai Chem Phvs Lett 472 134 (2009>1 Howewer because the cerrcct secondary structures are not knoun a pmem it Ts necessary to obtain thc infonnation from secondary structure prediLtion Since the prediction containg crrors "c extended our method so that it cdin use the result ot the uncertain secondaTL structurL prediction We uscd the PSIPRED pregram to predict the secondary structures Restraining potentials wcTe applied the backbone dihedral angles {q yJ) of edLh residue whose secondary structure ig predicted to be a hcl]x or P gtrand

DOI： 10.2142/biophys.51.S41_2

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Elucidating protein-RNA interactions (PRIs) is important for understanding cellular systems. We developed a PRI prediction method by using a rigid-body protein-RNA docking calculation with tertiary structure data. We evaluated this method by using 78 protein-RNA complex structures from the Protein Data Bank. We predicted the interactions for pairs in 78x78 combinations. Of these, 78 original complexes were defined as positive pairs, and the other 6,006 complexes were defined as negative pairs; then an F-measure value of 0.465 was obtained with our prediction system.

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The elucidation of the protein-protein interaction (PPI) network is an important problem in the understanding of the cellular system and structure-based drug design. An effective way to conduct exhaustive PPI screening is one of the computational solutions for this problem. To predict all-to-all PPI from protein structures, we have been studying the protein docking approach based on the physico-chemical properties and shape complementarity. To realize these procedures that require huge number of protein dockings, we have developed high-speed protein-protein docking software "MEGADOCK" that reduces calculation time needed for docking by using several techniques that include a novel scoring function called rPSC. MEGADOCK was shown to be capable of exhaustive PPI screening by making docking calculation four times faster than conventional docking software, ZDOCK, while keeping almost the same level of accuracy in docking predictions. Here we describe an architecture and calculation model of MEGADOCK. We yielded F-measure value of 0.415 which substantially higher than previous studies when our PPI prediction system was applied to a general benchmark data. The same prediction system was applied to bacterial chemotaxis pathway, which is a typical basic problem in systems biology, and obtained the same level of accuracy (F-measure value of 0.436) with the benchmark dataset. We also found some interactions such as CheY--CheD among "False-Positives" that were worthy of further analysis.

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Other Link： http://id.nii.ac.jp/1001/00070731/

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