Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.csbj.2025.01.022

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Accurate prediction of the difference in binding free energy between compounds is crucial for reducing the high costs associated with drug discovery. Relative binding free energy perturbation (RBFEP) calculations are effective for small structural changes; however, large topological changes pose significant challenges for calculations, leading to high errors and difficulties in convergence. To address such issues, we propose a new approach─PairMap─that focuses on introducing appropriate intermediates for complex transformations between two input compounds. PairMap-generated intermediates exhaustively, determined the optimal conversion paths, and introduced thermodynamic cycles into the perturbation map to improve accuracy and reduce computational cost. PairMap succeeded in introducing appropriate intermediates that could not be discovered by existing simple approaches by comprehensively considering intermediates. Furthermore, we evaluated the accuracy of the prediction of binding free energy using 9 compounds selected from Wang et al.'s benchmark set, which included particularly complex transformations. The perturbation map generated by PairMap achieved excellent accuracy with a mean absolute error of 0.93 kcal/mol compared to 1.70 kcal/mol when using the perturbation map generated by the conventional Flare FEP intermediate introduction method. Moreover, in a scaffold hopping experiment conducted with the PDE5a target involving complex transformations, PairMap provided more accurate free energy predictions than ABFEP calculations, yielding more reliable results compared to experimental data. Additionally, PairMap can be utilized to introduce intermediates into congeneric series, demonstrating that complex links on the perturbation map can be resolved with minimal addition of intermediates and links. In conclusion, PairMap overcomes the limitations of existing methods by enabling RBFEP calculations for more complex transformations, further streamlining lead optimization in drug discovery.

DOI： 10.1021/acs.jcim.4c01634

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Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s11227-024-06860-w

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Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s11227-024-06861-9

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Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.48550/arXiv.2409.10964

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

DOI： 10.1109/BIBM62325.2024.10822009

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Other Link： https://dblp.uni-trier.de/db/conf/bibm/bibm2024.html#TsutaokaKNLO24

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

DOI： 10.1109/BIBM62325.2024.10822666

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Other Link： https://dblp.uni-trier.de/db/conf/bibm/bibm2024.html#NishinoKTLO24

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

DOI： 10.1145/3673038.3673097

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Other Link： https://dblp.uni-trier.de/rec/conf/icpp/2024

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s11227-024-06007-x

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Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s11227-024-06006-y

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Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s11227-023-05887-9

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Other Link： https://dblp.uni-trier.de/db/journals/tjs/tjs80.html#UekiO24

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

Graph Neural Networks (GNNs) excel in compound property and activity prediction, but the choice of molecular graph representations significantly influences model learning and interpretation. While atom-level molecular graphs resemble natural topology, they overlook key substructures or functional groups and their interpretation partially aligns with chemical intuition. Recent research suggests alternative representations using reduced molecular graphs to integrate higher-level chemical information and leverages both representations for model. However, there is a lack of studies about applicability and impact of different molecular graphs on model learning and interpretation. Here, we introduce MMGX (Multiple Molecular Graph eXplainable discovery), investigating the effects of multiple molecular graphs, including Atom, Pharmacophore, JunctionTree, and FunctionalGroup, on model learning and interpretation with various perspectives. Our findings indicate that multiple graphs relatively improve model performance, but in varying degrees depending on datasets. Interpretation from multiple graphs in different views provides more comprehensive features and potential substructures consistent with background knowledge. These results help to understand model decisions and offer valuable insights for subsequent tasks. The concept of multiple molecular graph representations and diverse interpretation perspectives has broad applicability across tasks, architectures, and explanation techniques, enhancing model learning and interpretation for relevant applications in drug discovery.

DOI： 10.1038/s42004-024-01155-w

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Rapid advancements in protein sequencing technology have resulted in gaps between proteins with identified sequences and those with mapped structures. Although sequence-based predictions offer insights, they can be incomplete due to the absence of structural details. Conversely, structure-based methods face challenges with respect to newly sequenced proteins. The AlphaFold Multimer has remarkable accuracy in predicting the structure of protein complexes. However, it cannot distinguish whether the input protein sequences can interact. Nonetheless, by analyzing the information in the models predicted by the AlphaFold Multimer, we propose a highly accurate method for predicting protein interactions. This study focuses on the use of deep neural networks, specifically to analyze protein complex structures predicted by the AlphaFold Multimer. By transforming atomic coordinates and utilizing sophisticated image-processing techniques, vital 3D structural details were extracted from protein complexes. Recognizing the significance of evaluating residue distances in protein interactions, this study leveraged image recognition approaches by integrating Densely Connected Convolutional Networks (DenseNet) and Deep Residual Network (ResNet) within 3D convolutional networks for protein 3D structure analysis. When benchmarked against leading protein-protein interaction prediction methods, such as SpeedPPI, D-script, DeepTrio, and PEPPI, our proposed method, named SpatialPPI, exhibited notable efficacy, emphasizing the promising role of 3D spatial processing in advancing the realm of structural biology. The SpatialPPI code is available at: https://github.com/ohuelab/SpatialPPI.

DOI： 10.1016/j.csbj.2024.03.009

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

Abstract

The structural diversity of chemical libraries, which are systematic collections of compounds that have potential to bind to biomolecules, can be represented by chemical latent space. A chemical latent space is a projection of a compound structure into a mathematical space based on several molecular features, and it can express structural diversity within a compound library in order to explore a broader chemical space and generate novel compound structures for drug candidates. In this study, we developed a deep-learning method, called NP-VAE (Natural Product-oriented Variational Autoencoder), based on variational autoencoder for managing hard-to-analyze datasets from DrugBank and large molecular structures such as natural compounds with chirality, an essential factor in the 3D complexity of compounds. NP-VAE was successful in constructing the chemical latent space from large-sized compounds that were unable to be handled in existing methods, achieving higher reconstruction accuracy, and demonstrating stable performance as a generative model across various indices. Furthermore, by exploring the acquired latent space, we succeeded in comprehensively analyzing a compound library containing natural compounds and generating novel compound structures with optimized functions.

DOI： 10.1038/s42004-023-01054-6

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Other Link： https://www.nature.com/articles/s42004-023-01054-6

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MOTIVATION: Since the advent of ColabFold, numerous software packages have been provided with Google Colaboratory-compatible ipynb files, allowing users to effortlessly test and reproduce results without the need for local installation or configuration. MEGADOCK, a protein-protein docking tool, is particularly well-suited for Google Colaboratory due to its lightweight computations and GPU acceleration capabilities. To increase accessibility and promote widespread use, it is crucial to provide a computing environment compatible with Google Colaboratory. RESULTS: In this study, we report the development of a Google Colaboratory environment for running our protein-protein docking software, MEGADOCK. We provide a comprehensive ipynb file, including the compilation of MEGADOCK with the FFTW library installation on Colaboratory, the introduction of related tools using PyPI/apt, and the execution and visualization of docking structures. This streamlined environment enables users to visualize docking structures with just one click. The code is available under a CC-BY NC 4.0 license from https://github.com/ohuelab/MEGADOCK-on-Colab .

DOI： 10.1186/s13104-023-06505-w

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Authorship：Last author,　Corresponding author   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/CIBCB56990.2023.10264879

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Other Link： https://dblp.uni-trier.de/db/conf/cibcb/cibcb2023.html#KengkannaO23

Publishing type：Research paper (scientific journal)  

DOI： 10.3390/ijms241713257

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Protein-protein interactions (PPIs) are associated with various diseases; hence, they are important targets in drug discovery. However, the physicochemical empirical properties of PPI-targeted drugs are distinct from those of conventional small molecule oral pharmaceuticals, which adhere to the "rule of five (RO5)". Therefore, developing PPI-targeted drugs using conventional methods, such as molecular generation models, is challenging. In this study, we propose a molecular generation model based on deep reinforcement learning that is specialized for the production of PPI inhibitors. By introducing a scoring function that can represent the properties of PPI inhibitors, we successfully generated potential PPI inhibitor compounds. These newly constructed virtual compounds possess the desired properties for PPI inhibitors, and they show similarity to commercially available PPI libraries. The virtual compounds are freely available as a virtual library.

DOI： 10.3390/molecules28155652

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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

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

DOI： 10.1109/CSCE60160.2023.00349

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DOI： 10.3389/fmolb.2023.1190855

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DOI： 10.1109/CSCE60160.2023.00350

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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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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

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

New protein-protein interactions (PPIs) are identified, but PPIs have different physicochemical properties compared with conventional targets, making it difficult to use small molecules. Peptides offer a new modality to target PPIs, but designing appropriate peptide sequences by computation is challenging. Recently, AlphaFold and RoseTTAFold have made it possible to predict protein structures from amino acid sequences with ultra-high accuracy, enabling de novo protein design. We designed peptides likely to have PPI as the target protein using the "binder hallucination" protocol of AfDesign, a de novo protein design method using AlphaFold. However, the solubility of the peptides tended to be low. Therefore, we designed a solubility loss function using solubility indices for amino acids and developed a solubility-aware AfDesign binder hallucination protocol. The peptide solubility in sequences designed using the new protocol increased with the weight of the solubility loss function; moreover, they captured the characteristics of the solubility indices. Moreover, the new protocol sequences tended to have higher affinity than random or single residue substitution sequences when evaluated by docking binding affinity. Our approach shows that it is possible to design peptide sequences that can bind to the interface of PPI while controlling solubility.

DOI： 10.3390/biomedicines10071626

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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

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Language：English  

DOI： 10.3389/fmolb.2022.875859

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Authorship：Last author,　Corresponding author   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/CIBCB55180.2022.9863032

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Other Link： https://dblp.uni-trier.de/db/conf/cibcb/cibcb2022.html#FuruiO22

Publishing type：Part of collection (book)   Publisher：Springer International Publishing  

DOI： 10.1007/978-3-030-69984-0_53

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Drug-likeness quantification is useful for screening drug candidates. Quantitative estimates of drug-likeness (QED) are commonly used to assess quantitative drug efficacy but are not suitable for screening compounds targeting protein-protein interactions (PPIs), which have recently gained attention. Therefore, we developed a quantitative estimate index for compounds targeting PPIs (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs that models physicochemical properties based on the information available for drugs/compounds, specifically those reported to act on PPIs. FDA-approved drugs and compounds in iPPI-DB, which comprise PPI inhibitors and stabilizers, were evaluated using QEPPI. The results showed that QEPPI is more suitable than QED for early screening of PPI-targeting compounds. QEPPI was also considered an extended concept of the "Rule-of-Four" (RO4), a PPI inhibitor index. We evaluated the discriminatory performance of QEPPI and RO4 for datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. The F-scores of RO4 and QEPPI were 0.451 and 0.501, respectively. QEPPI showed better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it can be used as an initial filter to efficiently screen PPI-targeting compounds.

DOI： 10.3390/ijms222010925

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DOI： 10.3390/genes12091455

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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

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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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Other Link： https://dblp.uni-trier.de/db/journals/corr/corr2105.html#abs-2105-03617

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Cyclic peptides, with unique structural features, have emerged as new candidates for drug discovery; their association with human serum albumin (HSA; long blood half-life) is crucial to improve drug delivery and avoid renal clearance. Here, we present the crystal structure of HSA complexed with dalbavancin, a clinically used cyclic peptide. Small-angle X-ray scattering and isothermal titration calorimetry experiments showed that the HSA-dalbavancin complex exists in a monomeric state; dalbavancin is only bound to the subdomain IA of HSA in solution. Structural analysis and MD simulation revealed that the swing of Phe70 and movement of the helix near dalbavancin were necessary for binding. The flip of Leu251 promoted the formation of the binding pocket with an induced-fit mechanism; moreover, the movement of the loop region including Glu60 increased the number of noncovalent interactions with HSA. These findings may support the development of new cyclic peptides for clinical use, particularly the elucidation of their binding mechanism to HSA.

DOI： 10.1021/acs.jmedchem.0c01578

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DOI： 10.14529/js200102

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

DOI： 10.1007/978-3-030-48842-0_2

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

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Scoring is a challenging step in protein-protein docking, where typically thousands of solutions are generated. In this study, we ought to investigate the contribution of consensus-rescoring, as introduced by Oliva et al. (2013) with the CONSRANK method, where the set of solutions is used to build statistics in order to identify recurrent solutions. We explore several ways to perform consensus-based rescoring on the ZDOCK decoy set for Benchmark 4. We show that the information of the interface size is critical for successful rescoring in this context, but that consensus rescoring in itself performs less well than traditional physics-based evaluation. The results of physics-based and consensus-based rescoring are partially overlapping, supporting the use of a combination of these approaches.

DOI： 10.3389/fmolb.2020.559005

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DOI： 10.2142/biophysico.BSJ-2019050

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<title>Abstract</title>Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using <italic>in silico</italic> methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.

DOI： 10.1038/s41598-019-55069-y

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Other Link： http://www.nature.com/articles/s41598-019-55069-y

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DOI： 10.1145/3365610.3368409

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DOI： 10.1016/j.jmgm.2019.07.009

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

DOI： 10.1109/BIBE.2019.00035

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DOI： 10.1016/j.bbrep.2019.01.008

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

DOI： 10.1021/acs.orglett.8b03500

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Druglikeness is a useful concept for screening drug candidate compounds. We developed QEX, which is a new druglikeness index specific to individual targets. QEX is an improvement of the quantitative estimate of druglikeness (QED) method, which is a popular quantitative evaluation method of druglikeness proposed by Bickerton et al. QEX models the physicochemical properties of compounds that act on each target protein based on the concept of QED modeling physicochemical properties from information on US Food and Drug Administration-approved drugs. The result of the evaluation of PubChem assay data revealed that QEX showed better performance than the original QED did (the area under the curve value of the receiver operating characteristic curve improved by 0.069-0.236). We also present the c-Src inhibitor filtering results of the QEX constructed using Src family kinase inhibitors as a case study. QEX distinguished the inhibitors and non-inhibitors better than QED did. QEX works efficiently even when datasets of inactive compounds are unavailable. If both active and inactive compounds are present, QEX can be used as an initial filter to enhance the screening ability of conventional ligand-based virtual screenings.

DOI： 10.1007/s11030-018-9842-3

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Other Link： https://dblp.uni-trier.de/db/journals/corr/corr1907.html#abs-1907-01103

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DOI： 10.1186/s12859-018-2529-z

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

DOI： 10.1007/s10015-017-0416-8

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

DOI： 10.1016/j.compbiolchem.2018.03.013

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：BioMed Central Ltd.  

DOI： 10.1186/s12859-018-2073-x

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

DOI： 10.1016/j.compbiolchem.2018.02.008

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier Inc.  

DOI： 10.1016/j.jmgm.2017.11.011

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Cellular transitions and differentiation processes require mRNAs supporting the new phenotype but also the clearance of existing mRNAs for the parental phenotype. Cellular reprogramming from fibroblasts to induced pluripotent stem cells (iPSCs) occurs at the early stage of mesenchymal epithelial transition (MET) and involves drastic morphological changes. We examined the molecular mechanism for MET, focusing on RNA metabolism. DDX6, an RNA helicase, was indispensable for iPSC formation, in addition to RO60 and RNY1, a non-coding RNA, which form complexes involved in intracellular nucleotide sensing. RO60/RNY1/DDX6 complexes formed prior to processing body formation, which is central to RNA metabolism. The abrogation of DDX6 expression inhibited iPSC generation, which was mediated by RNA decay targeting parental mRNAs supporting mesenchymal phenotypes, along with microRNAs, such as miR-302b-3p. These results show that parental mRNA clearance is a prerequisite for cellular reprogramming and that DDX6 plays a central role in this process.

DOI： 10.1371/journal.pone.0203708

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DOI： 10.1093/bioinformatics/btx178

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DOI： 10.1007/10_2016_41

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

DOI： 10.1109/ICCABS.2017.8114299

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Language：English   Publishing type：Part of collection (book)   Publisher：Humana Press Inc.  

DOI： 10.1007/978-1-4939-7015-5_2

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer Verlag  

DOI： 10.1007/978-3-319-63312-1_48

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Analysis of protein-protein interaction networks has revealed the presence of proteins with multiple interaction ligand proteins, such as hub proteins. For such proteins, multiple ligands would be predicted as interacting partners when predicting all-to-all protein-protein interactions (PPIs). In this work, to obtain a better understanding of PPI mechanisms, we focused on protein interaction surfaces, which differ between protein pairs. We then performed rigid-body docking to obtain information of interfaces of a set of decoy structures, which include many possible interaction surfaces between a certain protein pair. Then, we investigated the specificity of sets of decoy interactions between true binding partners in each case of alpha-chymotrypsin, actin, and cyclin-dependent kinase 2 as test proteins having multiple true binding partners. To observe differences in interaction surfaces of docking decoys, we introduced broad interaction profiles (BIPs), generated by assembling interaction profiles of decoys for each protein pair. After cluster analysis, the specificity of BIPs of true binding partners was observed for each receptor. We used two types of BIPs: those involved in amino acid sequences (BIP-seqs) and those involved in the compositions of interacting amino acid residue pairs (BIP-AAs). The specificity of a BIP was defined as the number of group members including all true binding partners. We found that BIP-AA cases were more specific than BIP-seq cases. These results indicated that the composition of interacting amino acid residue pairs was sufficient for determining the properties of protein interaction surfaces.

DOI： 10.2142/biophysico.13.0_105

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DOI： 10.1186/1752-0509-9-S1-S6

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DOI： 10.1093/bioinformatics/btu532

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

DOI： 10.2142/biophys.54.S240_1

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：BioMed Central Ltd.  

DOI： 10.1186/1753-6561-7-S7-S6

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DOI： 10.1186/1751-0473-8-18

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DOI： 10.1371/journal.pone.0069365

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DOI： 10.1145/2506583.2506693

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DOI： 10.1145/2506583.2506669

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DOI： 10.1145/2506583.2506670

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DOI： 10.1007/978-3-642-34123-6_16

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DOI： 10.1016/j.jmb.2011.09.031

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Elucidating protein-RNA interactions (PRIs) is important for understanding many 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 78×78 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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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

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Language：English   Publishing type：Research paper, summary (international conference)  

DOI： 10.1016/j.bpj.2018.11.3026

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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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DOI： 10.1016/j.bpj.2015.11.1759

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DOI： 10.1016/j.bpj.2014.11.2579

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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：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：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：English   Publishing type：Research paper, summary (international conference)  

DOI： 10.1016/j.bpj.2013.11.2310

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

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  

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

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

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Other Link： http://id.nii.ac.jp/1001/00069426/

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Other Link： http://id.nii.ac.jp/1001/00068042/

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Other Link： http://id.nii.ac.jp/1001/00062131/

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Language：English   Presentation type：Oral presentation (general)  

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Applicant：国立大学法人東京工業大学

Application no：特願2021-023750  Date applied：2021.2

Announcement no：特開2022-078924  Date announced：2022.5

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Applicant：アヘッド・バイオコンピューティング株式会社

Application no：特願2021-023750  Date applied：2021.2

Announcement no：特開2022-078924  Date announced：2022.5

Patent/Registration no：特許第7125575号  Date registered：2022.8 

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