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Nishi-Tokyo-shi, Japan

Sugaya N.,PharmaDesign Inc.
Database : the journal of biological databases and curation | Year: 2012

Druggable Protein-protein Interaction Assessment System (Dr. PIAS) is a database of druggable protein-protein interactions (PPIs) predicted by our support vector machine (SVM)-based method. Since the first publication of this database, Dr. PIAS has been updated to version 2.0. PPI data have been increased considerably, from 71,500 to 83,324 entries. As the new positive instances in our method, 4 PPIs and 10 tertiary structures have been added. This addition increases the prediction accuracy of our SVM classifier in comparison with the previous classifier, despite the number of added PPIs and structures is small. We have introduced the novel concept of 'similar positives' of druggable PPIs, which will help researchers discover small compounds that can inhibit predicted druggable PPIs. Dr. PIAS will aid the effective search for druggable PPIs from a mine of interactome data being rapidly accumulated. Dr. PIAS 2.0 is available at http://www.drpias.net. Source

Machine learning methods based on ligand-protein interaction data in bioactivity databases are one of the current strategies for efficiently finding novel lead compounds as the first step in the drug discovery process. Although previous machine learning studies have succeeded in predicting novel ligand-protein interactions with high performance, all of the previous studies to date have been heavily dependent on the simple use of raw bioactivity data of ligand potencies measured by IC50, EC50, Ki, and Kd deposited in databases. ChEMBL provides us with a unique opportunity to investigate whether a machine-learning-based classifier created by reflecting ligand efficiency other than the IC50, EC50, Ki, and Kd values can also offer high predictive performance. Here we report that classifiers created from training data based on ligand efficiency show higher performance than those from data based on IC 50 or Ki values. Utilizing GPCRSARfari and KinaseSARfari databases in ChEMBL, we created IC50- or Ki-based training data and binding efficiency index (BEI) based training data then constructed classifiers using support vector machines (SVMs). The SVM classifiers from the BEI-based training data showed slightly higher area under curve (AUC), accuracy, sensitivity, and specificity in the cross-validation tests. Application of the classifiers to the validation data demonstrated that the AUCs and specificities of the BEI-based classifiers dramatically increased in comparison with the IC50- or Ki-based classifiers. The improvement of the predictive power by the BEI-based classifiers can be attributed to (i) the more separated distributions of positives and negatives, (ii) the higher diversity of negatives in the BEI-based training data in a feature space of SVMs, and (iii) a more balanced number of positives and negatives in the BEI-based training data. These results strongly suggest that training data based on ligand efficiency as well as data based on classical IC50, EC50, Kd, and Ki values are important when creating a classifier using a machine learning approach based on bioactivity data. © 2013 American Chemical Society. Source

Takahashi T.,Kobe University | Shibasaki T.,Kobe University | Takahashi H.,Kobe University | Sugawara K.,Kobe University | And 4 more authors.
Science Signaling | Year: 2013

Sulfonylureas are widely used drugs for treating insulin deficiency in patients with type 2 diabetes. Sulfonylureas bind to the regulatory subunit of the pancreatic β cell potassium channel that controls insulin secretion. Sulfonylureas also bind to and activate Epac2A, a member of the Epac family of cyclic adenosine monophosphate (cAMP) -binding proteins that promote insulin secretion through activation of the Ras-like guanosine triphosphatase Rap1. Using molecular docking simulation, we identified amino acid residues in one of two cyclic nucleotide - binding domains, cNBD-A, in Epac2A predicted to mediate the interaction with sulfonylureas. We confirmed the importance of the identified residues by site-directed mutagenesis and analysis of the response of the mutants to sulfonylureas using two assays: changes in fluorescence resonance energy transfer (FRET) of an Epac2A-FRET biosensor and direct sulfonylurea-binding experiments. These residues were also required for the sulfonylureadependent Rap1 activation by Epac2A. Binding of sulfonylureas to Epac2A depended on the concentration of cAMP and the structures of the drugs. Sulfonylureas and cAMP cooperatively activated Epac2A through binding to cNBD-A and cNBD-B, respectively. Our data suggest that sulfonylureas stabilize Epac2A in its open, active state and provide insight for the development of drugs that target Epac2A. Source

Yoshikawa Y.,PharmaDesign Inc. | Kobayashi K.,Kyoto University | Oishi S.,Kyoto University | Fujii N.,Kyoto University | And 2 more authors.
Bioorganic and Medicinal Chemistry Letters | Year: 2012

CXCR4 is a G-protein coupled receptor that is associated with many diseases such as breast cancer metastasis, HIV infection, leukemic disease and rheumatoid arthritis, and is thus considered an attractive drug target. Previously, we identified a cyclic pentapeptide, FC131, that is a potent antagonist for CXCR4. In this study, we constructed a three dimensional model of the CXCR4-FC131 complex. To investigate the backbone flexibility of FC131, we performed molecular dynamics simulations of FC131 based on the NMR structure of FC131, and obtained snapshot structures from the trajectories which were used to model the docking pose of FC131 into CXCR4. Our final model of the CXCR4-FC131 complex is partially different from the X-ray crystal structure of CXCR4-CVX15 and suggests water-mediated interactions. Nevertheless, this docking pose is consistent with the experimental data. We believe our model will aid in the discovery and development of small-molecule antagonists for CXCR4. © 2011 Elsevier Ltd. All rights reserved. Source

Kobayashi K.,Kyoto University | Oishi S.,Kyoto University | Hayashi R.,Kyoto University | Tomita K.,Kyoto University | And 7 more authors.
Journal of Medicinal Chemistry | Year: 2012

A structure-activity relationship study on a highly potent CXC chemokine receptor type 4 (CXCR4) antagonist, FC131 [cyclo(-d-Tyr 1-Arg 2-Arg 3-Nal 4-Gly 5-)], was carried out using a series of alkene isosteres of the d-Tyr 1-l/d-Arg 2 dipeptide to investigate the binding mode of FC131 and its derivatives with CXCR4. The structure-activity relationships of isostere-containing FC131 analogues were similar to those of the parent FC131 and its derivatives, suggesting that a trans-conformer of the d-Tyr 1-Arg 2 peptide bond is the dominant contributor to the bioactive conformations of FC131. Although NMR analysis demonstrated that the two conformations of the peptidomimetic containing the d-Tyr 1-d-Arg 2 isostere are possible, binding-mode prediction indicated that the orientations of the alkene motif within d-Tyr 1-MeArg 2 peptidomimetics depend on the chirality of Arg 2 and the β-methyl group of the isostere unit, which makes the dominant contribution for binding to the receptor. The most potent FC122 [cyclo(-d-Tyr 1-d-MeArg 2-Arg 3-Nal 4-Gly 5-)] bound with CXCR4 by a binding mode different from that of FC131. © 2012 American Chemical Society. Source

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