Daisuke Takaya

Fams-ace: a combined method to select the best model after remodeling all server models.

During Critical Assessment of Protein Structure Prediction (CASP7, Pacific Grove, CA, 2006), fams‐ace was entered in the 3D coordinate prediction category as a human expert group. The procedure can be summarized by the following three steps. (1) All the server models were refined and rebuilt utilizing our homology modeling method. (2) Representative structures were selected from each server, according to a model quality evaluation, based on a 3D1D profile score (like Verify3D). (3) The top five models were selected and submitted in the order of the consensus‐based score (like 3D‐Jury). Fams‐ace is a fully automated server and does not require human intervention. In this article, we introduce the methodology of fams‐ace and discuss the successes and failures of this approach during CASP7. In addition, we discuss possible improvements for the next CASP. Proteins 2007.

HCV NS3 protease enhances liver fibrosis via binding to and activating TGF-β type I receptor

Viruses sometimes mimic host proteins and hijack the host cell machinery. Hepatitis C virus (HCV) causes liver fibrosis, a process largely mediated by the overexpression of transforming growth factor (TGF)-β and collagen, although the precise underlying mechanism is unknown. Here, we report that HCV non-structural protein 3 (NS3) protease affects the antigenicity and bioactivity of TGF-β2 in (CAGA)9-Luc CCL64 cells and in human hepatic cell lines via binding to TGF-β type I receptor (TβRI). Tumor necrosis factor (TNF)-α facilitates this mechanism by increasing the colocalization of TβRI with NS3 protease on the surface of HCV-infected cells. An anti-NS3 antibody against computationally predicted binding sites for TβRI blocked the TGF-β mimetic activities of NS3 in vitro and attenuated liver fibrosis in HCV-infected chimeric mice. These data suggest that HCV NS3 protease mimics TGF-β2 and functions, at least in part, via directly binding to and activating TβRI, thereby enhancing liver fibrosis.

A new method for induced fit docking (GENIUS) and its application to virtual screening of novel HCV NS3-4A protease inhibitors.

Hepatitis C virus (HCV) is an etiologic agent of chronic liver disease, and approximately 170 million people worldwide are infected with the virus. HCV NS3-4A serine protease is essential for the replication of this virus, and thus has been investigated as an attractive target for anti-HCV drugs. In this study, we developed our new induced-fit docking program (genius), and applied it to the discovery of a new class of NS3-4A protease inhibitors (IC(50)=1-10 μM including high selectivity index). The new inhibitors thus identified were modified, based on the docking models, and revealed preliminary structure-activity relationships. Moreover, the genius in silico screening performance was validated by using an enrichment factor. We believe our designed scaffold could contribute to the improvement of HCV chemotherapy.

The SKE-DOCK server and human teams based on a combined method of shape complementarity and free energy estimation.

We participated in rounds 6–12 of the critical assessment of predicted interaction (CAPRI) contest as the SKE‐DOCK server and human teams. The SKE‐DOCK server is based on simple geometry docking and a knowledge base scoring function. The procedure is summarized in the following three steps: (1) protein docking according to shape complementarity, (2) evaluating complex models, and (3) repacking side‐chain of models. The SKE‐DOCK server did not make use of biological information. On the other hand, the human team tried various intervention approaches. In this article, we describe in detail the processes of the SKE‐DOCK server, together with results and reasons for success and failure. Good predicted models were obtained for target 25 by both the SKE‐DOCK server and human teams. When the modeled receptor proteins were superimposed on the experimental structures, the smallest Ligand‐rmsd values corresponding to the rmsd between the model and experimental structures were 3.307 and 3.324 Å, respectively. Moreover, the two teams obtained 4 and 2 acceptable models for target 25. The overall result for both the SKE‐DOCK server and human teams was medium accuracy for one (Target 25) out of nine targets. Proteins 2007.

Protein structure prediction in CASP6 using CHIMERA and FAMS

In CASP6, the CHIMERA‐group predicted full‐atom models of all targets using SKE‐CHIMERA, a Web‐user interface system for protein structure prediction that allows human intervention at necessary stages; we used a lot of information from our own data and from publicly available data. Using SKE‐CHIMERA, we iterated manual step (template selection and alignment by the in‐house program CHIMERA) and automatic step (three‐dimensional model building by the in‐house program FAMS). The official CASP6 assessment showed that CHIMERA‐group was one of the most successful predictors in homology modeling, especially for FR/H (Fold Recognition/Homologous). In this article, we introduce the method of CHIMERA‐group and discuss its successes and failures in CASP6. Proteins 2005;Suppl 7:122–127.

Searching for protein–protein interaction sites and docking by the methods of molecular dynamics, grid scoring, and the pairwise interaction potential of amino acid residues

In CAPRI Rounds 1 and 2, we assumed that because there are many ionic charges that weaken electrostatic interaction forces in living cells, the hydrophobic interaction force might be important entropically. As a result of Rounds 1 and 2, the predictions for binding sites and geometric centers were acceptable, but those of the binding axes were poor, because only the largest benzene cluster was used for generating the initial docking structures. These were generated by fitting of benzene clusters formed on the surface of receptor and ligand. In CAPRI Rounds 3–5, the grid‐scoring sum on the protein–protein interaction surface and the pairwise potential of the amino acid residues, which were indicated as coming easily into the protein–protein interaction regions, were used as the calculation methods, along with the smaller benzene clusters that participated in benzene cluster fitting. Good predicted models were obtained for Targets 11 and 12. When the modeled receptor proteins were superimposed on the experimental structures, the smallest ligand root‐mean‐square deviation (RMSD) values corresponding to the RMSD between the model and experimental structures were 6.2 Å and 7.3 Å, respectively. Proteins 2005;60:289–295.

Application of support vector machine to three-dimensional shape-based virtual screening using comprehensive three-dimensional molecular shape overlay with known inhibitors.

In this study, machine learning using support vector machine was combined with three-dimensional (3D) molecular shape overlay, to improve the screening efficiency. Since the 3D molecular shape overlay does not use fingerprints or descriptors to compare two compounds, unlike 2D similarity methods, the application of machine learning to a 3D shape-based method has not been extensively investigated. The 3D similarity profile of a compound is defined as the array of 3D shape similarities with multiple known active compounds of the target protein and is used as the explanatory variable of support vector machine. As the measures of 3D shape similarity for our new prediction models, the prediction performances of the 3D shape similarity metrics implemented in ROCS, such as ShapeTanimoto and ScaledColor, were validated, using the known inhibitors of 15 target proteins derived from the ChEMBL database. The learning models based on the 3D similarity profiles stably outperformed the original ROCS when more than 10 known inhibitors were available as the queries. The results demonstrated the advantages of combining machine learning with the 3D similarity profile to process the 3D shape information of plural active compounds.

FAMS complex: a fully automated homology modeling system for protein complex structures.

The formation of a protein-protein complex is responsible for many biological functions; therefore, three-dimensional structures of protein complexes are essential for deeper understandings of protein functions and the mechanisms of diseases at the atomic level. However, compared with individual proteins, complex structures are difficult to solve experimentally because of technical limitations. Thus a method that can predict protein complex structures would be invaluable. In this study, we developed new software, FAMS Complex; a fully automated homology modeling system for protein complex structures consisting of two or more molecules. FAMS Complex requires only sequences and alignments of the target protein as input and constructs all molecules simultaneously and automatically. FAMS Complex is likely to become an essential tool for structure-based drug design, such as in silico screening to accelerate drug discovery before an experimental structure is solved. Moreover, in this post-genomic era when huge amounts of protein sequence information are available, a major goal is the determination of protein-protein interaction networks on a genomic scale. FAMS Complex will contribute to this goal, because its procedure is fully automated and so is suited for large-scale genome wide modeling.

Characterization of crystal water molecules in a high-affinity inhibitor and hematopoietic prostaglandin D synthase complex by interaction energy studies.

Hematopoietic prostaglandin D synthase (H-PGDS) is one of the two enzymes that catalyze prostaglandin D2 synthesis and a potential therapeutic target of allergic and inflammatory responses. To reveal key molecular interactions between a high-affinity ligand and H-PGDS, we designed and synthesized a potent new inhibitor (KD: 0.14 nM), determined the crystal structure in complex with human H-PGDS, and quantitatively analyzed the ligand-protein interactions by the fragment molecular orbital calculation method. In the cavity, 10 water molecules were identified, and the interaction energy calculation indicated their stable binding to the surface amino acids in the cavity. Among them, 6 water molecules locating from the deep inner cavity to the peripheral part of the cavity contributed directly to the ligand binding by forming hydrogen bonding interactions. Arg12, Gly13, Gln36, Asp96, Trp104, Lys112 and an essential co-factor glutathione also had strong interactions with the ligand. A strong repulsive interaction between Leu199 and the ligand was canceled out by forming a hydrogen bonding network with the adjacent conserved water molecule. Our quantitative studies including crystal water molecules explained that compounds with an elongated backbone structure to fit from the deep inner cavity to the peripheral part of the cavity would have strong affinity to human H-PGDS.

A New Docking and Screening Method Using a New Operator Based upon Bioinformatics

Many disease‐protein targets have been found from biochemical experiments. Druggable compounds which inhibit or activate those significant protein targets must be researched rapidly. Many researches are using in‐sillico screening program such as DOCK, AutoDock and GOLD using classical mechanical potentials. We report the new method using a new operator which uses simulated annealing based on bioinformatics on the protein‐ligand flexible docking.