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ABSTRACT: Orotidine 5'-monophosphate decarboxylase from Plasmodium falciparum (PfOMPDC) catalyses the final step in the de novo synthesis of uridine 5'-monophosphate (UMP) from orotidine 5'-monophosphate (OMP). A defective PfOMPDC enzyme is lethal to the parasite. Novel in silico screening methods were performed to select 14 inhibitors against PfOMPDC, with a high hit rate of 9%. X-ray structure analysis of PfOMPDC in complex with one of the inhibitors, 4-(2-hydroxy-4-methoxyphenyl)-4-oxobutanoic acid, was carried out to at 2.1 Å resolution. The crystal structure revealed that the inhibitor molecule occupied a part of the active site that overlaps with the phosphate-binding region in the OMP- or UMP-bound complexes. Space occupied by the pyrimidine and ribose rings of OMP or UMP was not occupied by this inhibitor. The carboxyl group of the inhibitor caused a dramatic movement of the L1 and L2 loops that play a role in the recognition of the substrate and product molecules. Combining part of the inhibitor molecule with moieties of the pyrimidine and ribose rings of OMP and UMP represents a suitable avenue for further development of anti-malarial drugs.
Journal of biochemistry 06/2012; 152(2):133-8. · 1.95 Impact Factor
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ABSTRACT: Irradiation makes the difference: The relaxation-rate differences of individual ligand protons (H(A) , H(B) ) between the experiment with and that without (blue) saturation of the protons of the protein target reflect the proximity to the protein surface. Thus the binding portions of ligand molecules could be identified using this "difference of inversion recovery rate with and without target irradiation" (DIRECTION) methodology.
Angewandte Chemie International Edition 12/2011; 51(6):1362-5. · 13.45 Impact Factor
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ABSTRACT: Aldo-keto reductase 1B3 (AKR1B3) catalyzes the NADPH-dependent reduction of prostaglandin H(2) (PGH(2)), which is a common intermediate of various prostanoids, to form PGF(2α). AKR1B3 also reduces PGH(2) to PGD(2) in the absence of NADPH. AKR1B3 produced in Escherichia coli was crystallized in complex with NADPH by the sitting-drop vapour-diffusion method. The crystal was tetragonal, belonging to space group P4(1)2(1)2 or P4(3)2(1)2, with unit-cell parameters a = b = 107.62, c = 120.76 Å. X-ray diffraction data were collected to 2.4 Å resolution at 100 K using a synchrotron-radiation source.
Acta Crystallographica Section F Structural Biology and Crystallization Communications 12/2011; 67(Pt 12):1630-2. · 0.51 Impact Factor
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ABSTRACT: We developed a new protein-ligand docking calculation method using experimental NMR data. Recently, we proposed a novel ligand epitope-mapping experiment, which utilizes the difference between the longitudinal relaxation rates of ligand protons with and without irradiation of target protein protons (DIRECTION epitope-mapping experiment; Y. Mizukoshi, et al., An accurate pharmacophore mapping method by NMR, submitted for publication). Although the epitope-mapping experiment is simple and rapid, the result should reflect the proximity of ligand protons to the target protein surface. However, it cannot directly provide the protein-ligand complex structure without any other structural information. While the accuracy of protein-ligand docking software is insufficient, the software can provide many candidate complex structures. In many cases, the correct complex structure is included in the set of predicted complex structures and the correct structures could be selected by applying the above experimental result of ligand epitope mapping. In the current study, we combined the protein-ligand docking software with the NMR experimental information so as to improve the prediction of the protein-ligand complex structure. Consequently, the prediction accuracy was improved by 1.3-1.9 times (from ca. 50% to ca. 70%) in a self-docking test for the simulated epitope mapping result. Moreover, this method was applied to actual NMR experiments, and it successfully reconstructed the protein-ligand complex structures.
Journal of molecular graphics & modelling 09/2011; 31:20-7. · 2.17 Impact Factor
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ABSTRACT: We developed a new protocol for in silico drug screening for G-protein-coupled receptors (GPCRs) using a set of "universal active probes" (UAPs) with an ensemble docking procedure. UAPs are drug-like compounds, which are actual active compounds of a variety of known proteins. The current targets were nine human GPCRs whose three-dimensional (3D) structures are unknown, plus three GPCRs, namely β(2)-adrenergic receptor (ADRB2), A(2A) adenosine receptor (A(2A)), and dopamine D3 receptor (D(3)), whose 3D structures are known. Homology-based models of the GPCRs were constructed based on the crystal structures with careful sequence inspection. After subsequent molecular dynamics (MD) simulation taking into account the explicit lipid membrane molecules with periodic boundary conditions, we obtained multiple model structures of the GPCRs. For each target structure, docking-screening calculations were carried out via the ensemble docking procedure, using both true active compounds of the target proteins and the UAPs with the multiple target screening (MTS) method. Consequently, the multiple model structures showed various screening results with both poor and high hit ratios, the latter of which could be identified as promising for use in in silico screening to find candidate compounds to interact with the proteins. We found that the hit ratio of true active compounds showed a positive correlation to that of the UAPs. Thus, we could retrieve appropriate target structures from the GPCR models by applying the UAPs, even if no active compound is known for the GPCRs. Namely, the screening result that showed a high hit ratio for the UAPs could be used to identify actual hit compounds for the target GPCRs.
Journal of Chemical Information and Modeling 08/2011; 51(9):2398-407. · 4.68 Impact Factor
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Yuya Kodama,
Michael L Reese,
Nobuhisa Shimba,
Katsuki Ono,
Eiji Kanamori,
Volker Dötsch,
Shuji Noguchi, Yoshifumi Fukunishi,
Ei-Ichiro Suzuki,
Ichio Shimada,
Hideo Takahashi
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ABSTRACT: Protein-protein interactions are necessary for various cellular processes, and therefore, information related to protein-protein interactions and structural information of complexes is invaluable. To identify protein-protein interfaces using NMR, resonance assignments are generally necessary to analyze the data; however, they are time consuming to collect, especially for large proteins. In this paper, we present a rapid, effective, and unbiased approach for the identification of a protein-protein interface without resonance assignments. This approach requires only a single set of 2D titration experiments of a single protein sample, labeled with a unique combination of an (15)N-labeled amino acid and several amino acids (13)C-labeled on specific atoms. To rapidly obtain high resolution data, we applied a new pulse sequence for time-shared NMR measurements that allowed simultaneous detection of a ω(1)-TROSY-type backbone (1)H-(15)N and aromatic (1)H-(13)C shift correlations together with single quantum methyl (1)H-(13)C shift correlations. We developed a structure-based computational approach, that uses our experimental data to search the protein surfaces in an unbiased manner to identify the residues involved in the protein-protein interface. Finally, we demonstrated that the obtained information of the molecular interface could be directly leveraged to support protein-protein docking studies. Such rapid construction of a complex model provides valuable information and enables more efficient biochemical characterization of a protein-protein complex, for instance, as the first step in structure-guided drug development.
Journal of Structural Biology 06/2011; 174(3):434-42. · 3.41 Impact Factor
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ABSTRACT: We proposed a new definition of drug-likeness based on protein-compound docking simulation. Active and decoy compounds of 40 target proteins were investigated. These compounds were docked to protein sets consisting of 53-160 proteins. The protein sets did not include the target proteins. The average value and deviation of docking scores against the multiple proteins were calculated for each compound. Our study revealed that the docking scores of active compounds are more widely distributed than those of decoy compounds. Thus, the deviation of docking scores with multiple proteins should be a measure of drug-likeness for compound affinity.
Journal of Chemical Information and Modeling 04/2011; · 4.68 Impact Factor
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ABSTRACT: Aldo-keto reductase 1B1 and 1B3 (AKR1B1 and AKR1B3) are the primary human and mouse prostaglandin F(2α) (PGF(2α)) synthases, respectively, which catalyze the NADPH-dependent reduction of PGH(2), a common intermediate of various prostanoids, to form PGF(2α). In this study, we found that AKR1B1 and AKR1B3, but not AKR1B7 and AKR1C3, also catalyzed the isomerization of PGH(2) to PGD(2) in the absence of NADPH or NADP(+). Both PGD(2) and PGF(2α) synthase activities of AKR1B1 and AKR1B3 completely disappeared in the presence of NADP(+) or after heat treatment of these enzymes at 100 °C for 5 min. The K(m), V(max), pK and optimum pH values of the PGD(2) synthase activities of AKR1B1 and AKR1B3 were 23 and 18 μM, 151 and 57 nmol·min(-1)·(mg protein)(-1), 7.9 and 7.6, and pH 8.5 for both AKRs, respectively, and those of PGF(2α) synthase activity were 29 and 33 μM, 169 and 240 nmol·min(-1)·(mg protein)(-1), 6.2 and 5.4, and pH 5.5 and pH 5.0, respectively, in the presence of 0.5 mm NADPH. Site-directed mutagenesis of the catalytic tetrad of AKR1B1, composed of Tyr, Lys, His and Asp, revealed that the triad of Asp43, Lys77 and His110, but not Tyr48, acts as a proton donor in most AKR activities, and is crucial for PGD(2) and PGF(2α) synthase activities. These results, together with molecular docking simulation of PGH(2) to the crystallographic structure of AKR1B1, indicate that His110 acts as a base in concert with Asp43 and Lys77 and as an acid to generate PGD(2) and PGF(2α) in the absence of NADPH or NADP(+) and in the presence of NADPH, respectively.
FEBS Journal 02/2011; 278(8):1288-98. · 3.79 Impact Factor
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ABSTRACT: A new approach to predicting the ligand-binding sites of proteins was developed, using protein-ligand docking computation. In this method, many compounds in a random library are docked onto the whole protein surface. We assumed that the true ligand-binding site would exhibit stronger affinity to the compounds in the random library than the other sites, even if the random library did not include the ligand corresponding to the true binding site. We also assumed that the affinity of the true ligand-binding site would be correlated to the docking scores of the compounds in the random library, if the ligand-binding site was correctly predicted. We call this method the molecular-docking binding-site finding (MolSite) method. The MolSite method was applied to 89 known protein-ligand complex structures extracted from the Protein Data Bank, and it predicted the correct binding sites with about 80-99% accuracy, when only the single top-ranked site was adopted. In addition, the average docking score was weakly correlated to the experimental protein-ligand binding free energy, with a correlation coefficient of 0.44.
Protein Science 11/2010; 20(1):95-106. · 2.80 Impact Factor
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ABSTRACT: Amino acid selective cross-saturation (ASCS) method not only provides information about the interface of a protein assembly by the spin relaxation experiment, but also identifies the amino acid residues in the acceptor protein, which are located close to the selectively labeled amino acid residues in the donor protein. Here, a new method was developed to build a precise structural model of a protein assembly, which satisfies the experimental ASCS values, using simulated annealing computation. This method was applied to the ubiquitin-yeast ubiquitin hydrolase 1 (Ub-YUH1) complex to build a precise complex structure compatible with that determined by X-ray crystallography.
Proteins Structure Function and Bioinformatics 09/2010; 79(1):179-90. · 3.39 Impact Factor
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ABSTRACT: We developed a new method that uses a set of drug-like compounds to select reliable in silico drug screening results. If some active compounds are known, the screening results that rank these active compounds at the top should be reliable. If no active compound is known, how to select the result is in question. We propose a concept of a set of "universal active probes" (UAPs), which is a set of small active compounds that bind to different kinds of proteins. We found that the hit ratio of the true active compounds in in silico screening shows positive correlation to that of the UAPs, probably because UAPs form a set of drug-like compounds. Thus, if the UAPs were added to the compound library, the screening result that shows a high hit ratio of the UAPs could give reliable actual hit compounds for the target protein. We examined this method for several targets and found this idea useful.
Journal of Chemical Information and Modeling 07/2010; 50(7):1233-40. · 4.68 Impact Factor
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Yoshifumi Fukunishi
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ABSTRACT: Importance of the field: Structure-based in silico drug screening is now widely used in drug development projects. Structure-based in silico drug screening is generally performed using a protein-compound docking program and docking scoring function. Many docking programs have been developed over the last 2 decades, but their prediction accuracy remains insufficient. Areas covered in this review: This review highlights the recent progress of the post-processing of protein-compound complexes after docking. What the reader will gain: These methods utilize ensembles of docking poses of compounds to improve the prediction accuracy for the ligand-docking pose and screening results. While the individual docking poses are not reliable, the free energy surface or the most probable docking pose can be estimated from the ensemble of docking poses. Take home message: The protein-compound docking program provides an arbitral rather than a canonical ensemble of docking poses. When the ensemble of docking poses satisfies the canonical ensemble, we can discuss how these post-docking analysis methods work and fail. Thus, improvements to the docking software will be needed in order to generate well-defined ensembles of docking poses.
Expert Opinion on Drug Metabolism & Toxicology 07/2010; 6(7):835-49. · 3.12 Impact Factor
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ABSTRACT: Chemical compound libraries are the basic database for virtual (in silico) drug screening, and the number of entries has reached 20 million. Many drug-like compound libraries for virtual drug screening have been developed and released. In this review, the process of constructing a database for virtual screening is reviewed, and several popular databases are introduced. Several kinds of focused libraries have been developed. The author has developed databases for metalloproteases, and the details of the libraries are described. The library for metalloproteases was developed by improving the generation of the dominant-ion forms. For instance, the SH group is treated as S- in this library while all SH groups are protonated in the conventional libraries. In addition, metal complexes were examined as new candidates of drug-like compounds. Finally, a method for generating chemical space is introduced, and the diversity of compound libraries is discussed.
Current Computer - Aided Drug Design 04/2010; · 1.76 Impact Factor
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Yoshifumi Fukunishi
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ABSTRACT: For fragment-based drug development, both hit (active) compound prediction and docking-pose (protein-ligand complex structure) prediction of the hit compound are important, since chemical modification (fragment linking, fragment evolution) subsequent to the hit discovery must be performed based on the protein-ligand complex structure. However, the naïve protein-compound docking calculation shows poor accuracy in terms of docking-pose prediction. Thus, post-processing of the protein-compound docking is necessary. Recently, several methods for the post-processing of protein-compound docking have been proposed. In FBDD, the compounds are smaller than those for conventional drug screening. This makes it difficult to perform the protein-compound docking calculation. A method to avoid this problem has been reported. Protein-ligand binding free energy estimation is useful to reduce the procedures involved in the chemical modification of the hit fragment. Several prediction methods have been proposed for high-accuracy estimation of protein-ligand binding free energy. This paper summarizes the various computational methods proposed for docking-pose prediction and their usefulness in FBDD.
Current topics in medicinal chemistry 03/2010; 10(6):680-94. · 4.47 Impact Factor
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ABSTRACT: We compared the protein-ligand binding free energies (G) obtained by the explicit water model, the MM-GB/SA (molecular-mechanics generalized Born surface area) model, and the docking scoring function. The free energies by the explicit water model and the MM-GB/SA model were calculated by the previously developed Smooth Reaction Path Generation (SRPG) method. In the SRPG method, a smooth reaction path was generated by linking two coordinates, one a bound state and the other an unbound state. The free energy surface along the path was calculated by a molecular dynamics (MD) simulation, and the binding free energy was estimated from the free energy surface. We applied these methods to the streptavidin-and-biotin system. The G value by the explicit water model was close to the experimental value. The G value by the MM-GB/SA model was overestimated and that by the scoring function was underestimated. The free energy surface by the explicit water model was close to that by the GB/SA model around the bound state (distances of < 6 A), but the discrepancy appears at distances of > 6 A. Thus, the difference in long-range Coulomb interaction should cause the error in G. The scoring function cannot take into account the entropy change of the protein. Thus, the error of G could depend on the target protein.
Genome informatics. International Conference on Genome Informatics 10/2009; 23(1):85-97.
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ABSTRACT: We developed a new molecular dynamics simulation method for protein-ligand binding free energy calculation in an explicit water model. This method consists of three steps: (1) generation of a compound dissociation path starting from a stable protein-compound complex structure, (2) calculation of the free energy surface along the dissociation path, and (3) calculation of the free energy surface of a small area around the protein-compound complex structure, which is a free energy minimum. The protein-compound binding free energy is estimated from the information obtained by the above three steps. This method was applied to a small system, a 18-crown-6 ether with its ligand ion, and a realistic system consisting of a target protein with its inhibitor. This approximation worked well; the protein-inhibitor dissociation was successfully performed, and the binding free energies were calculated.
Journal of Chemical Information and Modeling 08/2009; 49(8):1944-51. · 4.68 Impact Factor
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Yoshifumi Fukunishi
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ABSTRACT: The initial stage of drug development is the hit (active) compound search from a pool of millions of compounds; for this process, in silico (virtual) screening has been successfully applied. One of the problems of in silico screening, however, is the low hit ratio in relation to the high computational cost and the long CPU time. This problem becomes serious in structure-based in silico screening. The major reason is the low accuracy of the estimation of protein-compound binding free energy. The problem of ligand-based in silico screening is that the conventional quantitative structure-activity relationship (QSAR) approach is not effective at predicting new hit compounds with new scaffolds. Recently, machine-learning approaches have been applied to in silico drug screening to overcome the above problems. We review here machine-learning approaches for both structure-based and ligand-based drug screening. Machine learning is used to improve database enrichment in two ways, namely by improving the docking score calculated by the protein-compound docking program and by calculating the optimal distance between the feature vectors of active and inactive compounds. Both approaches require compounds that are known to be active with respect to the target protein. In structure-based screening, the former approach is mainly used with a protein-compound affinity matrix. In ligand-based screening, both the former and latter approaches are used, and the latter approach can be applied to various kinds of descriptors, such as 1D/2D descriptors/fingerprints and the affinity fingerprint given by the protein-compound affinity matrix.
Combinatorial chemistry & high throughput screening 06/2009; 12(4):397-408. · 2.46 Impact Factor
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ABSTRACT: We developed a new in silico screening method, which is a structure-based virtual fragment screening with protein-compound docking. The structure-based in silico screening of small fragments is known to be difficult due to poor surface complementarity between protein surfaces and small compound (fragment) surfaces. In our method, several side chains were attached to the fragment in question to generate a set of replica molecules of different sizes. This chemical modification enabled us to select potentially active fragments more easily than basing the selection on the original form of the fragment. In addition, the Coulombic and hydrogen bonding interactions were ignored in the docking simulation to reduce the variety of chemical modifications. Namely, we focused on the sizes and the shapes of the side chains and could ignore the atomic charges and types of elements. This procedure was validated in the screenings of inhibitors of six target proteins using known active compounds, and the results revealed that our procedure was effective.
Journal of Chemical Information and Modeling 05/2009; 49(4):925-33. · 4.68 Impact Factor
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ABSTRACT: A molecular similarity measure has been developed using molecular topological graphs and atomic partial charges. Two kinds of topological graphs were used. One is the ordinary adjacency matrix and the other is a matrix which represents the minimum path length between two atoms of the molecule. The ordinary adjacency matrix is suitable to compare the local structures of molecules such as functional groups, and the other matrix is suitable to compare the global structures of molecules. The combination of these two matrices gave a similarity measure. This method was applied to in silico drug screening, and the results showed that it was effective as a similarity measure.
Journal of Biomedicine and Biotechnology 01/2009; 2009:231780. · 2.44 Impact Factor
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ABSTRACT: A variety of compounds with different chemical properties directly interact with the cardiac repolarizing K(+) channel encoded by the human ether-a-go-go-related gene (hERG). This causes acquired forms of QT prolongation, which can result in lethal cardiac arrhythmias including torsades de pointes one of the most serious adverse effects of various therapeutic agents. Prediction of this phenomenon will improve the safety of pharmacological therapy and also facilitate the process of drug development. Here we propose a strategy for the development of an in silico system to predict the potency of chemical compounds to block hERG. The system consists of two sequential processes. The first process is a ligand-based prediction to estimate half-maximal concentrations for the block of compounds inhibiting hERG current using the relationship between chemical features and activities of compounds. The second process is a protein-based prediction that comprises homology modeling of hERG, docking simulation of chemical-channel interaction, analysis of the shape of the channel pore cavity, and Brownian dynamics simulation to estimate hERG currents in the presence and absence of chemical blockers. Since each process is a combination of various calculations, the criterion for assessment at each calculation and the strategy to integrate these steps are significant for the construction of the system to predict a chemical's block of hERG current and also to predict the risk of inducing cardiac arrhythmias from the chemical information. The principles and criteria of elemental computations along this strategy are described.
The Journal of Physiological Sciences 12/2008; 58(7):459-70. · 1.61 Impact Factor
