Journal of Chemical Information and Modeling

Protein Surface Conservation in Binding Sites.

Publication Date: 2008 May 14 PMID: 18476685
Authors: Carl, N. - Konc, J. - Janezic, D.
Journal: J Chem Inf Model

An algorithm is described which uses the conservation of the 3D structure of protein surfaces, as opposed to their sequences, to detect protein-protein binding sites. The protein in which protein-protein binding sites are sought is compared with structures of multiple structurally related proteins and the surface that is conserved at least once is considered to be a part of the binding site. The binding site predictions obtained in this way for a set of protein-protein complexes correspond well with the actual protein-protein binding sites. A comparison of this method with an algorithm using the support vector machine approach for predicting protein-protein binding sites shows structural conservation to be an important characteristic that distinguishes binding sites from the remainder of protein surfaces.

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Combinatorial QSAR Modeling of Specificity and Subtype Selectivity of Ligands Binding to Serotonin Receptors 5HT1E and 5HT1F.

Publication Date: 2008 May 10 PMID: 18470978
Authors: Wang, X. S. - Tang, H. - Golbraikh, A. - Tropsha, A.
Journal: J Chem Inf Model

The Quantitative Structure-Activity Relationship (QSAR) approach has been applied to model binding affinity and receptor subtype selectivity of human 5HT1E and 5HT1F receptor-ligands. The experimental data were obtained from the PDSP Ki Database. Several descriptor types and data-mining approaches have been used in the context of combinatorial QSAR modeling. Data mining approaches included k Nearest Neighbor, Automated Lazy Learning (ALL), and PLS; descriptor types included MolConnZ, MOE, DRAGON, Frequent Subgraphs (FSG), and Molecular Hologram Fingerprints (MHFs). Highly predictive QSAR models were generated for all three data sets (i.e., for ligands of both receptor subtypes and for subtype selectivity), and different individual techniques were proved best in each case. For real value activity data available for 5HT1E and 5HT1F ligand binding, models were characterized by leave-one-out cross-validated R (2) ( q (2)) for the training sets and predictive R (2) values for the test sets. The best models for 5HT1E ligands were obtained with the kNN approach combined with MolConnZ descriptors ( q (2) = 0.69, R (2) = 0.92); for 5HT1F ligands ALL QSAR method using MolConnZ descriptors gave the best results ( R (2) = 0.92). Rigorously validated classification models were also developed for the 5HT1E/5HT1F subtype selectivity data set with high correct classification accuracy for both training (CCR train= 0.88) and test (CCR test = 1.00) sets using kNN with MolConnZ descriptors. The external predictive power of QSAR models was further validated by virtual screening of The Scripps Research Institute (TSRI) screening library to recover 5HT1E agonists and antagonists (not present in the original PDSP data set) with high enrichment factors. The successful development of externally predictive and interpretative QSAR models affords further design and discovery of novel subtype specific GPCR agents.

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Scores of Extended Connectivity Fingerprint as Descriptors in QSPR Study of Melting Point and Aqueous Solubility.

Publication Date: 2008 May 9 PMID: 18465850
Authors: Zhou, D. - Alelyunas, Y. - Liu, R.
Journal: J Chem Inf Model

QSPR studies, using scores of SciTegic's extended connectivity fingerprint as raw descriptors, were extended to the prediction of melting points and aqueous solubility of organic compounds. Robust partial least-squares models were developed that perform as well as the best published QSPR models for structurally diverse organic compounds. Satisfactory performance of the QSPR models indicates that the scores of extended connectivity fingerprint are high performance molecular descriptors for QSAR/QSPR studies. Performance of the fingerprint-based descriptors is further validated by the satisfactory prediction of aqueous solubility of nearly 1300 organic compounds (squared correlation coefficient of 0.83 and RMSE of 0.85 log unit) with Yalkowsky's general solubility equation using both calculated melting points and calculated octanol-water partition coefficients. It demonstrates for the first time that it is feasible to predict aqueous solubility of structurally diverse organic compounds with the general solubility equation using both the calculated melting points and the partition coefficients.

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Investigation of MM-PBSA Rescoring of Docking Poses.

Publication Date: 2008 May 9 PMID: 18465849
Authors: Thompson, D. C. - Humblet, C. - Joseph-McCarthy, D.
Journal: J Chem Inf Model

Target-based virtual screening is increasingly used to generate leads for targets for which high quality three-dimensional (3D) structures are available. To allow large molecular databases to be screened rapidly, a tiered scoring scheme is often employed whereby a simple scoring function is used as a fast filter of the entire database and a more rigorous and time-consuming scoring function is used to rescore the top hits to produce the final list of ranked compounds. Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) approaches are currently thought to be quite effective at incorporating implicit solvation into the estimation of ligand binding free energies. In this paper, the ability of a high-throughput MM-PBSA rescoring function to discriminate between correct and incorrect docking poses is investigated in detail. Various initial scoring functions are used to generate docked poses for a subset of the CCDC/Astex test set and to dock one set of actives/inactives from the DUD data set. The effectiveness of each of these initial scoring functions is discussed. Overall, the ability of the MM-PBSA rescoring function to (i) regenerate the set of X-ray complexes when docking the bound conformation of the ligand, (ii) regenerate the X-ray complexes when docking conformationally expanded databases for each ligand which include "conformation decoys" of the ligand, and (iii) enrich known actives in a virtual screen for the mineralocorticoid receptor in the presence of "ligand decoys" is assessed. While a pharmacophore-based molecular docking approach, PhDock, is used to carry out the docking, the results are expected to be general to use with any docking method.

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Scaffold Hopping in Drug Discovery Using Inductive Logic Programming.

Publication Date: 2008 May 6 PMID: 18457387
Authors: Tsunoyama, K. - Amini, A. - Sternberg, M. J. - Muggleton, S. H.
Journal: J Chem Inf Model

In chemoinformatics, searching for compounds which are structurally diverse and share a biological activity is called scaffold hopping. Scaffold hopping is important since it can be used to obtain alternative structures when the compound under development has unexpected side-effects. Pharmaceutical companies use scaffold hopping when they wish to circumvent prior patents for targets of interest. We propose a new method for scaffold hopping using inductive logic programming (ILP). ILP uses the observed spatial relationships between pharmacophore types in pretested active and inactive compounds and learns human-readable rules describing the diverse structures of active compounds. The ILP-based scaffold hopping method is compared to two previous algorithms (chemically advanced template search, CATS, and CATS3D) on 10 data sets with diverse scaffolds. The comparison shows that the ILP-based method is significantly better than random selection while the other two algorithms are not. In addition, the ILP-based method retrieves new active scaffolds which were not found by CATS and CATS3D. The results show that the ILP-based method is at least as good as the other methods in this study. ILP produces human-readable rules, which makes it possible to identify the three-dimensional features that lead to scaffold hopping. A minor variant of a rule learnt by ILP for scaffold hopping was subsequently found to cover an inhibitor identified by an independent study. This provides a successful result in a blind trial of the effectiveness of ILP to generate rules for scaffold hopping. We conclude that ILP provides a valuable new approach for scaffold hopping.

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Receptor-Based Modeling and 3D-QSAR for a Quantitative Production of the Butyrylcholinesterase Inhibitors Based on Genetic Algorithm.

Publication Date: 2008 Apr 29 PMID: 18444627
Authors: Zaheer-Ul-Haq - Uddin, R. - Yuan, H. - Petukhov, P. A. - Choudhary, M. I. - Madura, J. D.
Journal: J Chem Inf Model

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been constructed using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) for a series of structurally related steroidal alkaloids as butyrylcholinesterase (BuChE) inhibitors. Docking studies were employed to position the inhibitors into the BuChE active site to determine the most probable binding mode. The strategy was to explore multiple inhibitor conformations in producing a more reliable 3D-QSAR model. These multiple conformations were derived using the FlexS program. The conformation selection step for CoMFA was done by genetic algorithm. The genetic algorithm based CoMFA approach was found to be the best. Both CoMFA and CoMSIA yielded significant cross-validated q (2) values of 0.701 and 0.627 and the r (2) values of 0.979 and 0.982, respectively. These statistically significant models were validated by a test set of five compounds. Comparison of CoMFA and CoMSIA contour maps helped to identify structural requirements for the inhibitors and serves as a basis for the design of the next generation of the inhibitor analogues. The results demonstrate that the combination of ligand-based and receptor-based modeling with use of a genetic algorithm is a powerful approach to build 3D-QSAR models. These data can be used for the lead optimization process with respect to inhibition enhancement which is important for the drug discovery and development for Alzheimer's disease.

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Homology Model-Based Virtual Screening for GPCR Ligands Using Docking and Target-Biased Scoring.

Publication Date: 2008 Apr 26 PMID: 18442221
Authors: Radestock, S. - Weil, T. - Renner, S.
Journal: J Chem Inf Model

The current study investigates the combination of two recently reported techniques for the improvement of homology model-based virtual screening for G-protein coupled receptor (GPCR) ligands. First, ligand-supported homology modeling was used to generate receptor models that were in agreement with mutagenesis data and structure-activity relationship information of the ligands. Second, interaction patterns from known ligands to the receptor were applied for scoring and rank ordering compounds from a virtual library using ligand-receptor interaction fingerprint-based similarity (IFS). Our approach was evaluated in retrospective virtual screening experiments for antagonists of the metabotropic glutamate receptor (mGluR) subtype 5. The results of our approach were compared to the results obtained by conventional scoring functions (Dock-Score, PMF-Score, Gold-Score, ChemScore, and FlexX-Score). The IFS lead to significantly higher enrichment rates, relative to the competing scoring functions. Though using a target-biased scoring approach, the results were not biased toward the chemical classes of the reference structures. Our results indicate that the presented approach has the potential to serve as a general setup for successful structure-based GPCR virtual screening.

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Predictivity of QSAR.

Publication Date: 2008 Apr 22 PMID: 18426198
Authors: Benigni, R. - Bossa, C.
Journal: J Chem Inf Model

A range of good quality, local QSARs for mutagenicity and carcinogenicity have been assessed and challenged for their predictivity in respect to real external test sets (i.e., chemicals never considered by the authors while developing their models). The QSARs for potency (applicable only to toxic chemicals) generated predictions 30-70% correct, whereas the QSARs for discriminating between active and inactive chemicals were 70-100% correct in their external predictions: thus the latter can be used with good reliability for applicative purposes. On the other hand internal, statistical validation methods, which are often assumed to be good diagnostics for predictivity, did not correlate well with the predictivity of the QSARs when challenged in external prediction tests. Nonlocal models for noncongeneric chemicals were considered as well, pointing to the critical role of an adequate definition of the applicability domain.

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Bootstrap-Based Consensus Scoring Method for Protein-Ligand Docking.

Publication Date: 2008 Apr 22 PMID: 18426197
Authors: Fukunishi, H. - Teramoto, R. - Takada, T. - Shimada, J.
Journal: J Chem Inf Model

To improve the performance of a single scoring function used in a protein-ligand docking program, we developed a bootstrap-based consensus scoring (BBCS) method, which is based on ensemble learning. BBCS combines multiple scorings, each of which has the same function form but different energy-parameter sets. These multiple energy-parameter sets are generated in two steps: (1) generation of training sets by a bootstrap method and (2) optimization of energy-parameter set by a Z-score approach, which is based on energy landscape theory as used in protein folding, against each training set. In this study, we applied BBCS to the FlexX scoring function. Using given 50 complexes, we generated 100 training sets and obtained 100 optimized energy-parameter sets. These parameter sets were tested against 48 complexes different from the training sets. BBCS was shown to be an improvement over single scoring when using a parameter set optimized by the same Z-score approach. Comparing BBCS with the original FlexX scoring function, we found that (1) the success rate of recognizing the crystal structure at the top relative to decoys increased from 33.3% to 52.1% and that (2) the rank of the crystal structure improved for 54.2% of the complexes and worsened for none. We also found that BBCS performed better than conventional consensus scoring (CS).

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MM-GB/SA Rescoring of Docking Poses in Structure-Based Lead Optimization.

Publication Date: 2008 Apr 19 PMID: 18422307
Authors: Guimaraes, C. R. - Cardozo, M.
Journal: J Chem Inf Model

The critical issues in docking include the prediction of the correct binding pose and the accurate estimation of the corresponding binding affinity. Different docking methodologies have all been successful in reproducing the crystallographic binding modes but struggle when predicting the corresponding binding affinities. The aim of this work is to evaluate the performance of the MM-GB/SA rescoring of docking poses in structure-based lead optimization. To accomplish that, a diverse set of pharmaceutically relevant targets, including CDK2, FactorXa, Thrombin, and HIV-RT were selected. The correlation between the MM-GB/SA results and experimental data in all cases is remarkable. It even qualifies this approach as a more attractive alternative for rank-ordering than the Free Energy Perturbation and Thermodynamic Integration methodologies because, while as accurate, it can handle more structurally dissimilar ligands and provides results at a fraction of the computational cost. On the technical side, the benefit of performing a conformational analysis and having an ensemble of conformers to represent each ligand in the unbound state during the MM-GB/SA rescoring procedure was investigated. In addition, the estimation of conformational entropy penalties for the ligands upon binding, computed from the Boltzmann distribution in water, was evaluated and compared to a commonly used approach employed by many docking scoring functions.

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Probing the Structures of Leishmanial Farnesyl Pyrophosphate Synthases: Homology Modeling and Docking Studies.

Publication Date: 2008 Apr 18 PMID: 18419114
Authors: Mukherjee, P. - Desai, P. V. - Srivastava, A. - Tekwani, B. L. - Avery, M. A.
Journal: J Chem Inf Model

Leishmania donovani and Leishmania major farnesyl pyrophosphate synthase ( LdFPPS and LmFPPS) are potential targets for the development of antileishmanial therapy. The protein sequence for LdFPPS was recently elucidated in our laboratory. Highly refined homology models were generated using the protein sequences of LdFPPS and the closely related LmFPPS enzyme. A ligand-refined model of LmFPPS with a bound bisphosphonate ligand was generated using restraint-guided molecular mechanics followed by quantum mechanics/molecular mechanics refinement. The ligand-refined model of LmFPPS was further validated through extensive pose validation, enrichment, and other docking studies involving known bisphosphonate inhibitors. The model was able to explain the critical binding site interactions and site-directed mutagenesis data obtained from experimental studies on related FPPS enzymes. The ligand-refined model in conjunction with the validated docking protocol could be utilized in the future for structure-based virtual screening and rational drug design studies against these targets.

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Application of Belief Theory to Similarity Data Fusion for Use in Analog Searching and Lead Hopping.

Publication Date: 2008 Apr 17 PMID: 18416545
Authors: Muchmore, S. W. - Debe, D. A. - Metz, J. T. - Brown, S. P. - Martin, Y. C. - Hajduk, P. J.
Journal: J Chem Inf Model

A wide variety of computational algorithms have been developed that strive to capture the chemical similarity between two compounds for use in virtual screening and lead discovery. One limitation of such approaches is that, while a returned similarity value reflects the perceived degree of relatedness between any two compounds, there is no direct correlation between this value and the expectation or confidence that any two molecules will in fact be equally active. A lack of a common framework for interpretation of similarity measures also confounds the reliable fusion of information from different algorithms. Here, we present a probabilistic framework for interpreting similarity measures that directly correlates the similarity value to a quantitative expectation that two molecules will in fact be equipotent. The approach is based on extensive benchmarking of 10 different similarity methods (MACCS keys, Daylight fingerprints, maximum common subgraphs, rapid overlay of chemical structures (ROCS) shape similarity, and six connectivity-based fingerprints) against a database of more than 150 000 compounds with activity data against 23 protein targets. Given this unified and probabilistic framework for interpreting chemical similarity, principles derived from decision theory can then be applied to combine the evidence from different similarity measures in such a way that both capitalizes on the strengths of the individual approaches and maintains a quantitative estimate of the likelihood that any two molecules will exhibit similar biological activity.

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Functional Drift of Sequence Attributes in the FK506-Binding Proteins (FKBPs).

Publication Date: 2008 Apr 16 PMID: 18412331
Authors: Galat, A.
Journal: J Chem Inf Model

Diverse members of the FK506-binding proteins (FKBPs) group and their complexes with different macrocyclic ligands of fungal origins such as FK506, rapamycin, ascomycin, and their immunosuppressive and nonimmunosuppressive derivatives display a variety of cellular and biological activities. The functional relatedness of the FKBPs was estimated from the following attributes of their aligned sequences: 1 degrees conservation of the consensus sequence; 2 degrees sequence similarity; 3 degrees pI; 4 degrees hydrophobicity; 5 degrees amino acid hydrophobicity and bulkiness profiles. Analyses of the multiple sequence alignments and intramolecular interaction networks calculated from a series of structures of the FKBPs revealed some variations in the interaction clusters formed by the AA residues that are crucial for sustaining peptidylprolyl cis/trans isomerases (PPIases) activity and binding capacity of the FKBPs. Fine diversification of the sequences of the multiple paralogues and orthologues of the FKBPs encoded in different genomes alter the intramolecular interaction patterns of their structures and allowed them to gain some selectivity in binding to diverse targets (functional drift).

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Regioselectivity Prediction of CYP1A2-Mediated Phase I Metabolism.

Publication Date: 2008 Apr 16 PMID: 18412330
Authors: Jung, J. - Kim, N. D. - Kim, S. Y. - Choi, I. - Cho, K. H. - Oh, W. S. - Kim, D. N. - No, K. T.
Journal: J Chem Inf Model

A kinetic, reactivity-binding model has been proposed to predict the regioselectivity of substrates meditated by the CYP1A2 enzyme, which is responsible for the metabolism of planar-conjugated compounds such as caffeine. This model consists of a docking simulation for binding energy and a semiempirical molecular orbital calculation for activation energy. Possible binding modes of CYP1A2 substrates were first examined using automated docking based on the crystal structure of CYP1A2, and binding energy was calculated. Then, activation energies for CYP1A2-mediated metabolism reactions were calculated using the semiempirical molecular orbital calculation, AM1. Finally, the metabolic probability obtained from two energy terms, binding and activation energies, was used for predicting the most probable metabolic site. This model predicted 8 out of 12 substrates accurately as the primary preferred site among all possible metabolic sites, and the other four substrates were predicted into the secondary preferred site. This method can be applied for qualitative prediction of drug metabolism mediated by CYP1A2 and other CYP450 family enzymes, helping to develop drugs efficiently.

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Technique for Generating Three-Dimensional Alignments of Multiple Ligands from One-Dimensional Alignments.

Publication Date: 2008 Apr 16 PMID: 18412329
Authors: Anghelescu, A. V. - Delisle, R. K. - Lowrie, J. F. - Klon, A. E. - Xie, X. - Diller, D. J.
Journal: J Chem Inf Model

We describe and demonstrate a method for the simultaneous, fully flexible alignment of multiple molecules with a common biological activity. The key aspect of the algorithm is that the alignment problem is first solved in a lower dimensional space, in this case using the one-dimensional representations of the molecules. The three-dimensional alignment is then guided by constraints derived from the one-dimensional alignment. We demonstrate using 10 hERG channel blockers, with a total of 72 rotatable bonds, that the one-dimensional alignment is able to effectively isolate key conserved pharmacophoric features and that these conserved features can effectively guide the three-dimensional alignment. Further using 10 estrogen receptor agonists and 5 estrogen receptor antagonists with publicly available cocrystal structures we show that the method is able to produce superpositions comparable to those derived from crystal structures. Finally, we demonstrate, using examples from peptidic CXCR3 agonists, that the method is able to generate reasonable binding hypotheses.

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Ranking Targets in Structure-Based Virtual Screening of Three-Dimensional Protein Libraries: Methods and Problems.

Publication Date: 2008 Apr 16 PMID: 18412328
Authors: Kellenberger, E. - Foata, N. - Rognan, D.
Journal: J Chem Inf Model

Structure-based virtual screening is a promising tool to identify putative targets for a specific ligand. Instead of docking multiple ligands into a single protein cavity, a single ligand is docked in a collection of binding sites. In inverse screening, hits are in fact targets which have been prioritized within the pool of best ranked proteins. The target rate depends on specificity and promiscuity in protein-ligand interactions and, to a considerable extent, on the effectiveness of the scoring function, which still is the Achilles' heel of molecular docking. In the present retrospective study, virtual screening of the sc-PDB target library by GOLD docking was carried out for four compounds (biotin, 4-hydroxy-tamoxifen, 6-hydroxy-1,6-dihydropurine ribonucleoside, and methotrexate) of known sc-PDB targets and, several ranking protocols based on GOLD fitness score and topological molecular interaction fingerprint (IFP) comparison were evaluated. For the four investigated ligands, the fusion of GOLD fitness and two IFP scores allowed the recovery of most targets, including the rare proteins which are not readily suitable for statistical analysis, while significantly filtering out most false positive entries. The current survey suggests that selecting a small number of targets (<20) for experimental evaluation is achievable with a pure structure-based approach.

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Novel Approach to Structure-Based Pharmacophore Search Using Computational Geometry and Shape Matching Techniques.

Publication Date: 2008 Apr 28 PMID: 18396858
Authors: Ebalunode, J. O. - Ouyang, Z. - Liang, J. - Zheng, W.
Journal: J Chem Inf Model

Computationally efficient structure-based virtual screening methods have recently been reported that seek to find effective means to utilize experimental structure information without employing detailed molecular docking calculations. These tools can be coupled with efficient experimental screening technologies to improve the probability of identifying hits and leads for drug discovery research. Commercial software ROCS (rapid overlay of chemical structures) from Open Eye Scientific is such an example, which is a shape-based virtual screening method using the 3D structure of a ligand, typically from a bound X-ray costructure, as the query. We report here the development of a new structure-based pharmacophore search method (called Shape4) for virtual screening. This method adopts a variant of the ROCS shape technology and expands its use to work with an empty crystal structure. It employs a rigorous computational geometry method and a deterministic geometric casting algorithm to derive the negative image (i.e., pseudoligand) of a target binding site. Once the negative image (or pseudoligand) is generated, an efficient shape comparison algorithm in the commercial OE SHAPE Toolkit is adopted to compare and match small organic molecules with the shape of the pseudoligand. We report the detailed computational protocol and its computational validation using known biologically active compounds extracted from the WOMBAT database. Models derived for five selected targets were used to perform the virtual screening experiments to obtain the enrichment data for various virtual screening methods. It was found that our approach afforded similar or better enrichment ratios than other related methods, often with better diversity among the top ranking computational hits.

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Structure-Based Discovery of Novel Non-nucleosidic DNA Alkyltransferase Inhibitors: Virtual Screening and in Vitro and in Vivo Activities.

Publication Date: 2008 Apr 28 PMID: 18351730
Authors: Ruiz, F. M. - Gil-Redondo, R. - Morreale, A. - Ortiz, A. R. - Fabrega, C. - Bravo, J.
Journal: J Chem Inf Model

The human DNA-repair O (6)-alkylguanine DNA alkyltransferase (MGMT or hAGT) protein protects DNA from environmental alkylating agents and also plays an important role in tumor resistance to chemotherapy treatment. Available inhibitors, based on pseudosubstrate analogs, have been shown to induce substantial bone marrow toxicity in vivo. These deficiencies and the important role of MGMT as a resistance mechanism in the treatment of some tumors with dismal prognosis like glioblastoma multiforme, the most common and lethal primary malignant brain tumor, are increasing the attention toward the development of improved MGMT inhibitors. Here, we report the identification for the first time of novel non-nucleosidic MGMT inhibitors by using docking and virtual screening techniques. The discovered compounds are shown to be active in both in vitro and in vivo cellular assays, with activities in the low to medium micromolar range. The chemical structures of these new compounds can be classified into two families according to their chemical architecture. The first family corresponds to quinolinone derivatives, while the second is formed by alkylphenyl-triazolo-pyrimidine derivatives. The predicted inhibitor protein interactions suggest that the inhibitor binding mode mimics the complex between the excised, flipped out damaged base and MGMT. This study opens the door to the development of a new generation of MGMT inhibitors.

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Virtual Screening System for Finding Structurally Diverse Hits by Active Learning.

Publication Date: 2008 Apr 28 PMID: 18351729
Authors: Fujiwara, Y. - Yamashita, Y. - Osoda, T. - Asogawa, M. - Fukushima, C. - Asao, M. - Shimadzu, H. - Nakao, K. - Shimizu, R.
Journal: J Chem Inf Model

Two virtual screening strategies, "query by bagging" (QBag) and "query by bagging with descriptor-sampling" (QBagDS), based on active learning were devised. The QBag strategy generates multiple structure-activity relationship rules by bagging and selects compounds to improve the rules. To find many structurally diverse hits, the QBagDS strategy generates rules by bagging with descriptor sampling. They can also use prior knowledge about hits to improve the efficiency at the beginning of screening. We performed simulation experiments and clustering analysis for several G-protein coupled receptors and showed that the QBag and QBagDS strategies outperform the conventional similarity-based strategy and that using both descriptor sampling and prior knowledge are effective for finding many hits. We applied the bagging with descriptor sampling strategy to novel hit finding, and 4 of the 10 selected compounds showed high inhibition.

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Effect of Cobratoxin Binding on the Normal Mode Vibration within Acetylcholine Binding Protein.

Publication Date: 2008 Apr 28 PMID: 18348519
Authors: Bertaccini, E. J. - Lindahl, E. - Sixma, T. - Trudell, J. R.
Journal: J Chem Inf Model

Recent crystal structures of the acetylcholine binding protein (AChBP) have revealed surprisingly small structural alterations upon ligand binding. Here we investigate the extent to which ligand binding may affect receptor dynamics. AChBP is a homologue of the extracellular component of ligand-gated ion channels (LGICs). We have previously used an elastic network normal-mode analysis to propose a gating mechanism for the LGICs and to suggest the effects of various ligands on such motions. However, the difficulties with elastic network methods lie in their inability to account for the modest effects of a small ligand or mutation on ion channel motion. Here, we report the successful application of an elastic network normal mode technique to measure the effects of large ligand binding on receptor dynamics. The present calculations demonstrate a clear alteration in the native symmetric motions of a protein due to the presence of large protein cobratoxin ligands. In particular, normal-mode analysis revealed that cobratoxin binding to this protein significantly dampened the axially symmetric motion of the AChBP that may be associated with channel gating in the full nAChR. The results suggest that alterations in receptor dynamics could be a general feature of ligand binding.

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Enhancement of Ordinal CoMFA by Ridge Logistic Partial Least Squares.

Publication Date: 2008 Apr 28 PMID: 18338844
Authors: Ohgaru, T. - Shimizu, R. - Okamoto, K. - Kawashita, N. - Kawase, M. - Shirakuni, Y. - Nishikiori, R. - Takagi, T.
Journal: J Chem Inf Model

Conventional comparative molecular field analysis (CoMFA) requires at least 3 orders of experimental data, such as IC 50 and K i, to obtain a good model, although practically there are many screening assays where biological activity is measured only by rating scale. To improve three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis, we developed in this study a modified ordinal classification-oriented CoMFA using partial-least-squares generalized linear regression and ridge estimation. The modified Logistic CoMFA was validated using a corticosteroid binding globulin receptor binding data set, a benchmark for 3D-QSAR, and an acetylcholine esterase inhibitor data set. Our results show that modification of Logistic CoMFA enhanced both prediction accuracy and 3D graphical analysis. In addition, the 3D graphical analysis of the modified Logistic CoMFA was much improved. This improvement resulted in more accurate information on the binding mode between proteins and ligands than in the case of conventional CoMFA.

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Quantifying the Relationships among Drug Classes.

Publication Date: 2008 Apr 28 PMID: 18335977
Authors: Hert, J. - Keiser, M. J. - Irwin, J. J. - Oprea, T. I. - Shoichet, B. K.
Journal: J Chem Inf Model

The similarity of drug targets is typically measured using sequence or structural information. Here, we consider chemo-centric approaches that measure target similarity on the basis of their ligands, asking how chemoinformatics similarities differ from those derived bioinformatically, how stable the ligand networks are to changes in chemoinformatics metrics, and which network is the most reliable for prediction of pharmacology. We calculated the similarities between hundreds of drug targets and their ligands and mapped the relationship between them in a formal network. Bioinformatics networks were based on the BLAST similarity between sequences, while chemoinformatics networks were based on the ligand-set similarities calculated with either the Similarity Ensemble Approach (SEA) or a method derived from Bayesian statistics. By multiple criteria, bioinformatics and chemoinformatics networks differed substantially, and only occasionally did a high sequence similarity correspond to a high ligand-set similarity. In contrast, the chemoinformatics networks were stable to the method used to calculate the ligand-set similarities and to the chemical representation of the ligands. Also, the chemoinformatics networks were more natural and more organized, by network theory, than their bioinformatics counterparts: ligand-based networks were found to be small-world and broad-scale.

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A Probabilistic Approach to Classifying Metabolic Stability.

Publication Date: 2008 Apr 28 PMID: 18327900
Authors: Schwaighofer, A. - Schroeter, T. - Mika, S. - Hansen, K. - Ter Laak, A. - Lienau, P. - Reichel, A. - Heinrich, N. - Muller, K. R.
Journal: J Chem Inf Model

Metabolic stability is an important property of drug molecules that shouldoptimallybe taken into account early on in the drug design process. Along with numerous medium- or high-throughput assays being implemented in early drug discovery, a prediction tool for this property could be of high value. However, metabolic stability is inherently difficult to predict, and no commercial tools are available for this purpose. In this work, we present a machine learning approach to predicting metabolic stability that is tailored to compounds from the drug development process at Bayer Schering Pharma. For four different in vitro assays, we develop Bayesian classification models to predict the probability of a compound being metabolically stable. The chosen approach implicitly takes the "domain of applicability" into account. The developed models were validated on recent project data at Bayer Schering Pharma, showing that the predictions are highly accurate and the domain of applicability is estimated correctly. Furthermore, we evaluate the modeling method on a set of publicly available data.

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Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds.

Publication Date: 2008 Apr 28 PMID: 18318473
Authors: Geppert, H. - Horvath, T. - Gartner, T. - Wrobel, S. - Bajorath, J.
Journal: J Chem Inf Model

Similarity searching using molecular fingerprints is computationally efficient and a surprisingly effective virtual screening tool. In this study, we have compared ranking methods for similarity searching using multiple active reference molecules. Different 2D fingerprints were used as search tools and also as descriptors for a support vector machine (SVM) algorithm. In systematic database search calculations, a SVM-based ranking scheme consistently outperformed nearest neighbor and centroid approaches, regardless of the fingerprints that were tested, even if only very small training sets were used for SVM learning. The superiority of SVM-based ranking over conventional fingerprint methods is ascribed to the fact that SVM makes use of information about database molecules, in addition to known active compounds, during the learning phase.

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Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis.

Publication Date: 2008 Apr 28 PMID: 18311912
Authors: Zhu, H. - Tropsha, A. - Fourches, D. - Varnek, A. - Papa, E. - Gramatica, P. - Oberg, T. - Dao, P. - Cherkasov, A. - Tetko, I. V.
Journal: J Chem Inf Model

Selecting most rigorous quantitative structure-activity relationship (QSAR) approaches is of great importance in the development of robust and predictive models of chemical toxicity. To address this issue in a systematic way, we have formed an international virtual collaboratory consisting of six independent groups with shared interests in computational chemical toxicology. We have compiled an aqueous toxicity data set containing 983 unique compounds tested in the same laboratory over a decade against Tetrahymena pyriformis. A modeling set including 644 compounds was selected randomly from the original set and distributed to all groups that used their own QSAR tools for model development. The remaining 339 compounds in the original set (external set I) as well as 110 additional compounds (external set II) published recently by the same laboratory (after this computational study was already in progress) were used as two independent validation sets to assess the external predictive power of individual models. In total, our virtual collaboratory has developed 15 different types of QSAR models of aquatic toxicity for the training set. The internal prediction accuracy for the modeling set ranged from 0.76 to 0.93 as measured by the leave-one-out cross-validation correlation coefficient ( Q abs (2)). The prediction accuracy for the external validation sets I and II ranged from 0.71 to 0.85 (linear regression coefficient R absI (2)) and from 0.38 to 0.83 (linear regression coefficient R absII (2)), respectively. The use of an applicability domain threshold implemented in most models generally improved the external prediction accuracy but at the same time led to a decrease in chemical space coverage. Finally, several consensus models were developed by averaging the predicted aquatic toxicity for every compound using all 15 models, with or without taking into account their respective applicability domains. We find that consensus models afford higher prediction accuracy for the external validation data sets with the highest space coverage as compared to individual constituent models. Our studies prove the power of a collaborative and consensual approach to QSAR model development. The best validated models of aquatic toxicity developed by our collaboratory (both individual and consensus) can be used as reliable computational predictors of aquatic toxicity and are available from any of the participating laboratories.

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Structural Basis for Selective Inhibition of Trypanosomatid Glyceraldehyde-3-Phosphate Dehydrogenase: Molecular Docking and 3D QSAR Studies.

Publication Date: 2008 Apr 28 PMID: 18303835
Authors: Guido, R. V. - Oliva, G. - Montanari, C. A. - Andricopulo, A. D.
Journal: J Chem Inf Model

The glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is as an attractive target for the development of novel antitrypanosomatid agents. In the present work, comparative molecular field analysis and comparative molecular similarity index analysis were conducted on a large series of selective inhibitors of trypanosomatid GAPDH. Four statistically significant models were obtained ( r (2) > 0.90 and q (2) > 0.70), indicating their predictive ability for untested compounds. The models were then used to predict the potency of an external test set, and the predicted values were in good agreement with the experimental results. Molecular modeling studies provided further insight into the structural basis for selective inhibition of trypanosomatid GAPDH.

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