Journal of Chemical Information and Modeling

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Do not hesitate to use Tversky - and other hints for successful active analogue searches with feature count descriptors.

Publication Date: 2013 Jun 3 PMID: 23731338
Authors: Horvath, D. - Marcou, G. - Varnek, A.
Journal: J Chem Inf Model

This study is an exhaustive analysis of the Neighborhood Behavior over a high quality and large data set (ChEMBL target/ligand pairs of known Ki values, for 165 targets with more than 50 associated ligands each). It focuses of Similarity-Based Virtual Screening (SVS) success defined by the Ascertained Optimality Index, as a weighted compromise between purity and retrieval rate of active hits in the neighborhood of an active query. One key issue addressed here is the impact of asymmetric weighing, using Tversky scores, of query vs candidate features (represented as integer-value ISIDA colored fragment/pharmacophore triplet count descriptor vectors). The nearly a (3/4) million independent SVS experiments showed that Tversky scores with a strong bias in favor of query-specific features are, by far, the most successful and the least failure-prone out of a set of nine other dissimilarity scores (including the classical Tanimoto index, which failed to defend its privileged status in practical SVS applications). Tversky performance is not significantly conditioned by tuning of its bias parameter alpha. Both initial "guesses" of alpha=0.9 and alpha=0.7 were more successful than Tanimoto (at its turn, better than Euclid). Tversky was eventually tested in exhaustive similarity searching within the library of 1.6 M commercial + bioactive molecules at http://infochim.u-strasbg.fr/webserv/VSEngine.html, comparing favorably to Tanimoto in terms of "scaffold hopping" propensity. Therefore, it should be used at least as often as, perhaps in parallel to Tanimoto in SVS. Analysis with respect to query subclasses highlighted relationships of query complexity and/or target nature vs SVS success likelihood. SVS using more complex queries are more robust with respect to the choice of their operational premises (descriptors, metric) - but nevertheless best handled by "pro-query" Tversky scores at alpha>0.5. Amongst simpler queries, one may distinguish between "growable" (allowing for active analogs with additional features), and a few "conservative" queries not allowing any growth. These (typically bioactive amine transporter ligands) form the specific application domain of "pro-candidate" biased Tversky scores at alpha<0.5.

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DOLINA - Docking based on a Local Induced-Fit Algorithm: Application toward Small-Molecule Binding to Nuclear Receptors.

Publication Date: 2013 Jun 3 PMID: 23725336
Authors: Smiesko, M.
Journal: J Chem Inf Model

Docking algorithms allowing for ligand and - to various extent - also protein flexibility are nowadays replacing techniques based on rigid protocols. The algorithm implemented in the Dolina software relies on pharmacophore matching for generating potential ligand poses and treats associated local induced-fit changes by combinatorial rearrangement of side-chains lining the binding site. In Dolina, ligand flexibility is not treated internally, instead a pool of low-energy conformers identified in a conformational search is screened for extended binding-pose candidates. Grouping rearranged residues in sterically independent families and side-chain conformer clustering are employed to achieve efficient use of the computational resources along with a good accuracy of the generated poses. Dolina was applied toward docking of small-molecule ligands to three different nuclear receptor ligand binding domains for which in total 18 high-resolution crystal structures were used as reference. The selected nuclear receptors feature a deeply buried ligand binding site where local induced-fit is to be expected, particularly for receptor antagonists. For each receptor, a crystal structure with co-crystallized small steroid ligand (template) was chosen as target system, to which several synthetic ligands of different size were docked. Poses within an RMSD of 2.0 A from the crystal reference pose were generated in 91% of the cases. In 28%, the pose with the lowest RMSD to the reference pose was ranked as the top one and in 76% it was ranked among top five poses. Detailed description of the docking algorithm and observed results are included. Dolina is available free of charge for academic institutions.

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Water Network Perturbation in Ligand Binding: Adenosine A2A Antagonists as a Case Study.

Publication Date: 2013 Jun 1 PMID: 23725291
Authors: Bortolato, A. - Tehan, B. - Bodnarchuk, M. S. - Essex, J. W. - Mason, J. S.
Journal: J Chem Inf Model

Recent efforts in the computational evaluation of the thermodynamic properties of water molecules have resulted in the development of promising new in silico methods to evaluate the role of water in ligand binding. These methods include WaterMap, SZMAP, GRID/CRY probe and Grand Canonical Monte Carlo simulations. They allow the prediction of the position and relative free energy of the water molecule in the protein active site and the analysis of the perturbation of an explicit water network (WNP) as consequence of ligand binding. We have for the first time extended these approaches toward the prediction of kinetics for small molecules and of relative free energy of binding with a focus on the perturbation of the water network and application to large diverse data sets. Our results support a qualitative correlation between the residence time of 12 related triazine adenosine A2A receptor antagonists and the number and position of high energy trapped solvent molecules. From a quantitative view point, we successfully applied these computational techniques as an implicit solvent alternative, in linear combination with a molecular mechanics force field, to predict the relative ligand free energy of binding (WNP-MMSA). The applicability of this linear method, based on the thermodynamics additivity principle, did not extend to 375 diverse A2A receptor antagonists. However, a fast but effective method could be enabled by replacing the linear approach with a machine learning technique using probabilistic classification trees, which classified the binding affinity correctly for 90% of the ligands in the training set and 67% in the test set.

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A chemical genomics approach for GPCR-ligand interaction prediction and extraction of ligand binding determinants.

Publication Date: 2013 May 30 PMID: 23721295
Authors: Shiraishi, A. - Niijima, S. - Brown, J. B. - Nakatsui, M. - Okuno, Y.
Journal: J Chem Inf Model

Chemical genomics researches have revealed that G-protein coupled receptors (GPCRs) interact with a variety of ligands and that a large number of ligands are known to bind GPCRs even with low transmembrane (TM) sequence similarity. It is crucial to extract informative binding region propensities from large quantities of bioactivity data. To address this issue, we propose a machine learning approach that enables identification of both chemical substructures and amino acid properties that are associated with ligand binding, which can be applied to virtual ligand screening on a GPCR-wide scale. We also address the question of how to select plausible negative non-interaction pairs based on a statistical approach in order to develop reliable prediction models for GPCR-ligand interactions. The key interaction sites estimated by our approach can be of great use not only for screening of active compounds, but also for modification of active compounds with the aim of improving activity or selectivity.

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Pragmatic Approaches to Using Computational Methods to Predict Xenobiotic Metabolism.

Publication Date: 2013 May 29 PMID: 23718189
Authors: Piechota, P. - Cronin, M. - Hewitt, M. - Madden, J. C.
Journal: J Chem Inf Model

In this study the performance of a selection of computational models for the prediction of metabolites and / or sites of metabolism was investigated. These included models incorporated in MetaPrint2D-React, the OECD QSAR Toolbox, Meteor and SMARTCyp software. The algorithms were assessed using two datasets; one a homogenous dataset of 28 Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and paracetamol (DS1); the second a diverse dataset of 30 top-selling drugs (DS2). The prediction of metabolites for the diverse dataset (DS2) was better than for the more homogenous DS1 in all cases, indicating that some areas of chemical space may be better represented than others in the data used to develop and train the models. The study also identified compounds for which none of the packages could predict metabolites, again indicating areas of chemical space where more information is needed. Pragmatic approaches to using metabolism prediction software have also been proposed based on the results described herein. These approaches include using cut-off values instead of restrictive reasoning settings in Meteor, to reduce the output with little loss of sensitivity, and for directing metabolite prediction by pre-selection based on likely sites of metabolism.

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Evaluation and Optimization of Virtual Screening Workflows with DEKOIS 2.0 - A Public Library of Challenging Docking Benchmark Sets.

Publication Date: 2013 May 25 PMID: 23705874
Authors: Bauer, M. R. - Ibrahim, T. M. - Vogel, S. M. - Boeckler, F. M.
Journal: J Chem Inf Model

The application of molecular benchmarking sets helps to assess the actual performance of virtual screening (VS) workflows. To improve the efficiency of structure-based VS approaches, the selection and optimization of various parameters can be guided by benchmarking. With the DEKOIS 2.0 library, we aim to further extend and complement the collection of publicly available decoy sets. Based on BindingDB bioactivity, we provide 81 new and structurally diverse benchmark sets for a wide variety of different target classes. To ensure a meaningful selection of ligands, we address several issues that can be found in bioactivity data. We have improved our previously introduced DEKOIS methodology with enhanced physicochemical matching, now including the consideration of molecular charges, as well as a more sophisticated elimination of latent actives in the decoy set (LADS). We evaluate the docking performance of Glide, GOLD and AutoDock Vina with our data sets and highlight existing challenges for VS tools. All DEKOIS 2.0 benchmark sets will be made accessible at http://www.dekois.com.

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Revisiting a receptor-based pharmacophore hypothesis for human A2A adenosine receptor antagonists.

Publication Date: 2013 May 25 PMID: 23705857
Authors: Bacilieri, M. - Ciancetta, A. - Paoletta, S. - Federico, S. - Cosconati, S. - Cacciari, B. - Taliani, S. - Da Settimo, F. - Novellino, E. - Klotz, K. N. - Spalluto, G. - Moro, S.
Journal: J Chem Inf Model

The application of both structure- and ligand-based design approaches represents to date one of the most useful strategies in the discovery of new drug candidates. In the present paper, we investigated how the application of docking-driven conformational analysis can improve the predictive ability of 3D-QSAR statistical models. With the use of the crystallographic structure in complex with the high affinity antagonist ZM 241385 (4-(2-[7-amino-2-(2-furyl)[1,2,4]-triazolo[2,3-a][1,3,5]triazin-5-ylamino]ethyl)p henol) we revisited a general pharmacophore hypothesis for the human A2A adenosine receptor of a set of 751 known antagonists, by applying an integrated ligand- and structure-based approach. Our novel pharmacophore hypothesis has been validated by using an external test set of 29 newly synthesized human adenosine receptors antagonists.

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The Structural Role of Uracil DNA Glycosylase for the Recognition of Uracil in DNA duplexes. Clues from Atomistic Simulations.

Publication Date: 2013 May 25 PMID: 23705837
Authors: Franco, D. - Sgrignani, J. - Bussi, G. - Magistrato, A.
Journal: J Chem Inf Model

In the first stage of the base excision repair pathway the enzyme Uracil DNA Glycosylase (UNG) recognizes and excises uracil (U) from DNA filaments. U repair is believed to occur via a multistep base-flipping process, through which the damaged U base is initially detected and then engulfed into the enzyme active site, where it is cleaved. The subtle recognition mechanism by which UNG discriminates between U and the other similar pyrimidine nucleobases is still matter of active debate. Detailed structural information on the different steps of the base-flipping pathway may provide insights on it. However, to date only two intermediates have been trapped crystallographically thanks to chemical modifications of the target and/or of its complementary base. Here, we performed force-field based molecular dynamics (MD) simulations to explore the structural and dynamical properties of distinct UNG/dsDNA adducts, containing A:U, A:T, G:U or G:C base pairs, at different stages of the base-flipping pathway. Our simulations reveal that if U is present in the DNA sequence a short-lived extra-helical (EH) intermediate exists. This is stabilized by a water-mediated H-bond network, which connects U with His148, a residue pointed out by mutational studies to play a key role for U recognition and catalysis. Moreover, in this EH intermediate, UNG induces a remarkable overall axis bend to DNA. We believe this aspect may facilitate the flipping of U, with respect to other similar nucleobases, in the latter part of the base-extrusion process. In fact, a large DNA bend has been demonstrated to be associated with a lowering of the free energy barrier for base-flipping. A detailed comparison of our results with partially flipped intermediates identified crystallographically or computationally for other base-flipping enzymes allows us to validate our results and to formulate hypothesis on the recognition mechanism of UNG. Our study provides a first ground for a detailed understanding of the UNG repair pathway, which is necessary to devise new pharmaceutical strategies for targeting DNA-related pathologies.

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Effect of halogen substitutions on dUMP to stability of thymidylate synthase/dUMP/mTHF ternary complex using molecular dynamics simulation.

Publication Date: 2013 May 25 PMID: 23705822
Authors: Kaiyawet, N. - Rungrotmongkol, T. - Hannongbua, S.
Journal: J Chem Inf Model

The stability of thymidilate synthase (TS)/dUMP/mTHF complex formation and Michael addition are considered as important steps involved in the inhibition mechanism of a known anticancer drug, 5-FU. Here, the effect of three different halogen substitutions on the C-5 position of dUMP substrate (XdUMPs: FdUMP, CldUMP and BrdUMP) to the TS stability is investigated via molecular dynamics simulation technique in a presence and an absence of mTHF cofactor. The simulated results show that the stability of all systems is substantially increased by mTHF binding to the catalytic pocket. In ternary complex, much more stabilization on the dUMP and XdUMPs through electrostatic interactions including charge-charge and hydrogen bond interactions is in comparison to the mTHF. An additionally unique hydrogen bond between the substituted fluorine of FdUMP and the hydroxyl group of Y94 was observed in both binary and ternary complexes. The distance between the S- atom of C146 and the C6 atom of dUMP of < 4 A in all systems suggests that the Michael addition with a formation of S-C6 covalent bond is possible occurred although the hydrogen atom on C6 of dUMP is substituted by halogen atom. The MM/PBSA binding free energy provides the significant role of the bridging waters around the ligand(s) in increase of binding affinity of dUMP/XdUMP alone or together with mTHF towards TS enzyme by ~10 kcal/mol. The order of averaged binding affinity is of: CldUMP ~ FdUMP > dUMP > BrdUMP in ternary systems suggesting that CldUMP could be a potent candidate for TS inhibition as same as FdUMP, the metabolite form of 5-FU.

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SFCscore: A Random-Forest-Based Scoring Function for Improved Affinity Prediction of Protein-Ligand Complexes.

Publication Date: 2013 May 25 PMID: 23705795
Authors: Zilian, D. - Sotriffer, C. A.
Journal: J Chem Inf Model

A major shortcoming of empirical scoring functions for protein-ligand complexes is the low degree of correlation between predicted and experimental binding affinities, as frequently observed not only for large and diverse datasets, but also for SAR series of particular targets. Improvements can be envisaged by developing new descriptors, by employing larger training sets of higher quality, and by resorting to more sophisticated regression methods. Herein, we describe the use of SFCscore descriptors to develop an improved scoring function by means of a PDBbind training set of 1005 complexes in combination with Random Forest for regression. This provided SFCscoreRF as a new scoring function with significantly improved performance on the PDBbind and CSAR-NRC HiQ benchmarks in comparison to previously developed SFCscore functions. A leave-cluster-out cross validation and the performance in the CSAR 2012 scoring exercise point out remaining limitations, but also directions for further improvement of SFCscoreRF in particular and empirical scoring functions in general.

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X-ray crystallographic structures as a source of ligand alignment in 3D-QSAR.

Publication Date: 2013 May 24 PMID: 23705769
Authors: Urniaz, R. D. - Jozwiak, K.
Journal: J Chem Inf Model

Three-Dimensional Quantitative Structure - Activity Relationships (3D-QSAR) analyses are methods correlating a pharmacological property with mathematical representation of molecular property distribution around three dimensional molecular models for a set of congeners. 3D-QSAR methods are known to be highly sensitive to a ligand conformation and alignment method. The current study collects 32 unique positions of congeneric ligands co-crystallized with the binding domain of AMPA receptor and aligns them using protein coordinates. Thus, it allows for a unique opportunity to consider a ligands' orientation aligned by their mode of binding in a native molecular target. Comparative Molecular Field Analysis (CoMFA) models were generated for this alignment and compared with the results of analogous modeling using standard structure based alignment or obtained in docking simulations of ligands' molecules. In comparison with classically derived models, the model based on x-ray crystallographic studies showed much better performance and statistical significance. Although the 3D-QSAR methods are mainly employed when crystallographic information is limited, the current study underscores the importance that the selection of inappropriate molecular conformations and alignment methods can lead to generation of erroneous models and false conclusions.

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Rapid Scanning Structure-Activity Relationships in Combinatorial Data Sets: Identification of Activity Switches.

Publication Date: 2013 May 24 PMID: 23705689
Authors: Medina-Franco, J. L. - Edwards, B. S. - Pinilla, C. - Appel, J. R. - Giulianotti, M. - Santos, R. - Yongye, A. B. - Sklar, L. A. - Houghten, R. A.
Journal: J Chem Inf Model

We present a general approach to describe the structure-activity relationships (SAR) of combinatorial data sets with activity for two biological endpoints with emphasis on the rapid identification of substitutions that have a large impact on activity and selectivity. The approach uses Dual-Activity Difference (DAD) maps that represent a visual and quantitative analysis of all pairwise comparisons of one, two, or more substitutions around a molecular template. Scanning the SAR of data sets using DAD maps allows the visual and quantitative identification of activity switches defined as specific substitutions that have an opposite effect on the activity of the compounds against two targets. The approach also rapidly identifies single- and double-target R-cliffs, i.e., compounds where a single or double substitution around the central scaffold dramatically modifies the activity for one or two targets, respectively. The approach introduced in this report can be applied to any analogue series with two biological activity endpoints. To illustrate the approach, we discuss the SAR of 106 pyrrolidine bis-diketopiperazines tested against two formylpeptide receptors obtained from positional scanning deconvolution methods of mixture-based libraries.

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Prediction of Cytochrome P450 Xenobiotic Metabolism: Tethered Docking and Reactivity Derived from Ligand Molecular Orbital Analysis.

Publication Date: 2013 May 24 PMID: 23701380
Authors: Tyzack, J. D. - Williamson, M. J. - Torella, R. - Glen, R. C.
Journal: J Chem Inf Model

Metabolism of xenobiotic and endogenous compounds is frequently complex, not completely elucidated, and therefore often ambiguous. The prediction of sites of metabolism (SoM) can be particularly helpful as a first step toward the identification of metabolites, a process especially relevant to drug discovery. This paper describes a reactivity approach for predicting SoM whereby reactivity is derived directly from the ground state ligand molecular orbital analysis, calculated using Density Functional Theory, using a novel implementation of the average local ionization energy. Thus each potential SoM is sampled in the context of the whole ligand, in contrast to other popular approaches where activation energies are calculated for a predefined database of molecular fragments and assigned to matching moieties in a query ligand. In addition, one of the first descriptions of molecular dynamics of cytochrome P450 (CYP) isoforms 3A4, 2D6, and 2C9 in their Compound I state is reported, and, from the representative protein structures obtained, an analysis and evaluation of various docking approaches using GOLD is performed. In particular, a covalent docking approach is described coupled with the modeling of important electrostatic interactions between CYP and ligand using spherical constraints. Combining the docking and reactivity results, obtained using standard functionality from common docking and quantum chemical applications, enables a SoM to be identified in the top 2 predictions for 75%, 80%, and 78% of the data sets for 3A4, 2D6, and 2C9, respectively, results that are accessible and competitive with other recently published prediction tools.

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Modeling phospholipidosis induction: reliability and warnings.

Publication Date: 2013 May 21 PMID: 23692521
Authors: Goracci, L. - Ceccarelli, M. - Bonelli, D. - Cruciani, G.
Journal: J Chem Inf Model

Drug-induced phospholipidosis (PLD) is characterized by accumulation of phospholipids, the inducing drugs and lamellar inclusion bodies in the lysosomes of affected tissues. These side effects must be considered as early as possible during drug discovery and, in fact, numerous in silico models designed to predict PLD have been published. However, the quality of any in silico model cannot be better than the quality of the experimental dataset used to build it. The present paper reports an overview of the difficulties and errors encountered in the generation of databases used for the published PLD models. A new database of 466 compounds was constructed from seven literature sources, containing only publicly available compounds. A comparison of the PLD assignations in selected databases proved useful in revealing some inconsistencies and raised doubts about the previously assigned PLD+ and PLD- classifications for some chemicals. Finally, a Partial Least Squares Discriminant Analysis (PLS-DA) approach was also applied, revealing further anomalies and clearly showing that metabolism, as well as data quality, must be taken into account when generating accurate methods for predicting the likelihood that a compound will induce PLD. A new curated database of 330 compounds is proposed.

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Quantum Mechanics-based Properties for 3D-QSAR.

Publication Date: 2013 May 21 PMID: 23692495
Authors: El Kerdawy, A. - Gussregen, S. - Matter, H. - Hennemann, M. - Clark, T.
Journal: J Chem Inf Model

We have used a set of four local properties based on semiempirical molecular orbital calculations (the electron density (rho), hydrogen bond donor field (HDF), hydrogen bond acceptor field (HAF) and molecular lipophilicity potential (MLP)) for 3D-QSAR studies to overcome the limitations of the current force-field based molecular interaction fields (MIFs). These properties can be calculated rapidly and are thus amenable to high-throughput industrial applications. Their statistical performance was compared with that of conventional 3D-QSAR approaches using nine datasets (angiotensin converting enzyme inhibitors (ACE), acetylcholinesterase inhibitors (AchE), benzodiazepine receptor ligands (BZR), cyclooxygenase-2 inhibitors (COX2), dihydrofolate reductase inhibitors (DHFR), glycogen phosphorylase b inhibitors (GPB), thermolysin inhibitors (THER), thrombin inhibitors (THR) and serine protease factor Xa inhibitors (fXa)). The 3D-QSAR models generated were tested thoroughly for robustness and predictive ability. The average performance of the quantum mechanical molecular interaction field (QM-MIF) models for the nine datasets is better than that of the conventional force-field-based MIFs. In the individual datasets, the QM-MIF models always perform better than, or as well as, the conventional approaches. It is particularly encouraging that the relative performance of the QM-MIF models improves in the external validation. In addition, the models generated showed statistical stability with respect to model building procedure variations such as grid spacing size and grid orientation. QM-MIF contour maps reproduce the features important for ligand binding for the example dataset (factor Xa inhibitors), demonstrating the intuitive chemical interpretability of QM-MIFs.

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Classification of Compounds with Distinct or Overlapping Multi-Target Activities and Diverse Molecular Mechanisms Using Emerging Chemical Patterns.

Publication Date: 2013 Jun 3 PMID: 23692475
Authors: Namasivayam, V. - Hu, Y. - Balfer, J. - Bajorath, J.
Journal: J Chem Inf Model

The emerging chemical patterns (ECP) approach has been introduced for compound classification. Thus far, only very few ECP applications have been reported. Here, we further investigate the ECP methodology by studying complex classification problems. The analysis involves multi-target data sets with systematically organized subsets of compounds having distinct or overlapping target activities and, in addition, data sets containing classes of specifically active compounds with different mechanism-of-action. In systematic classification trials focusing on individual compound subsets or mechanistic classes, ECP calculations utilizing numerical descriptors achieve moderate to high sensitivity, dependent on the data set, and consistently high specificity. Accurate ECP predictions are already obtained on the basis of very small learning sets with only three positive training instances, which distinguishes the ECP approach from many other machine learning techniques.

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Estimation of Ligand Efficacies of Metabotropic Glutamate Receptors from Conformational Forces Obtained from Molecular Dynamics Simulations.

Publication Date: 2013 May 21 PMID: 23688150
Authors: Lakkaraju, S. K. - Xue, F. - Faden, A. I. - Mackerell, A. D. Jr
Journal: J Chem Inf Model

Group 1 metabotropic glutamate receptors (mGluR) are G-protein coupled receptors with a large bilobate extracellular ligand binding region (LBR) that resembles a Venus fly trap. Closing of this LBR in the presence of a ligand is associated with the activation of the receptor. From conformational sampling of the LBR-ligand complexes using all-atom molecular dynamics (MD) simulations, we characterized the conformational minima related to the hinge like motion associated with the LBR closing/opening in the presence of known agonists and antagonists. By applying a harmonic restraint on the LBR, we also determined the conformational forces generated by the different ligands. The change in the location of the minima and the conformational forces were used to quantify the efficacies of the ligands. This analysis shows that efficacies can be estimated from the forces of a single conformation of the receptor, indicating the potential of MD simulations as an efficient and useful technique to quantify efficacies, thereby facilitating the rational design of mGluR agonists and antagonists.

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Interactions between Voltage Sensor and Pore Domains in a hERG K Channel Model from Molecular Simulations and the Effects of a Voltage Sensor Mutation.

Publication Date: 2013 May 24 PMID: 23672495
Authors: Colenso, C. K. - Sessions, R. B. - Zhang, Y. H. - Hancox, J. C. - Dempsey, C. E.
Journal: J Chem Inf Model

The hERG K+ channel is important for establishing normal electrical activity in the human heart. The channel's unique gating response to membrane potential changes indicates specific interactions between voltage sensor and pore domains that are poorly understood. In the absence of a crystal structure we constructed a homology model of the full hERG membrane domain and performed 0.5 mus molecular dynamics (MD) simulations in a hydrated membrane. The simulations identify potential interactions involving residues at the extracellular surface of S1 in the voltage sensor and at the N-terminal end of the pore helix in the hERG model. In addition, a diffuse interface involving hydrophobic residues on S4 (voltage sensor) and pore domain S5 of an adjacent subunit was stable during 0.5 mus of simulation. To assess the ability of the model to give insight into the effects of channel mutation we simulated a hERG mutant that contains a Leu to Pro substitution in the voltage sensor S4 helical segment (hERG L532P). Consistent with the retention of gated K+ conductance, the L532P mutation was accommodated in the S4 helix with little disruption of helical structure. The mutation reduced the extent of interaction across the S4-S5 interface, suggesting a structural basis for the greatly enhanced deactivation rate in hERG L532P. The study indicates that pairwise comparison of wild-type and mutated channel models is a useful approach to interpreting functional data where uncertainty in model structures exist.

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Scaffold-Focused Virtual Screening: Prospective Application to the Discovery of TTK Inhibitors.

Publication Date: 2013 May 24 PMID: 23672464
Authors: Langdon, S. R. - Westwood, I. M. - van Montfort, R. L. - Brown, N. - Blagg, J.
Journal: J Chem Inf Model

We describe and apply a scaffold-focused virtual screen based upon scaffold trees to the mitotic kinase TTK (MPS1). Using level 1 of the scaffold tree, we perform both 2D and 3D similarity searches between a query scaffold and a level 1 scaffold library derived from a 2 million compound library; 98 compounds from 27 unique top-ranked level 1 scaffolds are selected for biochemical screening. We show that this scaffold-focused virtual screen prospectively identifies eight confirmed active compounds that are structurally differentiated from the query compound. In comparison, 100 compounds were selected for biochemical screening using a virtual screen based upon whole molecule similarity resulting in 12 confirmed active compounds that are structurally similar to the query compound. We elucidated the binding mode for four of the eight confirmed scaffold hops to TTK by determining their protein-ligand crystal structures; each represents a ligand-efficient scaffold for inhibitor design.

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Hydration Properties of Ligands and Drugs in Protein Binding Sites: Tightly-Bound, Bridging Water Molecules and Their Effects and Consequences on Molecular Design Strategies.

Publication Date: 2013 May 23 PMID: 23662606
Authors: Garcia-Sosa, A. T.
Journal: J Chem Inf Model

Some water molecules in binding sites are important for intermolecular interactions and stability. The way binding site explicit water molecules are dealt with affects the diversity and nature of designed ligand chemical structures and properties. The strategies commonly employed frequently assume that a gain in binding affinity will be achieved by their targeting or neglect. However, in the present work, 2332 high-resolution X-ray crystal structures of hydrated and nonhydrated, drug and nondrug compounds in biomolecular complexes with reported Ki or Kd show that compounds that use tightly bound, bridging water molecules are as potent as those that do not. The distribution of their energies, physicochemical properties, and ligand efficiency indices were compared for statistical significance, and the results were confirmed using 2000 permutation runs. Ligand cases were also split into agonists and antagonists, and crystal structure pairs with differing tightly bound water molecules were also compared. In addition, agonists and antagonists that use tightly bound water bridges are smaller, less lipophilic, and less planar; have deeper ligand efficiency indices; and in general, possess better physicochemical properties for further development. Therefore, tightly bound, bridging water molecules may in some cases be replaced and targeted as a strategy, though sometimes keeping them as bridges may be better from a pharmacodynamic perspective. The results suggest general indications on tightly hydrated and nontightly hydrated compounds in binding sites and practical considerations to adopt a strategy in drug and molecular design when faced with this special type of water molecules. There are also benefits of lower log P and better developability for tightly hydrated compounds, while stronger potency is not always required or beneficial. The hydrated binding site may be one of the many structure conformations available to the receptor, and different ligands will have a different ability to select either hydrated or nonhydrated receptor binding site conformations. Compounds may thus be designed, and if a tightly bound, bridging water molecule is observed in the binding site, attempts to replace it should only be made if the subsequent ligand modification would improve also its ligand efficiency, enthalpy, specificity, and pharmacokinetic properties. If the modification does succeed in replacing the tightly bound, bridging water molecule, it will have at least achieved benefits for ligand optimization and development independently of either positive or negative change in binding affinity outcome.

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Automated Large-Scale File Preparation, Docking, and Scoring: Evaluation of ITScore and STScore Using the 2012 Community Structure-Activity Resource Benchmark.

Publication Date: 2013 May 21 PMID: 23656179
Authors: Grinter, S. Z. - Yan, C. - Huang, S. Y. - Jiang, L. - Zou, X.
Journal: J Chem Inf Model

In this study, we use the recently released 2012 Community Structure-Activity Resource (CSAR) data set to evaluate two knowledge-based scoring functions, ITScore and STScore, and a simple force-field-based potential (VDWScore). The CSAR data set contains 757 compounds, most with known affinities, and 57 crystal structures. With the help of the script files for docking preparation, we use the full CSAR data set to evaluate the performances of the scoring functions on binding affinity prediction and active/inactive compound discrimination. The CSAR subset that includes crystal structures is used as well, to evaluate the performances of the scoring functions on binding mode and affinity predictions. Within this structure subset, we investigate the importance of accurate ligand and protein conformational sampling and find that the binding affinity predictions are less sensitive to non-native ligand and protein conformations than the binding mode predictions. We also find the full CSAR data set to be more challenging in making binding mode predictions than the subset with structures. The script files used for preparing the CSAR data set for docking, including scripts for canonicalization of the ligand atoms, are offered freely to the academic community.

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Compound Optimization through Data Set-Dependent Chemical Transformations.

Publication Date: 2013 May 20 PMID: 23654345
Authors: de la Vega de Leon, A. - Bajorath, J.
Journal: J Chem Inf Model

We have searched for chemical transformations that improve drug development-relevant properties within a given class of active compounds, regardless of the compounds they are applied to. For different compound data sets, varying numbers of frequently occurring data set-dependent transformations were identified that consistently induced favorable changes of selected molecular properties. Sequences of compound pairs representing such transformations were determined that formed pathways leading from unfavorable to favorable regions of property space. Data set-dependent transformations were then applied to predict a series of compounds with increasingly favorable property values. By database searching the desired biological activity was detected for several designed molecules or compounds that were very similar to these molecules. Taken together our findings indicate that data set-dependent transformations can be applied to predict compounds that map to favorable regions of molecular property space and retain their biological activity.

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Improving the Scoring of Protein-Ligand Binding Affinity by Including the Effects of Structural Water and Electronic Polarization.

Publication Date: 2013 May 20 PMID: 23651068
Authors: Liu, J. - He, X. - Zhang, J. Z.
Journal: J Chem Inf Model

Docking programs that use scoring functions to estimate binding affinities of small molecules to biological targets are widely applied in drug design and drug screening with partial success. But accurate and efficient scoring functions for protein-ligand binding affinity still present a grand challenge to computational chemists. In this study, the polarized protein-specific charge model (PPC) is incorporated into the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method to rescore the binding poses of some protein-ligand complexes, for which docking programs, such as Autodock, could not predict their binding modes correctly. Different sampling techniques (single minimized conformation and multiple molecular dynamics (MD) snapshots) are used to test the performance of MM/PBSA combined with the PPC model. Our results show the availability and effectiveness of this approach in correctly ranking the binding poses. More importantly, the bridging water molecules are found to play an important role in correctly determining the protein-ligand binding modes. Explicitly including these bridging water molecules in MM/PBSA calculations improves the prediction accuracy significantly. Our study sheds light on the importance of both bridging water molecules and the electronic polarization in the development of more reliable scoring functions for predicting molecular docking and protein-ligand binding affinity.

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Structural Basis for the Mutation-Induced Dysfunction of Human CYP2J2: A Computational Study.

Publication Date: 2013 May 20 PMID: 23647230
Authors: Cong, S. - Ma, X. T. - Li, Y. X. - Wang, J. F.
Journal: J Chem Inf Model

Arachidonic acid is an essential fatty acid in cells, acting as a key inflammatory intermediate in inflammatory reactions. In cardiac tissues, CYP2J2 can adopt arachidonic acid as a major substrate to produce epoxyeicosatrienoic acids (EETs), which can protect endothelial cells from ischemic or hypoxic injuries and have been implicated in the pathogenesis of coronary artery disease and hypertension. However, some CYP2J2 polymorphisms, i.e., T143A and N404Y, significantly reduce the metabolism of arachidonic acid. Lacking experimental structural data for CYP2J2, the detailed mechanism for the mutation-induced dysfunction in the metabolism of arachidonic acid is still unknown. In the current study, three-dimensional structural models of the wild-type CYP2J2 and two mutants (T143A and N404Y) were constructed by a coordinate reconstruction approach and ab initio modeling using CYP2R1 as a template. The structural analysis of the computational models showed that the wild-type CYP2J2 exhibited a typical CYP fold with 12 alpha-helices and three beta-sheets on one side and with the heme group buried deeply inside the core. Due to the small and hydrophobic side-chain, T143A mutation could destabilize the C helix, further placing the water access channel in a closed state to prevent the escape of the produced water molecules during the catalytic processes. N404Y mutation could reposition the side-chain of Leu378, making it no longer form a hydrogen bond with the carboxyl group of arachidonic acid. However, this hydrogen bond was essential for substrate recognition and positioning in a correct orientation.

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Structural Determinants of Drug Partitioning in n-Hexadecane/Water System.

Publication Date: 2013 May 21 PMID: 23641957
Authors: Natesan, S. - Wang, Z. - Lukacova, V. - Peng, M. - Subramaniam, R. - Lynch, S. - Balaz, S.
Journal: J Chem Inf Model

Surrogate phases have been widely used as correlates for modeling transport and partitioning of drugs in biological systems, taking advantage of chemical similarity between the surrogate and the phospholipid bilayer as the elementary unit of biological phases, which is responsible for most of the transport and partitioning. Solvation in strata of the phospholipid bilayer is an important drug characteristic because it affects the rates of absorption and distribution, as well as the interactions with the membrane proteins having the binding sites located inside the bilayer. The bilayer core can be emulated by n-hexadecane (C16), and the headgroup stratum is often considered a hydrophilic phase because of the high water content. Therefore, we tested the hypothesis that the C16/water partition coefficients (P) can predict the bilayer locations of drugs and other small molecules better than other surrogate systems. Altogether 514 PC16/W values for nonionizable (458) and completely ionized (56) compounds were collected from the literature or measured, when necessary. With the intent to create a fragment-based prediction system, the PC16/W values were factorized into the fragment solvation parameters (f) and correction factors based on the ClogP fragmentation scheme. A script for the PC16/W prediction using the ClogP output is provided. To further expand the prediction system and reveal solvation differences, the fC16/W values were correlated with their more widely available counterparts for the 1-octanol/water system (O/W) using solvatochromic parameters. The analysis for 50 compounds with known bilayer location shows that the available and predicted PC16/W and PO/W values alone or the PC16/O values representing their ratio do not satisfactorily predict the preference for drug accumulation in bilayer strata. These observations indicate that the headgroups stratum, albeit well hydrated, does not have solvation characteristics similar to water and is also poorly described by the O/W partition characteristics.

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Conformational determinants of the activity of antiproliferative factor glycopeptide.

Publication Date: 2013 May 24 PMID: 23627670
Authors: Mallajosyula, S. S. - Adams, K. M. - Barchi, J. J. - Mackerell, A. D.
Journal: J Chem Inf Model

The antiproliferative factor (APF) involved in interstitial cystitis is a glycosylated nonapeptide (TVPAAVVVA) containing a sialylated core 1 alpha-O-disaccharide linked to the N-terminal threonine. The chemical structure of APF was deduced using spectroscopic techniques and confirmed using total synthesis. The synthetic APF provided a platform to study amino acid modifications and their effect on APF activity, based on which a structure-activity relationship (SAR) for APF activity was previously proposed. However, this SAR model could not explain the change in activity associated with minor alterations in the peptide sequence. Presented is computational analysis of 14 APF derivatives to identify structural trends from which a more detailed SAR is obtained. The APF activity is found to be dictated by the close interplay between carbohydrate-peptide and peptide-peptide interactions. The former involves hydrogen bond and hydrophobic interactions, and the latter is dominated by hydrophobic interactions. The highly flexible hydrophobic peptide adopts collapsed conformations separated by low energy barriers. APF activity correlates with hydrophobic clustering associated with amino acids 4A, 6V, and 8V. Peptide conformations are highly sensitive to single point mutations, which explain the experimental trends. The presented SAR will act as a guide for lead optimization of more potent APF analogues of potential therapeutic utility.

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Characterization of Biaryl Torsional Energetics and its Treatment in OPLS All-Atom Force Fields.

Publication Date: 2013 May 24 PMID: 23621692
Authors: Dahlgren, M. K. - Schyman, P. - Tirado-Rives, J. - Jorgensen, W. L.
Journal: J Chem Inf Model

The frequency of biaryl substructures in a database of approved oral drugs has been analyzed. This led to designation of 20 prototypical biaryls plus 10 arylpyridinones for parametrization in the OPLS all-atom force fields. Bond stretching, angle-bending, and torsional parameters were developed to reproduce the MP2 geometries and torsional energy profiles. The transferability of the new parameters was tested through their application to three additional biaryls. The torsional energetics for the 33 biaryl molecules are analyzed and factors leading to preferences for planar and nonplanar geometries are identified. For liquid biphenyl, the computed density and heat of vaporization at the boiling point (255 degrees C) are also reported.

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Determination of toxicant mode of action by augmented top priority fragment class.

Publication Date: 2013 May 24 PMID: 23621653
Authors: Casalegno, M. - Sello, G.
Journal: J Chem Inf Model

Theoretical models can be an efficient tool to assess compound toxicity as an alternative to experimental determinations. Their application must follow some requirements that include the possibility of understanding the rationale that supports the prediction; here, the determination of the mode of action (MOA) is important. A combination of similarity and reactivity analysis has been applied to group chemical compounds with the aim at selecting groups that share structure and electronic state. The model is not based on experimental data but only on structural features. The result is a number of groups that contains similar compounds with similar reactivity and, possibly, similar MOA. The comparison of these groups to the experimentally determined MOAs available for the EPAFHAM database permits the discussion of the validity of both the model and the experimental data.

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Unraveling the Allosteric Inhibition Mechanism of PTP1B by Free Energy Calculation Based on Umbrella Sampling.

Publication Date: 2013 May 24 PMID: 23621621
Authors: Cui, W. - Cheng, Y. H. - Geng, L. L. - Liang, D. S. - Hou, T. J. - Ji, M. J.
Journal: J Chem Inf Model

Protein tyrosine phosphatase 1B (PTP1B) is a promising target for the treatment of obesity and type II diabetes. Allosteric inhibitors can stabilize an active conformation of PTP1B by hindering the conformational transition of the WPD loop of PTP1B from the open to the closed state. Here, the umbrella sampling molecular dynamics (MD) simulations were employed to compute the reaction path of the conformational transition of PTP1B, and the snapshots extracted from the MD trajectory were clustered into 58 conformational groups based on the key conformational parameter. Then, the impact of the conformational change of the WPD loop on the interactions between the allosteric site of PTP1B and an allosteric inhibitor BB3 was explored by using the MM/GBSA binding free energy calculations and free energy decomposition analysis. The simulation results show that the binding free energy of BB3 increases gradually from the open to the closed conformation of the WPD loop, providing the molecular mechanism of allosteric inhibition. Correlation analysis of the different energy terms indicates that the allosteric inhibitor with more negative van der Waals contribution cannot only exhibit stronger binding affinity but also hinder the swing of the WPD loop more effectively. Besides, it is found that the energy contribution of Lys292 in the alpha7 helix undergoes significant change, which reveals that Lys292 is not only the key residue for ligand binding but also plays an important role in hindering the conformational change of the WPD loop.

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TRAPP: A Tool for Analysis of Transient Binding Pockets in Proteins.

Publication Date: 2013 May 24 PMID: 23621586
Authors: Kokh, D. B. - Richter, S. - Henrich, S. - Czodrowski, P. - Rippmann, F. - Wade, R. C.
Journal: J Chem Inf Model

We present TRAPP (TRAnsient Pockets in Proteins), a new automated software platform for tracking, analysis, and visualization of binding pocket variations along a protein motion trajectory or within an ensemble of protein structures that may encompass conformational changes ranging from local side chain fluctuations to global backbone motions. TRAPP performs accurate grid-based calculations of the shape and physicochemical characteristics of a binding pocket for each structure and detects the conserved and transient regions of the pocket in an ensemble of protein conformations. It also provides tools for tracing the opening of a particular subpocket and residues that contribute to the binding site. TRAPP thus enables an assessment of the druggability of a disease-related target protein taking its flexibility into account.

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Exploring the potential of protein-based pharmacophore models in ligand pose prediction and ranking.

Publication Date: 2013 May 24 PMID: 23621564
Authors: Hu, B. - Lill, M. A.
Journal: J Chem Inf Model

Protein-based pharmacophore models derived from protein binding site atoms without the inclusion of any ligand information have become more popular in virtual screening studies. However, the accuracy of protein-based pharmacophore models for reproducing the critical protein-ligand interactions has never been explicitly assessed. In this study, we used known protein-ligand contacts from a large set of experimentally determined protein-ligand complexes to assess the quality of the protein-based pharmacophores in reproducing these critical contacts. We demonstrate how these contacts can be used to optimize the pharmacophore generation procedure to produce pharmacophore models that optimally cover the known protein-ligand interactions. Finally, we explored the potential of the optimized protein-based pharmacophore models for pose prediction and pose rankings. Our results demonstrate that there are significant variations in the success of protein-based pharmacophore models to reproduce native contacts and consequently native ligand poses dependent on the details of the pharmacophore generation process. We show that the generation of optimized protein-based pharmacophore models is a promising approach for ligand pose prediction and pose rankings.

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LiGen: A High Performance Workflow for Chemistry Driven de Novo Design.

Publication Date: 2013 May 28 PMID: 23617275
Authors: Beccari, A. R. - Cavazzoni, C. - Beato, C. - Costantino, G.
Journal: J Chem Inf Model

Tools for molecular de novo design are actively sought incorporating sets of chemical rules for fast and efficient identification of structurally new chemotypes endowed with a desired set of biological properties. In this paper, we present LiGen, a suite of programs which can be used sequentially or as stand-alone tools for specific purposes. In its standard application, LiGen modules are used to define input constraints, either structure-based, through active site identification, or ligand-based, through pharmacophore definition, to docking and to de novo generation. Alternatively, individual modules can be combined in a user-defined manner to generate project-centric workflows. Specific features of LiGen are the use of a pharmacophore-based docking procedure which allows flexible docking without conformer enumeration and accurate and flexible reactant mapping coupled with reactant tagging through substructure searching. The full description of LiGen functionalities is presented.

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Molecular Dynamics Simulation by GROMACS Using GUI Plugin for PyMOL.

Publication Date: 2013 May 24 PMID: 23611462
Authors: Makarewicz, T. - Kazmierkiewicz, R.
Journal: J Chem Inf Model

The molecular models stored as PDB formatted files are static, but most of the biomolecular systems display a dynamic behavior, in other words their conformations depend on time. To get the dynamic model from the static one, one needs to perform the molecular dynamics (MD) simulation using tools like GROMACS. This paper describes functionality of the newly created plugin for PyMOL (the popular and easy to use program for displaying and manipulating molecule models). This plugin enables the easy use of molecular dynamics simulations using GROMACS through a graphic interface. It transfers the results of those calculations and displays them back in PyMOL. All the components of the stack are open source and are available free of charge. This strategy gives researchers easy access to the molecular dynamics PYMOL plugin and creates an opportunity to modify its source when needed.

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Hit expansion approaches using multiple similarity methods and virtualized query structures.

Publication Date: 2013 May 24 PMID: 23600728
Authors: Bergner, A. - Parel, S. P.
Journal: J Chem Inf Model

Ligand-based virtual screening and computational hit expansion methods undoubtedly facilitate the finding of novel active chemical entities, utilizing already existing knowledge of active compounds. It has been demonstrated that the parallel execution of complementary similarity search methods enhances the performance of such virtual screening campaigns. In this article, we examine the use of virtualized template (query, seed) structures as an extension to common search methods, such as fingerprint and pharmacophore graph-based similarity searches. We demonstrate that template virtualization by bioisosteric enumeration and other rule-based methods, in combination with standard similarity search techniques, represents a powerful approach for hit expansion following high-throughput screening campaigns. The reliability of the methods is demonstrated by four different test data sets representing different target classes and two hit finding case studies on the epigenetic targets G9a and LSD1.

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Identification of Compounds with Potential Antibacterial Activity against Mycobacterium through Structure-Based Drug Screening.

Publication Date: 2013 May 24 PMID: 23600706
Authors: Kinjo, T. - Koseki, Y. - Kobayashi, M. - Yamada, A. - Morita, K. - Yamaguchi, K. - Tsurusawa, R. - Gulten, G. - Komatsu, H. - Sakamoto, H. - Sacchettini, J. C. - Kitamura, M. - Aoki, S.
Journal: J Chem Inf Model

To identify novel antibiotics against Mycobacterium tuberculosis, we performed a hierarchical structure-based drug screening (SBDS) targeting the enoyl-acyl carrier protein reductase (InhA) with a compound library of 154,118 chemicals. We then evaluated whether the candidate hit compounds exhibited inhibitory effects on the growth of two model mycobacterial strains: Mycobacterium smegmatis and Mycobacterium vanbaalenii. Two compounds (KE3 and KE4) showed potent inhibitory effects against both model mycobacterial strains. In addition, we rescreened KE4 analogs, which were identified from a compound library of 461,383 chemicals through fingerprint analysis and genetic algorithm-based docking simulations. All of the KE4 analogs (KES1-KES5) exhibited inhibitory effects on the growth of M. smegmatis and/or M. vanbaalenii. Based on the predicted binding modes, we probed the structure-activity relationships of KE4 and its analogs and found a correlative relationship between the IC50 values and the interaction residues/LogP values. The most potent inhibitor, compound KES4, strongly and stably inhibited the long-term growth of the model bacteria and showed higher inhibitory effects (IC50 = 4.8 muM) than isoniazid (IC50 = 5.4 muM), which is a first-line drug for tuberculosis therapy. Moreover, compound KES4 did not exhibit any toxic effects that impede cell growth in several mammalian cell lines and enterobacteria. The structural and experimental information of these novel chemical compounds will likely be useful for the development of new anti-TB drugs. Furthermore, the methodology that was used for the identification of the effective chemical compound is also likely to be effective in the SBDS of other candidate medicinal drugs.

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Reaction schemes visualized in network form: the syntheses of strychnine as an example.

Publication Date: 2013 May 24 PMID: 23597302
Authors: Proudfoot, J. R.
Journal: J Chem Inf Model

Representation of synthesis sequences in a network form provides an effective method for the comparison of multiple reaction schemes and an opportunity to emphasize features such as reaction scale that are often relegated to experimental sections. An example of data formatting that allows construction of network maps in Cytoscape is presented, along with maps that illustrate the comparison of multiple reaction sequences, comparison of scaffold changes within sequences, and consolidation to highlight common key intermediates used across sequences. The 17 different synthetic routes reported for strychnine are used as an example basis set. The reaction maps presented required a significant data extraction and curation, and a standardized tabular format for reporting reaction information, if applied in a consistent way, could allow the automated combination of reaction information across different sources.

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Fighting Obesity with a Sugar-Based Library: Discovery of Novel MCH-1R Antagonists by a New Computational-VAST Approach for Exploration of GPCR Binding Sites.

Publication Date: 2013 May 24 PMID: 23590178
Authors: Heifetz, A. - Barker, O. - Verquin, G. - Wimmer, N. - Meutermans, W. - Pal, S. - Law, R. J. - Whittaker, M.
Journal: J Chem Inf Model

Obesity is an increasingly common disease. While antagonism of the melanin-concentrating hormone-1 receptor (MCH-1R) has been widely reported as a promising therapeutic avenue for obesity treatment, no MCH-1R antagonists have reached the market. Discovery and optimization of new chemical matter targeting MCH-1R is hindered by reduced HTS success rates and a lack of structural information about the MCH-1R binding site. X-ray crystallography and NMR, the major experimental sources of structural information, are very slow processes for membrane proteins and are not currently feasible for every GPCR or GPCR-ligand complex. This situation significantly limits the ability of these methods to impact the drug discovery process for GPCR targets in "real-time", and hence, there is an urgent need for other practical and cost-efficient alternatives. We present here a conceptually pioneering approach that integrates GPCR modeling with design, synthesis, and screening of a diverse library of sugar-based compounds from the VAST technology (versatile assembly on stable templates) to provide structural insights on the MCH-1R binding site. This approach creates a cost-efficient new avenue for structure-based drug discovery (SBDD) against GPCR targets. In our work, a primary VAST hit was used to construct a high-quality MCH-1R model. Following model validation, a structure-based virtual screen yielded a 14% hit rate and 10 novel chemotypes of potent MCH-1R antagonists, including EOAI3367472 (IC50 = 131 nM) and EOAI3367474 (IC50 = 213 nM).

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Consensus methods for combining multiple clusterings of chemical structures.

Publication Date: 2013 May 24 PMID: 23581471
Authors: Saeed, F. - Salim, N. - Abdo, A.
Journal: J Chem Inf Model

The goal of consensus clustering methods is to find a consensus partition that optimally summarizes an ensemble and improves the quality of clustering compared with single clustering algorithms. In this paper, an enhanced voting-based consensus method was introduced and compared with other consensus clustering methods, including co-association-based, graph-based, and voting-based consensus methods. The MDDR and MUV data sets were used for the experiments and were represented by three 2D fingerprints: ALOGP, ECFP_4, and ECFC_4. The results were evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster using four criteria: F-measure, Quality Partition Index (QPI), Rand Index (RI), and Fowlkes-Mallows Index (FMI). The experiments suggest that the consensus methods can deliver significant improvements for the effectiveness of chemical structures clustering.

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Compound Pathway Model To Capture SAR Progression: Comparison of Activity Cliff-Dependent and -Independent Pathways.

Publication Date: 2013 May 24 PMID: 23581427
Authors: Stumpfe, D. - Dimova, D. - Heikamp, K. - Bajorath, J.
Journal: J Chem Inf Model

A compound pathway model is introduced to monitor SAR progression in compound data sets. Pathways are formed by sequences of structurally analogous compounds with stepwise increasing potency that ultimately yield highly potent compounds. Hence, the model was designed to mimic compound optimization efforts. Different pathway categories were defined. Pathways originating from any active compound in a data set were systematically identified including compounds forming activity cliffs. The relative frequency of activity cliff-dependent and -independent pathways was determined and compared. In 23 of 39 different compound data sets that qualified for our analysis, significant differences in the relative frequency of activity cliff-dependent and -independent pathways were observed. In 17 of these 23 data sets, activity cliff-dependent pathways occurred with higher relative frequency than cliff-independent pathways. In addition, pathways originating from the majority of activity cliff compounds displayed desired SAR progression, reflecting SAR information gain associated with activity cliffs.

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Identification of a New Binding Site in E. coli FabH using Molecular Dynamics Simulations: Validation by Computational Alanine Mutagenesis and Docking Studies.

Publication Date: 2013 May 24 PMID: 23581389
Authors: Ramamoorthy, D. - Turos, E. - Guida, W. C.
Journal: J Chem Inf Model

FabH (Fatty acid biosynthesis, enzyme H, also referred to as beta-ketoacyl-ACP-synthase III) is a key condensing enzyme in the type II fatty acid synthesis (FAS) system. The FAS pathway in bacteria is essential for growth and survival and vastly differs from the human FAS pathway. Enzymes involved in this pathway have arisen as promising biomolecular targets for discovery of new antibacterial drugs. However, currently there are no clinical drugs that selectively target FabH, and known inhibitors of FabH all act within the active site. FabH exerts its catalytic function as a dimer, which could potentially be exploited in developing new strategies for inhibitor design. The aim of this study was to elucidate structural details of the dimer interface region by means of computational modeling, including molecular dynamics (MD) simulations, in order to derive information for the structure-based design of new FabH inhibitors. The dimer interface region was analyzed by MD simulations, trajectory snapshots were collected for further analyses, and docking studies were performed with potential small molecule disruptors. Alanine mutation and docking studies strongly suggest that the dimer interface could be a potential target for anti-infection drug discovery.

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Highly SpecIfic and Sensitive Pharmacophore Model for Identifying CXCR4 Antagonists. Comparison with Docking and Shape-Matching Virtual Screening Performance.

Publication Date: 2013 May 24 PMID: 23577723
Authors: Karaboga, A. S. - Planesas, J. M. - Petronin, F. - Teixido, J. - Souchet, M. - Perez-Nueno, V. I.
Journal: J Chem Inf Model

HIV infection is initiated by fusion of the virus with the target cell through binding of the viral gp120 protein with the CD4 cell surface receptor protein and the CXCR4 or CCR5 coreceptors. There is currently considerable interest in developing novel ligands that can modulate the conformations of these coreceptors and, hence, ultimately block virus-cell fusion. Herein, we present a highly specific and sensitive pharmacophore model for identifying CXCR4 antagonists that could potentially serve as HIV entry inhibitors. Its performance was compared with docking and shape-matching virtual screening approaches using 3OE6 CXCR4 crystal structure and high-affinity ligands as query molecules, respectively. The performance of these methods was compared by virtually screening a library assembled by us, consisting of 228 high affinity known CXCR4 inhibitors from 20 different chemotype families and 4696 similar presumed inactive molecules. The area under the ROC plot (AUC), enrichment factors, and diversity of the resulting virtual hit lists was analyzed. Results show that our pharmacophore model achieves the highest VS performance among all the docking and shape-based scoring functions used. Its high selectivity and sensitivity makes our pharmacophore a very good filter for identifying CXCR4 antagonists.

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Characterizing Binding of Small Molecules. II. Evaluating the Potency of Small Molecules to Combat Resistance Based on Docking Structures.

Publication Date: 2013 May 24 PMID: 23570305
Authors: Ding, B. - Li, N. - Wang, W.
Journal: J Chem Inf Model

Drug resistance severely erodes the efficacy of therapeutic treatments for many diseases. Assessing the potency of a drug lead to combat resistance is no doubt critical for designing new drugs or new therapeutic combinations. Virtual screening is often the first step in drug discovery and a challenging problem is to accurately predict the resistant profile of an inhibitor based on the docking structures. Using a well studied system HIV-1 protease, we have illustrated the success of a computational method called MIEC-SVM on tackling this problem. We computed molecular interaction energy components (MIECs) between the ligand and the protease residues to characterize the docking poses, which were input to support vector machine (SVM) to distinguish resistant from nonresistant mutants. More importantly, the method is able to predict resistant profiles for new drugs based on the docking structures as indicated by its satisfactory performance in leave-one-drug-out and leave-drug/mutants-out tests. Therefore, the MIEC-SVM method can also facilitate designing effective therapeutic combinations by combining drugs with complementary resistant profiles.

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Development of the Knowledge-Based and Empirical Combined Scoring Algorithm (KECSA) To Score Protein-Ligand Interactions.

Publication Date: 2013 May 24 PMID: 23560465
Authors: Zheng, Z. - Merz, K. M. Jr
Journal: J Chem Inf Model

We describe a novel knowledge-based protein-ligand scoring function that employs a new definition for the reference state, allowing us to relate a statistical potential to a Lennard-Jones (LJ) potential. In this way, the LJ potential parameters were generated from protein-ligand complex structural data contained in the Protein Databank (PDB). Forty-nine (49) types of atomic pairwise interactions were derived using this method, which we call the knowledge-based and empirical combined scoring algorithm (KECSA). Two validation benchmarks were introduced to test the performance of KECSA. The first validation benchmark included two test sets that address the training set and enthalpy/entropy of KECSA. The second validation benchmark suite included two large-scale and five small-scale test sets, to compare the reproducibility of KECSA, with respect to two empirical score functions previously developed in our laboratory (LISA and LISA+), as well as to other well-known scoring methods. Validation results illustrate that KECSA shows improved performance in all test sets when compared with other scoring methods, especially in its ability to minimize the root mean square error (RMSE). LISA and LISA+ displayed similar performance using the correlation coefficient and Kendall tau as the metric of quality for some of the small test sets. Further pathways for improvement are discussed for which would allow KECSA to be more sensitive to subtle changes in ligand structure.

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Molecular dynamics simulations of the adenosine a2a receptor: structural stability, sampling, and convergence.

Publication Date: 2013 May 24 PMID: 23514445
Authors: Ng, H. W. - Laughton, C. A. - Doughty, S. W.
Journal: J Chem Inf Model

Molecular dynamics (MD) simulations of membrane-embedded G-protein coupled receptors (GPCRs) have rapidly gained popularity among the molecular simulation community in recent years, a trend which has an obvious link to the tremendous pharmaceutical importance of this group of receptors and the increasing availability of crystal structures. In view of the widespread use of this technique, it is of fundamental importance to ensure the reliability and robustness of the methodologies so they yield valid results and enable sufficiently accurate predictions to be made. In this work, 200 ns simulations of the A2a adenosine receptor (A2a AR) have been produced and evaluated in the light of these requirements. The conformational dynamics of the target protein, as obtained from replicate simulations in both the presence and absence of an inverse agonist ligand (ZM241385), have been investigated and compared using principal component analysis (PCA). Results show that, on this time scale, convergence of the replicates is not readily evident and dependent on the types of the protein motions considered. Thus rates of inter- as opposed to intrahelical relaxation and sampling can be different. When studied individually, we find that helices III and IV have noticeably greater stability than helices I, II, V, VI, and VII in the apo form. The addition of the inverse agonist ligand greatly improves the stability of all helices.

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Similarity boosted quantitative structure-activity relationship-a systematic study of enhancing structural descriptors by molecular similarity.

Publication Date: 2013 May 24 PMID: 23489025
Authors: Girschick, T. - Almeida, P. R. - Kramer, S. - Stalring, J.
Journal: J Chem Inf Model

The concept of molecular similarity is one of the most central in the fields of predictive toxicology and quantitative structure-activity relationship (QSAR) research. Many toxicological responses result from a multimechanistic process and, consequently, structural diversity among the active compounds is likely. Combining this knowledge, we introduce similarity boosted QSAR modeling, where we calculate molecular descriptors using similarities with respect to representative reference compounds to aid a statistical learning algorithm in distinguishing between different structural classes. We present three approaches for the selection of reference compounds, one by literature search and two by clustering. Our experimental evaluation on seven publicly available data sets shows that the similarity descriptors used on their own perform quite well compared to structural descriptors. We show that the combination of similarity and structural descriptors enhances the performance and that a simple stacking approach is able to use the complementary information encoded by the different descriptor sets to further improve predictive results. All software necessary for our experiments is available within the cheminformatics software framework AZOrange.

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ChemCalc: A Building Block for Tomorrow's Chemical Infrastructure.

Publication Date: 2013 May 24 PMID: 23480664
Authors: Patiny, L. - Borel, A.
Journal: J Chem Inf Model

Web services, as an aspect of cloud computing, are becoming an important part of the general IT infrastructure, and scientific computing is no exception to this trend. We propose a simple approach to develop chemical Web services, through which servers could expose the essential data manipulation functionality that students and researchers need for chemical calculations. These services return their results as JSON (JavaScript Object Notation) objects, which facilitates their use for Web applications. The ChemCalc project http://www.chemcalc.org demonstrates this approach: we present three Web services related with mass spectrometry, namely isotopic distribution simulation, peptide fragmentation simulation, and molecular formula determination. We also developed a complete Web application based on these three Web services, taking advantage of modern HTML5 and JavaScript libraries (ChemDoodle and jQuery).

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