Journal of Chemical Information and Modeling

pharmACOphore: Multiple Flexible Ligand Alignment Based on Ant Colony Optimization.

Publication Date: 2010 Aug 30 PMID: 20804159
Authors: Korb, O. - Monecke, P. - Hessler, G. - Stutzle, T. - Exner, T. E.
Journal: J Chem Inf Model

The flexible superimposition of biologically active ligands is a crucial step in ligand-based drug design. Here we present pharmACOphore, a new approach for pairwise as well as multiple flexible alignment of ligands based on ant colony optimization (ACO; Dorigo , M. ; Stutzle , T. Ant Colony Optimization ; MIT Press : Cambridge, MA, USA , 2004 ). An empirical scoring function is used, which describes ligand similarity by minimizing the distance of pharmacophoric features. The scoring function was parametrized on pairwise alignments of ligand sets for four proteins from diverse protein families (cyclooxygenase-2, cyclin-dependent kinase 2, factor Xa and peroxisome proliferator-activated receptor gamma). The derived parameters were assessed with respect to pose prediction performance on the independent FlexS data set ( Lemmen , C. ; Lengauer , T. ; Klebe , G. J. Med. Chem. 1998, 41 , 4502 - 4520 ) in exhausting pairwise alignments. Additionally, multiple flexible alignment experiments were carried out for the pharmacologically relevant targets trypsin and poly (ADP-ribose) polymerase (PARP). The results obtained show that the new procedure provides a robust and efficient way for the pairwise as well as multiple flexible alignment of small molecules.

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Setting Anchor in the Minor Groove: in Silico Investigation into Formamido N-Methylpyrrole and N-Methylimidazole Polyamides Bound by Cognate DNA Sequences.

Publication Date: 2010 Aug 30 PMID: 20804157
Authors: Collar, C. J. - Lee, M. - Wilson, W. D.
Journal: J Chem Inf Model

Tricyclic N-methylpyrrole (Py) and N-methylimidazole (Im) containing polyamide monocations are known to bind as stacked dimers within the minor groove of DNA, and those with N-terminal formamido (f) substituents bind in a staggered configuration with high specificity over a range of affinities. Although binding constants have been reported, there is not a clear understanding of why such constants vary significantly for polyamide dimers and their respective cognate DNA sequences. By employing computational tools, the following homodimer complexes have been addressed in this study: f-PyPyIm in complex with 5'-d(GAACTAGTTC)-3', f-ImPyPy in complex with 5'-d(GAATGCATTC)-3', and f-ImPyIm in complex with 5'-d(GAACGCGTTC)-3'. These complexes were selected based on their 10- to 100-fold differences in binding constants. From this study, it was possible to determine how polyamides anchor themselves within the minor groove of specific DNA sequences. This is done through several interactions that provide stability for specific recognition: (i) Py groups secure themselves between DNA base pairs, (ii) lone-pair-Pi interactions are formed between DNA deoxyribose O4' and Im groups nearest f, (iii) minor groove bases hydrogen bond to Im groups and amides of the polyamide backbone, (iv) f substituents rotate without leaving the minor groove of DNA and with this rotation form specific hydrogen bonds with electron-rich sites on the floor of the minor groove, and (v) flexible charged N,N-dimethylaminoalkyl substituents reside favorably in the minor groove of DNA. Results displayed the greatest amount of interactions and stability for dimer f-ImPyIm in complex with 5'-d(GAACGCGTTC)-3' and the least amount in dimer f-PyPyIm in complex with 5'-d(GAACTAGTTC)-3'. Hence, for cognate DNA sequences, the relative binding strength of compounds was determined as f-ImPyIm > f-ImPyPy > f-PyPyIm. This force-field-based computational study is in agreement with experimental results and provides a molecular rational for the binding constant values.

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Molecular Docking and 3D-QSAR CoMFA Studies on Indole Inhibitors of GIIA Secreted Phospholipase A(2).

Publication Date: 2010 Aug 26 PMID: 20795712
Authors: Mouchlis, V. D. - Mavromoustakos, T. M. - Kokotos, G.
Journal: J Chem Inf Model

Automated docking allowing a "protein-based" alignment was performed on a set of indole inhibitors of the GIIA secreted phospholipase A(2) (GIIA sPLA(2)). A correlation between the binding scores and the experimental inhibitory activity was observed (r(2) = 0.666, N = 34). All the indole inhibitors were docked in the active site of the GIIA sPLA(2) enzyme, and the best score docking pose of each inhibitor was used for the "protein-based" alignment of the compounds. A three-dimensional quantitative structure-activity relationship (3D-QSAR) model was then established using the comparative molecular field analysis (CoMFA) method. The set of 34 indole inhibitors was divided into two subsets: the training set, composed of 26 compounds, and the test set, consisting of eight compounds. The robustness and the predictive ability of the generated CoMFA model were examined by using the test set. A good correlation (r(2) = 0.997) between predicted and experimental inhibitory activity data allows the validation of the CoMFA model. Finally, the generated CoMFA model was used for the design and evaluation of new compounds. The new designed compounds exert improved predicted inhibitory activity and may be a target for the synthesis of new GIIA sPLA(2) indole inhibitors.

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From Structure Diagrams to Visual Chemical Patterns.

Publication Date: 2010 Aug 26 PMID: 20795706
Authors: Schomburg, K. - Ehrlich, H. C. - Stierand, K. - Rarey, M.
Journal: J Chem Inf Model

The intuitive way of chemists to communicate molecules is via two-dimensional structure diagrams. The straightforward visual representations are mostly preferred to the often complicated systematic chemical names. For chemical patterns, however, no comparable visualization standards have evolved so far. Chemical patterns denoting descriptions of chemical features are needed whenever a set of molecules is filtered for certain properties. The currently available representations are constrained to linear molecular pattern languages which are hardly human readable and therefore keep chemists without computational background from systematically formulating patterns. Therefore, we introduce a new visualization concept for chemical patterns. The common standard concept of structure diagrams is extended to account for property descriptions and logic combinations of chemical features in patterns. As a first application of the new concept, we developed the SMARTSviewer, a tool that converts chemical patterns encoded in SMARTS strings to a visual representation. The graphic pattern depiction provides an overview of the specified chemical features, variations, and similarities without needing to decode the often cryptic linear expressions. Taking recent chemical publications from various fields, we demonstrate the wide application range of a graphical chemical pattern language.

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Molecular Graph Augmentation with Rings and Functional Groups.

Publication Date: 2010 Aug 26 PMID: 20795702
Authors: Grave, K. D. - Costa, F.
Journal: J Chem Inf Model

Molecular graphs are a compact representation of molecules but may be too concise to obtain optimal generalization performance from graph-based machine learning algorithms. Over centuries, chemists have learned what are the important functional groups in molecules. This knowledge is normally not manifest in molecular graphs. In this paper, we introduce a simple method to incorporate this type of background knowledge: we insert additional vertices with corresponding edges for each functional group and ring structure identified in the molecule. We present experimental evidence that, on a wide range of ligand-based tasks and data sets, the proposed augmentation method improves the predictive performance over several graph kernel-based quantitative structure-activity relationship models. When the augmentation technique is used with the recent pairwise maximal common subgraphs kernel, we achieve a significant improvement over the current state-of-the-art on the NCI-60 cancer data set in 28 out of 60 cell lines, with the other 32 cell lines showing no significant difference in accuracy. Finally, on the Bursi mutagenicity data set, we obtain near-optimal predictions.

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GARLig: A Fully Automated Tool for Subset Selection of Large Fragment Spaces via a Self-Adaptive Genetic Algorithm.

Publication Date: 2010 Aug 26 PMID: 20795677
Authors: Pfeffer, P. - Fober, T. - Hullermeier, E. - Klebe, G.
Journal: J Chem Inf Model

In combinatorial chemistry, molecules are assembled according to combinatorial principles by linking suitable reagents or decorating a given scaffold with appropriate substituents from a large chemical space of starting materials. Often the number of possible combinations greatly exceeds the number feasible to handle by an in-depth in silico approach or even more if it should be experimentally synthesized. Therefore, powerful tools to efficiently enumerate large chemical spaces are required. They can be provided by genetic algorithms, which mimic Darwinian evolution. GARLig (genetic algorithm using reagents to compose ligands) has been developed to perform subset selection in large chemical compound spaces subject to target-specific 3D-scoring criteria. GARLig uses different scoring schemes, such as AutoDock4 Score, GOLDScore, and DrugScore(CSD), as fitness functions. Its genetic parameters have been optimized to characterize combinatorial libraries with respect to the binding to various targets of pharmaceutical interest. A large tripeptide library of 20(3) members has been used to profile amino acid frequencies in putative substrates for trypsin, thrombin, factor Xa, and plasmin. A peptidomimetic scaffold assembled from a selection of a 25(3) building block was used to test the performance of the evolutionary algorithm in suggesting potent inhibitors of the enzyme cathepsin D. In a final case study, our program was used to characterize and rank a combinatorial drug-like library comprising 33 750 potential thrombin inhibitors. These case studies demonstrate that GARLig finds experimentally confirmed potent leads by processing a significantly smaller subset of the fully enumerated combinatorial library. Furthermore, the profiles of amino acids computed by the genetic algorithm match the observed amino acid frequencies found by screening peptide libraries in substrate cleavage assays.

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TAMkin: A Versatile Package for Vibrational Analysis and Chemical Kinetics.

Publication Date: 2010 Aug 26 PMID: 20738140
Authors: Ghysels, A. - Verstraelen, T. - Hemelsoet, K. - Waroquier, M. - Van Speybroeck, V.
Journal: J Chem Inf Model

TAMkin is a program for the calculation and analysis of normal modes, thermochemical properties and chemical reaction rates. At present, the output from the frequently applied software programs ADF, CHARMM, CPMD, CP2K, Gaussian, Q-Chem, and VASP can be analyzed. The normal-mode analysis can be performed using a broad variety of advanced models, including the standard full Hessian, the Mobile Block Hessian, the Partial Hessian Vibrational approach, the Vibrational Subsystem Analysis with or without mass matrix correction, the Elastic Network Model, and other combinations. TAMkin is readily extensible because of its modular structure. Chemical kinetics of unimolecular and bimolecular reactions can be analyzed in a straightforward way using conventional transition state theory, including tunneling corrections and internal rotor refinements. A sensitivity analysis can also be performed, providing important insight into the theoretical error margins on the kinetic parameters. Two extensive examples demonstrate the capabilities of TAMkin: the conformational change of the biological system adenylate kinase is studied, as well as the reaction kinetics of the addition of ethene to the ethyl radical. The important feature of batch processing large amounts of data is highlighted by performing an extended level of theory study, which TAMkin can automate significantly.

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Rapid Context-Dependent Ligand Desolvation in Molecular Docking.

Publication Date: 2010 Aug 24 PMID: 20735049
Authors: Mysinger, M. M. - Shoichet, B. K.
Journal: J Chem Inf Model

In structure-based screens for new ligands, a molecular docking algorithm must rapidly score many molecules in multiple configurations, accounting for both the ligand's interactions with receptor and its competing interactions with solvent. Here we explore a context-dependent ligand desolvation scoring term for molecular docking. We relate the Generalized-Born effective Born radii for every ligand atom to a fractional desolvation and then use this fraction to scale an atom-by-atom decomposition of the full transfer free energy. The fractional desolvation is precomputed on a scoring grid by numerically integrating over the volume of receptor proximal to a ligand atom, weighted by distance. To test this method's performance, we dock ligands versus property-matched decoys over 40 DUD targets. Context-dependent desolvation better enriches ligands compared to both the raw full transfer free energy penalty and compared to ignoring desolvation altogether, though the improvement is modest. More compellingly, the new method improves docking performance across receptor types. Thus, whereas entirely ignoring desolvation works best for charged sites and overpenalizing with full desolvation works well for neutral sites, the physically more correct context-dependent ligand desolvation is competitive across both types of targets. The method also reliably discriminates ligands from highly charged molecules, where ignoring desolvation performs poorly. Since this context-dependent ligand desolvation may be precalculated, it improves docking reliability with minimal cost to calculation time and may be readily incorporated into any physics-based docking program.

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Molecular Dynamics Simulations and Elastic Network Analysis of Protein Kinase B (Akt/PKB) Inactivation.

Publication Date: 2010 Aug 24 PMID: 20735046
Authors: Cheng, S. - Niv, M. Y.
Journal: J Chem Inf Model

Akt (also called protein kinase B-PKB) is a key component of the phosphoinositide-3-kinase signaling pathway, which is responsible for cell proliferation and survival and is a novel target for antioncogenic indications. In its fully activated state, Akt is phosphorylated on the activation loop (A-loop) at residue Thr 309. We used molecular dynamics (MD) simulations and elastic network model normal-mode analysis (ENM-NMA) to study the initial stages of the active-inactive transition in the kinase catalytic domain. We first carried out MD simulations of the active phosphorylated Akt in complex with its ligands under different protonation states of His 196, the phosphothreonine-coordinating residue found in the alphaC helix. Analysis of trajectories suggested that the doubly protonated His 196 is most compatible with the crystallographic structure. Next we studied the dynamic processes involved in Akt inactivation: detachment of the ligands and A-loop dephosphorylation resulted in MD trajectories with increased mobility, particularly in the N-lobe and in the HJ-alphaG region of the C-lobe, and in stronger correlation and anticorrelation of motions. The first principal motions derived from the trajectories of phosphorylated and dephosphorylated apo structures were similar to each other but differed from the first principal motions derived from the complex trajectory. A rather large number of principal components obtained from MD trajectories and of ENM-NMA modes is required to describe the active-inactive conformational change of the kinase. The results are discussed in the context of related computational studies of kinase dynamics and kinase-specific inhibitor design.

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Pharmacophore modeling of substituted 1,2,4-Trioxanes for quantitative prediction of their antimalarial activity.

Publication Date: 2010 Aug 23 PMID: 20726605
Authors: Gupta, A. K. - Chakroborty, S. - Srivastava, K. - Puri, S. K. - Saxena, A. K.
Journal: J Chem Inf Model

A pharmacophore model has been developed for determining the essential structural requirements for antimalarial activity from the eight series of substituted 1,2,4-trioxanes. The best pharmacophore model possessing two aliphatic hydrophobic, one aromatic hydrophobic, one hydrogen-bond (H-bond) acceptor, and one H-bond acceptor (lipid) feature for antimalarial activity showed an excellent correlation coefficient for the training (r(2)(training) = 0.85) and a fair correlation coefficient for the test set (r(2)(test) = 0.51) molecules. The model predicts well to other known substituted 1,2,4-trioxanes including those which either are drugs or are undergoing clinical trials. In order to further validate this model, five substituted 1,2,4-trioxanes were synthesized from the generated focused library and screened for antimalarial activity. The observed activity of these molecules was consistent with the pharmacophore model, suggesting that the model may be useful in the design of potent antimalarial agents.

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Generation, validation, and utilization of a three-dimensional pharmacophore model for EP3 antagonists.

Publication Date: 2010 Aug 23 PMID: 20726604
Authors: Mishra, R. K. - Singh, J.
Journal: J Chem Inf Model

Studies reported here are aimed to investigate the important structural features that characterize the human EP(3) antagonists. Based on the knowledge of low-energy conformation of the endogenous ligand, the initial hit analogs were prepared. Subsequently, a ligand-based lead optimization approach using pharmacophore model generation was utilized. A 5-point pharmacophore using a training set of 19 compounds spanning the IC(50) data over 4-log order was constructed using the HypoGen module of Catalyst. Following pharmacophore customization, using a linear structure-activity regression equation, a six feature three-dimensional predictive pharmacophore model, P6, was built, which resulted in improved predictive power. The P6 model was validated using a test set of 11 compounds providing a correlation coefficient (R(2)) of 0.90 for predictive versus experimental EP(3) IC(50) values. This pharmacophore model has been expanded to include diverse chemotypes, and the predictive ability of the customized pharmacophore has been tested.

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HIV-1 TAR RNA spontaneously undergoes relevant apo-to-holo conformational transitions in molecular dynamics and constrained geometrical simulations.

Publication Date: 2010 Aug 23 PMID: 20726603
Authors: Fulle, S. - Christ, N. A. - Kestner, E. - Gohlke, H.
Journal: J Chem Inf Model

We report all-atom molecular dynamics and replica exchange molecular dynamics simulations on the unbound human immunodeficiency virus type-1 (HIV-1) transactivation responsive region (TAR) RNA structure and three TAR RNA structures in bound conformations of, in total, approximately 250 ns length. We compare the extent of observed conformational sampling with that of the conceptually simpler and computationally much cheaper constrained geometrical simulation approach framework rigidity optimized dynamic algorithm (FRODA). Atomic fluctuations obtained by replica-exchange molecular dynamics (REMD) simulations agree quantitatively with those obtained by molecular dynamics (MD) and FRODA simulations for the unbound TAR structure. Regarding the stereochemical quality of the generated conformations, backbone torsion angles and puckering modes of the sugar-phosphate backbone were reproduced equally well by MD and REMD simulations, but further improvement is needed in the case of FRODA simulations. Essential dynamics analysis reveals that all three simulation approaches show a tendency to sample bound conformations when starting from the unbound TAR structure, with MD and REMD simulations being superior with respect to FRODA. These results are consistent with the experimental view that bound TAR RNA conformations are transiently sampled in the free ensemble, following a conformation selection model. The simulation-generated TAR RNA conformations have been successfully used as receptor structures for docking. This finding has important implications for RNA-ligand docking in that docking into an ensemble of simulation-generated RNA structures is shown to be a valuable means to cope with large apo-to-holo conformational transitions of the receptor structure.

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Systematic classification and analysis of themes in protein-DNA recognition.

Publication Date: 2010 Aug 23 PMID: 20726602
Authors: Zhou, P. - Tian, F. - Ren, Y. - Shang, Z.
Journal: J Chem Inf Model

Protein-DNA recognition plays a central role in the regulation of gene expression. With the rapidly increasing number of protein-DNA complex structures available at atomic resolution in recent years, a systematic, complete, and intuitive framework to clarify the intrinsic relationship between the global binding modes of these complexes is needed. In this work, we modified, extended, and applied previously defined RNA-recognition themes to describe protein-DNA recognition and used a protocol that incorporates automatic methods into manual inspection to plant a comprehensive classification tree for currently available high-quality protein-DNA structures. Further, a nonredundant (representative) data set consisting of 200 thematically diverse complexes was extracted from the leaves of the classification tree by using a locally sensitive interface comparison algorithm. On the basis of the representative data set, various physical and chemical properties associated with protein-DNA interactions were analyzed using empirical or semiempirical methods. We also examined the individual energetic components involved in protein-DNA interactions and highlighted the importance of conformational entropy, which has been almost completely ignored in previous studies of protein-DNA binding energy.

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Molecular interaction fields and 3D-QSAR studies of p53-MDM2 inhibitors suggest additional features of ligand-target interaction.

Publication Date: 2010 Aug 23 PMID: 20726601
Authors: Dezi, C. - Carotti, A. - Magnani, M. - Baroni, M. - Padova, A. - Cruciani, G. - Macchiarulo, A. - Pellicciari, R.
Journal: J Chem Inf Model

The design and optimization of small molecule inhibitors of the murine double minute clone 2-p53 (p53-MDM2) interaction has attracted a great deal of interest as a way to novel anticancer therapies. Herein we report 3D-QSAR studies of 41 small molecule inhibitors based on the use of molecular interaction fields and docking experiments as part of an approach to generating predictive models of MDM2 affinity and shedding further light on the structural elements of the ligand-target interaction. These studies have yielded predictive models explaining much of the variance of the 41 compound training set and satisfactorily predicting with 75% success an external test set of 36 compounds. Not surprisingly, and in full agreement with previous data, inspection of the 3D-QSAR coefficients reveals that the major driving force for potent inhibition is given by the hydrophobic interaction between the inhibitors and the p53 binding cleft of MDM2. More surprisingly, and challenging previous suggestions, the projection of the 3D-QSAR coefficients back onto the experimental structures of MDM2 provides an intriguing hypothesis concerning an active role played by the N-terminal region of MDM2 in ligand binding.

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Protein kinases: docking and homology modeling reliability.

Publication Date: 2010 Aug 23 PMID: 20726600
Authors: Tuccinardi, T. - Botta, M. - Giordano, A. - Martinelli, A.
Journal: J Chem Inf Model

A database of about 700 high-resolution kinase structures was used to test the reliability of 17 docking procedures (using six docking software packages) by means of self- and cross-docking studies. The analysis of about 80 000 docking calculations suggests that the docking of an unknown ligand into a kinase has a probability of only 30-37% to be a correct ligand pose. However, based on the hypothesis that docking calculations are more reliable if the ligand to be docked is similar to the ligand present in the complex from which the target docking protein has been extracted, we propose an automated procedure that is able to improve the docking accuracy, suggest the best protein for docking studies, and assess the statistical reliability of docking calculations. The results were also transferred to the homology modeling field and led us to propose an alternative strategy based on ligand similarity for the development of kinase models whose experimental structure was not known. Our results suggest that in many cases this approach can give better results than the classical homology modeling procedure based exclusively on the sequence homology.

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Evolution of human receptor binding affinity of H1N1 hemagglutinins from 1918 to 2009 pandemic influenza A virus.

Publication Date: 2010 Aug 23 PMID: 20726599
Authors: Nunthaboot, N. - Rungrotmongkol, T. - Malaisree, M. - Kaiyawet, N. - Decha, P. - Sompornpisut, P. - Poovorawan, Y. - Hannongbua, S.
Journal: J Chem Inf Model

The recent outbreak of the novel 2009 H1N1 influenza in humans has focused global attention on this virus, which could potentially have introduced a more dangerous pandemic of influenza flu. In the initial step of the viral attachment, hemagglutinin (HA), a viral glycoprotein surface, is responsible for the binding to the human SIA alpha2,6-linked sialopentasaccharide host cell receptor (hHAR). Dynamical and structural properties, based on molecular dynamics simulations of the four different HAs of Spanish 1918 (H1-1918), swine 1930 (H1-1930), seasonal 2005 (H1-2005), and a novel 2009 (H1-2009) H1N1 bound to the hHAR were compared. In all four HA-hHAR complexes, major interactions with the receptor binding were gained from HA residue Y95 and the conserved HA residues of the 130-loop, 190-helix, and 220-loop. However, introduction of the charged HA residues K145 and E227 in the 2009 HA binding pocket was found to increase the HA-hHAR binding efficiency in comparison to the three previously recognized H1N1 strains. Changing of the noncharged HA G225 residue to a negatively charged D225 provides a larger number of hydrogen-bonding interactions. The increase in hydrophilicity of the receptor binding region is apparently an evolution of the current pandemic flu from the 1918 Spanish, 1930 swine, and 2005 seasonal strains. Detailed analysis could help the understanding of how different HAs effectively attach and bind with the hHAR.

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Similarity-potency trees: a method to search for SAR information in compound data sets and derive SAR rules.

Publication Date: 2010 Aug 23 PMID: 20726598
Authors: Wawer, M. - Bajorath, J.
Journal: J Chem Inf Model

An intuitive and generally applicable analysis method, termed similarity-potency tree (SPT), is introduced to mine structure-activity relationship (SAR) information in compound data sets of any source. Only compound potency values and nearest-neighbor similarity relationships are considered. Rather than analyzing a data set as a whole, in part overlapping compound neighborhoods are systematically generated and represented as SPTs. This local analysis scheme simplifies the evaluation of SAR information and SPTs of high SAR information content are easily identified. By inspecting only a limited number of compound neighborhoods, it is also straightforward to determine whether data sets contain only little or no interpretable SAR information. Interactive analysis of SPTs is facilitated by reading the trees in two directions, which makes it possible to extract SAR rules, if available, in a consistent manner. The simplicity and interpretability of the data structure and the ease of calculation are characteristic features of this approach. We apply the methodology to high-throughput screening and lead optimization data sets, compare the approach to standard clustering techniques, illustrate how SAR rules are derived, and provide some practical guidance how to best utilize the methodology. The SPT program is made freely available to the scientific community.

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Model-free drug-likeness from fragments.

Publication Date: 2010 Aug 23 PMID: 20726597
Authors: Ursu, O. - Oprea, T. I.
Journal: J Chem Inf Model

We developed a drug-likeness filter (DLF), starting from molecular fragments and molecular weight (MW), a key property relevant in drug design. The molecular fragments were selected from extended connectivity atom environments based on their occurrence ratio in our collection of drugs and "nondrugs". The DLF recalls 87.05% of compounds from DRUGS (N = 3823) and 40.25% of compounds from the Available Chemicals Directory, (ACD, N = 178 0 11), using molecular fragments only. By adding MW (under 600) as an additional filter, 78.81% of DRUGS and 40.17% of ACD are recalled. The DLF procedure was externally validated using the MDL Drug Data Report (MDDR) data set (N = 169 277): 78.45% of compounds were recalled using the molecular fragments only, while 65.64% pass the DLF-MW filter. Over 50% of a pesticides collection (N = 1482) passed the DLF, as these chemicals share molecular fragments with known drugs. Developed as a model-free filter, DLF is perhaps less useful in discriminating drugs from nondrugs but more likely to rapidly eliminate those chemicals rich in nondrug-like fragments. Since almost 40% of ACD, the standard reference set for nondrugs, contain drug-like molecules, by using a rule-based system such as DLF, one is less likely to mislabel nondrugs due to overfitting. Reliable benchmarks for nondrugs are not likely to exist since medicinal chemistry catalogs tend to be biased toward existing drugs.

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Introduction of jumping fragments in combination with QSARs for the assessment of classification in ecotoxicology.

Publication Date: 2010 Aug 23 PMID: 20726596
Authors: Lozano, S. - Poezevara, G. - Halm-Lemeille, M. P. - Lescot-Fontaine, E. - Lepailleur, A. - Bissell-Siders, R. - Cremilleux, B. - Rault, S. - Cuissart, B. - Bureau, R.
Journal: J Chem Inf Model

Starting from a random set of structures taken from the European Chemical Bureau (ECB) Web site, an estimation of the classification by acute category in ecotoxicology was carried out. This estimation was based on two approaches. One approach consists in starting with global quantitative structure-activity relationship (QSAR) equations, analyzing the results and defining an interpretation in terms of overall results and mode of action. The other starts with the notion of emerging fragments and more specifically with the introduction of a particular concept: the jumping fragments. This publication studies the scopes and limitations of each approach for the classification of the derivatives. A promising combination of the two methods is proposed for the classification and also for bringing new information about the importance, for the ecotoxicity, of specific chemical fragments considered alone or in association with others.

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Systematic signatures for organic reactions.

Publication Date: 2010 Aug 23 PMID: 20726595
Authors: Hendrickson, J. B.
Journal: J Chem Inf Model

A signature for any reaction is defined by just the net change in bonding of the reacting atoms in the conversion of reactant to product structures. This reaction signature is both unique and definitive for any reaction and consists of a simple linear string of letters suitable to index every reaction in a reaction database for computer access. This allows daily entry of new reactions to be easily incorporated and later retrieved with all related reactions from the reaction database.

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Replacement Method and Enhanced Replacement Method Versus the Genetic Algorithm Approach for the Selection of Molecular Descriptors in QSPR/QSAR Theories.

Publication Date: 2010 Aug 19 PMID: 20722426
Authors: Mercader, A. G. - Duchowicz, P. R. - Fernandez, F. M. - Castro, E. A.
Journal: J Chem Inf Model

We compare three methods for the selection of optimal subsets of molecular descriptors from a much greater pool of such regression variables. On the one hand is our enhanced replacement method (ERM) and on the other is the simpler replacement method (RM) and the genetic algorithm (GA). These methods avoid the impracticable full search for optimal variables in large sets of molecular descriptors. Present results for 10 different experimental databases suggest that the ERM is clearly preferable to the GA that is slightly better than the RM. However, the latter approach requires the smallest amount of linear regressions and, consequently, the lowest computation time.

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An Open-Source Java Platform for Automated Reaction Mapping.

Publication Date: 2010 Aug 17 PMID: 20715832
Authors: Crabtree, J. D. - Mehta, D. P. - Kouri, T. M.
Journal: J Chem Inf Model

This article presents software applications that have been built upon a modular, open-source, reaction mapping library that can be used in both cheminformatics and bioinformatics research. We first describe the theoretical underpinnings and modular architecture of the core software library. We then describe two applications that have been built upon that core. The first is a generic reaction viewer and mapper, and the second classifies reactions according to rules that can be modified by end users with little or no programming skills.

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Postprocessing of Protein-Ligand Docking Poses Using Linear Response MM-PB/SA: Application to Wee1 Kinase Inhibitors.

Publication Date: 2010 Aug 16 PMID: 20712342
Authors: Wichapong, K. - Lawson, M. - Pianwanit, S. - Kokpol, S. - Sippl, W.
Journal: J Chem Inf Model

Prediction of the binding strength of untested ligands is a central issue in structure-based drug design. In order to rapidly screen large compound databases, simple scoring schemes are often used in target-based virtual screening. The resulting scores often correlate poorly with biological affinities. More rigorous scoring methods, such as MM-PB/SA, correlate better with biological data by considering solvation effects and protein flexibility in the calculation of the binding free energy of a ligand. Here we describe the performance of a modified MM-PB/SA method on 222 Wee1 kinase inhibitors (48 pyridopyrimidine and 174 pyrrolocarbazole derivatives). Docking of these inhibitors into the available Wee1 kinase crystal structure yielded a consistent binding mode, and the derived MM-PB/SA models showed a significant correlation between calculated and experimental data (r(2) values between 0.64 and 0.67). Further study of these models on external test sets of Wee1 kinase inhibitors and structurally related decoys showed that a model based on a single kinase-inhibitor conformation can discriminate the active inhibitors from decoys. We also tested whether the linear interaction energy method with continuum electrostatics (LIECE) yields comparable results to MM-PB/SA and whether the LIECE and MM-PB/SA models can be applied for virtual screening of compound libraries.

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Rapid Flexible Docking Using a Stochastic Rotamer Library of Ligands.

Publication Date: 2010 Aug 16 PMID: 20712341
Authors: Ding, F. - Yin, S. - Dokholyan, N. V.
Journal: J Chem Inf Model

Existing flexible docking approaches model the ligand and receptor flexibility either separately or in a loosely coupled manner, which captures the conformational changes inefficiently. Here, we propose a flexible docking approach, MedusaDock, which models both ligand and receptor flexibility simultaneously with sets of discrete rotamers. We developed an algorithm to build the ligand rotamer library "on-the-fly" during docking simulations. MedusaDock benchmarks demonstrate a rapid sampling efficiency and high prediction accuracy in both self- (to the cocrystallized state) and cross-docking (to a state cocrystallized with a different ligand), the latter of which mimics the virtual screening procedure in computational drug discovery. We also perform a virtual screening test of four flexible kinase targets, including cyclin-dependent kinase 2, vascular endothelial growth factor receptor 2, HIV reverse transcriptase, and HIV protease. We find significant improvements of virtual screening enrichments when compared to rigid-receptor methods. The predictive power of MedusaDock in cross-docking and preliminary virtual-screening benchmarks highlights the importance to model both ligand and receptor flexibility simultaneously in computational docking.

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Corel WordPerfect X5 WordPerfect X5 Corel 1600 Carling Avenue, Ottawa, Ontario, K1Z 8R7 Canada . http://corel.com.

Publication Date: 2010 Aug 11 PMID: 20701303
Authors: Heller, S. R.
Journal: J Chem Inf Model

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Reducing Docking Score Variations Arising from Input Differences.

Publication Date: 2010 Aug 10 PMID: 20698562
Authors: Feher, M. - Williams, C. I.
Journal: J Chem Inf Model

The variability of docking results as a function of variations in ligand input conformations was studied for the GOLD, Glide, FlexX, and Surflex programs. It is concluded that there are two major effects leading to such variability: the adequacy of conformational search during docking and random "chaotic" effects arising from sensitivity to small input perturbations. It is shown that although the former is generally the stronger effect, the latter is also highly significant for almost all docking engines. The strong target-to-target variation of the magnitude of these effects is emphasized. The performance of different packages is compared using these measures. Guidelines are provided for different programs to reduce variability and improve reproducibility, which involve using a small number of input conformations as starting points for docking, followed by the selection of the top scoring docked pose from the results as the best docked solution.

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Test MM-PB/SA on True Conformational Ensembles of Protein-Ligand Complexes.

Publication Date: 2010 Aug 9 PMID: 20695488
Authors: Li, Y. - Liu, Z. - Wang, R.
Journal: J Chem Inf Model

The molecular mechanics Poisson-Boltzmann surface area (MM-PB/SA) method has been popular for computing protein-ligand binding free energies in recent years. All previous evaluations of the MM-PB/SA method are based upon computer-generated conformational ensembles, which may be affected by the defective computational methods used for preparing these conformational ensembles. In an attempt to reach more convincing conclusions, we have evaluated the MM-PB/SA method on a set of 24 diverse protein-ligand complexes, each of which has a set of conformations derived from NMR spectroscopy. Our results indicate that both MM-PB/SA and molecular mechanics generalized Born surface area (MM-GB/SA) are able to produce a modest correlation between their results and the experimentally measured binding free energies on our test set. In particular, both MM-PB/SA and MM-GB/SA produced better results by using a representative structure (R = 0.72-0.79) rather than averaging over the conformational ensemble of each given complex (R = 0.61-0.74). A head-to-head comparison with four selected scoring functions (X-Score, PLP, ChemScore, and DrugScore) on the same test set reveals that MM-PB/SA and MM-GB/SA results are marginally better than those produced by scoring funcitons, supporting the value of the MM-PB/SA method. Nevertheless, scoring functions are still more cost-effective options, especially for high-throughput tasks.

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Computational Modeling Toward Understanding Agonist Binding on Dopamine 3.

Publication Date: 2010 Aug 9 PMID: 20695484
Authors: Zhao, Y. - Lu, X. - Yang, C. Y. - Huang, Z. - Fu, W. - Hou, T. - Zhang, J.
Journal: J Chem Inf Model

The dopamine 3 (D3) receptor is a promising therapeutic target for the treatment of nervous system disorders, such as Parkinson's disease, and current research interests primarily focus on the discovery/design of potent D3 agonists. Herein, a well-designed computational protocol, which combines pharmacophore identification, homology modeling, molecular docking, and molecular dynamics (MD) simulations, was employed to understand the agonist binding on D3 aiming to provide insights into the development of novel potent D3 agonists. We (1) identified the chemical features required in effective D3 agonists by pharmacophore modeling based upon 18 known diverse D3 agonists; (2) constructed the three-dimensional (3D) structure of D3 based on homology modeling and the pharmacophore hypothesis; (3) identified the binding modes of the agonists to D3 by the correlation between the predicted binding free energies and the experimental values; and (4) investigated the induced fit of D3 upon agonist binding through MD simulations. The pharmacophore models of the D3 agonists and the 3D structure of D3 can be used for either ligand- or receptor-based drug design. Furthermore, the MD simulations further give the insight that the long and flexible EL2 acts as a "door" for agonist binding, and the "ionic lock" at the bottom of TM3 and TM6 is essential to transduce the activation signal.

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Benchmark Performance of MultiCASE Inc. Software in Ames Mutagenicity Set.

Publication Date: 2010 Aug 9 PMID: 20695480
Authors: Saiakhov, R. D. - Klopman, G.
Journal: J Chem Inf Model

The predictive performances of MC4PC were evaluated using its learning machine functionality. Its superior characteristics are demonstrated in this following up study using the newly published Ames mutagenicity benchmark set.

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Discovery of New Inhibitors of Schistosoma mansoni PNP by Pharmacophore-Based Virtual Screening.

Publication Date: 2010 Aug 9 PMID: 20695479
Authors: Postigo, M. P. - Guido, R. V. - Oliva, G. - Castilho, M. S. - da R Pitta, I. - de Albuquerque, J. F. - Andricopulo, A. D.
Journal: J Chem Inf Model

Schistosomiasis is considered the second most important tropical parasitic disease, with severe socioeconomic consequences for millions of people worldwide. Schistosoma mansoni , one of the causative agents of human schistosomiasis, is unable to synthesize purine nucleotides de novo, which makes the enzymes of the purine salvage pathway important targets for antischistosomal drug development. In the present work, we describe the development of a pharmacophore model for ligands of S. mansoni purine nucleoside phosphorylase (SmPNP) as well as a pharmacophore-based virtual screening approach, which resulted in the identification of three thioxothiazolidinones (1-3) with substantial in vitro inhibitory activity against SmPNP. Synthesis, biochemical evaluation, and structure-activity relationship investigations led to the successful development of a small set of thioxothiazolidinone derivatives harboring a novel chemical scaffold as new competitive inhibitors of SmPNP at the low-micromolar range. Seven compounds were identified with IC(50) values below 100 muM. The most potent inhibitors 7, 10, and 17 with IC(50) of 2, 18, and 38 muM, respectively, could represent new potential lead compounds for further development of the therapy of schistosomiasis.

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Pocket similarity: are alpha carbons enough?

Publication Date: 2010 Aug 23 PMID: 20690656
Authors: Feldman, H. J. - Labute, P.
Journal: J Chem Inf Model

A novel method for measuring protein pocket similarity was devised, using only the alpha carbon positions of the pocket residues. Pockets were compared pairwise using an exhaustive three-dimensional Calpha common subset search, grouping residues by physicochemical properties. At least five Calpha matches were required for each hit, and distances between corresponding points were fit to an Extreme Value Distribution resulting in a probabilistic score or likelihood for any given superposition. A set of 85 structures from 13 diverse protein families was clustered based on binding sites alone, using this score. It was also successfully used to cluster 25 kinases into a number of subfamilies. Using a test kinase query to retrieve other kinase pockets, it was found that a specificity of 99.2% and sensitivity of 97.5% could be achieved using an appropriate cutoff score. The search itself took from 2 to 10 min on a single 3.4 GHz CPU to search the entire Protein Data Bank (133 800 pockets), depending on the number of hits returned.

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WizePairZ: a novel algorithm to identify, encode, and exploit matched molecular pairs with unspecified cores in medicinal chemistry.

Publication Date: 2010 Aug 23 PMID: 20690655
Authors: Warner, D. J. - Griffen, E. J. - St-Gallay, S. A.
Journal: J Chem Inf Model

An algorithm to automatically identify and extract matched molecular pairs from a collection of compounds has been developed, allowing the learning associated with each molecular transformation to be readily exploited in drug discovery projects. Here, we present the application to an example data set of 11 histone deacetylase inhibitors. The matched pairs were identified, and corresponding differences in activity and lipophilicity were recorded. These property differences were associated with the chemical transformations encoded in the SMIRKS reaction notation. The transformations identified a subseries with the optimal balance of these two parameters. Enumeration of a virtual library of compounds using the extracted transformations identified two additional compounds initially excluded from the analysis with an accurate estimation of their biological activity. We describe how the WizePairZ system can be used to archive and apply medicinal chemistry knowledge from one drug discovery project to another as well as identify common bioisosteres.

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FLAP: GRID molecular interaction fields in virtual screening. validation using the DUD data set.

Publication Date: 2010 Aug 23 PMID: 20690627
Authors: Cross, S. - Baroni, M. - Carosati, E. - Benedetti, P. - Clementi, S.
Journal: J Chem Inf Model

The performance of FLAP (Fingerprints for Ligands and Proteins) in virtual screening is assessed using a subset of the DUD (Directory of Useful Decoys) benchmarking data set containing 13 targets each with more than 15 different chemotype classes. A variety of ligand and receptor-based virtual screening approaches are examined, using combinations of individual templates 2D structures of known actives, a cocrystallized ligand, a receptor structure, or a cocrystallized ligand-biased receptor structure. We examine several data fusion approaches to combine the results of the individual virtual screens. In doing so, we show that excellent chemotype enrichment is achieved in both single target ligand-based and receptor-based approaches, of approximately 17-fold over random on average at a false positive rate of 1%. We also show that using as much starting knowledge as possible improves chemotype enrichment, and that data fusion using Pareto ranking is an effective method to do this giving up to 50% improvement in enrichment over the single methods. Finally we show that if inactivity or decoy data is incorporated, automatically training the scoring function in FLAP improves recovery still further, with almost 2-fold improvement over the enrichments shown by the single methods. The results clearly demonstrate the utility of FLAP for virtual screening when either a limited or wide range of prior knowledge is available.

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Enthalpic Efficiency of Ligand Binding.

Publication Date: 2010 Aug 4 PMID: 20684566
Authors: Ferenczy, G. G. - Keseru, G. M.
Journal: J Chem Inf Model

The thermodynamics of ligand-protein binding has received much attention recently. In the present contribution we focus on the enthalpic component of binding. The dissociation constant, pK(d), was decomposed into enthalpic and entropic components (pK(d) = pK(H) + pK(S)), and pK(H), defined as pK(H) = -DeltaH/(2.303.RT) was used to characterize the enthalpy contribution to binding. It was found that the maximal achievable pK(H) decreases with increasing molecular size. This is in contrast to maximal pK(d) that increases with molecular size until it achieves a plateau. Size-independent enthalpic efficiency (SIHE) was defined as SIHE = pK(H)/40.HA(0.3), with HA being the number of heavy atoms. SIHE allows a size unbiased comparative binding characterization of compounds. It can find use in hit and lead selection and also in monitoring optimization in drug discovery programs. The physical background of decreasing maximal pK(H) with molecular size is discussed, and its consequences to drug discovery are analyzed. It is concluded that the feasibility of simultaneous optimization of affinity and enthalpy diminishes with increasing molecular size. Consequently, binding thermodynamics considerations are to be applied primarily in hit prioritization and hit-to-lead optimization.

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Knowledge-based scoring functions in drug design. 1. Developing a target-specific method for kinase-ligand interactions.

Publication Date: 2010 Aug 23 PMID: 20681607
Authors: Xue, M. - Zheng, M. - Xiong, B. - Li, Y. - Jiang, H. - Shen, J.
Journal: J Chem Inf Model

Protein kinases are attractive targets for therapeutic interventions in many diseases. Due to their importance in drug discovery, a kinase family-specific potential of mean force (PMF) scoring function, kinase-PMF, was developed to assess the binding of ATP-competitive kinase inhibitors. It is hypothesized that target-specific PMF scoring functions may achieve increased performance in scoring along with the growth of the PDB database. The kinase-PMF inherits the functions and atom types in PMF04 and uses a kinase data set of 872 complexes to derive the potentials. The performance of kinase-PMF was evaluated with an external test set containing 128 kinase crystal structures. We compared it with eight scoring functions commonly used in computer-aided drug design, either in terms of the retrieval rate of retrieving "right" conformations or a virtual screening study. The evaluation results clearly demonstrate that a target-specific scoring function is a promising way to improve prediction power in structure-based drug design compared with other general scoring functions. To provide this rescoring service for researchers, a publicly accessible Web site was established at http://202.127.30.184:8080/scoring/index.jsp .

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Search for complexity generating chemical transformations by combining connectivity analysis and cascade transformation patterns.

Publication Date: 2010 Aug 23 PMID: 20681604
Authors: Nowak, G. - Fic, G.
Journal: J Chem Inf Model

Retrosynthetic analysis involved in a backward search for strategic disconnections is still the most powerful strategy, recently advanced by topology-based complexity estimation, for discovering the shortest sequences of transformations and chemical synthesis planning. Therein, we propose an alternative strategy that combines backward and forward search embodied within a mathematical model of generating chemical transformations. The backward reasoning involves a new concept of the strategic bond tree for alternative multibond disconnections of a target molecule. In the forward direction, each combination of the resulted structural fragments is examined for reconstruction of the target structure by means of biomimetic transformation patterns that describe one-pot multibond forming reactions. The algorithm has been implemented into the CSB system, and its performance is illustrated by examples of published complex molecule syntheses for comparison and analysis. This paper describes the strategy for discovering the shortest synthetic pathways based on the multibond forming cascade transformations for application in synthesis design and generating synthetically accessible product libraries.

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Hashing algorithms and data structures for rapid searches of fingerprint vectors.

Publication Date: 2010 Aug 23 PMID: 20681581
Authors: Nasr, R. - Hirschberg, D. S. - Baldi, P.
Journal: J Chem Inf Model

In many large chemoinformatics database systems, molecules are represented by long binary fingerprint vectors whose components record the presence or absence of particular functional groups or combinatorial features. To speed up database searches, we propose to add to each fingerprint a short signature integer vector of length M. For a given fingerprint, the i component of the signature vector counts the number of 1-bits in the fingerprint that fall on components congruent to i modulo M. Given two signatures, we show how one can rapidly compute a bound on the Jaccard-Tanimoto similarity measure of the two corresponding fingerprints, using the intersection bound. Thus, these signatures allow one to significantly prune the search space by discarding molecules associated with unfavorable bounds. Analytical methods are developed to predict the resulting amount of pruning as a function of M. Data structures combining different values of M are also developed together with methods for predicting the optimal values of M for a given implementation. Simulations using a particular implementation show that the proposed approach leads to a 1 order of magnitude speedup over a linear search and a 3-fold speedup over a previous implementation. All theoretical results and predictions are corroborated by large-scale simulations using molecules from the ChemDB. Several possible algorithmic extensions are discussed.

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Inverse frequency weighting of fragments for similarity-based virtual screening.

Publication Date: 2010 Aug 23 PMID: 20672867
Authors: Arif, S. M. - Holliday, J. D. - Willett, P.
Journal: J Chem Inf Model

This paper discusses the weighting of two-dimensional fingerprints for similarity-based virtual screening, specifically the use of weights that assign greatest importance to the substructural fragments that occur least frequently in the database that is being screened. Virtual screening experiments using the MDL Drug Data Report and World of Molecular Bioactivity databases show that the use of such inverse frequency weighting schemes can result, in some circumstances, in marked increases in screening effectiveness when compared with the use of conventional, unweighted fingerprints. Analysis of the characteristics of the various schemes demonstrates that such weights are best used to weight the fingerprint of the reference structure in a similarity search, with the database structures' fingerprints unweighted. However, the increases in performance resulting from such weights are only observed with structurally homogeneous sets of active molecules; when the actives are diverse, the best results are obtained using conventional, unweighted fingerprints for both the reference structure and the database structures.

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Predicting polypharmacology by binding site similarity: from kinases to the protein universe.

Publication Date: 2010 Aug 23 PMID: 20666497
Authors: Milletti, F. - Vulpetti, A.
Journal: J Chem Inf Model

Polypharmacology is receiving increasing attention in the pharmaceutical industry, since finding new targets of a compound is useful not only for anticipating possible side effects but also for opening new therapeutic opportunities. Thus, while system biology and personalized medicine are becoming increasingly important, there is an urgent need to map the inhibition profile of a compound on a large panel of targets by using both experimental and computational methods. This is especially important for kinase inhibitors, given the high similarity at the binding site level for the 518 kinases in the human genome. In this paper, we propose and validate a new method to predict the inhibition map of a compound by comparison of binding pockets. We used a subset of the Ambit panel for the validation-17 inhibitors with K(d) measured on 189 kinases-and found that on average 37% of kinases inhibited with K(d) < 10 microM were retrieved at 10% ROC enrichment. These results make this method particularly suitable to rationalize and optimize the selectivity profile of a compound. In addition, the method was extended to explore all the proteins in the PDB by using as queries pockets occupied by compounds of biological interest (ATP and various marketed drugs). The profiling of compounds against the protein universe revealed that striking structural similarities at the subpocket level (RMSD < 0.5 A) may also occur among targets with different folds, which can be exploited not only to predict off-target effects but also to design novel inhibitors for the target of interest.

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