Journal of Chemical Information and Modeling

AsteriX: a web-server to automatically extract ligand coordinates from figures in PDF articles.

Publication Date: 2012 Feb 2 PMID: 22299625
Authors: Lounnas, V. - Vriend, G.
Journal: J Chem Inf Model

Coordinates describing the chemical structures of small molecules that are potential ligands for pharmaceutical targets are used at many stages of the drug design process. The coordinates of the vast majority of ligands can be obtained from either publicly accessible or commercial databases. However, interesting ligands sometimes are only available from the scientific literature in which case their coordinates need to be reconstructed manually; a process that consists of a series of time-consuming steps. We present a Web server that helps reconstruct the three dimensional (3D) coordinates of ligands for which a two dimensional (2D) picture is available in a PDF file. The software, called AsteriX, analyses every picture contained in the PDF file and attempts to determine automatically whether or not it contains ligands. Areas in pictures that may contain molecular structures are processed to extract connectivity and atom type information that allow coordinates to be subsequently reconstructed. The AsteriX Web server was tested on a series of articles containing a large diversity in graphical representations. In total, 88% of 3249 ligand structures present in the test set were identified as chemical diagrams. Of these, about half were interpreted correctly as 3D structures, and a further one-third required only minor manual corrections. It is principally impossible to always correctly reconstruct 3D coordinates from pictures because there are many different protocols for drawing a 2D image of a ligand, but more importantly a wide variety of semantic annotations are possible. The AsteriX Web server therefore includes facilities that allow the users to augment partial or partially correct 3D reconstructions. All 3D reconstructions submitted, checked, and corrected by the users remain at the server and are freely available for everybody. The coordinates of the reconstructed ligands are made available in a series of formats commonly used in drug design research. The AsteriX web-server is freely available at http://swift.cmbi.ru.nl/bitmapb/

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A Pragmatic Approach Using First Principle Methods to Address Site of Metabolism with Implications for Reactive Metabolite Formation.

Publication Date: 2012 Feb 2 PMID: 22299574
Authors: Norinder, U. - Hsiao, Y. W. - Petersson, C. - Svensson, M.
Journal: J Chem Inf Model

The majority of xenobiotics are metabolized by cytochrome P450 (CYP) enzymes. The discovery of drug candidates with low propensity to form reactive metabolites and low clearance can be facilitated by understanding CYP-mediated xenobiotic metabolism. Being able to predict the sites where reactive metabolites form is beneficial in drug design to produce drug candidates free of reactive metabolite issues. Herein, we report a pragmatic protocol using first principle density functional theory (DFT) calculations for predicting sites of epoxidation and hydroxylation of aromatic substrates mediated by CYP. The method is based on the relative stabilities of the CYP-substrate intermediates or the substrate epoxides. Consequently, it concerns mainly the electronic reactivity of the substrates. Comparing to the experimental findings, the presented protocol gave excellent first-ranked epoxidation site predictions of 83% and when the test was extended to CYP-mediated sites of aromatic hydroxylation, satisfactory results were also obtained (73%). This indicates that our assumptions are valid and also implies that the intrinsic reactivities of the substrates are in general more important than their binding poses in proteins, although the protocol may benefit from the addition of docking information.

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SiteBinder - an improved approach for comparing multiple protein structural motifs.

Publication Date: 2012 Feb 1 PMID: 22296449
Authors: Sehnal, D. - Svobodova Varekova, R. - Huber, H. J. - Geidl, S. - Ionescu, C. M. - Wimmerova, M. - Koca, J.
Journal: J Chem Inf Model

There is a paramount need to develop new techniques and tools that will extract as much information as possible from the ever growing repository of protein 3D structures. We report here on the development of a software tool for the multiple superimposition of large sets of protein structural motifs. Our superimposition methodology performs a systematic search for the atom pairing which provides the best fit. During this search, the RMSDs for all chemically relevant pairings are calculated by quaternion algebra. The number of evaluated pairings is markedly decreased by using PDB annotations for atoms. This approach guarantees that the best fit will be found, and can be applied even when sequence similarity is low or does not exist at all. We have implemented this methodology in the web application SiteBinder, which is able to process up to thousands of protein structural motifs in a very short time, and which provides an intuitive and user-friendly interface. Our benchmarking analysis has shown the robustness, efficiency and versatility of our methodology and its implementation by the successful superimposition of 1000 experimentally determined structures for each of 32 eukaryotic linear motifs. We also demonstrate the applicability of SiteBinder using three case studies. We first compared the structures of 61 PA-IIL sugar binding sites containing 9 different sugars and we found that the sugar binding sites of PA-IIL and its mutants have a conserved structure despite their binding different sugars. We then superimposed over 300 zinc finger central motifs, and revealed that the molecular structure in the vicinity of the Zn atom is highly conserved. Finally, we superimposed 12 BH3 domains from pro-apoptotic proteins. Our findings come to support the hypothesis that there is a structural basis for the functional segregation of BH3-only proteins into activators and enablers.

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A Scaffold-Independent Subcellular Event-Based Analysis: Characterization of Significant Structural Modifications.

Publication Date: 2012 Jan 30 PMID: 22288932
Authors: Lin, Y. T. - Chen, G. Y.
Journal: J Chem Inf Model

ABSTRACT The overall molecular description was separated using the Jurs descriptor and the ES descriptor from our initial study, allowing characterization of the significant structural modifications of given collected PPARgamma agonists directly from ligand-dependent cellular data. For ligand-dependent cellular data, the selected Jurs descriptor is the integrated description of all the general subcellular events of given collected agonists but is scaffold-dependent. The selected ES descriptors represent significant structural modifications corresponding to singular ligand-receptor interactions. To further elucidate the descriptor-event relationship in this subcellular event-based analysis, we used 3 biological data sets to reveal and validate that the Jurs descriptor can be further divided into 3 important descriptors, the logD, PSA (polar surface area), and shape-like descriptor. In general, logD is the general description of solvation and desolvation, PSA is the general description of membrane transport, and the shape-like descriptor describes general ligand-receptor interaction in a subcellular event-based context. The general description of subcellular events thus able to be represented by the 3 integral descriptors were characterized and discussed. In the end, the method that a scaffold-independent event-based working equation through regression fit for capturing significant structural modifications of the ligand-receptor binding event was developed. The significant electronic structural modifications of the 46 collected TZD PPARgamma agonists1-3 were captured and correlated to all the singular ligand-receptor interactions revealed in the crystallography of the rosiglitazone-PPARgamma complexes. KEYWORDS Ligand-depenedent cellular data, Jurs descriptor, Electrotopological State (ES) descriptor, subcellular events, descriptor-event relationship, significant structural modifications.

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ProBiS-Database: Pre-calculated Binding Site Similarities and Local Pairwise Alignments of PDB Structures.

Publication Date: 2012 Jan 23 PMID: 22268964
Authors: Konc, J. - Cesnik, T. - Trykowska Konc, J. - Penca, M. - Janezic, D.
Journal: J Chem Inf Model

ProBiS-Database is a searchable repository of pre-calculated local structural alignments in proteins detected by the ProBiS algorithm in the Protein Data Bank. Identification of functionally important binding regions of the protein is facilitated by structural similarity scores mapped to the query protein structure. PDB structures that have been aligned with a query protein may be rapidly retrieved from the ProBiS-database which is thus able to generate hypotheses concerning the roles of uncharacterized proteins. Presented with uncharacterized protein structure, ProBiS-Database can discern relationships between such a query protein and other, better known proteins in the PDB. Fast access and a user-friendly graphical interface promote easy exploration of this database of over 420 million local structural alignments. The ProBiS-Database is updated weekly and is freely available online at http://probis.cmm.ki.si/database.

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EXPLORING INHIBITOR RELEASE PATHWAYS IN HISTONE DEACETYLASES USING RANDOM ACCELERATION MOLECULAR DYNAMICS SIMULATIONS.

Publication Date: 2012 Jan 23 PMID: 22263580
Authors: Kalyaanamoorthy, S. - Chen, Y. P.
Journal: J Chem Inf Model

Molecular channel exploration perseveres to be the prominent solution for eliciting structure and accessibility of active site and other internal spaces of macromolecules. The volume and silhouette characterization of these channels provides answers for the issues of substrate access and ligand swapping between the obscured active site and the exterior of the protein. Histone deacetylases (HDACs) are metal-dependent enzymes that are involved in the cell growth, cell cycle regulation and progression, and their deregulations have been linked with different types of cancers. Hence HDACs, especially the class I family, are widely recognized as the important cancer targets and the characterizations of their structures and functions have been of special interest in cancer drug discovery. The class I HDACs are known to possess two different protein channels, a 11 A (named as channel A) and a 14 A (named as channel B1) of which, the former is a ligand or substrate occupying tunnel that leads to the buried active site zinc ion and the latter is speculated to be involved in product release. In this work, we have carried out Random acceleration molecular dynamics (RAMD) simulations coupled with the classical molecular dynamics to explore the release of the ligand, N-(2-aminophenyl) benzamide (LLX) from the active sites of the recently solved X-ray crystal structure of HDAC2 and the computationally modeled HDAC1 proteins. The RAMD simulations identified significant structural and dynamic features of the HDAC channels, especially the key 'gate-keeping' amino acid residues that control these channels and the ligand release events. Further, this study identified a novel and unique channel B2, a sub-channel from channel B1, in the HDAC1 protein structure. The roles of water molecules in the LLX release from the HDAC1 and HDAC2 enzymes are also discussed. Such structural and dynamic properties of the HDAC protein channels that govern the ligand escape reactions will provide further mechanistic insights into the HDAC enzymes which, on a long run, have a potential to bring new ideas for developing more promising HDAC inhibitors as well as extend our atomic level understandings on their mechanisms of action.

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Conformational Analysis of 6alpha- and 6beta-Naltrexol and Derivatives and Relationship to Opioid Receptor Affinity.

Publication Date: 2012 Jan 20 PMID: 22263545
Authors: Bayron, J. A. - Deveau, A. M. - Stubbs, J. M.
Journal: J Chem Inf Model

Naltrexol and its C(6) alpha and beta desoxy, iodo, mesyl, tosyl, trifyl, dimethylcarbamyl, and diphenylcarbamyl derivatives were studied in their energy-minimized C ring chair-like and boat-like conformations using B3LYP/6-31G** and SM5.4/A to estimate aqueous solvation free energy. The results were compared to experimental opioid receptor binding affinities. The total energy difference between beta conformers correlated well with MOR binding affinity, while the aqueous solvation free energy correlated well with the KOR binding affinity.

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Homology Model-Guided 3D-QSAR Studies of HIV-1 Integrase Inhibitors.

Publication Date: 2012 Jan 18 PMID: 22256860
Authors: Sharma, H. - Cheng, X. - Buolamwini, J. K.
Journal: J Chem Inf Model

In the present study, we report the exploration of binding modes of potent HIV-1 integrase (IN) inhibitors MK-0518 (raltegravir) and GS-9137 (elvitegravir) as well as chalcone and related amide IN inhibitors we recently synthesized; and the development of 3D-QSAR models for integrase inhibition. Homology models of DNA-bound HIV-1 IN were constructed based on the X-ray crystal structure of the foamy virus IN-DNA complex (PDB ID: 3L2T), and used for docking. The binding modes of raltegravir and elvitegravir in our homology models are in accordance with those in the foamy virus structure revealing interactions important for inhibitor-IN binding. To gain further insights into the structural requirements for IN inhibition, three-dimensional quantitative structure activity relationship (3D-QSAR) studies were conducted using raltegravir, elvitegravir and their analogs, our synthesized 3-keto salicylic acid IN inhibitor series as well as other structurally related HIV-1 IN inhibitors. In the first part of the study with 103 compounds, atom-fit alignments, I and II and docking-based alignment, III, were used to develop 3D-QSAR Models, 1, 2 and 3, respectively, each comprising comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSARs. This initial analysis indicated that the docking-based (structure-based) Model 3 performed better than the atom-fit (ligand-based) Models 1 and 2, in terms of statistical significance, robustness. Thus, the docking-based alignment was then subsequently used with an expanded data set of 296 compounds for building a more comprehensive 3D-QSAR, Model 4. Model 4 afforded good q2 values of 0.70 and 0.75 for CoMFA and CoMSIA 3D-QSARs, respectively, and showed good predictive performance on an external validation test set of 59 compounds with predictive r2 values up to 0.71. The HIV IN-DNA homology model of biological relevance and the comprehensive 3D-QSAR models developed in the present study provide insights and new predictive tools for structure-based design and optimization of IN inhibitors.

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Fighting High Molecular Weight in Bioactive Molecules with Sub-Pharmacophore-Based Virtual Screening.

Publication Date: 2012 Jan 18 PMID: 22251316
Authors: von Korff, M. - Freyss, J. - Sander, T. - Boss, C. - Ciana, C. L.
Journal: J Chem Inf Model

A new sub-pharmacophore-based virtual screening method is introduced. Sub-pharmacophores are derived from large active molecules to detect small bioactive molecules as seeds for starting points in medicinal chemistry programs. A large dataset was assembled from the ChEMBL database to check the validity of this approach. Molecules for 133 targets with molecular weights between 450 and 850 were selected as queries. For the query molecules, the pharmacophore descriptors were calculated. Up to 56,000 sub-pharmacophore descriptors with five to seven pharmacophore points were derived from the query pharmacophores. The sub-pharmacophore descriptors were used as query to screen 1079 test datasets, containing decoys and spike molecules. A maximum upper molecular weight limit of 400 Dalton was set for the test molecules. Three different chemical fingerprint descriptors were used for comparison purposes. The sub-pharmacophore approach detected active molecules for 85 out of 133 targets and outperformed the chemical fingerprints. This ligand-based virtual screening experiment was triggered by the needs of medicinal chemistry. Applying the sub-pharmacophore method in a medicinal chemistry program, where a lead molecule with a molecular weight of 800 Dalton was available, resulted in a new series of molecules with molecular weights below 400.

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Analyzing the Molecular Basis of Enzyme Stability in Ethanol/Water Mixtures Using Molecular Dynamics Simulations.

Publication Date: 2012 Jan 30 PMID: 22243049
Authors: Lousa, D. - Baptista, A. M. - Soares, C. M.
Journal: J Chem Inf Model

One of the drawbacks of nonaqueous enzymology is the fact that enzymes tend to be less stable in organic solvents than in water. There are, however, some enzymes that display very high stabilities in nonaqueous media. In order to take full advantage of the use of nonaqueous solvents in enzyme catalysis, it is essential to elucidate the molecular basis of enzyme stability in these media. Toward this end, we performed mus-long molecular dynamics simulations using two homologous proteases, pseudolysin, and thermolysin, which are known to have considerably different stabilities in solutions containing ethanol. (1) The analysis of the simulations indicates that pseudolysin is more stable than thermolysin in ethanol/water mixtures and that the disulfide bridge between C30 and C58 is important for the stability of the former enzyme, which is consistent with previous experimental observations. (1, 2) Our results indicate that thermolysin has a higher tendency to interact with ethanol molecules (especially through van der Waals contacts) than pseudolysin, which can lead to the disruption of intraprotein hydrophobic interactions and ultimately result in protein unfolding. In the absence of the C30-C58 disulfide bridge, pseudolysin undergoes larger conformational changes, becoming more open and more permeable to ethanol molecules which accumulate in its interior and form hydrophobic interactions with the enzyme, destroying its structure. Our observations are not only in good agreement with several previous experimental findings on the stability of the enzymes studied in ethanol/water mixtures but also give an insight on the molecular determinants of this stability. Our findings may, therefore, be useful in the rational development of enzymes with increased stability in these media.

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Understanding Product Specificity of Protein Lysine Methyltransferases from QM/MM MD and Free Energy Simulations: the Effects of Mutation on SET7/9 beyond the Tyr/Phe Switch.

Publication Date: 2012 Jan 16 PMID: 22242964
Authors: Guo, H. - Yao, J. - Chu, Y. - Ran, A.
Journal: J Chem Inf Model

The results of hybrid quantum mechanical/molecular mechanical (QM/MM) free energy (potential of mean force) simulations for methyl transfer processes in SET7/9 and its Y245A mutant are compared to address the question concerning the change of the product specificity as well as catalytic efficiency due to the mutation. One of the key questions is whether or not the free energy profiles of methyl transfers may be used to predict the change of the product specificity as a result of the mutations for the residues that are not located at the Tyr/Phe switch position. The simulations show that while the wild type SET7/9 is a mono-methylase, the Y245-->A mutation increases the ability of the enzyme to add more methyl groups on the target lysine (i.e., acting as a tri-methylase). However, the first methyl transfer process seems to become less efficient in the mutant compared to that in wild-type. All these results are consistent with experimental observations concerning the effects of the mutation on the product specificity and catalytic efficiency. Thus, the previous suggestion that the energetics of the methyl transfer reactions may determine the product specificity, at least in some cases, is confirmed by the present work. Moreover, the dynamic information of the reactant complexes obtained from the QM/MM MD simulations shows that the ability of the reactant complexes to form the reactive TS-like configurations may be used as an important indicator for the prediction of the product specificity of PKMTs, consistent with previous computational studies.

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Development of Surface-SFED Models for Polar Solvents.

Publication Date: 2012 Feb 1 PMID: 22242933
Authors: Lee, S. - Cho, K. H. - Acree, W. E. - No, K. T.
Journal: J Chem Inf Model

We developed surface grid-based solvation free energy density (Surface-SFED) models for 36 commonly used polar solvents. The parametrization was performed with a large and diverse set of experimental solvation free energies mainly consisting of combinations of polar solvent and multipolar solute. Therefore, the contribution of hydrogen bonds was dominant in the model. In order to increase the accuracy of the model, an elaborate version of a previous hydrogen bond acidity and basicity prediction model was introduced. We present two parametrizations for use with experimentally determined (Surface-SFED/HB(exp)) and empirical (Surface-SFED/HB(cal)) hydrogen bond acidity and basicity values. Our computational results agreed well with experimental results, and inaccuracy of empirical hydrogen bond acidity and basicity values was the main source of error in Surface-SFED/HB(cal). The mean absolute errors of Surface-SFED/HB(exp) and Surface-SFED/HB(cal) were 0.49 and 0.54 kcal/mol, respectively.

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Prospects for Tertiary Structure Prediction of RNA Based on Secondary Structure Information.

Publication Date: 2012 Jan 26 PMID: 22239168
Authors: Yamasaki, S. - Nakamura, S. - Fukui, K.
Journal: J Chem Inf Model

We developed a method, called RNA Assembler using Secondary Structure Information Effectively (RASSIE), for predicting RNA tertiary structures using known secondary structure information. We attempted a fragment assembly-based method that uses a secondary structure-based fragment library. For several typical target structures such as stem-loops, bulge-loops, and 2-way junctions, our method provided numerous good quality candidate structures in less computational time than previously proposed methods. By using a high-resolution potential energy function, we were able to select good predicted structures from candidate structures. This method of efficient conformational search and detailed structure evaluation using high-resolution potential is potentially useful for the tertiary structure prediction of RNA.

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Modelling Flexible Pharmacophores with Distance Geometry, Scoring and Bound Stretching.

Publication Date: 2012 Jan 11 PMID: 22235879
Authors: Binns, M. - Devisser, S. P. - Theodoropoulos, C.
Journal: J Chem Inf Model

The study of pharmacophores, i.e. of common features between different ligands, is important for the quantitative identification of "compatible" enzymes and binding species. A pharmacophore-based technique is developed which combines multiple conformations with a distance geometry method to create flexible pharmacophore representations. It uses a set of low-energy conformations combined with a new process we call bound stretching to create sets of distance bounds, which contain all or most of the low-energy conformations. The bounds can be obtained using the exact distances between pairs of atoms from the different low energy conformations. To avoid missing conformations we can take advantage of the triangle distance inequality between sets of three points to logically expand a set of upper and lower distance bounds (bound stretching). The flexible pharmacophore can be found using a 3D maximal common subgraph method, which uses the overlap of distance bounds to determine the overlapping structure. A scoring routine is implemented to select the substructures with the largest overlap since there will typically be many overlaps with the maximum number of overlapping bounds. A case study is presented in which 3D flexible pharmacophores are generated and used to eliminate potential binding species identified by a 2D pharmacophore method. A second case study creates flexible pharmacophores from a set of Thrombin ligands. These are used to compare the new method with existing pharmacophore identification software.

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Proteins as Sponges: A Statistical Journey along Protein Structure Organization Principles.

Publication Date: 2012 Jan 19 PMID: 22235848
Authors: Di Paola, L. - Paci, P. - Santoni, D. - De Ruvo, M. - Giuliani, A.
Journal: J Chem Inf Model

The analysis of a large database of protein structures by means of topological and shape indexes inspired by complex network and fractal analysis shed light on some organizational principles of proteins. Proteins appear much more similar to "fractal" sponges than to closely packed spheres, casting doubts on the tenability of the hydrophobic core concept. Principal component analysis highlighted three main order parameters shaping the protein universe: (1) "size", with the consequent generation of progressively less dense and more empty structures at an increasing number of residues, (2) "microscopic structuring", linked to the existence of a spectrum going from the prevalence of heterologous (different hydrophobicity) to the prevalence of homologous (similar hydrophobicity) contacts, and (3) "fractal shape", an organizing protein data set along a continuum going from approximately linear to very intermingled structures. Perhaps the time has come for seriously taking into consideration the real relevance of time-honored principles like the hydrophobic core and hydrophobic effect.

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Molecular Dynamics Simulations for Human CAR Inverse Agonists.

Publication Date: 2012 Jan 25 PMID: 22233089
Authors: Jyrkkarinne, J. - Kublbeck, J. - Pulkkinen, J. - Honkakoski, P. - Laatikainen, R. - Poso, A. - Laitinen, T.
Journal: J Chem Inf Model

Constitutive androstane receptor (CAR), along with pregnane x receptor (PXR), is an important metabolic sensor in the hepatocytes. Like all other nuclear receptors (NRs), CAR works in concert with coregulator proteins, coactivators, and corepressors which bind to the NRs. The main basis for the receptor to distinguish between coactivators and corepressors is the position of the C-terminal helix 12 (H12), which is determined by the bound NR ligand. CAR, having constitutive activity, can be repressed or further activated by its ligands. Crystal structure of human CAR bound to an agonist and a coactivator peptide is available, but no structural information on an inverse agonist-bound human CAR and a corepressor exists. In our previous molecular dynamics (MD) studies, no corepressor peptide was included. Therefore, probably due to the strong interactions which keep the relatively short H12 of CAR in the active position, the structural changes elicited by inverse agonists were very subtle, and H12 of CAR seemed to more or less retain its active conformation. Here, we have run a series of MD simulations to study the movement of H12 in the presence of both activating and repressing ligands as well as a corepressor peptide. The presence of the corepressor on the coregulator surface of CAR induced a clear shift of H12 of the inverse agonists-bound CAR. In general, H12 moved toward H10 and not away from the ligand binding domain, as seen in some other NRs. However, H12 of CAR is short enough that this movement seems to be adequate to accommodate the binding of the corepressor.

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Insights into the Conformational Switching Mechanism of the Human Vascular Endothelial Growth Factor Receptor Type 2 Kinase Domain.

Publication Date: 2012 Jan 23 PMID: 22229497
Authors: Chioccioli, M. - Marsili, S. - Bonaccini, C. - Procacci, P. - Gratteri, P.
Journal: J Chem Inf Model

Human vascular endothelial growth factor receptor type 2 (h-VEFGR2) is a receptor tyrosine kinase involved in the angiogenesis process and regarded as an interesting target for the design of anticancer drugs. Its activation/inactivation mechanism is related to conformational changes in its cytoplasmatic kinase domain, involving first among all the alphaC-helix in N-lobe and the A-loop in C-lobe. Affinity of inhibitors for the active or inactive kinase form could dictate the open or closed conformation of the A-loop, thus making the different conformations of the kinase domain receptor (KDR) domain different drug targets in drug discovery. In this view, a detailed knowledge of the conformational landscape of KDR domain is of central relevance to rationalize the efficiency and selectivity of kinase inhibitors. Here, molecular dynamics simulations were used to gain insight into the conformational switching activity of the KDR domain and to identify intermediate conformations between the two limiting active and inactive conformations. Specific energy barriers have been selectively removed to induce, and hence highlight at the atomistic level, the regulation mechanism of the A-loop opening. The proposed strategy allowed to repeatedly observe the escape of the KDR domain from the DFG-out free energy basin and to identify rare intermediate conformations between the DFG-out and the DFG-in structures to be employed in a structure-based drug discovery process.

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Protein Secondary Structure Prediction with SPARROW.

Publication Date: 2012 Jan 23 PMID: 22224407
Authors: Bettella, F. - Rasinski, D. - Knapp, E. W.
Journal: J Chem Inf Model

A first step toward predicting the structure of a protein is to determine its secondary structure. The secondary structure information is generally used as starting point to solve protein crystal structures. In the present study, a machine learning approach based on a complete set of two-class scoring functions was used. Such functions discriminate between two specific structural classes or between a single specific class and the rest. The approach uses a hierarchical scheme of scoring functions and a neural network. The parameters are determined by optimizing the recall of learning data. Quality control is performed by predicting separate independent test data. A first set of scoring functions is trained to correlate the secondary structures of residues with profiles of sequence windows of width 15, centered at these residues. The sequence profiles are obtained by multiple sequence alignment with PSI-BLAST. A second set of scoring functions is trained to correlate the secondary structures of the center residues with the secondary structures of all other residues in the sequence windows used in the first step. Finally, a neural network is trained using the results from the second set of scoring functions as input to make a decision on the secondary structure class of the residue in the center of the sequence window. Here, we consider the three-class problem of helix, strand, and other secondary structures. The corresponding prediction scheme "SPARROW" was trained with the ASTRAL40 database, which contains protein domain structures with less than 40% sequence identity. The secondary structures were determined with DSSP. In a loose assignment, the helix class contains all DSSP helix types (alpha, 3-10, pi), the strand class contains beta-strand and beta-bridge, and the third class contains the other structures. In a tight assignment, the helix and strand classes contain only alpha-helix and beta-strand classes, respectively. A 10-fold cross validation showed less than 0.8% deviation in the fraction of correct structure assignments between true prediction and recall of data used for training. Using sequences of 140,000 residues as a test data set, 80.46% +/- 0.35% of secondary structures are predicted correctly in the loose assignment, a prediction performance, which is very close to the best results in the field. Most applications are done with the loose assignment. However, the tight assignment yields 2.25% better prediction performance. With each individual prediction, we also provide a confidence measure providing the probability that the prediction is correct. The SPARROW software can be used and downloaded on the Web page http://agknapp.chemie.fu-berlin.de/sparrow/ .

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Comparative Studies on Some Metrics for External Validation of QSPR Models.

Publication Date: 2012 Jan 17 PMID: 22201416
Authors: Roy, K. - Mitra, I. - Kar, S. - Ojha, P. K. - Das, R. N. - Kabir, H.
Journal: J Chem Inf Model

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Exploring Polypharmacology Using a ROCS-Based Target Fishing Approach.

Publication Date: 2012 Jan 23 PMID: 22196353
Authors: Abdulhameed, M. D. - Chaudhury, S. - Singh, N. - Sun, H. - Wallqvist, A. - Tawa, G. J.
Journal: J Chem Inf Model

Polypharmacology has emerged as a new theme in drug discovery. In this paper, we studied polypharmacology using a ligand-based target fishing (LBTF) protocol. To implement the protocol, we first generated a chemogenomic database that links individual protein targets with a specified set of drugs or target representatives. Target profiles were then generated for a given query molecule by computing maximal shape/chemistry overlap between the query molecule and the drug sets assigned to each protein target. The overlap was computed using the program ROCS (Rapid Overlay of Chemical Structures). We validated this approach using the Directory of Useful Decoys (DUD). DUD contains 2950 active compounds, each with 36 property-matched decoys, against 40 protein targets. We chose a set of known drugs to represent each DUD target, and we carried out ligand-based virtual screens using data sets of DUD actives seeded into DUD decoys for each target. We computed Receiver Operator Characteristic (ROC) curves and associated area under the curve (AUC) values. For the majority of targets studied, the AUC values were significantly better than for the case of a random selection of compounds. In a second test, the method successfully identified off-targets for drugs such as rimantadine, propranolol, and domperidone that were consistent with those identified by recent experiments. The results from our ROCS-based target fishing approach are promising and have potential application in drug repurposing for single and multiple targets, identifying targets for orphan compounds, and adverse effect prediction.

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Structural Characterizations of Oligopyridyl Foldamers, alpha-Helix Mimetics.

Publication Date: 2012 Jan 17 PMID: 22196240
Authors: Sopkova-de Oliveira Santos, J. - Voisin-Chiret, A. S. - Burzicki, G. - Sebaoun, L. - Sebban, M. - Lohier, J. F. - Legay, R. - Oulyadi, H. - Bureau, R. - Rault, S.
Journal: J Chem Inf Model

Protein-protein interactions are central to many biological processes, from intracellular communication to cytoskeleton assembly, and therefore represent an important class of targets for new therapeutics. The most common secondary structure in natural proteins is an alpha-helix. Small molecules seem to be attractive candidates for stabilizing or disrupting protein-protein interactions based on alpha-helices. In our study, we assessed the ability of oligopyridyl scaffolds to mimic the alpha-helical twist. The theoretical as well as experimental studies (X-ray diffraction and NMR) on conformations of bipyridines in the function of substituent and pyridine nitrogen positions were carried out. Furthermore, the experimental techniques showed that the conformations observed in bipyridines are maintained within a longer oligopyridyl scaffold (quaterpyridines). The alignment of the synthesized quaterpyridine with two methyl substituents showed that it is an alpha-helix foldamer; their methyl groups overlap very well with side chain positions, i and i + 3, of an ideal alpha-helix.

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Revisiting the General Solubility Equation: In Silico Prediction of Aqueous Solubility Incorporating the Effect of Topographical Polar Surface Area.

Publication Date: 2012 Jan 13 PMID: 22196228
Authors: Ali, J. - Camilleri, P. - Brown, M. B. - Hutt, A. J. - Kirton, S. B.
Journal: J Chem Inf Model

The General Solubility Equation (GSE) is a QSPR model based on the melting point and log P of a chemical substance. It is used to predict the aqueous solubility of nonionizable chemical compounds. However, its reliance on experimentally derived descriptors, particularly melting point, limits its applicability to virtual compounds. The studies presented show that the GSE is able to predict, to within 1 log unit, the experimental aqueous solubility (log S) for 81% of the compounds in a data set of 1265 diverse chemical structures (-8.48 < log S < 1.58). However, the predictive ability of the GSE is reduced to 75% when applied to a subset of the data (1160 compounds -6.00 < log S < 0.00), which discounts those compounds occupying the sparsely populated regions of data space. This highlights how sparsely populated extremities of data sets can significantly skew results for linear regression-based models. Replacing the melting point descriptor of the GSE with a descriptor which accounts for topographical polar surface area (TPSA) produces a model of comparable quality to the GSE (the solubility of 81% of compounds in the full data set predicted accurately). As such, we propose an alternative simple model for predicting aqueous solubility which replaces the melting point descriptor of the GSE with TPSA and hence can be applied to virtual compounds. In addition, incorporating TPSA into the GSE in addition to log P and melting point gives a three descriptor model that improves accurate prediction of aqueous solubility over the GSE by 5.1% for the full and 6.6% for the reduced data set, respectively.

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Binding Conformation of 2-Oxoamide Inhibitors to Group IVA Cytosolic Phospholipase A(2) Determined by Molecular Docking Combined with Molecular Dynamics.

Publication Date: 2012 Jan 23 PMID: 22196172
Authors: Mouchlis, V. D. - Michopoulou, V. - Constantinou-Kokotou, V. - Mavromoustakos, T. - Dennis, E. A. - Kokotos, G.
Journal: J Chem Inf Model

The group IVA cytosolic phospholipase A(2) (GIVA cPLA(2)) plays a central role in inflammation. Long chain 2-oxoamides constitute a class of potent GIVA cPLA(2) inhibitors that exhibit potent in vivo anti-inflammatory and analgesic activity. We have now gained insight into the binding of 2-oxoamide inhibitors in the GIVA cPLA(2) active site through a combination of molecular docking calculations and molecular dynamics simulations. Recently, the location of the 2-oxoamide inhibitor AX007 within the active site of the GIVA cPLA(2) was determined using a combination of deuterium exchange mass spectrometry followed by molecular dynamics simulations. After the optimization of the AX007-GIVA cPLA(2) complex using the docking algorithm Surflex-Dock, a series of additional 2-oxoamide inhibitors have been docked in the enzyme active site. The calculated binding affinity presents a good statistical correlation with the experimental inhibitory activity (r(2) = 0.76, N = 11). A molecular dynamics simulation of the docking complex of the most active compound has revealed persistent interactions of the inhibitor with the enzyme active site and proves the stability of the docking complex and the validity of the binding suggested by the docking calculations. The combination of molecular docking calculations and molecular dynamics simulations is useful in defining the binding of small-molecule inhibitors and provides a valuable tool for the design of new compounds with improved inhibitory activity against GIVA cPLA(2).

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JGromacs: A Java Package for Analyzing Protein Simulations.

Publication Date: 2012 Jan 23 PMID: 22191855
Authors: Munz, M. - Biggin, P. C.
Journal: J Chem Inf Model

In this paper, we introduce JGromacs, a Java API (Application Programming Interface) that facilitates the development of cross-platform data analysis applications for Molecular Dynamics (MD) simulations. The API supports parsing and writing file formats applied by GROMACS (GROningen MAchine for Chemical Simulations), one of the most widely used MD simulation packages. JGromacs builds on the strengths of object-oriented programming in Java by providing a multilevel object-oriented representation of simulation data to integrate and interconvert sequence, structure, and dynamics information. The easy-to-learn, easy-to-use, and easy-to-extend framework is intended to simplify and accelerate the implementation and development of complex data analysis algorithms. Furthermore, a basic analysis toolkit is included in the package. The programmer is also provided with simple tools (e.g., XML-based configuration) to create applications with a user interface resembling the command-line interface of GROMACS applications. Availability: JGromacs and detailed documentation is freely available from http://sbcb.bioch.ox.ac.uk/jgromacs under a GPLv3 license .

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Assessment of Weak Intermolecular Interactions Across QM/MM Noncovalent Boundaries.

Publication Date: 2012 Jan 23 PMID: 22185219
Authors: Kumbhar, S. - Fischer, F. D. - Waller, M. P.
Journal: J Chem Inf Model

An assessment of a number of quantum mechanical/molecular mechanical (QM/MM) combinations was performed for weak intermolecular interactions across noncovalent QM/MM 'boundaries'. The popular S22 data set, comprising of a number of weak hydrogen-bonded, dispersion-bound and complexes with mixed interactions was used for the assessment. A range of QM methods was combined with a number of popular MM force fields. The single-point interaction energies, at reference geometries, are presented as deviations to accurate CCSD(T)/CBS reference values. This investigation employed both additive and subtractive QM/MM schemes. The density functional has only a negligible effect; the choice of basis set was also negligible in terms of accuracy. The importance of selecting the most appropriate MM force field for accurately describing interactions at the noncovalent 'boundary' region has a dramatic effect on the accuracy.

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Visual Characterization and Diversity Quantification of Chemical Libraries: 2. Analysis and Selection of Size-Independent, Subspace-Specific Diversity Indices.

Publication Date: 2012 Jan 5 PMID: 22181665
Authors: Colliandre, L. - Le Guilloux, V. - Bourg, S. - Morin-Allory, L.
Journal: J Chem Inf Model

High Throughput Screening (HTS) is a standard technique widely used to find hit compounds in drug discovery projects. The high costs associated with such experiments have highlighted the need to carefully design screening libraries in order to avoid wasting resources. Molecular diversity is an established concept that has been used to this end for many years. In this article, a new approach to quantify the molecular diversity of screening libraries is presented. The approach is based on the Delimited Reference Chemical Subspace (DRCS) methodology, a new method that can be used to delimit the densest subspace spanned by a reference library in a reduced 2D continuous space. A total of 22 diversity indices were implemented or adapted to this methodology, which is used here to remove outliers and obtain a relevant cell-based partition of the subspace. The behavior of these indices was assessed and compared in various extreme situations and with respect to a set of theoretical rules that a diversity function should satisfy when libraries of different sizes have to be compared. Some gold standard indices are found inappropriate in such a context, while none of the tested indices behave perfectly in all cases. Five DRCS-based indices accounting for different aspects of diversity were finally selected, and a simple framework is proposed to use them effectively. Various libraries have been profiled with respect to more specific subspaces, which further illustrate the interest of the method.

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Molecular Dynamic Behavior and Binding Affinity of Flavonoid Analogues to the Cyclin Dependent Kinase 6/cyclin D Complex.

Publication Date: 2012 Jan 23 PMID: 22172011
Authors: Khuntawee, W. - Rungrotmongkol, T. - Hannongbua, S.
Journal: J Chem Inf Model

The cyclin dependent kinases (CDKs), each with their respective regulatory partner cyclin that are involved in the regulation of the cell cycle, apoptosis, and transcription, are potentially interesting targets for cancer therapy. The CDK6 complex with cyclin D (CDK6/cycD) drives cellular proliferation by phosphorylation of specific key target proteins. To understand the flavonoids that inhibit the CDK6/cycD functions, molecular dynamics simulations (MDSs) were performed on three inhibitors, fisetin (FST), apigenin (AGN), and chrysin (CHS), complexed with CDK6/cycD, including the two different binding orientations of CHS: FST-like (CHS_A) and deschloro-flavopiridol-like (CHS_B). For all three inhibitors, including both CHS orientations, the conserved interaction between the 4-keto group of the flavonoid and the backbone V101 nitrogen of CDK6 was strongly detected. The 3'- and 4'-OH groups on the flavonoid phenyl ring and the 3-OH group on the benzopyranone ring of inhibitor were found to significantly increase the binding and inhibitory efficiency. Besides the electrostatic interactions, especially through hydrogen bond formation, the van der Waals (vdW) interactions with the I19, V27, F98, H100, and L152 residues of CDK6 are also important factors in the binding efficiency of flavonoids against the CDK6/cycD complex. On the basis of the docking calculation and MM-PBSA method, the order of the predicted inhibitory affinities of these three inhibitors toward the CDK6/cycD was FST > AGN > CHS, which is in good agreement with the experimental data. In addition, CHS preferentially binds to the active CDK6 in a different orientation to FST and AGN but similar to its related analog, deschloro-flavopiridol. The obtained results are useful as the basic information for the further design of potent anticancer drugs specifically targeting the CDK6 enzyme.

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Molecular dynamics simulations of a hyperthermophilic and a mesophilic protein l30e.

Publication Date: 2012 Jan 23 PMID: 22168407
Authors: Lee, K. J.
Journal: J Chem Inf Model

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Discovery of Novel Histamine H4 and Serotonin Transporter Ligands Using the Topological Feature Tree Descriptor.

Publication Date: 2012 Jan 23 PMID: 22168379
Authors: Kiss, R. - Sandor, M. - Gere, A. - Schmidt, E. - Balogh, G. T. - Kiss, B. - Molnar, L. - Lemmen, C. - Keseru, G. M.
Journal: J Chem Inf Model

Ligand-based approaches are particularly important in the hit identification process of drug discovery when no structural information on the target is available. Pharmacophore descriptors that use a topological representation of the ligands are usually fast enough to screen large compound libraries effectively when seeking novel lead candidates. One example of this kind is the Feature Tree descriptor, a reduced graph representation implemented in the FTrees software. In this study, we tested the screening efficiency of FTrees by both retrospective and prospective screens using known histamine H4 antagonists and serotonin transporter (SERT) inhibitors as query molecules. Our results demonstrate that FTrees can effectively find actives. Particularly when combined with a subsequent 2D fingerprint-based diversity selection, FTrees was found to be extremely effective at discovering a diverse set of scaffolds. Prospective screening of our in-house compound deck provided several novel H4 and SERT ligands that could serve as suitable starting points for further optimization.

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Ligand and decoy sets for docking to g protein-coupled receptors.

Publication Date: 2012 Jan 23 PMID: 22168315
Authors: Gatica, E. A. - Cavasotto, C. N.
Journal: J Chem Inf Model

We compiled a G protein-coupled receptor (GPCR) ligand library (GLL) for 147 targets, selecting for each ligand 39 decoy molecules, collected in the GPCR Decoy Database (GDD). Decoys were chosen ensuring a ligand-decoy similarity of six physical properties, while enforcing ligand-decoy chemical dissimilarity. The performance in docking of the GDD was evaluated on 19 GPCRs, showing a marked decrease in enrichment compared to bias-uncorrected decoy sets. Both the GLL and GDD are freely available for the scientific community.

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Improved chemical text mining of patents with infinite dictionaries and automatic spelling correction.

Publication Date: 2012 Jan 23 PMID: 22148717
Authors: Sayle, R. - Xie, P. H. - Muresan, S.
Journal: J Chem Inf Model

The text mining of patents of pharmaceutical interest poses a number of unique challenges not encountered in other fields of text mining. Unlike fields, such as bioinformatics, where the number of terms of interest is enumerable and essentially static, systematic chemical nomenclature can describe an infinite number of molecules. Hence, the dictionary- and ontology-based techniques that are commonly used for gene names, diseases, species, etc., have limited utility when searching for novel therapeutic compounds in patents. Additionally, the length and the composition of IUPAC-like names make them more susceptible to typographic problems: OCR failures, human spelling errors, and hyphenation and line breaking issues. This work describes a novel technique, called CaffeineFix, designed to efficiently identify chemical names in free text, even in the presence of typographical errors. Corrected chemical names are generated as input for name-to-structure software. This forms a preprocessing pass, independent of the name-to-structure software used, and is shown to greatly improve the results of chemical text mining in our study.

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Virtual screening data fusion using both structure- and ligand-based methods.

Publication Date: 2012 Jan 23 PMID: 22148635
Authors: Svensson, F. - Karlen, A. - Skold, C.
Journal: J Chem Inf Model

Virtual screening is widely applied in drug discovery, and significant effort has been put into improving current methods. In this study, we have evaluated the performance of compound ranking in virtual screening using five different data fusion algorithms on a total of 16 data sets. The data were generated by docking, pharmacophore search, shape similarity, and electrostatic similarity, spanning both structure- and ligand-based methods. The algorithms used for data fusion were sum rank, rank vote, sum score, Pareto ranking, and parallel selection. None of the fusion methods require any prior knowledge or input other than the results from the single methods and, thus, are readily applicable. The results show that compound ranking using data fusion improves the performance and consistency of virtual screening compared to the single methods alone. The best performing data fusion algorithm was parallel selection, but both rank voting and Pareto ranking also have good performance.

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A Structure-Based Approach to Understanding Somatostatin Receptor-4 Agonism (sst4).

Publication Date: 2012 Jan 23 PMID: 22148589
Authors: Liu, Z. - Crider, A. M. - Ansbro, D. - Hayes, C. - Kontoyianni, M.
Journal: J Chem Inf Model

It has been reported that somatostatin receptor subtypes 4 and 5 would be high-impact templates for homology modeling if their 3D structures became available. We have generated a homology model of the somatostatin receptor subtype 4 (sst4), using the newest active state beta(2) adrenoreceptor crystal structure, and subsequently docked a variety of agonists into the model-built receptor to elucidate the binding modes of reported agonists. Using experimental restraints, we were able to explain observed activity profiles. We propose two binding modes that can consistently explain findings for high-affinity agonists and reason why certain structures display low affinities for the receptor.

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Combining Global and Local Measures for Structure-Based Druggability Predictions.

Publication Date: 2012 Jan 5 PMID: 22148551
Authors: Volkamer, A. - Kuhn, D. - Grombacher, T. - Rippmann, F. - Rarey, M.
Journal: J Chem Inf Model

Predicting druggability and prioritizing certain disease modifying targets for the drug development process is of high practical relevance in pharmaceutical research. DoGSiteScorer is a fully automatic algorithm for pocket and druggability prediction. Besides consideration of global properties of the pocket, also local similarities shared between pockets are reflected. Druggability scores are predicted by means of a support vector machine (SVM), trained, and tested on the druggability data set (DD) and its nonredundant version (NRDD). The DD consists of 1069 targets with assigned druggable, difficult, and undruggable classes. In 90% of the NRDD, the SVM model based on global descriptors correctly classifies a target as either druggable or undruggable. Nevertheless, global properties suffer from binding site changes due to ligand binding and from the pocket boundary definition. Therefore, local pocket properties are additionally investigated in terms of a nearest neighbor search. Local similarities are described by distance dependent histograms between atom pairs. In 88% of the DD pocket set, the nearest neighbor and the structure itself conform with their druggability type. A discriminant feature between druggable and undruggable pockets is having less short-range hydrophilic-hydrophilic pairs and more short-range lipophilic-lipophilic pairs. Our findings for global pocket descriptors coincide with previously published methods affirming that size, shape, and hydrophobicity are important global pocket descriptors for automatic druggability prediction. Nevertheless, the variety of pocket shapes and their flexibility upon ligand binding limit the automatic projection of druggable features onto descriptors. Incorporating local pocket properties is another step toward a reliable descriptor-based druggability prediction.

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Hydride Dissociation Energies of Six-Membered Heterocyclic Organic Hydrides Predicted by ONIOM-G4Method.

Publication Date: 2012 Jan 23 PMID: 22146106
Authors: Shi, J. - Huang, X. Y. - Wang, H. J. - Fu, Y.
Journal: J Chem Inf Model

Hydride dissociation energy is of great importance in understanding the hydride-donating abilities of organic hydrides. Although the hydride dissociation energies of some organic hydrides have been experimentally measured, much less attention has been focused on the investigation of these quantities from the first principles of physics. Herein, we developed an ONIOM-G4 method and carefully benchmarked this new method against 48 experimental hydride dissociation energies of diverse bulky molecules. It was found that with the combined methods of the HF/6-31+G(d,p)//IEFPCM/Bondi1.15 solvation model, the ONIOM-G4 method can predict the hydride dissociation energies with an error bar of only 1.7 kcal/mol. With the newly developed ONIOM-G4 method, we then systematically studied the hydride dissociation energies of six categories of biologically and pharmaceutically important six-membered heterocyclic organic hydrides, namely, the organic hydrides containing 1,4-dihydropyridine, 1,4-dihydropyrazine, 1,4-oxazine, 1,4-thiazine, 4H-pyran, and 4H-thiopyran ring structures. An extensive hydride dissociation energy scale containing over 100 six-memebered heterocyclic organic hydrides has been established, which may find applications in both synthetic organic chemistry and mechanistic studies of various chemical or biological processes involving transferring of the hydride anion.

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Utilizing experimental data for reducing ensemble size in flexible-protein docking.

Publication Date: 2012 Jan 23 PMID: 22146074
Authors: Xu, M. - Lill, M. A.
Journal: J Chem Inf Model

Efficient and sufficient incorporation of protein flexibility into docking is still a challenging task. Docking to an ensemble of protein structures has proven its utility for docking, but using a large ensemble of structures can reduce the efficiency of docking and can increase the number of false positives in virtual screening. In this paper, we describe the application of our new methodology, Limoc, to generate an ensemble of holo-like protein structures in combination with the relaxed complex scheme (RCS), to virtual screening. We describe different schemes to reduce the ensemble of protein structures to increase efficiency and enrichment quality. Utilizing experimental knowledge about actives for a target protein allows the reduction of ensemble members to a minimum of three protein structures, increasing enrichment quality and efficiency simultaneously.

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Searching the "biologically relevant"conformation of dopamine: a computational approach.

Publication Date: 2012 Jan 23 PMID: 22146008
Authors: Andujar, S. A. - Tosso, R. D. - Suvire, F. D. - Angelina, E. - Peruchena, N. - Cabedo, N. - Cortes, D. - Enriz, R. D.
Journal: J Chem Inf Model

We report here an exhaustive and complete conformational study on the conformational potential energy hypersurface (PEHS) of dopamine (DA) interacting with the dopamine D2 receptor (D2-DR). A reduced 3D model for the binding pocket of the human D2-DR was constructed on the basis of the theoretical model structure of bacteriorhodopsin. In our reduced model system, only 13 amino acids were included to perform the quantum mechanics calculations. To obtain the different complexes of DA/D2-DR, we combined semiempirical (PM6), DFT (B3LYP/6-31G(d)), and QTAIM calculations. The molecular flexibility of DA interacting with the D2-DR was evaluated from potential energy surfaces and potential energy curves. A comparative study between the molecular flexibility of DA in the gas phase and at D2-DR was carried out. In addition, several molecular dynamics simulations were carried out to evaluate the molecular flexibility of the different complexes obtained. Our results allow us to postulate the complexes of type A as the "biologically relevant conformations" of DA. In addition, the theoretical calculations reported here suggested that a mechanistic stepwise process takes place for DA in which the protonated nitrogen group (in any conformation) acts as the anchoring portion, and this process is followed by a rapid rearrangement of the conformation allowing the interaction of the catecholic OH groups.

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Hot spot analysis for driving the development of hits into leads in fragment-based drug discovery.

Publication Date: 2012 Jan 23 PMID: 22145575
Authors: Hall, D. R. - Ngan, C. H. - Zerbe, B. S. - Kozakov, D. - Vajda, S.
Journal: J Chem Inf Model

Fragment-based drug design (FBDD) starts with finding fragment-sized compounds that are highly ligand efficient and can serve as a core moiety for developing high-affinity leads. Although the core-bound structure of a protein facilitates the construction of leads, effective design is far from straightforward. We show that protein mapping, a computational method developed to find binding hot spots and implemented as the FTMap server, provides information that complements the fragment screening results and can drive the evolution of core fragments into larger leads with a minimal loss or, in some cases, even a gain in ligand efficiency. The method places small molecular probes, the size of organic solvents, on a dense grid around the protein and identifies the hot spots as consensus clusters formed by clusters of several probes. The hot spots are ranked based on the number of probe clusters, which predicts the binding propensity of the subsites and hence their importance for drug design. Accordingly, with a single exception the main hot spot identified by FTMap binds the core compound found by fragment screening. The most useful information is provided by the neighboring secondary hot spots, indicating the regions where the core can be extended to increase its affinity. To quantify this information, we calculate the density of probes from mapping, which describes the binding propensity at each point, and show that the change in the correlation between a ligand position and the probe density upon extending or repositioning the core moiety predicts the expected change in ligand efficiency.

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Design of Novel FLT-3 Inhibitors Based on Dual-Layer 3D-QSAR Model and Fragment-Based Compounds in Silico.

Publication Date: 2012 Jan 23 PMID: 22142286
Authors: Shih, K. C. - Lin, C. Y. - Chi, H. C. - Hwang, C. S. - Chen, T. S. - Tang, C. Y. - Hsiao, N. W.
Journal: J Chem Inf Model

FMS-like tyrosine kinase 3 (FLT-3) is strongly correlated with acute myeloid leukemia, but no FLT-3-inhibitor cocomplex structure is available to assist the design of therapeutic inhibitors. Hence, we propose a dual-layer 3D-QSAR model for FLT-3 that integrates the pharmacophore, CoMFA, and CoMSIA. We then coupled the model with the fragment-based design strategy to identify novel FLT-3 inhibitors. In the first layer, the previously established model, Hypo02, was evaluated in terms of its correlation coefficient (r), RMS, cost difference, and configuration cost, with values of 0.930, 1.24, 106.45, and 16.44, respectively. Moreover, Fischer's cross-validation test of data generated by Hypo02 yielded a 98% confidence level, and the validation of the testing set yielded a best r value of 0.87. The features of Hypo02 were separated into two parts and then used to screen the MiniMaybridge fragment compound database. Nine novel FLT-3 inhibitors were generated in this layer. In the second layer, Hypo02 was subjected to an alignment rule to generate CoMFA- and CoMSIA-based models, for which the partial least-squares validation method was utilized. The values of q(2), r(2), and predictive r(2) were 0.58, 0.98, and 0.76, respectively, derived from the CoMFA model with steric and electrostatic fields. The CoMSIA model with five different fields yielded values of 0.54, 0.97, and 0.76 for q(2), r(2), and predictive r(2), respectively. The CoMFA and CoMSIA models were used to constrain 3D structures of the nine novel FLT-3 inhibitors. This dual-layer 3D-QSAR model constitutes a valuable tool to easily and quickly screen and optimize novel potential FLT-3 inhibitors for the treatment of acute myeloid leukemia.

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Kinase-Kernel Models: Accurate In silico Screening of 4 Million Compounds Across the Entire Human Kinome.

Publication Date: 2012 Jan 23 PMID: 22133092
Authors: Martin, E. - Mukherjee, P.
Journal: J Chem Inf Model

Reliable in silico prediction methods promise many advantages over experimental high-throughput screening (HTS): vastly lower time and cost, affinity magnitude estimates, no requirement for a physical sample, and a knowledge-driven exploration of chemical space. For the specific case of kinases, given several hundred experimental IC(50) training measurements, the empirically parametrized profile-quantitative structure-activity relationship (profile-QSAR) and surrogate AutoShim methods developed at Novartis can predict IC(50) with a reliability approaching experimental HTS. However, in the absence of training data, prediction is much harder. The most common a priori prediction method is docking, which suffers from many limitations: It requires a protein structure, is slow, and cannot predict affinity. (1) Highly accurate profile-QSAR (2) models have now been built for roughly 100 kinases covering most of the kinome. Analyzing correlations among neighboring kinases shows that near neighbors share a high degree of SAR similarity. The novel chemogenomic kinase-kernel method reported here predicts activity for new kinases as a weighted average of predicted activities from profile-QSAR models for nearby neighbor kinases. Three different factors for weighting the neighbors were evaluated: binding site sequence identity to the kinase neighbors, similarity of the training set for each neighbor model to the compound being predicted, and accuracy of each neighbor model. Binding site sequence identity was by far most important, followed by chemical similarity. Model quality had almost no relevance. The median R(2) = 0.55 for kinase-kernel interpolations on 25% of the data of each set held out from method optimization for 51 kinase assays, approached the accuracy of median R(2) = 0.61 for the trained profile-QSAR predictions on the same held out 25% data of each set, far faster and far more accurate than docking. Validation on the full data sets from 18 additional kinase assays not part of method optimization studies also showed strong performance with median R(2) = 0.48. Genetic algorithm optimization of the binding site residues used to compute binding site sequence identity identified 16 privileged residues from a larger set of 46. These 16 are consistent with the kinase selectivity literature and structural biology, further supporting the scientific validity of the approach. A priori kinase-kernel predictions for 4 million compounds were interpolated from 51 existing profile-QSAR models for the remaining >400 novel kinases, totaling 2 billion activity predictions covering the entire kinome. The method has been successfully applied in two therapeutic projects to generate predictions and select compounds for activity testing.

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Integrating medicinal chemistry, organic/combinatorial chemistry, and computational chemistry for the discovery of selective estrogen receptor modulators with forecaster, a novel platform for drug discovery.

Publication Date: 2012 Jan 23 PMID: 22133077
Authors: Therrien, E. - Englebienne, P. - Arrowsmith, A. G. - Mendoza-Sanchez, R. - Corbeil, C. R. - Weill, N. - Campagna-Slater, V. - Moitessier, N.
Journal: J Chem Inf Model

As part of a large medicinal chemistry program, we wish to develop novel selective estrogen receptor modulators (SERMs) as potential breast cancer treatments using a combination of experimental and computational approaches. However, one of the remaining difficulties nowadays is to fully integrate computational (i.e., virtual, theoretical) and medicinal (i.e., experimental, intuitive) chemistry to take advantage of the full potential of both. For this purpose, we have developed a Web-based platform, Forecaster, and a number of programs (e.g., Prepare, React, Select) with the aim of combining computational chemistry and medicinal chemistry expertise to facilitate drug discovery and development and more specifically to integrate synthesis into computer-aided drug design. In our quest for potent SERMs, this platform was used to build virtual combinatorial libraries, filter and extract a highly diverse library from the NCI database, and dock them to the estrogen receptor (ER), with all of these steps being fully automated by computational chemists for use by medicinal chemists. As a result, virtual screening of a diverse library seeded with active compounds followed by a search for analogs yielded an enrichment factor of 129, with 98% of the seeded active compounds recovered, while the screening of a designed virtual combinatorial library including known actives yielded an area under the receiver operating characteristic (AU-ROC) of 0.78. The lead optimization proved less successful, further demonstrating the challenge to simulate structure activity relationship studies.

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Utility-aware screening with clique-oriented prioritization.

Publication Date: 2012 Jan 23 PMID: 22117901
Authors: Swamidass, S. J. - Calhoun, B. T. - Bittker, J. A. - Bodycombe, N. E. - Clemons, P. A.
Journal: J Chem Inf Model

Most methods of deciding which hits from a screen to send for confirmatory testing assume that all confirmed actives are equally valuable and aim only to maximize the number of confirmed hits. In contrast, "utility-aware" methods are informed by models of screeners' preferences and can increase the rate at which the useful information is discovered. Clique-oriented prioritization (COP) extends a recently proposed economic framework and aims-by changing which hits are sent for confirmatory testing-to maximize the number of scaffolds with at least two confirmed active examples. In both retrospective and prospective experiments, COP enables accurate predictions of the number of clique discoveries in a batch of confirmatory experiments and improves the rate of clique discovery by more than 3-fold. In contrast, other similarity-based methods like ontology-based pattern identification (OPI) and local hit-rate analysis (LHR) reduce the rate of scaffold discovery by about half. The utility-aware algorithm used to implement COP is general enough to implement several other important models of screener preferences.

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ThermoData Engine (TDE) Software Implementation of the Dynamic Data Evaluation Concept. 7. Ternary Mixtures.

Publication Date: 2012 Jan 23 PMID: 22107452
Authors: Diky, V. - Chirico, R. D. - Muzny, C. D. - Kazakov, A. F. - Kroenlein, K. - Magee, J. W. - Abdulagatov, I. - Kang, J. W. - Frenkel, M.
Journal: J Chem Inf Model

ThermoData Engine (TDE) is the first full-scale software implementation of the dynamic data evaluation concept, as reported in this journal. The present paper describes the first application of this concept to the evaluation of thermophysical properties for ternary chemical systems. The method involves construction of Redlich-Kister type equations for individual properties (excess volume, thermal conductivity, viscosity, surface tension, and excess enthalpy) and activity coefficient models for phase equilibrium properties (vapor-liquid and liquid-liquid equilibrium). Constructed ternary models are based on those for the three pure component and three binary subsystems evaluated on demand through the TDE software algorithms. All models are described in detail, and extensions to the class structure of the program are provided. Reliable evaluation of properties for the binary subsystems is essential for successful property evaluations for ternary systems, and algorithms are described to aid appropriate parameter selection and fitting for the implemented activity coefficient models (NRTL, Wilson, Van Laar, Redlich-Kister, and UNIQUAC). Two activity coefficient models based on group contributions (original UNIFAC and NIST-KT-UNIFAC) are also implemented. Novel features of the user interface are shown, and directions for future enhancements are outlined.

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Improved machine learning models for predicting selective compounds.

Publication Date: 2012 Jan 23 PMID: 22107358
Authors: Ning, X. - Walters, M. - Karypisxy, G.
Journal: J Chem Inf Model

The identification of small potent compounds that selectively bind to the target under consideration with high affinities is a critical step toward successful drug discovery. However, there is still a lack of efficient and accurate computational methods to predict compound selectivity properties. In this paper, we propose a set of machine learning methods to do compound selectivity prediction. In particular, we propose a novel cascaded learning method and a multitask learning method. The cascaded method decomposes the selectivity prediction into two steps, one model for each step, so as to effectively filter out nonselective compounds. The multitask method incorporates both activity and selectivity models into one multitask model so as to better differentiate compound selectivity properties. We conducted a comprehensive set of experiments and compared the results with those of other conventional selectivity prediction methods, and our results demonstrated that the cascaded and multitask methods significantly improve the selectivity prediction performance.

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Structural Perspective on the Direct Inhibition Mechanism of EGCG on Mammalian Histidine Decarboxylase and DOPA Decarboxylase.

Publication Date: 2012 Jan 23 PMID: 22107329
Authors: Ruiz-Perez, M. V. - Pino-Angeles, A. - Medina, M. A. - Sanchez-Jimenez, F. - Moya-Garcia, A. A.
Journal: J Chem Inf Model

Histidine decarboxylase (HDC) and l-aromatic amino acid decarboxylase (DDC) are homologous enzymes that are responsible for the synthesis of important neuroactive amines related to inflammatory, neurodegenerative, and neoplastic diseases. Epigallocatechin-3-gallate (EGCG), the most abundant catechin in green tea, has been shown to target histamine-producing cells and to promote anti-inflammatory, antitumor, and antiangiogenic effects. Previous experimental work has demonstrated that EGCG has a direct inhibitory effect on both HDC and DDC. In this study, we investigated the binding modes of EGCG to HDC and DDC as a first step for designing new polyphenol-based HDC/DDC-specific inhibitors.

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Stereochemically Consistent Reaction Mapping and Identification of Multiple Reaction Mechanisms through Integer Linear Optimization.

Publication Date: 2012 Jan 23 PMID: 22098204
Authors: First, E. L. - Gounaris, C. E. - Floudas, C. A.
Journal: J Chem Inf Model

Reaction mappings are of fundamental importance to researchers studying the mechanisms of chemical reactions and analyzing biochemical pathways. We have developed an automated method based on integer linear optimization, ILP, to identify optimal reaction mappings that minimize the number of bond changes. An alternate objective function is also proposed that minimizes the number of bond order changes. In contrast to previous approaches, our method produces mappings that respect stereochemistry. We also show how to locate multiple reaction mappings efficiently and determine which of those mappings correspond to distinct reaction mechanisms by automatically detecting molecular symmetries. We demonstrate our techniques through a number of computational studies on the GRI-Mech, KEGG LIGAND, and BioPath databases. The computational studies indicate that 99% of the 8078 reactions tested can be addressed within 1 CPU hour. The proposed framework has been incorporated into the Web tool DREAM ( http://selene.princeton.edu/dream/ ), which is freely available to the scientific community.

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Drug Effect Prediction by Polypharmacology-Based Interaction Profiling.

Publication Date: 2012 Jan 23 PMID: 22098080
Authors: Simon, Z. - Peragovics, A. - Vigh-Smeller, M. - Csukly, G. - Tombor, L. - Yang, Z. - Zahoranszky-Kohalmi, G. - Vegner, L. - Jelinek, B. - Hari, P. - Hetenyi, C. - Bitter, I. - Czobor, P. - Malnasi-Csizmadia, A.
Journal: J Chem Inf Model

Most drugs exert their effects via multitarget interactions, as hypothesized by polypharmacology. While these multitarget interactions are responsible for the clinical effect profiles of drugs, current methods have failed to uncover the complex relationships between them. Here, we introduce an approach which is able to relate complex drug-protein interaction profiles with effect profiles. Structural data and registered effect profiles of all small-molecule drugs were collected, and interactions to a series of nontarget protein binding sites of each drug were calculated. Statistical analyses confirmed a close relationship between the studied 177 major effect categories and interaction profiles of ca. 1200 FDA-approved small-molecule drugs. On the basis of this relationship, the effect profiles of drugs were revealed in their entirety, and hitherto uncovered effects could be predicted in a systematic manner. Our results show that the prediction power is independent of the composition of the protein set used for interaction profile generation.

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Hot spots and transient pockets: predicting the determinants of small-molecule binding to a protein-protein interface.

Publication Date: 2012 Jan 23 PMID: 22087639
Authors: Metz, A. - Pfleger, C. - Kopitz, H. - Pfeiffer-Marek, S. - Baringhaus, K. H. - Gohlke, H.
Journal: J Chem Inf Model

Protein-protein interfaces are considered difficult targets for small-molecule protein-protein interaction modulators (PPIMs ). Here, we present for the first time a computational strategy that simultaneously considers aspects of energetics and plasticity in the context of PPIM binding to a protein interface. The strategy aims at identifying the determinants of small-molecule binding, hot spots, and transient pockets, in a protein-protein interface in order to make use of this knowledge for predicting binding modes of and ranking PPIMs with respect to their affinity. When applied to interleukin-2 (IL-2), the computationally inexpensive constrained geometric simulation method FRODA outperforms molecular dynamics simulations in sampling hydrophobic transient pockets. We introduce the PPIAnalyzer approach for identifying transient pockets on the basis of geometrical criteria only. A sequence of docking to identified transient pockets, starting structure selection based on hot spot information, RMSD clustering and intermolecular docking energies, and MM-PBSA calculations allows one to enrich IL-2 PPIMs from a set of decoys and to discriminate between subgroups of IL-2 PPIMs with low and high affinity. Our strategy will be applicable in a prospective manner where nothing else than a protein-protein complex structure is known; hence, it can well be the first step in a structure-based endeavor to identify PPIMs.

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Cheminformatics meets molecular mechanics: a combined application of knowledge-based pose scoring and physical force field-based hit scoring functions improves the accuracy of structure-based virtual screening.

Publication Date: 2012 Jan 23 PMID: 22017385
Authors: Hsieh, J. H. - Yin, S. - Wang, X. S. - Liu, S. - Dokholyan, N. V. - Tropsha, A.
Journal: J Chem Inf Model

Poor performance of scoring functions is a well-known bottleneck in structure-based virtual screening (VS), which is most frequently manifested in the scoring functions' inability to discriminate between true ligands vs known nonbinders (therefore designated as binding decoys). This deficiency leads to a large number of false positive hits resulting from VS. We have hypothesized that filtering out or penalizing docking poses recognized as non-native (i.e., pose decoys) should improve the performance of VS in terms of improved identification of true binders. Using several concepts from the field of cheminformatics, we have developed a novel approach to identifying pose decoys from an ensemble of poses generated by computational docking procedures. We demonstrate that the use of target-specific pose (scoring) filter in combination with a physical force field-based scoring function (MedusaScore) leads to significant improvement of hit rates in VS studies for 12 of the 13 benchmark sets from the clustered version of the Database of Useful Decoys (DUD). This new hybrid scoring function outperforms several conventional structure-based scoring functions, including XSCORE::HMSCORE, ChemScore, PLP, and Chemgauss3, in 6 out of 13 data sets at early stage of VS (up 1% decoys of the screening database). We compare our hybrid method with several novel VS methods that were recently reported to have good performances on the same DUD data sets. We find that the retrieved ligands using our method are chemically more diverse in comparison with two ligand-based methods (FieldScreen and FLAP::LBX). We also compare our method with FLAP::RBLB, a high-performance VS method that also utilizes both the receptor and the cognate ligand structures. Interestingly, we find that the top ligands retrieved using our method are highly complementary to those retrieved using FLAP::RBLB, hinting effective directions for best VS applications. We suggest that this integrative VS approach combining cheminformatics and molecular mechanics methodologies may be applied to a broad variety of protein targets to improve the outcome of structure-based drug discovery studies.

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A Robust Force Field Based Method for Calculating Conformational Energies of Charged Drug-Like Molecules.

Publication Date: 2012 Jan 20 PMID: 21985436
Authors: Poehlsgaard, J. - Harpsoe, K. - Jorgensen, F. S. - Olsen, L.
Journal: J Chem Inf Model

The binding affinity of a drug-like molecule depends among other things on the availability of the bioactive conformation. If the bioactive conformation has a significantly higher energy than the global minimum energy conformation, then the molecule is unlikely to bind to its target. Determination of the global minimum energy conformation and calculation of conformational penalties of binding is a prerequisite for prediction of reliable binding affinities. Here, we present a simple and computationally efficient procedure to estimate the global energy minimum for a wide variety of structurally diverse molecules, including polar and charged compounds. Identifying global energy minimum conformations of such compounds with force field methods is problematic due to the exaggeration of intramolecular electrostatic interactions. We demonstrate that the global energy minimum conformations of zwitterionic compounds generated by conformational analysis with modified electrostatics are good approximations of the conformational distributions predicted by experimental data and with molecular dynamics performed in explicit solvent. Finally the method is used to calculate conformational penalties for zwitterionic GluA2 agonists and to filter false positives from a docking study.

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