Journal of Chemical Information and Modeling (J CHEM INF MODEL)

Publications in this journal

• Article: New Group IV Chemical Motifs for Improved Dielectric Permittivity of Polyethylene

  G. Pilania, C. C. Wang, K. Wu, N. Sukumar, C. Breneman, G. Sotzing, R. Ramprasad
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  ABSTRACT: An enhanced dielectric permittivity of polyethylene and related polymers, while not overly sacrificing their excellent insulating properties, is highly desirable for various electrical energy storage applications. In this computational study, we use density functional theory (DFT) in combination with modified group additivity based high throughput techniques to identify promising chemical motifs that can increase the dielectric permittivity of polyethylene. We consider isolated polyethylene chains and allow the CH2 units in the backbone to be replaced by a number of Group IV halides (viz., SiF2, SiCl2, GeF2, GeCl2, SnF2, or SnCl2 units) in a systematic, progressive, and exhaustive manner. The dielectric permittivity of the chemically modified polyethylene chains is determined by employing DFT computations in combination with the effective medium theory for a limited set of compositions and configurations. The underlying chemical trends in the DFT data are first rationalized in terms of various tabulated atomic properties of the constituent atoms. Next, by parametrizing a modified group contribution expansion using the DFT data set, we are able to predict the dielectric permittivity and bandgap of nearly 30 000 systems spanning a much larger part of the configurational and compositional space. Promising motifs which lead to simultaneously large dielectric constant and band gap in the modified polyethylene chains have been identified. Our theoretical work is expected to serve as a possible motivation for future experimental efforts.
  Journal of Chemical Information and Modeling 03/2013; 53(4):879–886.
• Article: Pharmacophore Assessment Through 3-D QSAR:evaluation of the predictive ability on new derivatives by the application on a serie of antitubercularagents.

  Laura Friggeri, † Flavio Ballante, ‡ Rino Ragno, ‡ Ira Musmuca, ‡ Daniela De Vita, † Fabrizio Manetti, ¤ Mariangela Biava, † Luigi Scipione, † Roberto Di Santo, † Roberta Costi, † Marta Feroci, Silvano Tortorella
  Journal of Chemical Information and Modeling 01/2013;
• Article: Improving the selectivity of antimicrobial peptides from anuran skin

  Nédia Kamech, Damir Vukičević, Ali Ladram, Christophe Piesse, Julie Vasseur, Viktor Bojović, Juraj Simunić, Davor Juretić
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  ABSTRACT: ABSTRACT: Anuran skin is known to be a rich source of antimicrobial peptides although their therapeutic potential is often limited due to their toxicity against mammalian cells. The analysis of structure−activity relationships among anuran antimicrobial peptides provided the parameters to construct the “Mutator” tool for improving their selectivity for bacterial cells, by suggesting appropriate point substitutions. Double substitution analogues [K2, K16] of the Xenopus tropicalis peptide XT-7 and [I2, K19] of the Ascaphus truei peptide ascaphin-8 were predicted by this tool to have an increased ‘therapeutic index’ (TI = HC50/MIC for erythrocytes with respect to bacteria) > 80. The mutated peptides were synthesized and respectively found to have experimental TI values > 130 for S. aureus or E. coli, a considerable improvement with respect to TI < 37 for the parent compounds. Circular dichroism studies of the mutated peptides suggested this may in part be due to variations in the α-helical structure. For P. aeruginosa, which is more resistant to XT-7, the TI increased in the mutated peptide from 5 to >270, also due to a significant improvement in minimal inhibitory concentration. We have shown that the Mutator tool is capable of suggesting limited variations in natural anuran peptides capable of increasing peptide selectivity, by decreasing toxicity against mammalian erythrocytes, in general without compromising antibacterial activity. The tool is freely available on the Mutator Web server at http://split4.pmfst.hr/mutator/.
  Journal of Chemical Information and Modeling 10/2012; 52(12):3341-3351.
• Article: Histone Deacetylase Inhibitors: Structure-Based Modeling and Isoform-Selectivity Prediction

  Laura Silvestri, Flavio Ballante, Antonello Mai, Garland R. Marshall, Rino Ragno
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  ABSTRACT: An enhanced version of comparative binding energy (COMBINE) analysis, named COMBINEr, based on both ligand-based and structure-based alignments has been used to build several 3-D QSAR models for the eleven human zinc-based histone deacetylases (HDACs). When faced with an abundance of data from diverse structure–activity sources, choosing the best paradigm for an integrative analysis is difficult. A common example from studies on enzyme–inhibitors is the abundance of crystal structures characterized by diverse ligands complexed with different enzyme isoforms. A novel comprehensive tool for data mining on such inhomogeneous set of structure–activity data was developed based on the original approach of Ortiz, Gago, and Wade, and applied to predict HDAC inhibitors’ isoform selectivity. The COMBINEr approach (apart from the AMBER programs) has been developed to use only software freely available to academics.
  Journal of Chemical Information and Modeling 07/2012;
• Article: 3-D QSAutogrid/R: An Alternative Procedure To Build 3-D QSAR Models. Methodologies and Applications

  Flavio Ballante, Rino Ragno
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  ABSTRACT: Since it first appeared in 1988 3-D QSAR has proved its potential in the field of drug design and activity prediction. Although thousands of citations now exist in 3-D QSAR, its development was rather slow with the majority of new 3-D QSAR applications just extensions of CoMFA. An alternative way to build 3-D QSAR models, based on an evolution of software, has been named 3-D QSAutogrid/R and has been developed to use only software freely available to academics. 3-D QSAutogrid/R covers all the main features of CoMFA and GRID/GOLPE with implementation by multiprobe/multiregion variable selection (MPGRS) that improves the simplification of interpretation of the 3-D QSAR map. The methodology is based on the integration of the molecular interaction fields as calculated by AutoGrid and the R statistical environment that can be easily coupled with many free graphical molecular interfaces such as UCSF-Chimera, AutoDock Tools, JMol, and others. The description of each R package is reported in detail, and, to assess its validity, 3-D QSAutogrid/R has been applied to three molecular data sets of which either CoMFA or GRID/GOLPE models were reported in order to compare the results. 3-D QSAutogrid/R has been used as the core engine to prepare more that 240 3-D QSAR models forming the very first 3-D QSAR server (www.3d-qsar.com) with its code freely available through R-Cran distribution.
  Journal of Chemical Information and Modeling 05/2012;
• Article: Influence of the Membrane Lipophilic Environment on the Structure and on the Substrate Access/Egress Routes of the Human Aromatase Enzyme. A Computational Study.

  Jacopo Sgrignani, Alessandra Magistrato
  Journal of Chemical Information and Modeling 01/2012;
• Article: EXPLORING INHIBITOR RELEASE PATHWAYS IN HISTONE DEACETYLASES USING RANDOM ACCELERATION MOLECULAR DYNAMICS SIMULATIONS

  Subha Kalyaanamoorthy, Yi-Ping Phoebe Chen
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  ABSTRACT: 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 Å (named as channel A) and a 14 Å (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.
  Journal of Chemical Information and Modeling 01/2012;
• Article: S Chakroborty

  Gupta AK, Chakroborty S, Srivastava K, Puri SK, Saxena AK
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  ABSTRACT: 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 (r2training = 0.85) and a fair correlation coefficient for the test set (r2test = 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.
  Journal of Chemical Information and Modeling 07/2010; 52(5):1376.
• Article: Book Review of Google: The Digital Gutenberg Google: The Digital Gutenberg . By Stephen Arnold. Infonortics: Tetbury, England. 2009 . approx. 113 pp. E-book available only in online PDF download version direct from publisher; no ISBN. US$350, Euro 260.

  Stephen R Heller
  Journal of Chemical Information and Modeling 09/2009;
• Article: Concept-based semi-automatic classification of drugs.

  Harsha Gurulingappa, Corinna Kolárik, Martin Hofmann-Apitius, Juliane Fluck
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  ABSTRACT: The anatomical therapeutic chemical (ATC) classification system maintained by the World Health Organization provides a global standard for the classification of medical substances and serves as a source for drug repurposing research. Nevertheless, it lacks several drugs that are major players in the global drug market. In order to establish classifications for yet unclassified drugs, this paper presents a newly developed approach based on a combination of information extraction (IE) and machine learning (ML) techniques. Most of the information about drugs is published in the scientific articles. Therefore, an IE-based framework is employed to extract terms from free text that express drug's chemical, pharmacological, therapeutic, and systemic effects. The extracted terms are used as features within a ML framework to predict putative ATC class labels for unclassified drugs. The system was tested on a portion of ATC containing drugs with an indication on the cardiovascular system. The class prediction turned out to be successful with the best predictive accuracy of 89.47% validated by a 100-fold bootstrapping of the training set and an accuracy of 77.12% on an independent test set. The presented concept-based classification system outperformed state-of-the-art classification methods based on chemical structure properties.
  Journal of Chemical Information and Modeling 09/2009; 49(8):1986-92.
• Article: Docking studies on DNA-ligand interactions: building and application of a protocol to identify the binding mode.

  Clarisse G Ricci, Paulo A Netz
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  ABSTRACT: Despite DNA being an important target for several drugs, most of the docking programs are validated only for proteins and their ligands. In this paper, we used AutoDock 4.0 to perform self-dockings and cross dockings between two DNA ligands (a minor groove binder and an intercalator) and four distinct receptors: 1) crystallographic DNA without intercalation gap; 2) crystallographic DNA with intercalation gap; 3) canonical B-DNA; and 4) modified B-DNA with intercalation gap. Besides being efficient in self-dockings, AutoDock is capable of correctly identifying two of the main DNA binding modes with the condition that the target possesses an artificial intercalation gap. Therefore, we suggest a default protocol to identify DNA binding modes which uses a modified canonical DNA (with gap) as receptor. This protocol was applied to dock two different Troger bases to DNA and the predicted binding modes agree with those suggested, yet not established, by experimental data. We also applied the protocol to dock aflatoxin B(1) exo-8,9-epoxide, and the results are in complete agreement with experimental data from the literature. We propose that this approach can be used to investigate other ligands whose binding mode to DNA remains unknown, yielding a suitable starting point for further theoretical studies such as molecular dynamics simulations.
  Journal of Chemical Information and Modeling 09/2009; 49(8):1925-35.
• Article: pK(a) prediction from "Quantum Chemical Topology" descriptors.

  A P Harding, D C Wedge, P L A Popelier
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  ABSTRACT: Knowing the pK(a) of a compound gives insight into many properties relevant to many industries, in particular the pharmaceutical industry during drug development processes. In light of this, we have used the theory of Quantum Chemical Topology (QCT), to provide ab initio descriptors that are able to accurately predict pK(a) values for 228 carboxylic acids. This Quantum Topological Molecular Similarity (QTMS) study involved the comparison of 5 increasingly more expensive levels of theory to conclude that HF/6-31G(d) and B3LYP/6-311+G(2d,p) provided an accurate representation of the compounds studies. We created global and subset models for the carboxylic acids using Partial Least Square (PLS), Support Vector Machines (SVM), and Radial Basis Function Neural Networks (RBFNN). The models were extensively validated using 4-, 7-, and 10-fold cross-validation, with the validation sets selected based on systematic and random sampling. HF/6-31G(d) in conjunction with SVM provided the best statistics when taking into account the large increase in CPU time required to optimize the geometries at the B3LYP/6-311+G(2d,p) level. The SVM models provided an average q(2) value of 0.886 and an RMSE value of 0.293 for all the carboxylic acids, a q(2) of 0.825 and RMSE of 0.378 for the ortho-substituted acids, a q(2) of 0.923 and RMSE of 0.112 for the para- and meta-substituted acids, and a q(2) of 0.906 and RMSE of 0.268 for the aliphatic acids. Our method compares favorably to ACD/Laboratories, VCCLAB, SPARC, and ChemAxon's pK(a) prediction software based of the RMSE calculated by the leave-one-out method.
  Journal of Chemical Information and Modeling 08/2009; 49(8):1914-24.
• Article: Tunable machine vision-based strategy for automated annotation of chemical databases.

  Jungkap Park, Gus R Rosania, Kazuhiro Saitou
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  ABSTRACT: We present a tunable, machine vision-based strategy for automated annotation of virtual small molecule databases. The proposed strategy is based on the use of a machine vision-based tool for extracting structure diagrams in research articles and converting them into connection tables, a virtual "Chemical Expert" system for screening the converted structures based on the adjustable levels of estimated conversion accuracy, and a fragment-based measure for calculating intermolecular similarity. For annotation, calculated chemical similarity between the converted structures and entries in a virtual small molecule database is used to establish the links. The overall annotation performances can be tuned by adjusting the cutoff threshold of the estimated conversion accuracy. We perform an annotation test which attempts to link 121 journal articles registered in PubMed to entries in PubChem which is the largest, publicly accessible chemical database. Two cases of tests are performed, and their results are compared to see how the overall annotation performances are affected by the different threshold levels of the estimated accuracy of the converted structure. Our work demonstrates that over 45% of the articles could have true positive links to entries in the PubChem database with promising recall and precision rates in both tests. Furthermore, we illustrate that the Chemical Expert system which can screen converted structures based on the adjustable levels of estimated conversion accuracy is a key factor impacting the overall annotation performance. We propose that this machine vision-based strategy can be incorporated with the text-mining approach to facilitate extraction of contextual scientific knowledge about a chemical structure, from the scientific literature.
  Journal of Chemical Information and Modeling 08/2009; 49(8):1993-2001.
• Article: On transversal hydrophobicity of some proteins and their modules.

  Andrzej Galat
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  ABSTRACT: Hydrophobicity of proteins encoded in the genomes of diverse organisms was quantified using two novel concepts: (A) amino acid (AA) bulkiness-dependent hydrophobicity profiles and (B) spatial context of hydrophobicity distribution in AA triads. Both concepts were introduced into an algorithm that was used for extracting protein clusters from diverse genomic databases whose sequence attributes were similar to those in the multiple sequence alignment (MSA) of a given family of proteins. The sequences of the G protein-coupled receptors (GPCRs) encoded in different genomes were used as templates for testing the above concepts. The following sequence attributes were used for protein clustering: (A) sequence similarity scores (IDs); (B) amino acid composition (AAC); (C) hydrophobicity; (D) AA-bulkiness; and (E) alpha-helical propensity potentials. Diverse GPCRs display variable distributions of AA bulkiness-dependent buildups and declines in the hydrophobicity profiles that may be related to their function-dependent way of packing and allostery in the membrane. It is shown that intramolecular transversal nonbonded interactions between the TM segments in diverse GPCRs involve about 50% of hydrophobic atoms. Similar interaction networks exist between alpha-helices of tetratricopeptide (TPR) motifs-containing immunophilins and other proteins containing alpha-helical bundles.
  Journal of Chemical Information and Modeling 08/2009; 49(7):1821-30.
• Article: Testing assumptions and hypotheses for rescoring success in protein-ligand docking.

  Noel M O'Boyle, John W Liebeschuetz, Jason C Cole
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  ABSTRACT: In protein-ligand docking, the scoring function is responsible for identifying the correct pose of a particular ligand as well as separating ligands from nonligands. Recently there has been considerable interest in schemes that combine results from several scoring functions in an effort to achieve improved performance in virtual screens. One such scheme is consensus scoring, which involves combining the results from several rescoring experiments. Although there have been a number of studies that have investigated factors affecting success in consensus scoring, these studies have not addressed the question of why a rescoring strategy works in the first place. Here we propose and test two alternative hypotheses for why rescoring has the potential to improve results, using GOLD 4.0. The "consensus" hypothesis is that rescoring is a way of combining results from two scoring functions such that only true positives are likely to score highly. The "complementary" hypothesis is that the two scoring functions used in rescoring have complementary strengths; one is better at ranking actives with respect to inactives while the other is better at ranking poses of actives. We find that in general it is this hypothesis that explains success in a rescoring experiment. We also test an assumption of any rescoring method, which is that the scores obtained are representative of the fitness of the docked pose. We find that although rescored poses tended to have slightly higher clash values than their docked equivalents, in general the scores were representative.
  Journal of Chemical Information and Modeling 08/2009; 49(8):1871-8.
• Article: Protein-ligand binding free energy calculation by the Smooth Reaction Path Generation (SRPG) Method.

  Yoshifumi Fukunishi, Daisuke Mitomo, Haruki Nakamura
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  ABSTRACT: We developed a new molecular dynamics simulation method for protein-ligand binding free energy calculation in an explicit water model. This method consists of three steps: (1) generation of a compound dissociation path starting from a stable protein-compound complex structure, (2) calculation of the free energy surface along the dissociation path, and (3) calculation of the free energy surface of a small area around the protein-compound complex structure, which is a free energy minimum. The protein-compound binding free energy is estimated from the information obtained by the above three steps. This method was applied to a small system, a 18-crown-6 ether with its ligand ion, and a realistic system consisting of a target protein with its inhibitor. This approximation worked well; the protein-inhibitor dissociation was successfully performed, and the binding free energies were calculated.
  Journal of Chemical Information and Modeling 08/2009; 49(8):1944-51.
• Article: GARD: a Generally Applicable Replacement for RMSD.

  J Christian Baber, David C Thompson, Jason B Cross, Christine Humblet
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  ABSTRACT: The root-mean-squared deviation (rmsd) is a widely used measure of distance between two aligned objects -- often chemical structures. However, rmsd has a number of known limitations including difficulty of interpretation, no limit on weighting for any portion of the alignment, and a lack of normalization. In this work, a Generally Applicable Replacement for rmsD (GARD) is proposed. In this implementation atomic contributions are weighted by their relative importance to binding, as determined statistically by Andrews et al. (1) , and as such this method is 'chemically aware'. This novel measure is normalized and does not have many of the failings of traditional rmsd. It is, thus, perfectly suited for a wide variety of uses, including the assessment of the quality of poses produced from molecular docking programs and the comparison of conformers. Rmsd and GARD are compared in their ability to assess docking software and multiple examples of the use of GARD to rescue essentially correct poses with a high rmsd are presented.
  Journal of Chemical Information and Modeling 08/2009; 49(8):1889-900.
• Article: An intersection inequality sharper than the tanimoto triangle inequality for efficiently searching large databases.

  Pierre Baldi, Daniel S Hirschberg
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  ABSTRACT: Bounds on distances or similarity measures can be useful to help search large databases efficiently. Here we consider the case of large databases of small molecules represented by molecular fingerprint vectors with the Tanimoto similarity measure. We derive a new intersection inequality which provides a bound on the Tanimoto similarity between two fingerprint vectors and show that this bound is considerably sharper than the bound associated with the triangle inequality of the Tanimoto distance. The inequality can be applied to other intersection-based similarity measures. We introduce a new integer representation which relies on partitioning the fingerprint components, for instance by taking components modulo some integer M and reporting the total number of 1-bits falling in each partition. We show how the intersection inequality can be generalized immediately to these integer representations and used to search large databases of binary fingerprint vectors efficiently.
  Journal of Chemical Information and Modeling 08/2009; 49(8):1866-70.