Journal of Chemical Information and Modeling

Molecular Dynamics and DFT Study on HIV-1 Nucleocapsid Protein-7 in Complex with Viral Genome.

Publication Date: 2010 Mar 4 PMID: 20201584
Authors: Mori, M. - Dietrich, U. - Manetti, F. - Botta, M.
Journal: J Chem Inf Model

The HIV-1 nucleocapsid protein-7 (NCp7) is a highly basic, small zinc-binding protein involved in both deoxyribonucleic (DNA) and ribonucleic (RNA) acids annealing and in viral particle maturation including genome encapsidation, with an additional chaperoning activity toward reverse transcriptase by promoting the two obligatory strand transfers during reverse transcription. Because of its interaction with highly conserved sequences of the HIV-1 genome, NCp7 is being considered a new potential drug target, resistant to mutation, for antiviral activity. The high flexibility of this protein has, however, limited the identification of structural determinants involved in the interaction with stranded sequences of DNA and RNA. Here, we provide a quantum mechanics (density functional theory) study of the zinc-binding motifs and a molecular dynamics simulation of the protein in complex with RNA and DNA, starting from available nuclear magnetic resonance (NMR) structures. Results show that the interaction between the NCp7 and the viral genome is probably based on electrostatic interactions due to a cluster of basic residues, which is reinforced by the exploitation of nonelectrostatic contacts that further stabilize the complexes. Moreover, a possible mechanism for DNA destabilization that involves amino acids T24 and R26 is also hypothesized. Finally, a network of hydrophobic and hydrogen-bond interactions for the stabilization of complexes with DNA and, especially, with RNA is described here for the first time. The complexes between NCp7 and both DNA and RNA, resulting from computer simulations, showed structural properties that are in agreement with most of the currently available molecular biology evidence and could be considered as reliable models (better than NMR structures currently available) for subsequent structure-based ligand design approaches.

post to: CiteULike

High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing.

Publication Date: 2010 Mar 3 PMID: 20199097
Authors: Buch, I. - Harvey, M. J. - Giorgino, T. - Anderson, D. P. - De Fabritiis, G.
Journal: J Chem Inf Model

Although molecular dynamics simulation methods are useful in the modeling of macromolecular systems, they remain computationally expensive, with production work requiring costly high-performance computing (HPC) resources. We review recent innovations in accelerating molecular dynamics on graphics processing units (GPUs), and we describe GPUGRID, a volunteer computing project that uses the GPU resources of nondedicated desktop and workstation computers. In particular, we demonstrate the capability of simulating thousands of all-atom molecular trajectories generated at an average of 20 ns/day each (for systems of approximately 30 000-80 000 atoms). In conjunction with a potential of mean force (PMF) protocol for computing binding free energies, we demonstrate the use of GPUGRID in the computation of accurate binding affinities of the Src SH2 domain/pYEEI ligand complex by reconstructing the PMF over 373 umbrella sampling windows of 55 ns each (20.5 mus of total data). We obtain a standard free energy of binding of -8.7 +/- 0.4 kcal/mol within 0.7 kcal/mol from experimental results. This infrastructure will provide the basis for a robust system for high-throughput accurate binding affinity prediction.

post to: CiteULike

Receptor- and Ligand-Based Study on Novel 2,2'-Bithienyl Derivatives as Non-Peptidic AANAT Inhibitors.

Publication Date: 2010 Mar 2 PMID: 20196559
Authors: Lepailleur, A. - Lemaitre, S. - Feng, X. - Sopkova-de Oliveira Santos, J. - Delagrange, P. - Boutin, J. - Renard, P. - Bureau, R. - Rault, S.
Journal: J Chem Inf Model

Arylalkylamine N-acetyl transferase (serotonin N-acetyl transferase, AANAT) is a critical enzyme in the light-mediated regulation of melatonin production and circadian rythm. With the objective of discovering new chemical entities with inhibitory potencies against AANAT, a medium-throughput screening campaign was performed on a chemolibrary. We found a class of molecules based on a 2,2'-bithienyl scaffold, and compound 1 emerged as a first hit. Herein, we describe our progress from hit discovery and to optimization of this new class of compounds. To complete the study, computational approaches were carried out: a docking study which provided insights into the plausible binding modes of these new AANAT inhibitors and a three-dimensional quantitative structure-activity relationship study that applied comparative molecular field analysis (CoMFA) methodology. Several CoMFA models were developed (variable alignments and options), and the best predictive one yields good statistical results (q(2) = 0.744, r(2) = 0.891, and s = 0.273). The resulting CoMFA contour maps were used to illustrate the pharmacomodulations relevant to the biological activities in this series of analogs and to design new active inhibitors. This novel series of 2,2'-bithienyl derivatives gives new insights into the design of AANAT inhibitors.

post to: CiteULike

Virtual Screening Discovery of New Acetylcholinesterase Inhibitors Issued from CERMN Chemical Library.

Publication Date: 2010 Mar 2 PMID: 20196555
Authors: Sopkova-de Oliveira Santos, J. - Lesnard, A. - Agondanou, J. H. - Dupont, N. - Godard, A. M. - Stiebing, S. - Rochais, C. - Fabis, F. - Dallemagne, P. - Bureau, R. - Rault, S.
Journal: J Chem Inf Model

In our quest to find new inhibitors able to inhibit acetylcholinesterase (AChE) and, at the same time, to protect neurons from beta amyloid toxicity, i.e., inhibitors interacting with the catalytic anionic subsite as well as with the peripherical anionic site of AChE, a virtual screening of the Centre d'Etudes et de Recherche sur le Medicament de Normandie (CERMN) chemical library was carried out. Two complementary approaches were applied, i.e., a ligand- and a structure-based screening. Each screening led to the selection of different compounds, but only two were present in both screening results. In vitro tests on AChE showed that one of those compounds presented a very good inhibition activity, of the same order as Donepezil. This result shows the real complementary of both methods for the discovery of new ligands.

post to: CiteULike

Homology Modeling and Docking Evaluation of Aminergic G Protein-Coupled Receptors.

Publication Date: 2010 Mar 1 PMID: 20187660
Authors: McRobb, F. M. - Capuano, B. - Crosby, I. T. - Chalmers, D. K. - Yuriev, E.
Journal: J Chem Inf Model

We report the development of homology models of dopamine (D(2), D(3), and D(4)), serotonin (5-HT(1B), 5-HT(2A), 5-HT(2B), and 5-HT(2C)), histamine (H(1)), and muscarinic (M(1)) receptors, based on the high-resolution structure of the beta(2)-adrenergic receptor. The homology models were built and refined using Prime. We have addressed the required modeling of extracellular loop 2, which is often implicated in ligand binding. The orthosteric sites of the models were optimized using induced fit docking, to allow for side-chain flexibility, and the resulting receptor models have been evaluated using protein validation tools. Of the nine homology models developed, six models showed moderate to good enrichment in virtual screening experiments (5-HT(2A), 5-HT(1B), D(2), 5-HT(2C), D(3), and M(1)). The 5-HT(2A) receptor displayed the highest enrichment in virtual screening experiments with enrichment factors of 6.1, 6.9, and 5.9 at 2, 5, and 10%, respectively, of the screened database. However, three of the models require further refinement (5-HT(2B), D(4), and H(1)), due to difficulties in modeling some of the binding site residues as well as the extracellular loop 2. Our effort also aims to supplement the limited number of tested G protein-coupled receptor homology models based on the beta(2) crystal structure that are freely available to the research community.

post to: CiteULike

Construction of Functional Group Reactivity Database under Various Reaction Conditions Automatically Extracted from Reaction Database in a Synthesis Design System.

Publication Date: 2010 Mar 1 PMID: 20187659
Authors: Tanaka, A. - Okamoto, H. - Bersohn, M.
Journal: J Chem Inf Model

To be able to estimate the reactivity of functional groups under certain reaction conditions, we have stored three types of data: (1) data of change or destruction of the functional groups by the conditions of the reaction conditions; (2) data showing no influence of the reaction conditions on the functional groups; and (3) data showing the relative reactivity of two functional groups in the presence of certain reaction conditions. These three types of data, considered together, form entities that are referenced as "interaction data". These interaction data are used in a synthesis design system called SYNSUP. A new module in our system has been constructed that automatically generates interaction data from the reaction databases. From 15 265 reactions in the database, our program selected 2763 useful reactions with yields of >/=90% and one functional group change. From these useful reactions, data regarding 465 interferences, 815 cases of inert functional groups (under the reaction conditions), and 62 relative rate data could be extracted. In addition, with the use of multiple relative rate datasets, the reactivity of more than two functional groups could be deduced.

post to: CiteULike

Structure-Based Design of Peptides against G3BP with Cytotoxicity on Tumor Cells.

Publication Date: 2010 Feb 24 PMID: 20180532
Authors: Cui, W. - Wei, Z. - Chen, Q. - Cheng, Y. - Geng, L. - Zhang, J. - Chen, J. - Hou, T. - Ji, M.
Journal: J Chem Inf Model

Herein, we report a successful application of molecular modeling techniques to design two novel peptides with cytotoxicity on tumor cells. First, the interactions between the nuclear transport factor 2 (NTF2)-like domain of G3BP and the SH3 domain of RasGAP were studied by a well-designed protocol, which combines homology modeling, protein/protein docking, molecular dynamics simulations, molecular mechanics/generalized born surface area (MM/GBSA) free energy calculations, and MM/GBSA free energy decomposition analysis together. Then, based on the theoretical predictions, two novel peptides were designed and synthesized for biological assays, and they showed an obvious sensitizing effect on cis-platin. Furthermore, the deigned peptides had no significant effects on normal cells, while cis-platin did. Our results demonstrate that it is feasible to use the peptides to enhance the efficacy of clinical drugs and to kill cancer cells selectively. We believe that our work should be very useful for finding new therapies for cancers.

post to: CiteULike

Inhibitors of PIM-1 Kinase: A Computational Analysis of the Binding Free Energies of a Range of Imidazo [1,2-b] Pyridazines.

Publication Date: 2010 Feb 23 PMID: 20175582
Authors: Doudou, S. - Sharma, R. - Henchman, R. H. - Sheppard, D. W. - Burton, N. A.
Journal: J Chem Inf Model

The binding of a selection of competitive imidazo [1,2-b] pyridazine inhibitors of PIM-1 kinase with nanomolar activity has been analyzed using computational methods. Molecular dynamics simulations using umbrella sampling to determine a potential of mean force have been used to accurately predict the relative free energies of binding of these inhibitors, from -4.3 to -9.5 kcal mol(-1), in excellent agreement with the trends observed in previous experimental assays. The relative activity of the inhibitors could not be accounted for by any single effect or interaction within the active site and could only be fully reproduced when the overall free energies were considered, including important contributions from interactions outside the hinge region and using explicit solvent in the active site. The potential of mean force for the displacement of the glycine-rich phosphate binding loop (P-loop) has also been estimated and shown to be an important feature in the binding of these ligands.

post to: CiteULike

Predicting Oral Druglikeness by Iterative Stochastic Elimination.

Publication Date: 2010 Feb 19 PMID: 20170135
Authors: Rayan, A. - Marcus, D. - Goldblum, A.
Journal: J Chem Inf Model

Integration of computational methods in the early stages of drug discovery has been one of the key trends in the pharmaceutical industry. Starting with high quality drug candidates should ultimately minimize clinical attrition rates and give rise to higher success rates. In this paper, we present a novel approach for indexing oral druglikeness of compounds. With the Iterative Stochastic Elimination (ISE) Algorithm, we distinguish between orally available drugs and nondrugs by generating sets of optimized descriptors' ranges, each set constituting a "filter". We delineate in this paper how to produce an ensemble of best k-descriptor sets out of the huge number of possibilities, and how to construct a "filter bank" that retains diverse filters by clustering. Finally, we define the "orally bioavailable drug-like" character of individual molecules by combining the filters into an "Orally Bioavailable Druglike Index" (OB-DLI) which may be used to prioritize molecules in databases and discuss its uses in several potential applications. The predictive power with sets of 4-6 descriptors is high (i.e., one filter of 5 descriptors retrieved 81% true positives and >77% true negatives). Thus, OB-DLI has advantages over binary decisions (that use only one filter) not only in raising discriminative power but also in ranking drug candidates according to their chance to be successful oral drugs. We demonstrate the ability of our approach to discover molecular entities with the required property, orally bioavailable drug likeness, that are structurally dissimilar to those of the training set. Comparison of this ISE application to some of the current main methods for classification reveals that our approach has >13% improvement in the Matthews Correlation Coefficient, which measures the success of identifying true and false positives and negatives.

post to: CiteULike

A Computational Approach to the Study of the Binding Mode of Dual ACE/NEP Inhibitors.

Publication Date: 2010 Feb 19 PMID: 20170101
Authors: Dimitropoulos, N. - Papakyriakou, A. - Dalkas, G. A. - Sturrock, E. D. - Spyroulias, G. A.
Journal: J Chem Inf Model

Combined blockade of the renin-angiotensin-aldosterone system (RAAS) is an attractive therapeutic strategy for the treatment of cardiovascular diseases. Vasopeptidase inhibitors are a group of compounds capable of inhibiting more than one enzyme, which leads to potentiation of natriuretic peptide actions and suppression of the RAAS. In this study, molecular modeling has been used to elucidate key structural features that govern the binding and/or selectivity of a single compound toward the zinc catalytic sites of the N- and C-domains of the angiotensin-converting enzyme (ACE) and the neutral endopeptidase (NEP). Eleven dual inhibitors were categorized in three classes, according to their zinc binding groups. Analysis of their docked conformers revealed the molecular environment of the catalytic sites and the specific interactions between the inhibitors and amino acid residues that are important for selectivity and cooperativity. In addition, inhibitors were predicted to bind to the C-domain of the ACE with greater affinity than the N-domain, with an average difference in the free energy of binding approximately 2-3 kcal mol(-1). Residues that were identified to actively participate in the binding and stabilizating of the enzyme-inhibitor complexes were analyzed in a consensus way for both the ACE and the NEP. These atomic-level insights into enzyme-ligand binding can be used to drive new structure-based drug design processes in the quest for more selective and effective vasopeptidase inhibitors.

post to: CiteULike

Ligand-Protein Cross-Docking with Water Molecules.

Publication Date: 2010 Feb 17 PMID: 20158272
Authors: Thilagavathi, R. - Mancera, R. L.
Journal: J Chem Inf Model

The accuracy of ligand-protein docking may be affected by the presence of water molecules on the surface of the protein. Cross-docking simulations have been performed on a number of ligand-protein complexes for various proteins whose crystal structures contain water molecules in their binding sites. Only common sets of water molecules found in the binding site of the proteins were considered. A statistically significant overall increase in accuracy was observed when water molecules were included in cross-docking simulations. These results confirm the importance of including water molecules whenever possible in ligand-protein docking simulations.

post to: CiteULike

Force Field Optimization using Dynamics and Ensemble Averaged Data: Vibrational Spectra and Relaxation in Bound MbCO.

Publication Date: 2010 Feb 10 PMID: 20146509
Authors: Devereux, M. - Meuwly, M.
Journal: J Chem Inf Model

Force field parameters are ingredients for realistic atomistic simulations of gas- and condensed-phase systems. Here we discuss the effect of including averaged data from explicit MD simulations in optimizing potential energy functions. It is shown that vibrational frequencies (FeC and CO stretch and FeCO bend) and CO vibrational relaxation times ((v = 1) --> (v = 0) (T(10)) and (v = 2) --> (v = 1) (T(21))) in the active site of CO-bound myoglobin (MbCO) can be well represented with a single set of force field parameters. It is further demonstrated that parameters fitted in a subsystem of MbCO comprising the CO ligand, heme group and proximal histidine, are transferable to investigating the full protein and to providing quantitatively correct results. In particular, it is possible to calculate the CO and FeC stretch and the FeCO bending frequency to within approximately 5%; the relaxation time of the first vibrationally excited state including quantum corrections of T(10) approximately 25 ps is calculated close to the experimental value (17 ps), and the ratio T(10)/T(21) approximately 2 agrees favorably with experimental estimates. In contrast, following the more traditional approach of fitting frequencies from analyzing the Hessian matrix leads to a force field that captures frequencies correctly but not relaxation of vibrations.

post to: CiteULike

Toward the Comprehensive Systematic Enumeration and Synthesis of Novel Kinase Inhibitors Based on a 4-Anilinoquinazoline Binding Mode.

Publication Date: 2010 Feb 9 PMID: 20141221
Authors: Kettle, J. G. - Ward, R. A.
Journal: J Chem Inf Model

There are currently eight small-molecule kinase inhibitors approved as cancer treatments, and a significantly larger number of compounds are in the earlier stages of clinical development. Although kinase inhibitors are most commonly developed in a cancer setting, other disease areas have been targeted. The vast majority of reported kinase small-molecule inhibitors contain functionalities that interact with the adenosine triphosphate (ATP) binding site of the kinase. The 4-anilinoquinazolines have previously been reported as potent epidermal growth factor receptor (EGFR) inhibitors, binding at the 'hinge' region of the ATP site. Subsequently, this chemical series has been optimized against a number of different kinases including Src and Aurora B. Here, we detail the computational enumeration of ring systems that have the ability to make comparable interactions to the 4-anilinoquinazoline core. These were prioritized by computational, medicinal, and synthetic chemistry input, and a number of libraries were subsequently synthesized.

post to: CiteULike

The ensemble bridge algorithm: a new modeling tool for drug discovery problems.

Publication Date: 2010 Feb 22 PMID: 20121044
Authors: Culp, M. - Johnson, K. - Michailidis, G.
Journal: J Chem Inf Model

Ensemble algorithms have been historically categorized into two separate paradigms, boosting and random forests, which differ significantly in the way each ensemble is constructed. Boosting algorithms represent one extreme, where an iterative greedy optimization strategy, weak learners (e.g., small classification trees), and stage weights are employed to target difficult-to-classify regions in the training space. On the other extreme, random forests rely on randomly selected features and complex learners (learners that exhibit low bias, e.g., large regression trees) to classify well over the entire training data. Because the approach is not targeting the next learner for inclusion, it tends to provide a natural robustness to noisy labels. In this work, we introduce the ensemble bridge algorithm, which is capable of transitioning between boosting and random forests using a regularization parameter nu in [0,1]. Because the ensemble bridge algorithm is a compromise between the greedy nature of boosting and the randomness present in random forests, it yields robust performance in the presence of a noisy response and superior performance in the presence of a clean response. Often, drug discovery data (e.g., computational chemistry data) have varying levels of noise. Hence, this method enables a practitioner to employ a single method to evaluate ensemble performance. The method's robustness is verified across a variety of data sets where the algorithm repeatedly yields better performance than either boosting or random forests alone. Finally, we provide diagnostic tools for the new algorithm, including a measure of variable importance and an observational clustering tool.

post to: CiteULike

Graph-mining algorithm for the evaluation of bond formability.

Publication Date: 2010 Feb 22 PMID: 20112969
Authors: Pennerath, F. - Niel, G. - Vismara, P. - Jauffret, P. - Laurenco, C. - Napoli, A.
Journal: J Chem Inf Model

The formability of a bond in a target molecule is a bond property related to the problem of finding a reaction that synthesizes the target by forming the bond: the easier this problem, the higher the formability. Bond formability provides an interesting piece of information that might be used for selecting strategic bonds during a retrosynthesic analysis or for assessing synthetic accessibility in virtual screening. The article describes a graph-mining algorithm called GemsBond that evaluates formability of bonds by mining structural environments contained in several thousand molecular graphs of reaction products. When tested on reaction databases, GemsBond recognizes most formed bonds in reaction products and provides explanations consistent with knowledge in organic synthesis.

post to: CiteULike

Computational estimation of lanthanoid-water bond lengths by semiempirical methods.

Publication Date: 2010 Feb 22 PMID: 20108893
Authors: Seitz, M. - Alzakhem, N.
Journal: J Chem Inf Model

Over 650 lanthanoid complexes with Ln-OH(2) motifs have been modeled by the three semiempirical methods (AM1, PM3, or PM6)/SPARKLE. The geometrical deviations from the corresponding crystal structures can be described by normal distributions. Statistical inference analysis shows that AM1/SPARKLE is surprisingly accurate for the estimation of the average bond lengths Ln-OH(2) for the technologically important central lanthanoids (Ln = Eu-Tb) in complexes with pyridine-like ligands with a worst-case error of only 4.9%.

post to: CiteULike

A novel structure-based multimode QSAR method affords predictive models for phosphodiesterase inhibitors.

Publication Date: 2010 Feb 22 PMID: 20095527
Authors: Dong, X. - Ebalunode, J. O. - Cho, S. J. - Zheng, W.
Journal: J Chem Inf Model

Quantitative structure-activity relationship (QSAR) methods aim to build quantitatively predictive models for the discovery of new molecules. It has been widely used in medicinal chemistry for drug discovery. Many QSAR techniques have been developed since Hansch's seminal work, and more are still being developed. Motivated by Hopfinger's receptor-dependent QSAR (RD-QSAR) formalism and the Lukacova-Balaz scheme to treat multimode issues, we have initiated studies that focus on a structure-based multimode QSAR (SBMM QSAR) method, where the structure of the target protein is used in characterizing the ligand, and the multimode issue of ligand binding is systematically treated with a modified Lukacova-Balaz scheme. All ligand molecules are first docked to the target binding pocket to obtain a set of aligned ligand poses. A structure-based pharmacophore concept is adopted to characterize the binding pocket. Specifically, we represent the binding pocket as a geometric grid labeled by pharmacophoric features. Each pose of the ligand is also represented as a labeled grid, where each grid point is labeled according to the atom types of nearby ligand atoms. These labeled grids or three-dimensional (3D) maps (both the receptor map (R-map) and the ligand map (L-map)) are compared to each other to derive descriptors for each pose of the ligand, resulting in a multimode structure-activity relationship (SAR) table. Iterative partial least-squares (PLS) is employed to build the QSAR models. When we applied this method to analyze PDE-4 inhibitors, predictive models have been developed, obtaining models with excellent training correlation (r(2) = 0.65-0.66), as well as test correlation (R(2) = 0.64-0.65). A comparative analysis with 4 other QSAR techniques demonstrates that this new method affords better models, in terms of the prediction power for the test set.

post to: CiteULike

Binding affinity prediction with property-encoded shape distribution signatures.

Publication Date: 2010 Feb 22 PMID: 20095526
Authors: Das, S. - Krein, M. P. - Breneman, C. M.
Journal: J Chem Inf Model

We report the use of the molecular signatures known as "property-encoded shape distributions" (PESD) together with standard support vector machine (SVM) techniques to produce validated models that can predict the binding affinity of a large number of protein ligand complexes. This "PESD-SVM" method uses PESD signatures that encode molecular shapes and property distributions on protein and ligand surfaces as features to build SVM models that require no subjective feature selection. A simple protocol was employed for tuning the SVM models during their development, and the results were compared to SFCscore, a regression-based method that was previously shown to perform better than 14 other scoring functions. Although the PESD-SVM method is based on only two surface property maps, the overall results were comparable. For most complexes with a dominant enthalpic contribution to binding (DeltaH/-TDeltaS > 3), a good correlation between true and predicted affinities was observed. Entropy and solvent were not considered in the present approach, and further improvement in accuracy would require accounting for these components rigorously.

post to: CiteULike

Inclusion of solvation and entropy in the knowledge-based scoring function for protein-ligand interactions.

Publication Date: 2010 Feb 22 PMID: 20088605
Authors: Huang, S. Y. - Zou, X.
Journal: J Chem Inf Model

The effects of solvation and entropy play a critical role in determining the binding free energy in protein-ligand interactions. Despite the good balance between speed and accuracy, no current knowledge-based scoring functions account for the effects of solvation and configurational entropy explicitly due to the difficulty in deriving the corresponding pair potentials and the resulting double counting problem. In the present work, we have included the solvation effect and configurational entropy in the knowledge-based scoring function by an iterative method. The newly developed scoring function has yielded a success rate of 91% in identifying near-native binding modes with Wang et al.'s benchmark of 100 diverse protein-ligand complexes. The results have been compared with the results of 15 other scoring functions for validation purpose. In binding affinity prediction, our scoring function has yielded a correlation of R(2) = 0.76 between the predicted binding scores and the experimentally measured binding affinities on the PMF validation sets of 77 diverse complexes. The results have been compared with R(2) of four other well-known knowledge-based scoring functions. Finally, our scoring function was also validated on the large PDBbind database of 1299 protein-ligand complexes and yielded a correlation coefficient of 0.474. The present computational model can be applied to other scoring functions to account for solvation and entropic effects.

post to: CiteULike

Structure-based rational screening of novel hit compounds with structural diversity for cytochrome P450 sterol 14alpha-demethylase from Penicillium digitatum.

Publication Date: 2010 Feb 22 PMID: 20088581
Authors: Zhang, Q. - Li, D. - Wei, P. - Zhang, J. - Wan, J. - Ren, Y. - Chen, Z. - Liu, D. - Yu, Z. - Feng, L.
Journal: J Chem Inf Model

Cytochrome P450 sterol 14alpha-demethylases (CYP51s) are essential enzymes in sterol biosynthesis and well-known as the target of antifungal drugs. All fungal CYP51s are integral membrane proteins, making structural and biophysical characterization more challenging. The X-ray crystallographic structure of CYP51 isolated from Mycobacterium tuberculosis (MT-CYP51) is the unique reported one hitherto. In the present study, a homology modeling three-dimensional structure of CYP51 from Penicillium digitatum (PD-CYP51) was generated by CPHmodels, in which the accuracy of sequence alignment could be improved by taking into account further structural conservation information, using MT-CYP51 as the template. Interaction mechanism between the active site of PD-CYP51 and its inhibitors were further investigated by molecular dynamics simulating and molecular docking. With the effective docking process and interaction analysis information, structure-based virtual screening was performed to pick out the thirty new potential inhibiting compounds with structural diversity by using a new virtual screening strategy including Flex-Pharm/PMF/GOLD//FlexX/PMF/GOLD molecular docking procedures, and finally, seven new hit compounds out of SPECs database with potent inhibitory ability were validated by bioaffinity assays at enzyme level and on P. digitatum in vitro. The positive results indicated that all modeling strategies and screening processes presented in the current study most like to be an encouraging way in search of novel lead compounds with structural diversity for the specifically individual fungal CYP51s of both plants and human pathogens in the future.

post to: CiteULike

Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation.

Publication Date: 2010 Feb 22 PMID: 20088575
Authors: Geppert, H. - Vogt, M. - Bajorath, J.
Journal: J Chem Inf Model

post to: CiteULike

SPECTRa-T: machine-based data extraction and semantic searching of chemistry e-theses.

Publication Date: 2010 Feb 22 PMID: 20088574
Authors: Downing, J. - Harvey, M. J. - Morgan, P. B. - Murray-Rust, P. - Rzepa, H. S. - Stewart, D. C. - Tonge, A. P. - Townsend, J. A.
Journal: J Chem Inf Model

The SPECTRa-T project has developed text-mining tools to extract named chemical entities (NCEs), such as chemical names and terms, and chemical objects (COs), e.g., experimental spectral assignments and physical chemistry properties, from electronic theses (e-theses). Although NCEs were readily identified within the two major document formats studied, only the use of structured documents enabled identification of chemical objects and their association with the relevant chemical entity (e.g., systematic chemical name). A corpus of theses was analyzed and it is shown that a high degree of semantic information can be extracted from structured documents. This integrated information has been deposited in a persistent Resource Description Framework (RDF) triple-store that allows users to conduct semantic searches. The strength and weaknesses of several document formats are reviewed.

post to: CiteULike

An in silico method for predicting ames activities of primary aromatic amines by calculating the stabilities of nitrenium ions.

Publication Date: 2010 Feb 22 PMID: 20078034
Authors: Bentzien, J. - Hickey, E. R. - Kemper, R. A. - Brewer, M. L. - Dyekjaer, J. D. - East, S. P. - Whittaker, M.
Journal: J Chem Inf Model

In this paper, we describe an in silico first principal approach to predict the mutagenic potential of primary aromatic amines. This approach is based on the so-called "nitrenium hypothesis", which was developed by Ford et al. in the early 1990s. This hypothesis asserts that the mutagenic effect for this class of molecules is mediated through the transient formation of a nitrenium ion and that the stability of this cation is correlated with the mutagenic potential. Here we use quantum mechanical calculations at different levels of theory (semiempirical AM1, ab initio HF/3-21G, HF/6-311G(d,p), and DFT/B3LYP/6-311G(d,p)) to compute the stability of nitrenium ions. When applied to a test set of 257 primary aromatic amines, we show that this method can correctly differentiate between Ames active and inactive compounds, and furthermore that it is able to rationalize and predict SAR trends within structurally related chemical series. For this test set, the AM1 nitrenium stability calculations are found to provide a good balance between speed and accuracy, resulting in an overall accuracy of 85%, and sensitivity and specificity of 91% and 72%, respectively. The nitrenium-based predictions are also compared to the commercial software packages DEREK, MULTICASE, and the MOE-Toxicophore descriptor. One advantage of the approach presented here is that the calculation of relative stabilities results in a continuous spectrum of activities and not a simple yes/no answer. This allows us to observe and rationalize subtle trends due to the different electrostatic properties of the organic molecules. Our results strongly indicate that nitrenium ion stability calculations should be used as a complementary approach to assist the medicinal chemist in prioritizing and selecting nonmutagenic primary aromatic amines during preclinical drug discovery programs.

post to: CiteULike