Showing papers in "Journal of Computer-aided Molecular Design in 2005"

Virtual computational chemistry laboratory - design and description

Abstract: Internet technology offers an excellent opportunity for the development of tools by the cooperative effort of various groups and institutions. We have developed a multi-platform software system, Virtual Computational Chemistry Laboratory, http://www.vcclab.org, allowing the computational chemist to perform a comprehensive series of molecular indices/properties calculations and data analysis. The implemented software is based on a three-tier architecture that is one of the standard technologies to provide client-server services on the Internet. The developed software includes several popular programs, including the indices generation program, DRAGON, a 3D structure generator, CORINA, a program to predict lipophilicity and aqueous solubility of chemicals, ALOGPS and others. All these programs are running at the host institutes located in five countries over Europe. In this article we review the main features and statistics of the developed system that can be used as a prototype for academic and industry models.

Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures

Abstract: Substructural fragments are proposed as a simple and safe way to encode molecular structures in a matrix containing the occurrence of fragments of a given type. The knowledge retrieved from QSPR modelling can also be stored in that matrix in addition to the information about fragments. Complex supramolecular systems (using special bond types) and chemical reactions (represented as Condensed Graphs of Reactions, CGR) can be treated similarly. The efficiency of fragments as descriptors has been demonstrated in QSPR studies of aqueous solubility for a diverse set of organic compounds as well as in the analysis of thermodynamic parameters for hydrogen-bonding in some supramolecular complexes. It has also been shown that CGR may be an interesting opportunity to perform similarity searches for chemical reactions. The relationship between the density of information in descriptors/knowledge matrices and the robustness of QSPR models is discussed.

Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network.

Abstract: Artificial neural network (ANN) implementing the back-propagation algorithm was applied for the calculation of the imprinting factors (IF) of molecularly imprinted polymers (MIP) as a function of the computed molecular descriptors of template and functional monomer molecules and mobile phase descriptors. The dataset used in our study were obtained from the literature and classified into two distinctive datasets on the basis of the polymer's morphology, irregularly sized MIP and uniformly sized MIP datasets. Results revealed that artificial neural network was able to perform well on datasets derived from uniformly sized MIP (n = 23, r = 0.946, RMS = 2.944) while performing poorly on datasets derived from irregularly sized MIP (n = 75, r = 0.382, RMS = 6.123). The superior performance of the uniformly sized MIP dataset over the irregularly sized MIP dataset could be attributed to its more predictable nature owing to the consistency of MIP particles, uniform number and association constant of binding sites, and minimal deviation of the imprinted polymers. The ability to predict the imprinting factor of imprinted polymer prior to performing actual experimental work provide great insights on the feasibility of the interaction between template-functional monomer pairs.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network.

Abstract: P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure–activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 ± 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.

A support vector machine approach to classify human cytochrome P450 3A4 inhibitors.

Abstract: The cytochrome P450 (CYP) enzyme superfamily plays a major role in the metabolism of commercially available drugs. Inhibition of these enzymes by a drug may result in a plasma level increase of another drug, thus leading to unwanted drug-drug interactions when two or more drugs are coadministered. Therefore, fast and reliable in silico methods predicting CYP inhibition from calculated molecular properties are an important tool which can be applied to assess both already synthesized as well as virtual compounds. We have studied the performance of support vector machines (SVMs) to classify compounds according to their potency to inhibit CYP3A4. The data set for model generation consists of more than 1300 structural diverse drug-like research molecules which were divided into training and test sets. The predictive power of SVMs crucially depends on a careful selection of parameters specifying the kernel function and the penalty for misclassifications. In this study we have investigated a procedure to identify a valid set of SVM parameters which is based on a sampling of the parameter space on a regular grid. From this set of parameters, either single SVMs or SVM committees were trained to distinguish between strong and weak inhibitors or to achieve a more realistic three-class assignment, with one class representing medium inhibitors. This workflow was studied for several kernel functions and descriptor sets. All SVM models performed significantly better than PLS-DA models which were generated from the corresponding descriptor sets. As a very promising result, simple two-dimensional (2D) descriptors yield a three-class model which correctly classifies more than 70% of the test set. Our work illustrates that SVMs used in combination with simple 2D descriptors provide a very effective and reliable tool which allows a fast assessment of CYP3A4 inhibition potency in an early in silico filtering process.

A Three-dimensional Model of the Human Immunodeficiency Virus Type 1 Integration Complex

Abstract: While the general features of HIV-1 integrase function are understood, there is still uncertainty about the composition of the integration complex and how integrase interacts with viral and host DNA. We propose an improved model of the integration complex based on current experimental evidence including a comparison with the homologous Tn5 transposase containing bound DNA and an analysis of DNA binding sites using Goodford’s GRID. Our model comprises a pair of integrase dimers, two strands of DNA to represent the viral DNA ends and a strand of bent DNA representing the host chromosome. In our model, the terminal four base pairs of each of the viral DNA strands interact with the integrase dimer providing the active site, while bases one turn away interact with a flexible loop (residues 186–194) on the second integrase dimer. We propose that residues E152, Q148 and K156 are involved in the specific recognition of the conserved CA dinucleotide and that the active site mobile loop (residues 140–149) stabilises the integration complex by acting as a barrier to separate the two viral DNA ends. In addition, the residues responsible for DNA binding in our model show a high level of amino acid conservation.

Automated QSPR through Competitive Workflow

Abstract: This paper describes a novel software architecture, Competitive Workflow, which implements workflow as a distributed and competitive multi-agent system. The implementation of a competitive workflow architecture designed to model important computer-aided molecular design workflows, the Discovery Bus, is described. QSPR modelling results for three example ADME datasets, for solubility, human plasma protein binding and P-glycoprotein substrates using an autonomous QSPR modelling workflow implemented on the Discovery Bus are presented. The autonomous QSPR system allows exhaustive exploration of descriptor and model space, automated model validation and continuous updating as new data and methods are made available. Prediction of properties of novel structures by an ensemble of models is also a feature of the system.

BALLView: an object-oriented molecular visualization and modeling framework.

Abstract: We present BALLView, an extensible tool for visualizing and modeling bio-molecular structures. It provides a variety of different models for bio-molecular visualization, e.g. ball-and-stick models, molecular surfaces, or ribbon models. In contrast to most existing visualization tools, BALLView also offers rich functionality for molecular modeling and simulation, including molecular mechanics methods (AMBER and CHARMM force fields), continuum electrostatics methods employing a Finite-Difference Poisson Boltzmann solver, and secondary structure calculation. Results of these computations can be exported as publication quality images or as movies. Even unexperienced users have direct access to this functionality through an intuitive graphical user interface, which makes BALLView particularly useful for teaching. For more advanced users, BALLView is extensible in different ways. Owing to its framework design, extension on the level of C‰+‰‰+ code is very convenient. In addition, an interface to the scripting language Python allows the interactive rapid prototyping of new methods. BALLView is portable and runs on all major platforms (Windows, MacOS X, Linux, most Unix flavors). It is available free of charge under the GNU Public License (GPL) from our website http://www.ballview.org.

Can free energy calculations be fast and accurate at the same time? Binding of low-affinity, non-peptide inhibitors to the SH2 domain of the src protein.

Abstract: The usefulness of free-energy calculations in non-academic environments, in general, and in the pharmaceutical industry, in particular, is a long-time debated issue, often considered from the angle of cost/performance criteria. In the context of the rational drug design of low-affinity, non-peptide inhibitors to the SH2 domain of the (pp60)src tyrosine kinase, the continuing difficulties encountered in an attempt to obtain accurate free-energy estimates are addressed. free-energy calculations can provide a convincing answer, assuming that two key-requirements are fulfilled: (i) thorough sampling of the configurational space is necessary to minimize the statistical error, hence raising the question: to which extent can we sacrifice the computational effort, yet without jeopardizing the precision of the free-energy calculation? (ii) the sensitivity of binding free-energies to the parameters utilized imposes an appropriate parametrization of the potential energy function, especially for non-peptide molecules that are usually poorly described by multipurpose macromolecular force fields. Employing the free-energy perturbation method, accurate ranking, within +/-0.7 kcal/mol, is obtained in the case of four non-peptide mimes of a sequence recognized by the (pp60)src SH2 domain.

A recursive-partitioning model for blood–brain barrier permeation

Abstract: A series of bagged recursive partitioning models for log(BB) is presented. Using a LGO-CV, three sets of physical property descriptors are evaluated and found to have Q2 values of 0.51 (CPSA), 0.53 (Ro5x) and 0.53 (MOE). Extrapolating these models to Pfizer chemical space is difficult due to P-glycoprotein (P-gp) mediated efflux. Low correlation coefficients for this test set are improved (R2 = 0.39) when compounds known to be P-gp substrates or statistical extrapolations are removed. The use of simple linear models for specific chemical series is also found to improve the correlation over a limited chemical space.

Structure-activity relationships for a new family of sulfonylurea herbicides.

Abstract: Acetohydroxyacid synthase (AHAS; EC 2.2.1.6) catalyzes the first common step in branched-chain amino acid biosynthesis. The enzyme is inhibited by several chemical classes of compounds and this inhibition is the basis of action of the sulfonylurea and imidazolinone herbicides. The commercial sulfonylureas contain a pyrimidine or a triazine ring that is substituted at both meta positions, thus obeying the initial rules proposed by Levitt. Here we assess the activity of 69 monosubstituted sulfonylurea analogs and related compounds as inhibitors of pure recombinant Arabidopsis thaliana AHAS and show that disubstitution is not absolutely essential as exemplified by our novel herbicide, monosulfuron (2-nitro-N-(4'-methyl-pyrimidin-2'-yl) phenyl-sulfonylurea), which has a pyrimidine ring with a single meta substituent. A subset of these compounds was tested for herbicidal activity and it was shown that their effect in vivo correlates well with their potency in vitro as AHAS inhibitors. Three-dimensional quantitative structure-activity relationships were developed using comparative molecular field analysis and comparative molecular similarity indices analysis. For the latter, the best result was obtained when steric, electrostatic, hydrophobic and H-bond acceptor factors were taken into consideration. The resulting fields were mapped on to the published crystal structure of the yeast enzyme and it was shown that the steric and hydrophobic fields are in good agreement with sulfonylurea-AHAS interaction geometry.

Towards the chemometric dissection of peptide--HLA-A*0201 binding affinity: comparison of local and global QSAR models.

Abstract: The affinities of 177 nonameric peptides binding to the HLA-A*0201 molecule were measured using a FACS-based MHC stabilisation assay and analysed using chemometrics. Their structures were described by global and local descriptors, QSAR models were derived by genetic algorithm, stepwise regression and PLS. The global molecular descriptors included molecular connectivity chi indices, kappa shape indices, E-state indices, molecular properties like molecular weight and log P, and three-dimensional descriptors like polarizability, surface area and volume. The local descriptors were of two types. The first used a binary string to indicate the presence of each amino acid type at each position of the peptide. The second was also position-dependent but used five z-scales to describe the main physicochemical properties of the amino acids forming the peptides. The models were developed using a representative training set of 131 peptides and validated using an independent test set of 46 peptides. It was found that the global descriptors could not explain the variance in the training set nor predict the affinities of the test set accurately. Both types of local descriptors gave QSAR models with better explained variance and predictive ability. The results suggest that, in their interactions with the MHC molecule, the peptide acts as a complicated ensemble of multiple amino acids mutually potentiating each other.

Molecular design of two sterol 14α-demethylase homology models and their interactions with the azole antifungals ketoconazole and bifonazole

Abstract: Sterol 14α-demethylase (CYP51) is one of the known major targets for azole antifungals. Therapeutic side effects of these antifungals are based on interactions of the azoles with the human analogue enzyme. This study describes for the first time a comparison of a human CYP51 (HU-CYP51) homology model with a homology model of the fungal CYP51 of Candida albicans (CA-CYP51). Both models are constructed by using the crystal structure of Mycobacterium tuberculosis MT-CYP51 (PDB code: 1EA1).

ENPDA: an evolutionary structure-based de novo peptide design algorithm

Abstract: One of the goals of computational chemists is to automate the de novo design of bioactive molecules. Despite significant advances in computational approaches to ligand design and binding energy evaluation, novel procedures for ligand design are required. Evolutionary computation provides a new approach to this design endeavor. We propose an evolutionary tool for de novo peptide design, based on the evaluation of energies for peptide binding to a user-defined protein surface patch. Special emphasis has been placed on the evaluation of the proposed peptides, leading to two different evaluation heuristics. The software developed was successfully tested on the design of ligands for the proteins prolyl oligopeptidase, p53, and DNA gyrase.

VolSurf analysis of pharmacokinetic properties for several antifungal sesquiterpene lactones isolated from Greek Centaurea sp.

Abstract: Sesquiterpene lactones are terpenoid compounds characteristic of the Asteraceae (Compositae) possessing a variety of biological activities, such as cytotoxic, antitumor, antibacterial, and antifungal The prediction of the pharmacokinetic profile of several antifungal sesquiterpene lactones, isolated from Greek taxa of Centaurea sp, was undertaken in this study using the VolSurf procedure The molecules were projected on the following pre-calculated ADME models: Caco-2 cell permeability, plasma protein affinity, blood–brain barrier permeation and thermodynamic solubility The in silico projection revealed a non optimal pharmacokinetic profile for the studied compounds ADME in silico screening of a semi-synthetic derivatives virtual library has been performed in order to optimize the pharmacokinetic properties A number of derivatives were proposed as it was predicted to have higher Caco-2 cell permeability, while the pharmacokinetic behaviour regarding BBB penetration, protein binding and solubility was mainly preserved

In-silico Screening using Flexible Ligand Binding Pockets: A Molecular Dynamics-based Approach

Abstract: In-silico screening of flexible ligands against flexible ligand binding pockets (LBP) is an emerging approach in structure-based drug discovery. Here, we describe a molecular dynamics (MD) based docking approach to investigate the influence on the high-throughput in-silico screening of small molecules against flexible ligand binding pockets. In our approach, an ensemble of 51 energetically favorable structures of the LBP of human estrogen receptor α (hERα) were collected from 3 ns MD simulations. In-silico screening of 3500 endocrine disrupting compounds against these flexible ligand binding pockets resulted in thousands of ER–ligand complexes of which 582 compounds were unique. Detailed analysis of MD generated structures showed that only 17 of the LBP residues significantly contribute to the overall binding pocket flexibility. Using the flexible LBP conformations generated, we have identified 32 compounds that bind better to the flexible ligand-binding pockets compared to the crystal structure. These compounds, though chemically divergent, are structurally similar to the natural hormone. Our MD-based approach in conjunction with grid–based distributed computing could be applied routinely for in-silico screening of large databases against any given target.

Molecular shape and electrostatics in the encoding of relevant chemical information

Abstract: We propose a molecule's chemistry can be hidden by representations of its shape and electrostatic field while retaining crucial, pharmaceutically relevant, information. Necessary, but not sufficient, to this proposition are the importance of shape and electrostatics to activity, the facility to easily represent, store and compare field properties, and knowledge of the density of possible drug-like molecules within a given radius of physical similarity. We provide methods and evidence to support the conclusion that a useful encoding is practical and propose tests for falsification.

3D-chiral atom, atom-type, and total non-stochastic and stochastic molecular linear indices and their applications to central chirality codification.

Abstract: Non-stochastic and stochastic 2D linear indices have been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. These descriptors circumvent the inability of conventional 2D non-stochastic [Y. Marrero-Ponce. J. Chem. Inf. Comp., Sci. l 44 (2004) 2010] and stochastic [Y. Marrero-Ponce, et al. Bioorg. Med. Chem., 13 (2005) 1293] linear indices to distinguish σ-stereoisomers. In order to test the potential of this novel approach in drug design we have modelled the angiotensin-converting enzyme inhibitory activity of perindoprilate’s σ-stereoisomers combinatorial library. Two linear discriminant analysis models, using non-stochastic and stochastic linear indices, were obtained. The models showed an accuracy of 100% and 96.65% for the training set; and 88.88% and 100% in the external test set, respectively. Canonical regression analysis corroborated the statistical quality of these models (Rcan of 0.78 and of 0.77) and was also used to compute biology activity canonical scores for each compound. After that, the prediction of the σ-receptor antagonists of chiral 3-(3-hydroxyphenyl)piperidines by linear multiple regression analysis was carried out. Two statistically significant QSAR models were obtained when non-stochastic (R2 = 0.982 and s = 0.157) and stochastic (R2 = 0.941 and s = 0.267) 3D-chiral linear indices were used. The predictive power was assessed by the leave-one-out cross-validation experiment, yielding values of q2 = 0.982 (scv = 0.186) and q2 = 0.90 (scv = 0.319), respectively. Finally, the prediction of the corticosteroid-binding globulin binding affinity of steroids set was performed. The best results obtained in the cross-validation procedure with non-stochastic (q2 = 0.904) and stochastic (q2 = 0.88) 3D-chiral linear indices are rather similar to most of the 3D-QSAR approaches reported so far. The validation of this method was achieved by comparison with previous reports applied to the same data set. The non-stochastic and stochastic 3D-chiral linear indices appear to provide an interesting alternative to other more common 3D-QSAR descriptors.

The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine.

Abstract: Support vector machine (SVM), as a novel machine learning technique, was used for the prediction of the human oral absorption for a large and diverse data set using the five descriptors calculated from the molecular structure alone The molecular descriptors were selected by heuristic method (HM) implemented in CODESSA At the same time, in order to show the influence of different molecular descriptors on absorption and to well understand the absorption mechanism, HM was used to build several multivariable linear models using different numbers of molecular descriptors Both the linear and non-linear model can give satisfactory prediction results: the square of correlation coefficient R2 was 078 and 086 for the training set, and 070 and 073 for the test set respectively In addition, this paper provides a new and effective method for predicting the absorption of the drugs from their structures and gives some insight into structural features related to the absorption of the drugs

A very large diversity space of synthetically accessible compounds for use with drug design programs.

Abstract: We have constructed a very large virtual diversity space containing more than 1013 chemical compounds. The diversity space is built from about 400 combinatorial libraries, which have been expanded by choosing sizeable collections of suitable R-groups that can be attached to each link point of their scaffolds. These R-group collections have been created by selecting reagents that have drug-like properties from catalogs of available chemicals. As members of known combinatorial libraries, the compounds in the diversity space are in general synthetically accessible and useful as potential drug leads. Hence, the diversity space can be used as a vast source of compounds by a de novo drug design program. For example, we have used such a program to generate inhibitors of HIV integrase enzyme that exhibited activity in the micromolar range.

Quantitative structure-activity relationship analysis of pyridinone HIV-1 reverse transcriptase inhibitors using the k nearest neighbor method and QSAR-based database mining.

Abstract: We have developed quantitative structure–activity relationship (QSAR) models for 44 non-nucleoside HIV-1 reverse transcriptase inhibitors (NNRTIs) of the pyridinone derivative type. The k nearest neighbor (kNN) variable selection approach was used. This method utilizes multiple descriptors such as molecular connectivity indices, which are derived from two-dimensional molecular topology. The modeling process entailed extensive validation including the randomization of the target property (Y-randomization) test and the division of the dataset into multiple training and test sets to establish the external predictive power of the training set models. QSAR models with high internal and external accuracy were generated, with leave-one-out cross-validated R2 (q2) values ranging between 0.5 and 0.8 for the training sets and R2 values exceeding 0.6 for the test sets. The best models with the highest internal and external predictive power were used to search the National Cancer Institute database. Derivatives of the pyrazolo[3,4-d]pyrimidine and phenothiazine type were identified as promising novel NNRTIs leads. Several candidates were docked into the binding pocket of nevirapine with the AutoDock (version 3.0) software. Docking results suggested that these types of compounds could be binding in the NNRTI binding site in a similar mode to a known non-nucleoside inhibitor nevirapine.

Construction of 4D-QSAR models for use in the design of novel p38-MAPK inhibitors.

Abstract: The p38-mitogen-activated protein kinase (p38-MAPK) plays a key role in lipopolysaccharide-induced tumor necrosis factor-alpha (TNF-alpha) and interleukin-1 (IL-1) release during the inflammatory process, emerging as an attractive target for new anti-inflammatory agents. Four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis [Hopfinger et al., J. Am. Chem. Soc., 119 (1997) 10509] was applied to a series of 33 (a training set of 28 and a test set of 5) pyridinyl-imidazole and pyrimidinyl-imidazole inhibitors of p38-MAPK, with IC50 ranging from 0.11 to 2100 nM [Liverton et al., J. Med. Chem., 42 (1999) 2180]. Five thousand conformations of each analogue were sampled from a molecular dynamics simulation (MDS) during 50 ps at a constant temperature of 303 K. Each conformation was placed in a 2 angstroms grid cell lattice for each of three trial alignments. 4D-QSAR models were constructed by genetic algorithm (GA) optimization and partial least squares (PLS) fitting, and evaluated by leave-one-out cross-validation technique. In the best models, with three to six terms, the adjusted cross-validated squared correlation coefficients, Q2adj, ranged from 0.67 to 0.85. Model D (Q2adj = 0.84) was identified as the most robust model from alignment 1, and it is representative of the other best models. This model encompasses new molecular regions as containing pharmacophore sites, such as the amino-benzyl moiety of pyrimidine analogs and the N1-substituent in the imidazole ring. These regions of the ligands should be further explored to identify better anti-inflammatory inhibitors of p38-MAPK.

Homology modeling and molecular interaction field studies of α-glucosidases as a guide to structure-based design of novel proposed anti-HIV inhibitors

Abstract: For AIDS therapy, there are currently a number of compounds available for multiple targets already approved by the FDA and in clinic, e.g. protease inhibitors, reverse transcriptase inhibitors (NRTI, NNRTI), fusion inhibitors, CCR4, CCR5 among others. Some pharmaceuticals act against the virus before the entrance of HIV into the host cells. One of these targets is the glucosidase protein. This novel fusion target has been recently explored because the synthesis of viral glycoproteins depends on the activity of enzymes, such as glucosidase and transferase, for the elaboration of the polysaccharides. In this work we have built an homology model of Saccharomyces cerevisiae glucosidase and superimposed all relevant glucosidase-like enzymes in complex with carbohydrates, and calculated as well molecular interaction fields in our S. cerevisiae active site model. Our results suggest that there are two saccharide binding sites which are the most important for the binding of inhibitors with this family of enzymes which supports the possibility of inhibitors containing only two sugar units. Based on these results, we have proposed a novel pseudo-dissacharide which is a potential pharmaceutical for AIDS treatment.

Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines.

Abstract: The accurate nonlinear model for predicting the tissue/blood partition coefficients (PC) of organic compounds in different tissues was firstly developed based on least-squares support vector machines (LS-SVM), as a novel machine learning technique, by using the compounds' molecular descriptors calculated from the structure alone and the composition features of tissues. The heuristic method (HM) was used to select the appropriate molecular descriptors and build the linear model. The prediction result of the LS-SVM model is much better than that obtained by HM method and the prediction values of tissue/blood partition coefficients based on the LS-SVM model are in good agreement with the experimental values, which proved that nonlinear model can simulate the relationship between the structural descriptors, the tissue composition and the tissue/blood partition coefficients more accurately as well as LS-SVM was a powerful and promising tool in the prediction of the tissue/blood partition behaviour of compounds. Furthermore, this paper provided a new and effective method for predicting the tissue/blood partition behaviour of the compounds in the different tissues from their structures and gave some insight into structural features related to the partition process of the organic compounds in different tissues.

Characterization of β3-adrenergic receptor : determination of pharmacophore and 3D QSAR model for β3 adrenergic receptor agonism

Abstract: The β3-adrenoreceptor (β3-AR) has been shown to mediate various pharmacological and physiological effects such as lipolysis, thermogenesis, and intestinal smooth muscle relaxation. It also plays an important role in glucose homeostasis and energy balance. Molecular modeling studies were undertaken to develop predictive pharmacophoric hypothesis and 3D-QSAR model, which may explain variations in β3-AR agonistic activity in terms of chemical features and physicochemical properties. The two softwares, CATALYST for pharmacophoric alignment and APEX-3D for 3D-QSAR modeling were used to establish the structure activity relationships for β3-AR agonistic activity. Among the several statistically significant models, the selection of the best pharmacophore and 3D-QSAR model was based on its ability to estimate the activity of external test sets of similar and different structural types along with the reasonable consistency of the model with the limited information of the active site of β3-AR. The final 3D-QSAR model was derived using the pharmacophoric alignments from the hypothesis which consisted of four chemical features: basic or positive ionizable feature on the nitrogen of the aryloxypropylamino group, two ring aromatic features corresponding to the phenyl ring of the phenoxide and the benzenesulphonamido groups and a hydrogen-bond donor (HBD) in the vicinity of the nitrogen atom of the benzenesulphonamido group with the most active molecule mapping in an energetically favorable extended conformation. This hypothesis was in agreement with the site directed mutagenesis studies on human β3-AR and correlated well the observed and estimated activity both in, training and both the external test sets. It also mapped reasonably well to six β3-AR agonists of different structural classes under clinical development and thus this hypothesis may have a universal applicability in providing a powerful template for virtual screening and also for designing new chemical entities (NCEs) as β3-AR agonists.

Scoring functions--the first 100 years.

Abstract: The use of simple linear mathematical models to estimate chemical properties is not a new idea. Albert Einstein used very simple 'gravity-like' forces to explain the capillarity of different liquids in 1900-1901. Today such models are used in more complicated situations, and a great many have been developed to analyse interactions between proteins and their ligands. This is not surprising, since proteins are too complicated to model accurately without lengthy numerical analysis, and simple models often do at least as good a job in predicting binding constants as much more computationally expensive methods. One hundred years after Einstein's 'miraculous year' in which he transformed physics, it is instructive to recall some of his even earlier work. As approximations, 'scoring functions' are excellent, but it is dangerous to read too much into them. A few cautionary tales are presented for the beginner to the field of ligand affinity prediction by linear models.

Dynamical Aspects of TEM-1 β-Lactamase Probed by Molecular Dynamics

Abstract: The dynamical aspects of the fully hydrated TEM-1 β-lactamase have been determined by a 5 ns Molecular Dynamics simulation. Starting from the crystallographic coordinates, the protein shows a relaxation in water with an overall root mean square deviation from the crystal structure increasing up to 0.17 nm, within the first nanosecond. Then a plateau is reached and the molecule fluctuates around an equilibrium conformation. The results obtained in the first nanosecond are in agreement with those of a previous simulation (Diaz et al., J. Am. Chem. Soc., (2003) 125, 672–684). The successive equilibrium conformation in solution shows an increased mobility characterized by the following aspects. A flap-like translational motion anchores the Ω-loop to the body of the enzyme. A relevant part of the backbone dynamics implies a rotational motion of one domain relative to the other. The water molecules in the active site can exchange with different residence times. The H-bonding networks formed by the catalytic residues are frequently interrupted by water molecules that could favour proton transfer reactions. An additional simulation, where the aspartyl dyad D214–D233 was considered fully deprotonated, shows that the active site is destabilized.

Association of Cytochrome P450 Enzymes is a Determining Factor in their Catalytic Activity

Abstract: Previously, our laboratory demonstrated that one cytochrome P450 isoenzyme can influence the catalytic properties of another P450 isoenzyme when combined in a reconstituted system. Moreover, our data and that of other investigators indicate that P450 interaction is required for catalytic activity even when one isoenzyme is present. The goal of the current study was to examine the possible mechanism of these interactions in more detail. Analyzing recently published X-ray data of microsomal P450 enzymes and protein docking studies, four types of dimer formations of P450 enzymes were examined in more detail. In case of two dimer types, the aggregating partner was shown to contribute to NADPH cytochrome P450 reductase (CPR) binding-a flavoprotein whose interaction with P450 is required for expressing P450 functional activity of the neighboring P450 moiety. Thus, it was shown that dimerization of P450 enzymes might result in an altered affinity towards the CPR. Two dimer types were shown to exist only in the presence of a substrate, while the other two types exist also without a substrate present. The molecular basis was established for the fact that the presence of a substrate and other P450 enzymes simultaneously determine the catalytic activity. Furthermore, a kinetic model was improved describing the catalytic activity of P450 enzymes as a function of CPR concentration based on equilibrium between different supramolecular organizations of P450 enzymes. This model was successfully applied in order to explain our experimental data and that of other investigators.

Surrogate docking: structure-based virtual screening at high throughput speed.

Abstract: Structure-based screening using fully flexible docking is still too slow for large molecular libraries. High quality docking of a million molecule library can take days even on a cluster with hundreds of CPUs. This performance issue prohibits the use of fully flexible docking in the design of large combinatorial libraries. We have developed a fast structure-based screening method, which utilizes docking of a limited number of compounds to build a 2D QSAR model used to rapidly score the rest of the database. We compare here a model based on radial basis functions and a Bayesian categorization model. The number of compounds that need to be actually docked depends on the number of docking hits found. In our case studies reasonable quality models are built after docking of the number of molecules containing 50 docking hits. The rest of the library is screened by the QSAR model. Optionally a fraction of the QSAR-prioritized library can be docked in order to find the true docking hits. The quality of the model only depends on the training set size – not on the size of the library to be screened. Therefore, for larger libraries the method yields higher gain in speed no change in performance. Prioritizing a large library with these models provides a significant enrichment with docking hits: it attains the values of 13 and 35 at the beginning of the score-sorted libraries in our two case studies: screening of the NCI collection and a combinatorial libraries on CDK2 kinase structure. With such enrichments, only a fraction of the database must actually be docked to find many of the true hits. The throughput of the method allows its use in screening of large compound collections and in the design of large combinatorial libraries. The strategy proposed has an important effect on efficiency but does not affect retrieval of actives, the latter being determined by the quality of the docking method itself.

Validated ligand mapping of ACE active site.

Abstract: Crystal structures of angiotensin-converting enzyme (ACE) complexed with three inhibitors (lisinopril, captopril, enalapril) provided experimental data for testing the validity of a prior active site model predicting the bound conformation of the inhibitors. The ACE active site model - predicted over 18 years ago using a series of potent ACE inhibitors of diverse chemical structure - was recreated using published data and commercial software. Comparison between the predicted structures of the three inhibitors bound to the active site of ACE and those determined experimentally yielded root mean square deviation (RMSD) values of 0.43-0.81 A, among the distances defining the active site map. The bound conformations of the chemically relevant atoms were accurately deduced from the geometry of ligands, applying the assumption that the geometry of the active site groups responsible for binding and catalysis of amide hydrolysis was constrained. The mapping of bound inhibitors at the ACE active site was validated for known experimental compounds, so that the constrained conformational search methodology may be applied with confidence when no experimentally determined structure of the enzyme yet exists, but potent, diverse inhibitors are available.