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PubMed2006

Biochemical profiling in silico--predicting substrate specificities of large enzyme families.

A general high-throughput method for in silico biochemical profiling of enzyme families has been developed based on covalent docking of potential substrates into the binding sites of target enzymes. The method has been tested by systematically docking transition state--analogous intermediates of 12 substrates into the binding sites of 20 alpha/beta hydrolases from 15 homologous families. To evaluate the effect of side chain orientations to the docking results, 137 crystal structures were included in the analysis. A good substrate must fulfil two criteria: it must bind in a productive geometry with four hydrogen bonds between the substrate and the catalytic histidine and the oxyanion hole, and a high affinity of the enzyme-substrate complex as predicted by a high docking score. The modelling results in general reproduce experimental data on substrate specificity and stereoselectivity: the differences in substrate specificity of cholinesterases toward acetyl- and butyrylcholine, the changes of activity of lipases and esterases upon the size of the acid moieties, activity of lipases and esterases toward tertiary alcohols, and the stereopreference of lipases and esterases toward chiral secondary alcohols. Rigidity of the docking procedure was the major reason for false positive and false negative predictions, as the geometry of the complex and docking score may sensitively depend on the orientation of individual side chains. Therefore, appropriate structures have to be identified. In silico biochemical profiling provides a time efficient and cost saving protocol for virtual screening to identify the potential substrates of the members of large enzyme family from a library of molecules.

Binding Sites↗
PubMed2003

Ligand-supported homology modelling of protein binding-sites using knowledge-based potentials.

A new approach, MOBILE, is presented that models protein binding-sites including bound ligand molecules as restraints. Initially generated, homology models of the target protein are refined iteratively by including information about bioactive ligands as spatial restraints and optimising the mutual interactions between the ligands and the binding-sites. Thus optimised models can be used for structure-based drug design and virtual screening. In a first step, ligands are docked into an averaged ensemble of crude homology models of the target protein. In the next step, improved homology models are generated, considering explicitly the previously placed ligands by defining restraints between protein and ligand atoms. These restraints are expressed in terms of knowledge-based distance-dependent pair potentials, which were compiled from crystallographically determined protein-ligand complexes. Subsequently, the most favourable models are selected by ranking the interactions between the ligands and the generated pockets using these potentials. Final models are obtained by selecting the best-ranked side-chain conformers from various models, followed by an energy optimisation of the entire complex using a common force-field. Application of the knowledge-based pair potentials proved efficient to restrain the homology modelling process and to score and optimise the modelled protein-ligand complexes. For a test set of 46 protein-ligand complexes, taken from the Protein Data Bank (PDB), the success rate of producing near-native binding-site geometries (rmsd<2.0A) with MODELLER is 70% when the ligand restrains the homology modelling process in its native orientation. Scoring these complexes with the knowledge-based potentials, in 66% of the cases a pose with rmsd <2.0A is found on rank 1. Finally, MOBILE has been applied to two case studies modelling factor Xa based on trypsin and aldose reductase based on aldehyde reductase.

Binding Sites↗
PubMed2004

Crystal structure of the motor domain of the human kinetochore protein CENP-E.

The human kinetochore is a highly complex macromolecular structure that connects chromosomes to spindle microtubules (MTs) in order to facilitate accurate chromosome segregation. Centromere-associated protein E (CENP-E), a member of the kinesin superfamily, is an essential component of the kinetochore, since it is required to stabilize the attachment of chromosomes to spindle MTs, to develop tension across aligned chromosomes, to stabilize spindle poles and to satisfy the mitotic checkpoint. Here we report the 2.5A resolution crystal structure of the motor domain and linker region of human CENP-E with MgADP bound in the active site. This structure displays subtle but important differences compared to the structures of human Eg5 and conventional kinesin. Our structure reveals that the CENP-E linker region is in a "docked" position identical to that in the human plus-end directed conventional kinesin. CENP-E has many advantages as a potential anti-mitotic drug target and this crystal structure of human CENP-E will provide a starting point for high throughput virtual screening of potential inhibitors.

Adenosine Diphosphate↗
PubMed2004

A practical approach to docking of zinc metalloproteinase inhibitors.

Forty zinc-dependent metalloproteinase/ligand complexes with known crystal structures were re-docked using five docking/scoring approaches (DOCK, FlexX, DrugScore, GOLD, and AutoDock). Correct geometry of the coordination bonds between the ligand's zinc binding group (ZBG) and the catalytic zinc is important for docking accuracy and scoring reliability. More than 75% of docked poses with RMSD less than 2A were found to have appropriate ZBG binding, but for poor ZBG binding, about 95% of poses failed to dock correctly. Elimination of poses with inappropriate zinc binding resulted in better binding energy predictions that were further improved by dividing the ligands into subsets according to the ZBG (carboxylates, hydroxamates, and phosphorus containing groups). After a subset re-scoring using the regression functions obtained for individual subsets, DrugScore was able to explain 77% and the consensus scoring scheme X-CSCORE even 88% of variance in binding energies. The approach combining ZBG-based pose selection and subset re-scoring improved the hit rate in virtual screening for metalloproteinase inhibitors for all tested methods by 4-16%.

Algorithms↗
PubMed2004

Modeling of p38 mitogen-activated protein kinase inhibitors using the Catalyst HypoGen and k-nearest neighbor QSAR methods.

We have employed in parallel the Catalyst HypoGen pharmacophore modeling approach and the variable selection k-nearest neighbor quantitative structure-activity relationship (kNN QSAR) method to model a diverse data set of p38 mitogen-activated protein (MAP) kinase inhibitors. The HypoGen pharmacophore model, developed from a novel automated training set selection protocol, identified chemical functional features that were characteristic of the active compounds and differentiated the active from the inactive inhibitors. The kNN QSAR modeling employed topological descriptors and afforded predictive QSAR models with consistently high values of both leave-one-out cross-validated R2 for the training set and predictive R2 for the test set. The results of both modeling approaches were sensitive to the selection of the training and test sets used for model development and validation. The resulting Catalyst pharmacophore and kNN QSAR models can be used concurrently for rapid virtual screening of chemical databases to identify novel p38 MAP kinase inhibitors.

Algorithms↗
PubMed2006

The orphan G-protein coupled receptor RDC1: evidence for a role in chondrocyte hypertrophy and articular cartilage matrix turnover.

OBJECTIVE: RDC1 is a class A orphan G-protein coupled receptor of unknown function. The purpose of this study was to identify compound RDC1 agonists and use these as tools to determine the effect of RDC1 activation in human chondrocytes and cartilage explant tissue. METHODS: Computational chemistry was employed to build a homology model of the RDC1 receptor. A virtual screen of in-house compounds was then performed and positive hits screened for their ability to invoke a Ca2+ response in a recombinant RDC1 HEK293 cell line, as measured by FLIPR. The effect of RDC1 activation on human chondrocytes and cartilage explant gene expression was determined by quantitative real-time polymerase chain reaction (PCR), and these effects validated as being mediated by RDC1 using siRNA antisense. RESULTS: Tissue expression profiling demonstrated that RDC1 expression was predominant in cartilage tissue. Treatment of human primary chondrocytes with RDC1 agonist induced a Ca2+ response, suggesting the receptor is active in this tissue type. Treatment for 24h with RDC1 agonist led to altered expression of a number of genes associated with chondrocyte hypertrophy and increased matrix degradation in human primary chondrocytes, and elevated total matrix metalloproteinase (MMP) activity in cartilage explant. Transfection with RDC1 siRNA caused a >90% reduction in human primary chondrocyte RDC1 expression and significantly reduced the impact of RDC1 agonist on the previously identified RDC1-regulated genes. CONCLUSIONS: RDC1 activation in human chondrocytes and cartilage explant leads to changes in gene expression and activity associated with chondrocyte hypertrophy, angiogenesis and increased matrix degradation, suggesting signalling via the RDC1 receptor may play an important role in the early development of osteoarthritis (OA).

Calcium↗
PubMed2005

Are hERG channel inhibition and QT interval prolongation all there is in drug-induced torsadogenesis? A review of emerging trends.

Contemporary preclinical in vitro and in vivo methods have been imperfect in predicting drug-induced Torsades de Pointes (TdP) in humans. A better understanding of additional relevant factors in the genesis of drug-induced TdP is necessary. New sophisticated in vitro techniques, such as arterially perfused ventricular wedge preparations or isolated perfused hearts, potentially offer a better understanding of torsadogenic mechanisms and a refinement of drug testing. Of particular interest are the dispersion of repolarization and the refractoriness of different cell types across the ventricular wall, triangulation of the action potential, reverse use dependence and instability of the action potential duration. In vivo models are currently refined by establishing parameters such as beat-to-beat variability and T-wave morphology as derived from the in vitro proarrhythmia indices. Animal models of proarrhythmia are to date not recommended for routine evaluation. A pharmacodynamic interaction with combinations of torsadogenic compounds is another area to be considered. Little is known about channel/receptor cross talk, although considerable evidence exists that cardiac G protein-coupled receptors can modulate hERG channel function. More investigations are necessary to further evaluate the role of altered gene expression, mutations, and polymorphisms in drug-induced TdP. A novel mechanism of drug-induced torsadogenesis is the reduced expression of hERG channel protein on the plasma membrane due to a trafficking defect. Pharmacokinetic and metabolism data are crucial for calculating the risk of a torsadogenic potential in man. Consideration of intracardiac accumulation can help in delineating pharmacokinetic-pharmacodyamic relationships. In silico virtual screening procedures with new chemical entities to predict hERG block may develop as a promising tool. The role of in silico modeling of TdP arrhythmia is likely to become increasingly important for organizing and integrating the vast amount of generated data. At present, however, in silico methods cannot replace existing preclinical in vitro and in vivo models.

Animals↗
PubMed2002

Identification and mapping of small-molecule binding sites in proteins: computational tools for structure-based drug design.

The number of protein structures is currently increasing at an impressive rate. The growing wealth of data calls for methods to efficiently exploit structural information for medicinal and pharmaceutical purposes. Given the three-dimensional (3D) structure of a validated protein target, the identification of functionally relevant binding sites and the analysis ('mapping') of these sites with respect to molecular recognition properties are important initial tasks in structure-based drug design. To address these tasks, a variety of computational tools have been developed. Approaches to identify binding pockets include geometric analyses of protein surfaces, comparisons of protein structures, similarity searches in databases of protein cavities, and docking scans to reveal areas of high ligand complementarity. In the context of binding-site analysis, powerful data mining tools help to retrieve experimental information about related protein-ligand complexes. To identify interaction hot spots, various potential functions and knowledge-based approaches are available for mapping binding regions. The results may subsequently be used to guide virtual screenings for new ligands via pharmacophore searches or docking simulations.

Artificial Intelligence↗
PubMed2005

Screening for selective small molecule inhibitors of the proteasome using activity-based probes.

The proteasome's role in fundamental biological processes ranging from control of the cell cycle to production of peptides for display to immune cells has been uncovered with the help of small molecule inhibitors. Most of the commonly used inhibitors have been designed and synthesized by organic chemists or by Nature. To continue to develop new inhibitors and reagents for the proteasome, a rapid screening method is required that allows not only assessment of potency but also selectivity of inhibitors for each of the primary catalytic sites in the complex. This chapter outlines methods for the solid-phase synthesis of diverse peptide vinyl sulfone libraries and a rapid screen for potent and selective inhibitors that makes use of an active site label (Nazif and Bogyo, 2001). This assay can be performed with small quantities of total cellular extracts as a source of enzyme and can be used to rapidly screen virtually any potential inhibitor.

Animals↗
PubMed2006

Rational design and applications of a Rac GTPase-specific small molecule inhibitor.

Rac GTPases are involved in the regulation of multiple cell functions and have been implicated in the pathology of certain human diseases. Dominant negative mutants of Rac have been the tool of choice in studying Rac function in cells. Given the difficulty of introducing high concentrations of the Rac mutants into primary cells and nonspecific effects of the mutants on Rho guanine nucleotide exchange factor (GEF) activities, it is desirable to develop small molecule inhibitors that could specifically inhibit Rac activities. Here we describe the rational design, characterization, and applications of a first-generation Rac-specific small molecule inhibitor. On the basis of the structure-function information of Rac interaction with GEFs, in a computer-based virtual screening we have identified NSC23766, a highly soluble and membrane permeable compound, as a specific inhibitor of a subset of GEF binding to Rac and, therefore, Rac activation by these GEFs. In fibroblast cells, NSC23766 inhibited Rac1 GTP-loading without affecting Cdc42 or RhoA activity and suppressed cell proliferation induced by a Rac GEF Tiam1. It has little effect on cell growth induced by a constitutively active Rac1 mutant. In addition, NSC23766 inhibited: (1) the anchorage-independent growth and invasion phenotypes of human prostate cancer PC-3 cells; (2) Rac activation and Rac-dependent aggregation of platelets stimulated by thrombin; and (3) Rac1 and Rac2 activities of hematopoietic stem/progenitor cells and induced their mobilization from mouse bone marrow to peripheral blood. Thus, NSC23766 is a lead small molecule inhibitor of Rac activity and could be useful for studying Rac-mediated cellular functions and for modulating pathological conditions in which Rac-deregulation may play a role.

Adenocarcinoma↗
PubMed2004

Computer modelling and visualization of active site of monoamine oxidases.

In spite of significant progress in MAO research culminating in the crystallization of the MAO B, many structure-functional aspects of these enzymes still require better characterization. Computer modelling of the substrate/inhibitory binding region of the active site includes consensus overlay of several series of fully reversible and/or tightly bound inhibitors onto a rigid referent inhibitor(s). The shape of resultant mould obviously reflects spatial characteristic features of the substrate/inhibitor binding region. The comparison of the active site mould of MAO B with its crystal structure revealed correctness of this approach. The resultant moulds may be effectively used for virtual screening of molecular databases for new lead-structures. Using this approach we have found several selective MAO A inhibitors in databases and these compounds have never been tested for MAO inhibitory activity.

Animals↗
PubMed2001

In silico research in drug discovery.

Target and lead discovery constitute the main components of today's early pharmaceutical research. The aim of target discovery is the identification and validation of suitable drug targets for therapeutic intervention, whereas lead discovery identifies novel chemical molecules that act on those targets. With the near completion of the human genome sequencing, bioinformatics has established itself as an essential tool in target discovery and the in silico analysis of gene expression and gene function are now an integral part of it, facilitating the selection of the most relevant targets for a disease under study. In lead discovery, advances in chemoinformatics have led to the design of compound libraries in silico that can be screened virtually. Moreover, computational methods are being developed to predict the drug-likeness of compounds. Thus, drug discovery is already on the road towards electronic R&D.

Computational Biology↗
PubMed2002

A simple model to predict blood-brain barrier permeation from 3D molecular fields.

We report a four-component partial least squares discriminant analysis (PLS) model for the prediction of blood-brain barrier (BBB) permeation using descriptors derived from 3D molecular fields. The 3D fields were transformed by VolSurf into suitable 1D descriptors, which were correlated to the ratio of blood-brain partitioning measured at steady state in rats (log C(brain)/C(blood)). The model so obtained sheds light on molecular properties influencing BBB permeation. It can also be used in the virtual screening of new chemicals.

Animals↗
PubMed2000

VolSurf: a new tool for the pharmacokinetic optimization of lead compounds.

A method for the modeling and prediction of pharmacokinetic properties based on computed molecular interaction fields and multivariate statistics has been investigated in different experimental datasets. The program VolSurf was used to correlate 3D molecular structures with physico-chemical and pharmacokinetic properties. In membrane partitioning, VolSurf produced a two-component model explaining 94% of the total variation with a predictive q(2) of 0.90. This result was achieved without conformational sampling and without any quantum-chemical calculation. For the prediction of blood-brain barrier penetration the VolSurf model was able to predict the BBB profile for most of the drugs in the external prediction set. In Caco-2 and MDCK permeation experiments, VolSurf was used with success to establish statistical models and to predict the behaviour of new compounds. The method thus appears as a valuable new property filter in virtual screening and as a novel tool in optimizing the pharmacokinetic profile of pharmaceutically relevant compounds.

Animals↗
PubMed2002

In silico prediction of ADME and pharmacokinetics. Report of an expert meeting organised by COST B15.

The computational approach is one of the newest and fastest developing techniques in pharmacokinetics, ADME (absorption, distribution, metabolism, excretion) evaluation, drug discovery and toxicity. However, to date, the software packages devoted to ADME prediction, especially of metabolism, have not yet been adequately validated and still require improvements to be effective. Most are 'open' systems, under constant evolution and able to incorporate rapidly, and often easily, new information from user or developer databases. Quantitative in silico predictions are now possible for several pharmacokinetic (PK) parameters, particularly absorption and distribution. The emerging consensus is that the predictions are no worse than those made using in vitro tests, with the decisive advantage that much less investment in technology, resources and time is needed. In addition, and of critical importance, it is possible to screen virtual compounds. Some packages are able to handle thousands of molecules in a few hours. However, common experience shows that, in part at least for essentially irrational reasons, there is currently a lack of confidence in these approaches. An effort should be made by the software producers towards more transparency, in order to improve the confidence of their consumers. It seems highly probable that in silico approaches will evolve rapidly, as did in vitro methods during the last decade. Past experience with the latter should be helpful in avoiding repetition of similar errors and in taking the necessary steps to ensure effective implementation. A general concern is the lack of access to the large amounts of data on compounds no longer in development, but still kept secret by the pharmaceutical industry. Controlled access to these data could be particularly helpful in validating new in silico approaches.

Adsorption↗
PubMed2001

Statistical potentials and scoring functions applied to protein-ligand binding.

In virtual screening, small-molecule ligands are docked into protein binding sites and their binding affinity is predicted. Knowledge-based, regression-based and first-principle-based methods have been developed to rank computer-generated binding modes. As a result of still existing deficiencies, a best compromise might be the combination of several scoring schemes into a consensus scoring approach.

Drug Evaluation, Preclinical↗
PubMed2002

Scaffold hopping and optimization towards libraries of glycogen synthase kinase-3 inhibitors.

Using a virtual screening strategy based on a methodology derived from the CATS molecular descriptor, a novel compound class with inhibitory activity against the GSK-3 enzyme was identified through scaffold hopping. These compounds were readily synthesized, either by solid-phase or solution-phase chemistry. Compounds with inhibitory activity below 1 microM were identified.

Combinatorial Chemistry Techniques↗
PubMedDate not supplied

Progress in predicting human ADME parameters in silico.

Understanding the development of a scientific approach is a valuable exercise in gauging the potential directions the process could take in the future. The relatively short history of applying computational methods to absorption, distribution, metabolism and excretion (ADME) can be split into defined periods. The first began in the 1960s and continued through the 1970s with the work of Corwin Hansch et al. Their models utilized small sets of in vivo ADME data. The second era from the 1980s through 1990s witnessed the widespread incorporation of in vitro approaches as surrogates of in vivo ADME studies. These approaches fostered the initiation and increase in interpretable computational ADME models available in the literature. The third era is the present were there are many literature data sets derived from in vitro data for absorption, drug-drug interactions (DDI), drug transporters and efflux pumps [P-glycoprotein (P-gp), MRP], intrinsic clearance and brain penetration, which can theoretically be used to predict the situation in vivo in humans. Combinatorial synthesis, high throughput screening and computational approaches have emerged as a result of continual pressure on pharmaceutical companies to accelerate drug discovery while decreasing drug development costs. The goal has become to reduce the drop-out rate of drug candidates in the latter, most expensive stages of drug development. This is accomplished by increasing the failure rate of candidate compounds in the preclinical stages and increasing the speed of nomination of likely clinical candidates. The industry now understands the reasons for clinical failure other than efficacy are mainly related to pharmacokinetics and toxicity. The late 1990s saw significant company investment in ADME and drug safety departments to assess properties such as metabolic stability, cytochrome P-450 inhibition, absorption and genotoxicity earlier in the drug discovery paradigm. The next logical step in this process is the evaluation of higher throughput data to determine if computational (in silico) models can be constructed and validated from it. Such models would allow an exponential increase in the number of compounds screened virtually for ADME parameters. A number of researchers have started to utilize in silico, in vitro and in vivo approaches in parallel to address intestinal permeability and cytochrome P-450-mediated DDI. This review will assess how computational approaches for ADME parameters have evolved and how they are likely to progress.

Catalysis↗