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At least 613 records · Page 34Linked to original sources
PubMed2006

Predictions of CCR1 chemokine receptor structure and BX 471 antagonist binding followed by experimental validation.

A major challenge in the application of structure-based drug design methods to proteins belonging to the superfamily of G protein-coupled receptors (GPCRs) is the paucity of structural information (1). The 19 chemokine receptors, belonging to the Class A family of GPCRs, are important drug targets not only for autoimmune diseases like multiple sclerosis but also for the blockade of human immunodeficiency virus type 1 entry (2). Using the MembStruk computational method (3), we predicted the three-dimensional structure of the human CCR1 receptor. In addition, we predicted the binding site of the small molecule CCR1 antagonist BX 471, which is currently in Phase II clinical trials (4). Based on the predicted antagonist binding site we designed 17 point mutants of CCR1 to validate the predictions. Subsequent competitive ligand binding and chemotaxis experiments with these mutants gave an excellent correlation to these predictions. In particular, we find that Tyr-113 and Tyr-114 on transmembrane domain 3 and Ile-259 on transmembrane 6 contribute significantly to the binding of BX 471. Finally, we used the predicted and validated structure of CCR1 in a virtual screening validation of the Maybridge data base, seeded with selective CCR1 antagonists. The screen identified 63% of CCR1 antagonists in the top 5% of the hits. Our results indicate that rational drug design for GPCR targets is a feasible approach.

Animals↗
PubMed2000

On the topological sub-structural molecular design (TOSS-MODE) in QSPR/QSAR and drug design research.

A recently introduced graph-theoretical approach to the study of structure-property-activity relationships is presented. The theoretical approach and the computational strategy for the use of the TOSS-MODE approach are given with details. Several QSPR and QSAR applications are reviewed including the study of physical properties of organic compounds, diamagnetic susceptibilities, and biological properties. The applications of the TOSS-MODE approach to discrimination of active/inactive compounds, the virtual screening of compounds with a desired property from databases of chemical structures, identification of active/inactive fragments and its relationships with 2D/3D pharmacophores, and to the design of novel compounds with desired biological activities are also reviewed.

Algorithms↗
PubMed2001

Quantitative structure-property relationships generated with optimizable even/odd Wiener polynomial descriptors.

Chemical structures of organic compounds are characterized numerically by a variety of structural descriptors, one of the earliest and most widely used being the Wiener index W, derived from the interatomic distances in a molecular graph. Extensive use of distance-based structural descriptors or topological indices has been made in QSPR and QSAR models, drug design, toxicology, virtual screening of combinatorial libraries, similarity and diversity assessment. Novel topological indices are introduced representing a partitioning of the Wiener polynomial based on counts of even and odd molecular graph distances. During the QSAR/QSPR modeling process the variables of the even and odd power functions are optimized in order to offer an improved mapping of the investigated property. These novel topological indices are tested in QSPR models for the boiling temperature, molar heat capacity, standard Gibbs energy of formation, vaporization enthalpy, refractive index, and density of alkanes. In many cases, the even/odd Wiener polynomial indices proposed here give notably improved correlations or suggest simpler QSPR models.

Chemical Phenomena↗
PubMedDate not supplied

Web-based cheminformatics and molecular property prediction tools supporting drug design and development at Novartis.

Web-based tools offer many advantages for processing chemical information, most notably ease of use and high interactivity. Therefore more and more pharmaceutical companies are using web technology to deliver sophisticated molecular processing tools directly to the desks of their chemists, to assist them in the process of designing and developing new drugs. In this paper, the web-based cheminformatics system developed at Novartis and currently used by more than thousand users is described. The system allows various molecular modeling and molecular processing tasks, including the calculation of molecular and substituent properties, property-based virtual screening, visualization of molecules, bioisosteric design, diversity analysis, and support of combinatorial chemistry. The methodology to calculate various molecular properties relevant to drug design is described, including the prediction of intestinal absorption, blood-brain barrier penetration, efflux, and water solubility. Information about the web technology used is also provided.

Drug Design↗
PubMed2006

Comparison of methods for chemical-compound affinity prediction.

The selection of effective features from various descriptors of chemical compounds and the exploitation of the most appropriate classifier is a momentous issue in improving overall accuracies of virtual screening of chemical compounds. In this article, the performance of various feature-selection methods and various classifiers of chemical compound-protein binding affinities are compared by using six series of compounds: cytochrome P450 2C9 inhibitors, multi-drug-resistance reversal compounds, estrogen receptor ligands, inhibitors of human ether-a-go-go-related genes, and ligands of serotonin receptor 5HT1A and 5HT2A. As a result, it was found that the genetic algorithm was superior to the other feature-selection methods, and its combination with Random Forests and Adaboosts or Baggings gave almost the same performance as support-vector machines and was superior to the other classifiers. The precision and recall of these methods were almost the same or ascendant to those of previous work. The automatically selected descriptors for each protein-compound affinity prediction were plausible and would be informative to interpret the resulting model.

Algorithms↗
PubMed2005

Simulators in surgery.

The introduction of minimally invasive surgery has demonstrated the need for training surgical skills outside the operating room using animal models or simulators. As laparoscopic surgery involves displaying images on a screen, virtual reality simulation of the surgical tasks is feasible. Different types of simulators have become available. The existing trainers can be divided into three groups: mechanical, hybrid, and virtual reality. This article aims at giving an overview of the different simulators available and the potential of simulators in the education of surgeons with focus on virtual reality simulators. All simulators aim at training psychomotoric skills and some simulators also allow training in decision-making and anatomical orientation. In the future virtual reality simulators may become a tool for training and validation of surgical skills and monitoring the training progress.

Journal Article↗
PubMed2006

BDT: an easy-to-use front-end application for automation of massive docking tasks and complex docking strategies with AutoDock.

MOTIVATION: AutoGrid/AutoDock is one of the most popular software packages for docking, but its automation is not trivial for tasks such as (1) the virtual screening of a library of ligands against a set of possible receptors; (2) the use of receptor flexibility and (3) making a blind-docking experiment with the whole receptor surface. This is an obstacle for research teams in the fields of Chemistry and the Life Sciences who are interested in conducting this kind of experiment but do not have enough programming skills. To overcome these limitations, we have designed BDT, an easy-to-use graphic interface for AutoGrid/AutoDock. AVAILABILITY: BDT is available for free, upon request, for non-commercial research.

Algorithms↗
PubMed2005

Mycobacterium tuberculosis NAD+-dependent DNA ligase is selectively inhibited by glycosylamines compared with human DNA ligase I.

DNA ligases are important enzymes which catalyze the joining of nicks between adjacent bases of double-stranded DNA. NAD+-dependent DNA ligases (LigA) are essential in bacteria and are absent in humans. They have therefore been identified as novel, validated and attractive drug targets. Using virtual screening against an in-house database of compounds and our recently determined crystal structure of the NAD+ binding domain of the Mycobacterium tuberculosis LigA, we have identified N1, N(n)-bis-(5-deoxy-alpha-D-xylofuranosylated) diamines as a novel class of inhibitors for this enzyme. Assays involving M.tuberculosis LigA, T4 ligase and human DNA ligase I show that these compounds specifically inhibit LigA from M.tuberculosis. In vitro kinetic and inhibition assays demonstrate that the compounds compete with NAD+ for binding and inhibit enzyme activity with IC50 values in the microM range. Docking studies rationalize the observed specificities and show that among several glycofuranosylated diamines, bis xylofuranosylated diamines with aminoalkyl and 1, 3-phenylene carbamoyl spacers mimic the binding modes of NAD+ with the enzyme. Assays involving LigA-deficient bacterial strains show that in vivo inhibition of ligase by the compounds causes the observed antibacterial activities. They also demonstrate that the compounds exhibit in vivo specificity for LigA over ATP-dependent ligase. This class of inhibitors holds out the promise of rational development of new anti-tubercular agents.

Antitubercular Agents↗
PubMed2006

kinDOCK: a tool for comparative docking of protein kinase ligands.

KinDOCK is a new web server for the analysis of ATP-binding sites of protein kinases. This characterization is based on the docking of ligands already co-crystallized with other protein kinases. A structural library of protein kinase-ligand complexes has been extracted from the Protein Data Bank (PDB). This library can provide both potential ligands and their putative binding orientation for a given protein kinase. After protein-protein structural superposition, the ligands are transferred from the template complexes to the target protein kinase. The resulting complexes are evaluated using the program SCORE to compute a theoretical affinity. They can be dynamically visualized to allow a rapid mapping of important steric clashes and potential substitutions relevant for specificity and affinity. These characteristics allow a quick characterization of protein kinase active sites including conformation changes potentially required to accommodate particular ligands. Additionally, promising pharmacophores can be identified in the focussed library. These features will help to rationalize or optimize virtual screening (VS) on larger chemical compound libraries. The server and its documentation are freely available at http://abcis.cbs.cnrs.fr/kindock/.

Adenosine Triphosphate↗
PubMed2006

BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities.

BindingDB (http://www.bindingdb.org) is a publicly accessible database currently containing approximately 20,000 experimentally determined binding affinities of protein-ligand complexes, for 110 protein targets including isoforms and mutational variants, and approximately 11,000 small molecule ligands. The data are extracted from the scientific literature, data collection focusing on proteins that are drug-targets or candidate drug-targets and for which structural data are present in the Protein Data Bank. The BindingDB website supports a range of query types, including searches by chemical structure, substructure and similarity; protein sequence; ligand and protein names; affinity ranges and molecular weight. Data sets generated by BindingDB queries can be downloaded in the form of annotated SDfiles for further analysis, or used as the basis for virtual screening of a compound database uploaded by the user. The data in BindingDB are linked both to structural data in the PDB via PDB IDs and chemical and sequence searches, and to the literature in PubMed via PubMed IDs.

Databases, Protein↗
PubMed1996

Tri-state myoelectric control of bilateral upper extremity neuroprostheses for tetraplegic individuals.

For the purpose of bimanual control of tetraplegic hands that have useful movement restored by a neuroprosthetic device, the use of myoelectric signals from bilateral sternoclei-domastoid muscles is proposed. Three state control has been proposed where each sternocleidomastoid controls its ipsilateral hand. Demonstration was made with spinal-cord-injured and nonspinal-cord-injured subjects providing three levels of activation that can be repeatably made with each of these muscles. The agonist and antagonist sternocleidomastoids during this command control were differentiated so that the desired hand will respond to a command. Neither normal head movements nor head position within its comfortable range of motion were shown to interfere with the proposed command. The provision of feedback was shown as important to provide robustness in the operation for the users selection of the right or left hand. The performance of spinal cord injured and noninjured persons using this controller was quantitatively measured through the completion of precision tracking tasks by the manipulation of on-screen virtual hands. All subjects were able to operate the controller with a degree of skill acceptable for completion of functional tasks with bilateral stimulated hand grasps. The sensitivity of the subjects performance to variation in controller parameters was also measured.

Arm↗
PubMed2007

OCIA domain containing 2 is highly expressed in adenocarcinoma mixed subtype with bronchioloalveolar carcinoma component and is associated with better prognosis.

Although lung adenocarcinoma is a major cause of cancer death worldwide, details of its molecular carcinogenesis and stepwise progression are still unclear. To characterize the sequential progression from bronchioloalveolar adenocarcinoma of the lung (BAC, in situ carcinoma) to adenocarcinoma mixed subtype with BAC component polymerase chain reaction-based cDNA suppression subtractive hybridization (SSH) was carried out using two representative cases of BAC (non-invasive tumors) and adenocarcinoma mixed subtype with BAC (invasive tumors). Through differential screening, virtual reverse northern hybridization and quantitative real-time reverse-transcription-polymerase chain reaction (qRT-PCR) we selected five genes (TncRNA, OCIAD2, ANXA2, TMED4 and LGALS4) that were expressed at significantly higher levels in invasive adenocarcinoma mixed subtype with BAC than in BAC. After in situ hybridization and qRT-PCR analyses, we confirmed that only the OCIAD2 gene showed significantly higher expression in the tumor cells of invasive adenocarcinoma mixed subtype with BAC than in BAC (P = 0.026). We then carried out in situ hybridization of OCIAD2 in 56 adenocarcinoma mixed subtype with BAC component and assessed the correlation between OCIAD2 expression and clinicopathological features. In contrast to our expectation, the patients with OCIAD2 expression showed a better clinical outcome than those without OCIAD2 expression, and OCIAD2 expression showed an inverse correlation with lymphatic invasion, blood vessel invasion and lymph node metastasis. These results suggest that OCIAD2 begins to express at the progression from in situ to invasive carcinoma, and is associated with the favorable prognosis of adenocarcinoma mixed subtype with BAC component.

Adenocarcinoma↗
PubMed2006

The challenge of predicting drug toxicity in silico.

Poor pharmacokinetics, side effects and compound toxicity are frequent causes of late-stage failures in drug development. A safe in silico identification of adverse effects triggered by drugs and chemicals would be highly desirable as it not only bears economical potential but also spawns a variety of ecological benefits: sustainable resource management, reduction of animal models and possibly less risky clinical trials. In computer-aided drug discovery, both existing and hypothetical compounds may be studied; the methods are fast, reproducible, and typically based on human bioregulators, making the question of transferability obsolete. In the recent past, our laboratory contributed towards the development of in silico concepts (--> multi-dimensional QSAR) and validated a series of "virtual test kits" based on the oestrogen, androgen, thyroid, and aryl hydrocarbon receptor (endocrine disruption, receptor-mediated toxicity) as well as on the enzyme cytochrome P450 3A4 (metabolic transformations, drug-drug interactions). The test kits are based on the three-dimensional structure of their target protein (i.e. ER(alphabeta), AR, TR(alphabeta), CYP450) or a surrogate thereof (AhR) and were trained using a representative selection of 362 substances. Subsequent evaluation of 107 compounds different therefrom showed that binding affinities are predicted close to experimental uncertainty. These results suggest that our approach is suited for the in silico identification of adverse effects triggered by drugs and chemicals and encouraged us to compile an Internet Database for the virtual screening of drugs and chemicals for toxic effects.

Computational Biology↗
PubMed2006

Design, synthesis and antitumor evaluation of a new series of N-substituted-thiourea derivatives.

AIM: To design and synthesize a novel class of protein tyrosine kinase inhibitors, featuring the N-(2-oxo-1,2-dihydroquinolin-3-yl-methyl)-thiourea framework. METHODS: First, compounds 1 and 2 were identified using the virtual screening approach in conjunction with binding assay based on surface plasmon resonance. Subsequently, 3 regions of compounds 1 and 2 were selected for chemical modification. All compounds were characterized potent inhibitory activities toward the human lung adenocarcinoma cell line SPAC1. RESULTS: Forty new compounds (1-2, 3a-g, 4a-w, and 5a-l) were designed, synthesized and bioassayed. Six compounds (1, 3e, 4l, 4w, 5a, and 5b) were found to show promising inhibitory activity against the SPAC1 tumor cell line. The inhibitory activity of compound 5a increases approximately 10 times more than that of the original compound 1. CONCLUSION: This study provides a promising new template with potential antitumor activity.

Adenocarcinoma↗
PubMed2006

Identification of novel small-molecule inducers of fetal hemoglobin using pharmacophore and 'PSEUDO' receptor models.

Pharmacologic reinduction of the developmentally silenced fetal (gamma) globin genes has been achieved in hemoglobinopathy patients using short chain fatty acid derivatives, with therapeutic effects. However, higher-potency inducers than are available in currently identified short chain fatty acid derivatives are desirable for long-term use. Using several short-chain fatty acids with established gamma-globin induction activity, a pharmacophore template was constructed with the TFIT module of the flo software and used to select several new candidate compounds, three of which exhibited significant activity in a gamma-globin gene reporter transcriptional assay which detects only strong inducers. The data were used to construct a new pharmacophore and a 'pseudo' receptor around it. Six hundred and thirty low-molecular weight compounds were docked into this receptor model. Of 26 compounds selected and tested in functional assays, two compounds showed activity >500% over control levels and two had activity 200% over control range, significantly more active than previously identified short chain fatty acid derivative fetal globin gene inducers. Three compounds had less activity; the remainder showed moderate activity. These findings demonstrate the feasibility of using iterative construction of pharmacophores, pseudo-binding site modeling, and virtual screening to identify small molecules with the ability to induce transcription of specific target genes, for potential therapeutics.

Animals↗
PubMed2005

In silico predictions of blood-brain barrier penetration: considerations to "keep in mind".

Within drug discovery, it is desirable to determine whether a compound will penetrate and distribute within the central nervous system (CNS) with the requisite pharmacokinetic and pharmacodynamic performance required for a CNS target or if it will be excluded from the CNS, wherein potential toxicities would mitigate its applicability. A variety of in vivo and in vitro methods for assessing CNS penetration have therefore been developed and applied to advancing drug candidates with the desired properties. In silico methods to predict CNS penetration from chemical structures have been developed to address virtual screening and prospective design. In silico predictive methods are impacted by the quality, quantity, sources, and generation of the measured data available for model development. Key considerations for predictions of CNS penetration include the comparison of local (in chemistry space) versus global (more structurally diverse) models and where in the drug discovery process such models may be best deployed. Preference should also be given to in vitro and in vivo measurements of greater mechanistic clarity that better support the development of structure-property relationships. Although there are numerous statistical methods that have been brought to bear on the prediction of CNS penetration, a greater concern is that such models are appropriate for the quality of measured data available and are statistically validated. In addition, the assessment of prediction uncertainty and relevance of predictive models to structures of interest are critical. This article will address these key considerations for the development and application of in silico methods in drug discovery.

Animals↗
PubMed2007

Structure-based identification of novel human gamma-glutamylcysteine synthetase inhibitors.

Glutathione depletion represents a potentially important strategy to sensitize tumors to cytotoxic drugs. l-Buthionine-(R,S)-sulfoximine (l-BSO) has been studied in both preclinical and early clinical trials, but limitation on its access has led to a search for alternatives. Using a 3D molecular model of human gamma-glutamylcysteine synthetase (gamma-GCS(H)), the major subunit of the rate-limiting GSH synthetic enzyme, we virtually screened the National Cancer Institute chemical database to identify compounds that could bind to and potentially inhibit gamma-GCS(H). We identified 51 test chemicals, all with structures very distinct from l-BSO. We subjected these compounds to biological assays measuring gamma-GCS(H) inhibition and glutathione (GSH) depletion. Among 10 novel gamma -GCS inhibitors identified, 4 compounds depleted glutathione in cells, and 2 with related structures sensitized tumor cells to melphalan treatment. This work validates the use of model-based database mining and identified inhibitors of gamma-GCS(H) with novel chemical structures.

Antineoplastic Agents↗
PubMed2004

The many roles of computation in drug discovery.

An overview is given on the diverse uses of computational chemistry in drug discovery. Particular emphasis is placed on virtual screening, de novo design, evaluation of drug-likeness, and advanced methods for determining protein-ligand binding.

Computer Simulation↗