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PubMed1994

Analysis of mutations in the SCH gene in schwannomas.

Schwannomas are benign tumors of cranial, spinal, and other nerve sheaths that develop sporadically or are inherited as part of neurofibromatosis type 2 (NF2). The NF2 gene (SCH) on chromosome 22 has recently been identified and shown to be inactivated by mutation and allele loss in some schwannomas. However, only limited regions in the SCH coding region were examined for mutations. We have extended these studies by screening virtually all coding sequences of the SCH gene (95% coverage) and adjacent splice site sequences for the presence of mutations in 48 schwannomas. All tumors (34 vestibular schwannomas and 14 schwannomas of other locations) were additionally characterized for allele loss on chromosome 22. By PCR-DGGE screening of the 16 known exons of the SCH gene, 22 mutations were found. Most of these give rise to a premature stop codon and are expected to result in the synthesis of a truncated gene product (schwannomin). Although there was no apparent hotspot for mutations, 16 of the 22 mutations occurred in the first eight exons or adjacent splice site sequences of the SCH gene. In several vestibular as well as other schwannomas loss of one SCH allele and mutational inactivation of the second allele were identified in the same tumor. Our data indicate that the SCH gene is implicated in the development of schwannomas of all locations in the nervous system.

Alleles↗

PubMed2006

A QSAR model for in silico screening of MAO-A inhibitors. Prediction, synthesis, and biological assay of novel coumarins.

This work explores the potential of the MARCH-INSIDE methodology to seek a QSAR for MAO-A inhibitors from a heterogeneous series of compounds. A Markov model was used to quickly calculate the molecular electron delocalization, polarizability, refractivity, and n-octanol/water partition coefficients for a series of 1406 active/nonactive compounds. LDA was subsequently used to fit a classification function. The model showed 92.8% and 91.8% global accuracy and predictability in training and validation studies. This QSAR model was validated through a virtual screening of a series of coumarin derivatives. The 15 selected compounds were prepared and evaluated as in vitro MAO-A inhibitors. The theoretical prediction was compared with the experimental results and the model correctly predicted 13 compounds with only two mistakes on compounds with activities very close to the cutoff point established for the model. Consequently, this method represents a useful tool for the "in silico" screening of MAO-A inhibitors.

Algorithms↗

PubMed2005

Rational inhibitor design and iterative screening in the identification of selective plasmodial cyclin dependent kinase inhibitors.

New chemical classes of compounds must be introduced into the malaria drug development pipeline in an effort to develop new chemotherapy options for the fight against malaria. In this review we describe an iterative approach designed to identify potent inhibitors of a kinase family that collectively functions as key regulators of the cell cycle. Cyclin-dependent protein kinases (CDKs) are attractive drug targets in numerous diseases and, most recently, they have become the focus of rational drug design programs for the development of new antimalarial agents. Our approach uses experimental and virtual screening methodologies to identify and refine chemical inhibitors and increase the success rate of discovering potent and selective inhibitors. The active pockets of the plasmodial CDKs are unique in terms of size, shape and amino acid composition compared with those of the mammalian orthologues. These differences exemplified through the use of screening assays, molecular modeling, and crystallography can be exploited for inhibitor design. To date, several classes of compounds including quinolines and oxindoles have been identified as selective inhibitors of the plasmodial CDK7 homologue, Pfmrk. From these initial studies and through the iterative rational drug design process, more potent, selective, and most importantly, chemically unique compound classes have been identified as effective inhibitors of the plasmodial CDKs and the malarial parasite.

Amino Acid Sequence↗

PubMed2002

Fragment analysis in small molecule discovery.

Cheminformatics is playing an ever-increasing role in small molecule drug discovery. The widespread use of high-throughput screening (HTS) and combinatorial chemistry techniques has led to the generation of large amounts of pharmacological data which, in turn, has catalyzed the development of computational methods designed to reduce the time and cost in identifying molecules suitable for pharmaceutical development. This review focuses on recent advances in the field of substructure analysis, an increasingly popular data mining technique with applications at many levels of the discovery process, including HTS, compound library design, virtual screening and the prediction of biological activity.

Animals↗

PubMed2006

Comparison of a homology model and the crystallographic structure of human 11beta-hydroxysteroid dehydrogenase type 1 (11betaHSD1) in a structure-based identification of inhibitors.

Human 11beta-hydroxysteroid dehydrogenase type 1 (11betaHSD1) catalyzes the interconversion of cortisone into active cortisol. 11betaHSD1 inhibition is a tempting target for the treatment of a host of human disorders that might benefit from blockade of glucocorticoid action, such as obesity, metabolic syndrome, and diabetes type 2. Here, we report an in silico screening study aimed at identifying new selective inhibitors of human 11betaHSD1 enzyme. In the first step, homology modeling was employed to build the 3D structure of 11betaHSD1. Further, molecular docking was used to validate the predicted model by showing that it was able to discriminate between known 11betaHSD1 inhibitors or substrates and non-inhibitors. The homology model was found to reproduce closely the crystal structure that became publicly available in the final stages of this work. Finally, we carried out structure-based virtual screening experiments on both the homology model and the crystallographic structure with a database of 114,000 natural molecules. Among these, 15 molecules were consistently selected as inhibitors based on both the model and crystal structures of the enzyme, implying a good quality for the homology model. Among these putative 11betaHSD1 inhibitors, two were flavonone derivatives that have already been shown to be potent inhibitors of the enzyme.

11-beta-Hydroxysteroid Dehydrogenase Type 1↗

PubMed2004

Pharmacognosy and reverse pharmacognosy: a new concept for accelerating natural drug discovery.

Combinatorial chemistry and high-throughput screening (HTS) have led to the identification of numerous agents that are active and selective in vitro. Identifying drugs that are active in vivo, however, remains a challenge. Traditional medicinal cures based on natural materials have proven useful for many populations worldwide, representing huge and disperse tracts of knowledge that are sometimes neglected in Western research due to differences in the concepts of illness. In this review we introduce a new approach, termed 'reverse pharmacognosy' (from diverse molecules to plants), which can be coupled with pharmacognosy (from biodiverse plants to molecules). Reverse pharmacognosy utilizes new techniques, such as HTS, virtual screening and a knowledge database containing the traditional uses of plants. Integrating pharmacognosy and reverse pharmacognosy in the research process may provide an efficient and rapid tool for natural drug discovery.

Animals↗

PubMed2002

Estimation of aqueous solubility of organic compounds with QSPR approach.

PURPOSE: To derive a QSPR model for estimation of aqueous solubility of organic compounds. METHODS: Solubility data for 930 diverse compounds was investigated with principal component regression analysis. This set of compounds consists of pharmaceuticals, pollutants, nutrients, herbicides, and pesticides. The diversity of this collection was analyzed using MACCS fingerprint and BCUT chemistry space. RESULTS: The training set of the solubility data is as diverse as the Available Chemicals Directory, and more diverse than the MDL Drug Data Report. Forty-six molecular descriptors were screened using a genetic algorithm. A QSPR model with a squared correlation coefficient (r2) of 0.92, a root mean square error of 0.53 log molar solubility (log S(w)), an average absolute estimation error of 0.36 log S(w), and a cross-validated q2 of 0.91 was derived. The QSPR model was validated with a test set of 249 compounds not included in the training set. The absolute estimation error for the test set of compounds was 0.39 log S(w). CONCLUSIONS: A highly predictive QSPR model for estimating aqueous solubility was derived and validated. This model can be used to estimate aqueous solubility for virtual screening and combinatorial library design.

Algorithms↗

PubMed2004

GAsDock: a new approach for rapid flexible docking based on an improved multi-population genetic algorithm.

Based on an improved multi-population genetic algorithm, a new fast flexible docking program, GAsDock, was developed. The docking accuracy, screening efficiency, and docking speed of GAsDock were evaluated by the docking results of thymidine kinase (TK) and HIV-1 reverse transcriptase (RT) enzyme with 10 available inhibitors of each protein and 990 randomly selected ligands. Nine of the ten known inhibitors of TK were accurately docked into the protein active site, the root-mean-square deviation (RMSD) values between the docking and X-ray crystal structures are less than 1.7A; binding poses (conformation and orientation) of 9 of the 10 known inhibitors of RT were reproduced by GAsDock with RMSD values less than 2.0A. The docking time is approximately in proportion to the number of rotatable bonds of ligands; GAsDock can finish a docking simulation within 60s for a ligand with no more than 20 rotatable bonds. Results indicate that GAsDock is an accurate and remarkably faster docking program in comparison with other docking programs, which is applausive in the application of virtual screening.

Algorithms↗

PubMed2004

Binding analyses between Human PPARgamma-LBD and ligands.

The binding characteristics of a series of PPARgamma ligands (GW9662, GI 262570, cis-parinaric acid, 15-deoxy-Delta(12,14)-prostaglandin J(2), LY171883, indomethacin, linoleic acid, palmitic acid and troglitazone) to human PPARgamma ligand binding domain have been investigated for the first time by using surface plasmon resonance biosensor technology, CD spectroscopy and molecular docking simulation. The surface plasmon resonance biosensor determined equilibrium dissociation constants (KD values) are in agreement with the results reported in the literature measured by other methods, indicating that the surface plasmon resonance biosensor can assume a direct assay method in screening new PPARgamma agonists or antagonists. Conformational changes of PPARgamma caused by the ligand binding were detected by CD determination. It is interesting that the thermal stability of the receptor, reflected by the increase of the transition temperature (T(m)), was enhanced by the binding of the ligands. The increment of the transition temperature (DeltaT(m)) of PPARgamma owing to ligand binding correlated well with the binding affinity. This finding implies that CD could possibly be a complementary technology with which to determine the binding affinities of ligands to PPARgamma. Molecular docking simulation provided reasonable and reliable binding models of the ligands to PPARgamma at the atomic level, which gave a good explanation of the structure-binding affinity relationship for the ligands interacting with PPARgamma. Moreover, the predicted binding free energies for the ligands correlated well with the binding constants measured by the surface plasmon resonance biosensor, indicating that the docking paradigm used in this study could possibly be employed in virtual screening to discover new PPARgamma ligands, although the docking program cannot accurately predict the absolute ligand-PPARgamma binding affinity.

Apoproteins↗

PubMed2001

Design, docking, and evaluation of multiple libraries against multiple targets.

We present a general approach to the design, docking, and virtual screening of multiple combinatorial libraries against a family of proteins. The method consists of three main stages: docking the scaffold, selecting the best substituents at each site of diversity, and comparing the resultant molecules within and between the libraries. The core "divide-and-conquer" algorithm for side-chain selection, developed from an earlier version (Sun et al., J Comp Aided Mol Design 1998;12:597-604), provides a way to explore large lists of substituents with linear rather than combinatorial time dependence. We have applied our method to three combinatorial libraries and three serine proteases: trypsin, chymotrypsin, and elastase. We show that the scaffold docking procedure, in conjunction with a novel vector-based orientation filter, reproduces crystallographic binding modes. In addition, the free-energy-based scoring procedure (Zou et al., J Am Chem Soc 1999;121:8033-8043) is able to reproduce experimental binding data for P1 mutants of macromolecular protease inhibitors. Finally, we show that our method discriminates between a peptide library and virtual libraries built on benzodiazepine and tetrahydroisoquinolinone scaffolds. Implications of the docking results for library design are explored.

Amino Acids↗

PubMedDate not supplied

A chemical class-based approach to predictive model generation.

We make a quantitative comparison of two distinct approaches to predictive model generation in the context of diverse screening data. In the default approach, a single recursive partitioning model is constructed using all of the training data at one time. In the "class-based" approach, the same data are first partitioned into homogeneous, scaffold-based classes, and models are constructed within each class independently. Both approaches are tested on the identical set of hold-out data, using a formal protocol that includes consensus scoring to handle the multiple class-based models. The entire process is performed using three different descriptor sets and is repeated using five separate random trials, such that the trial-averaged prediction rates for the two approaches can be quantitatively compared. We find that although the predictive performances of the class-based and default approaches are similar, the former has at least two distinct advantages. The first is greater interpretability, in that chemists can more easily extract useful structure-activity information from the models. The second is greater reliability, allowing models to be applied with increased confidence to unseen data in virtual-screening applications.

Journal Article↗

PubMedDate not supplied

Retrospective docking study of PDE4B ligands and an analysis of the behavior of selected scoring functions.

Scoring forms a major obstacle to the success of any docking study. In general, fast scoring functions perform poorly when used to determine the relative affinity of ligands for their receptors. In this study, the objective was not to rank compounds with confidence but simply to identify a scoring method which could provide a 4-fold hit enrichment in a screening sample over random selection. To this end, LigandFit, a fast shape matching docking algorithm, was used to dock a variety of known inhibitors of type 4 phosphodiesterase (PDE4B) into its binding site determined crystallographically for a series of pyrazolopyridine inhibitors. The success of identifying good poses with this technique was explored through RMSD comparisons with 19 known inhibitors for which crystallographic structures were available. The effectiveness of five scoring functions (PMF, JAIN, PLP2, LigScore2, and DockScore) was then evaluated through consideration of the success in enriching the top ranked fractions of nine artificial databases, constructed by seeding 1980 inactive ligands (pIC50 < 5) with 20 randomly selected inhibitors (pIC50 > 6.5). PMF and JAIN showed high average enrichment factors (greater than 4 times) in the top 5-10% of the ranked databases. Rank-based consensus scoring was then investigated, and the rational combination of 3 scoring functions resulted in more robust scoring schemes with (cScore)-DPmJ (consensus score of DockScore, PMF, and JAIN) and (cScore)-PPmJ (PLP2, PMF, and JAIN) yielding particularly good results. These cScores are believed to be of greater general application. Finally, the analysis of the behavior of the scoring functions across different chemotypes uncovered the inherent bias of the docking and scoring toward compounds in the same structural family as that employed for the crystal structure, suggesting the need to use multiple versions of the binding site for more successful virtual screening strategies.

3',5'-Cyclic-AMP Phosphodiesterases↗

PubMed2004

Guided docking approaches to structure-based design and screening.

With the number of protein-ligand complexes available in the Protein Data Bank constantly growing, structure-based approaches to drug design and screening have become increasingly important. Alongside this explosion of structural information, a number of molecular docking methods have been developed over the last years with the aim of maximally exploiting all available structural and chemical information that can be derived from proteins, from ligands, and from protein-ligand complexes. In this respect, the term 'guided docking' is introduced to refer to docking approaches that incorporate some degree of chemical information to actively guide the orientation of the ligand into the binding site. To reflect the focus on the use of chemical information, a classification scheme for guided docking approaches is proposed. In general terms, guided docking approaches can be divided into indirect and direct approaches. Indirect approaches incorporate chemical information implicitly, having an effect on scoring but not on orienting the ligand during sampling. In contrast, direct approaches incorporate chemical information explicitly, thus actively guiding the orientation of the ligand during sampling. Direct approaches can be further divided into protein-based, mapping-based, and ligand-based approaches to reflect the source used to derive the features capturing the chemical information inside the protein cavity. Within each category, a representative list of docking approaches is discussed. In view of the limitations of current scoring functions, it was generally found that making optimal use of chemical information represents an efficient knowledge-based strategy for improving binding affinity estimations, ligand binding-mode predictions, and virtual screening enrichments obtained from protein-ligand docking.

Binding Sites↗

PubMed1998

Molecular analysis reveals a high mutation frequency in the first untranslated exon of the PPOX gene and largely excludes variegate porphyria in a subset of clinically affected Afrikaner families.

A subset of probands from 11 South African families with clinical and/or biochemical features of variegate porphyria (VP), but without the known protoporphyrinogen oxidase (PPOX) gene defects identified previously in the South African population, were subjected to mutation analysis. Disease-related mutation(s) could not be identified after screening virtually the entire PPOX gene by heteroduplex single-strand conformation polymorphism analysis (HEX-SSCP), although three new sequence variants were detected in exon 1 of the gene in three normal controls. The presence of these single base changes at nucleotide positions 22 (C/G), 27 (C/A) and 127 (C/A), in addition to the known exon 1 polymorphisms I-26 and I-150, indicates that this untranslated region of the PPOX gene is particularly mutation-prone. Furthermore, microsatellite markers flanking the PPOX and alpha-1 antitrypsin (PI) gene, on chromosomes 1 and 14, respectively, were used to assess the probability of involvement of these loci in disease presentation. Common alleles transmitted from affected parent to affected child were determined where possible in the mutation-negative index cases. Allelic frequencies of these < > alleles were compared to findings in the normal population, but no predominant disease-associated allele could be identified. Co-segregation of a specific haplotype with the disease phenotype could also not be demonstrated in a large Afrikaner family. It is concluded that further studies are warranted to determine the genetic factor(s) underlying the autosomal dominant pattern of inheritance in molecularly uncharacterized cases showing clinical symptoms of an acute porphyria.

Base Sequence↗

PubMed2002

Drug design strategies for targeting G-protein-coupled receptors.

G-protein-coupled receptors (GPCRs) form a large protein family that plays an important role in many physiological and pathophysiological processes. Since the sequencing of the human genome has revealed several hundred new members of this receptor family, many new opportunities for developing novel therapeutics have emerged. The increasing knowledge of GPCRs (biological target space) and their ligands (chemical ligand space) enables novel drug design strategies to accelerate the finding and optimization of GPCR leads: The crystal structure of rhodopsin provides the first three-dimensional GPCR information, which now supports homology modeling studies and structure-based drug design approaches within the GPCR target family. On the other hand, the classical ligand-based design approaches (for example, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR)) are still powerful methods for lead finding and optimization. In addition, the cross-target analysis of GPCR ligands has revealed more and more common structural motifs and three-dimensional pharmacophores. Such GPCR privileged structural motifs have been successfully used by many pharmaceutical companies to design and synthesize combinatorial libraries, which are subsequently tested against novel GPCR targets for lead finding. In the near future structural biology and chemogenomics might allow the mapping of the ligand binding to the receptor. The linking of chemical and biological spaces will aid in generating lead-finding libraries, which are tailor-made for their respective receptor.

Amino Acid Motifs↗

PubMed2003

Application and limitations of X-ray crystallographic data in structure-based ligand and drug design.

Structure-based design usually focuses upon the optimization of ligand affinity. However, successful drug design also requires the optimization of many other properties. The primary source of structural information for protein-ligand complexes is X-ray crystallography. The uncertainties introduced during the derivation of an atomic model from the experimentally observed electron density data are not always appreciated. Uncertainties in the atomic model can have significant consequences when this model is subsequently used as the basis of manual design, docking, scoring, and virtual screening efforts. Docking and scoring algorithms are currently imperfect. A good correlation between observed and calculated binding affinities is usually only observed only when very large ranges of affinity are considered. Errors in the correlation often exceed the range of affinities commonly encountered during lead optimization. Some structure-based design approaches now involve screening libraries by using technologies based on NMR spectroscopy and X-ray crystallography to discover small polar templates, which are used for further optimization. Such compounds are defined as leadlike and are also sought by more traditional high-throughput screening technologies. Structure-based design and HTS technologies show important complementarity and a degree of convergence.

Crystallization↗

PubMed2006

An integrated database of flavonoids.

Flavonoids are polyphenolic compounds that occur ubiquitously in foods of plant origin. Some of these molecules exhibit various physiological activities. Among existing drugs, there are a huge number of compounds bearing a flavonoid-related skeleton. Because of the relevance for pharmaceutical research, it would be beneficial to collect these compounds into a database. Recently, various databases of chemicals were compiled to help biological and/or chemical research, but no comprehensive database of flavonoids with chemical structures and physicochemical parameters, supposedly related to their activity, is available yet. The aim of this research was to merge the information about flavonoids of plant origin and flavonoids used as medicines into a database. Moreover, predictions of activities against various targets were performed using a virtual screening procedure to demonstrate a possible application of the database for pharmaceutical research.

Chemistry, Physical↗

PubMed2006

An efficient method for the synthesis of peptide aldehyde libraries employed in the discovery of reversible SARS coronavirus main protease (SARS-CoV Mpro) inhibitors.

A method for the parallel solid-phase synthesis of peptide aldehydes has been developed. Protected amino acid aldehydes obtained by the racemization-free oxidation of amino alcohols with Dess-Martin periodinane were immobilized on threonyl resins as oxazolidines. Following Boc protection of the ring nitrogen to yield the N-protected oxazolidine linker, peptide synthesis was performed efficiently on this resin. A peptide aldehyde library was designed for targeting the SARS coronavirus main protease, SARS-CoV M(pro)(also known as 3CL(pro)), on the basis of three different reported binding modes and supported by virtual screening. A set of 25 peptide aldehydes was prepared by this method and investigated in inhibition assays against SARS-CoV M(pro). Several potent inhibitors were found with IC(50) values in the low micromolar range. An IC(50) of 7.5 muM was found for AcNSTSQ-H and AcESTLQ-H. Interestingly, the most potent inhibitors seem to bind to SARS-CoV M(pro) in a noncanonical binding mode.

Aldehydes↗