Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “virtual screening”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12Linked to original sources
PubMedDate not supplied

Structure-based rational quest for potential novel inhibitors of human HMG-CoA reductase by combining CoMFA 3D QSAR modeling and virtual screening.

3-Hydroxy-3-methylglutaryl-coenzyme A reductase (HMGR) catalyzes the formation of mevalonate. In many classes of organisms, this is the committed step leading to the synthesis of essential compounds, such as cholesterol. However, a high level of cholesterol is an important risk factor for coronary heart disease, for which an effective clinical treatment is to block HMGR using inhibitors like statins. Recently the structures of catalytic portion of human HMGR complexed with six different statins have been determined by a delicate crystallography study (Istvan and Deisenhofer Science 2001, 292, 1160-1164), which established a solid basis of structure and mechanism for the rational design, optimization, and development of even better HMGR inhibitors. In this study, three-dimensional quantitative structure-activity relationship (3D QSAR) with comparative molecular field analysis (CoMFA) was performed on a training set of up to 35 statins and statin-like compounds. Predictive models were established by using two different ways: (1) Models-fit, obtained by SYBYL conventional fit-atom molecular alignment rule, has cross-validated coefficients (q2) up to 0.652 and regression coefficients (r2) up to 0.977. (2) Models-dock, obtained by FlexE by docking compounds into the HMGR active site, has cross-validated coefficients (q2) up to 0.731 and regression coefficients (r2) up to 0.947. These models were further validated by an external testing set of 12 statins and statin-like compounds. Integrated with CoMFA 3D QSAR predictive models, molecular surface property (electrostatic and steric) mapping and structure-based (both ligand and receptor) virtual screening have been employed to explore potential novel hits for the HMGR inhibitors. A representative set of eight new compounds of non-statin-like structures but with high pIC(50) values were sorted out in the present study.

Binding Sites↗
PubMed2003

Crystallography, NMR and virtual screening: integrated tools for drug discovery.

Crystallography, nuclear magnetic resonance and virtual ligand screening have become common tools in structural approaches to drug discovery. Appropriately used, these techniques are highly complementary and synergistic, significantly enhancing the pace of the discovery process and the quality of compounds selected for further development. The integration of these discovery tools will be discussed, and examples in which the combination of these technologies has impacted on the drug discovery cycle will be highlighted.

Animals↗
PubMed2004

A novel class of inhibitors of peptide deformylase discovered through high-throughput screening and virtual ligand screening.

Peptide deformylase (PDF) has been identified as a promising antibacterial and herbicide target. A structurally novel class of inhibitors containing a 2-thioxo-thiazolidin-4-one heterocycle substituted by an arylidene group at the 5-position and a hexanoic acid side chain at the 3-position was discovered independently via high-throughput screening and virtual ligand screening. Data mining and analogue synthesis established a structure--activity relationship for the side chain region that is consistent with the docked structure.

Amidohydrolases↗
PubMed2005

Virtual screening against highly charged active sites: identifying substrates of alpha-beta barrel enzymes.

We have developed a virtual ligand screening method designed to help assign enzymatic function for alpha-beta barrel proteins. We dock a library of approximately 19,000 known metabolites against the active site and attempt to identify the relevant substrate based on predicted relative binding free energies. These energies are computed using a physics-based energy function based on an all-atom force field (OPLS-AA) and a generalized Born implicit solvent model. We evaluate the ability of this method to identify the known substrates of several members of the enolase superfamily of enzymes, including both holo and apo structures (11 total). The active sites of these enzymes contain numerous charged groups (lysines, carboxylates, histidines, and one or more metal ions) and thus provide a challenge for most docking scoring functions, which treat electrostatics and solvation in a highly approximate manner. Using the physics-based scoring procedure, the known substrate is ranked within the top 6% of the database in all cases, and in 8 of 11 cases, it is ranked within the top 1%. Moreover, the top-ranked ligands are strongly enriched in compounds with high chemical similarity to the substrate (e.g., different substitution patterns on a similar scaffold). These results suggest that our method can be used, in conjunction with other information including genomic context and known metabolic pathways, to suggest possible substrates or classes of substrates for experimental testing. More broadly, the physics-based scoring method performs well on highly charged binding sites and is likely to be useful in inhibitor docking against polar binding sites as well. The method is fast (<1 min per ligand), due largely to an efficient minimization algorithm based on the truncated Newton method, and thus, it can be applied to thousands of ligands within a few hours on a small Linux cluster.

Alanine Racemase↗
PubMed2001

Virtual screening of combinatorial libraries across a gene family: in search of inhibitors of Giardia lamblia guanine phosphoribosyltransferase.

Parasitic protozoa lack the ability to synthesize purine nucleotides de novo, relying instead on purine salvage enzymes for their survival. Guanine phosphoribosyltransferase (GPRT) from the protozoan parasite Giardia lamblia is a potential target for rational antiparasitic drug design, based on the experimental evidence, which indicates the lack of interconversion between adenine and guanine nucleotide pools. The present study is a continuation of our efforts to use three-dimensional structures of parasitic phosphoribosyltransferases (PRTs) to design novel antiparasitic agents. Two micromolar phthalimide-based GPRT inhibitors were identified by screening the in-house phthalimide library. A combination of structure-based scaffold selection using virtual library screening across the PRT gene family and solid phase library synthesis led to identification of smaller (molecular weight, <300) ligands with moderate to low specificity for GPRT; the best inhibitors, GP3 and GP5, had K(i) values in the 23 to 25 microM range. These results represent significant progress toward the goal of designing potent inhibitors of purine salvage in Giardia parasites. As a second step in this process, altering the phthalimide moiety to optimize interactions in the guanine-binding pocket of GPRT is expected to lead to compounds with promising activity against G. lamblia PRT.

Animals↗
PubMed2006

Ligand-based virtual screening, parallel solution-phase and microwave-assisted synthesis as tools to identify and synthesize new inhibitors of mycobacterium tuberculosis.

In an attempt to identify new inhibitors of the growth of Mycobacterium tuberculosis (MTB), the causative agent of tuberculosis, a procedure for the generation, design, and screening of a ligand-based virtual library was applied. This used both an in silico protocol centered on a recursive partitioning (RP) model described herein, and a pharmacophoric model for antitubercular agents previously generated by our research group. Two candidates emerged from databases of commercially available compounds, both characterized by a minimum inhibitory concentration (MIC) of 25 microg mL(-1). Based on these compounds, two series of derivatives were synthesized by both parallel solution-phase and microwave-assisted synthesis, leading to enhanced antimycobacterial activity. During both the design and synthesis, attention was focused on the efficient allocation of available resources with the aim of reducing the overall costs associated with calculation and synthesis.

Antitubercular Agents↗
PubMed2006

Discovery of protein phosphatase 2C inhibitors by virtual screening.

Protein phosphatase 2C (PP2C) is an archetype of the PPM Ser/Thr phosphatases, characterized by dependence on divalent magnesium or manganese cofactors, absence of known regulatory proteins, and resistance to all known Ser/Thr phosphatase inhibitors. We have used virtual ligand screening with the AutoDock method and the National Cancer Institute Diversity Set to identify small-molecule inhibitors of PP2Calpha activity at a protein substrate. These inhibitors are active in the micromolar range and represent the first non-phosphate-based molecules found to inhibit a type 2C phosphatase. The compounds docked to three recurrent binding sites near the PP2Calpha active site and displayed novel Ser/Thr phosphatase selectivity profiles. Common chemical features of these compounds may form the basis for development of a PP2C inhibitor pharmacophore and may facilitate investigation of PP2C control and cellular function.

Binding Sites↗
PubMed2006

Use of an induced fit receptor structure in virtual screening.

Structured-based drug design has traditionally relied on a single receptor structure as a target for docking and screening studies. However, it has become increasingly clear that in many cases where protein flexibility is an issue, it is critical to accurately model ligand-induced receptor movement in order to obtain high enrichment factors. We present a novel protein-ligand docking method that accounts for both ligand and receptor flexibility and accurately predicts the conformation of protein-ligand binding complexes. This method can generate viable receptor ensembles that can be used in virtual database screens.

Databases, Protein↗
PubMed2005

Inhibitors of Trypanosoma cruzi trypanothione reductase revealed by virtual screening and parallel synthesis.

In an approach to discover new inhibitors of trypanothione reductase from Trypanosoma cruzi, the causative agent of Chagas' disease, a virtual high-throughput screening was performed. Two structurally new types of inhibitors emerged, the antimicrobial chlorhexidine {1,1'-hexamethylenebis[5-(4-chlorophenyl)biguanide]}, a linear competitive inhibitor (K(i) = 2 +/- 1 microM), and a piperidine derivative acting as mixed inhibitor (K(i) = 6.2 +/- 2 microM and K(i)' = 8.5 +/- 2 microM). Neither compound interferes with human glutathione reductase. Based on chlorhexidine, different series of compounds were synthesized and studied as inhibitors of T. cruzi trypanothione reductase. Most efficient derivatives were three bis(amidines) showing mixed type inhibition with K(i,slope) and K(i,int) values of 2-5 microM and 16-47 microM, respectively. Although these compounds did not exert an improved inhibitory potency compared to chlorhexidine, the change from competitive to mixed-type inhibition is advantageous, since substrate accumulation does not overcome inhibition. Remarkably, all three derivatives carried two copies of an identical 2-methoxy-4-methyl-1-(phenylmethoxy)benzene substituent.

Amidines↗
PubMedDate not supplied

Applications of random sampling to virtual screening of combinatorial libraries.

We describe statistical techniques for effective evaluation of large virtual combinatorial libraries (> 10(10) potential compounds). The methods described are used for computationally evaluating templates (prioritization of candidate libraries for synthesis and screening) and for the design of individual combinatorial libraries (e.g., for a given diversity site, reagents can be selected based on the estimated frequency with which they appear in products that pass a computational filter). These statistical methods are powerful because they provide a simple way to estimate the properties of the overall library without explicitly enumerating all of the possible products. In addition, they are fast and simple, and the amount of sampling required to achieve a desired precision is calculable. In this article, we discuss the computational methods that allow random product selection from a combinatorial library and the statistics involved in estimating errors from quantities obtained from such samples. We then describe three examples: (1) an estimate of average molecular weight for the several billion possible products in a four-component Ugi reaction, a quantity that can be calculated exactly for comparison; (2) the prioritization of four templates for combinatorial synthesis using a computational filter based on four-point pharmacophores; and (3) selection of reagents for the four-component Ugi reaction based on their frequency of occurrence in products that pass a pharmacophore filter.

Combinatorial Chemistry Techniques↗
PubMed2000

Generation and evaluation of putative neuroregenerative drugs. Part 2: screening virtual libraries of novel polyketides which possess the binding domain of rapamycin.

The use of computational methods to direct engineered biosynthesis toward candidates based on the desired properties of the target compounds has been explored. The objective for this study has been the modification of rapamycin in order to eliminate its immunosuppressive activity and retain its neuroregenerative abilities. We have designed analogues of rapamycin which have truncated effector domains but retain the ability to bind to FKBP proteins, which is a prerequisite for the neuroregenerative abilities of the drugs. The procedures described here consist of the screening of large virtual libraries of molecules which retain the binding domain of rapamycin but in which different substitute ketide units replace the effector domain. These methods have provided analogues of rapamycin that cannot retain the immunosuppressive abilities of rapamycin, have a binding affinity to FKBP12 identical to that of rapamycin (by linear interaction energy calculations), and are suitable for synthesis by modified polyketide synthases.

Anti-Bacterial Agents↗
PubMed2000

Hybrid systems for virtual screening: interest of fuzzy clustering applied to olfaction.

Kohonen neural networks, also known as Self Organizing Map (SOM), offer a useful 2D representation of the compound distribution inside a large chemical database. This distribution results from the compound organization in a molecular diversity hyperspace derived from a large set of molecular descriptors. Fuzzy techniques based on the "concept of partial truth" reveal to be also a valuable tool for the direct exploitation of chemical databases or SOM. In such cases a fuzzy clustering algorithm is used. In this paper, a complete hybrid system, combining SOM and fuzzy clustering, is applied. As example, a series of olfactory compounds was selected. The complexity of such information is that a same compound may exhibit different odors. It is shown how fuzzy logic helps to have a better understanding of the organization of the compounds. These hybrid systems, using simultaneously SOM and fuzzy clustering, are foreseen as powerful tools for "virtual pre-screening".

Fuzzy Logic↗
PubMed2005

Atom, atom-type and total molecular linear indices as a promising approach for bioorganic and medicinal chemistry: theoretical and experimental assessment of a novel method for virtual screening and rational design of new lead anthelmintic.

Helminth infections are a medical problem in the world nowadays. In this paper a novel atom-level chemical descriptor has been applied to estimate the anthelmintic activity. Total and local linear indices and linear discriminant analysis were used to obtain a quantitative model that discriminates between anthelmintic and non-anthelmintic drug-like compounds. The discriminant model has an accuracy of 90.11% in the training set, with a high Matthews' correlation coefficient (MCC=0.80). To assess the robustness and predictive power of the obtained model, internal (leave-n-out) and external validation process was performed. The QSAR model correctly classified 88.55% of compounds in this external prediction set, yielding a MCC of 0.77. Another LDA model was carried out to outline some conclusions about the possible modes of action of anthelmintic drugs. It has an accuracy of 93.50% in the training set, and 80.00% in the external prediction set. After that, the developed model was used in the virtual--in silico--screening and several compounds from the Merck Index, Negwer's Handbook and Goodman and Gilman were identified by the model as anthelmintic. Finally, the experimental assay of an organic chemical (a furylethylene derivative) by an in vivo test permits us to carry out an assessment of the model. An accuracy of 100% with the theoretical predictions was observed. These results suggest that the proposed method will be a good tool for studying the biological properties of drug candidates during the early state of the drug-development process.

Anthelmintics↗
PubMed2003

Virtual screening and rational drug design method using structure generation system based on 3D-QSAR and docking.

An efficient virtual and rational drug design method is presented. It combines virtual bioactive compound generation with 3D-QSAR model and docking. Using this method, it is possible to generate a lot of highly diverse molecules and find virtual active lead compounds. The method was validated by the study of a set of anti-tumor drugs. With the constraints of pharmacophore obtained by DISCO implemented in SYBYL 6.8, 97 virtual bioactive compounds were generated, and their anti-tumor activities were predicted by CoMFA. Eight structures with high activity were selected and screened by the 3D-QSAR model. The most active generated structure was further investigated by modifying its structure in order to increase the activity. A comparative docking study with telomeric receptor was carried out, and the results showed that the generated structures could form more stable complexes with receptor than the reference compound selected from experimental data. This investigation showed that the proposed method was a feasible way for rational drug design with high screening efficiency.

Colchicine↗
PubMed2025

Chromosome scale genomes of two invasive Adelges species enable virtual screening for selective adelgicides.

Two invasive hemipteran adelgids are associated with widespread damage to several North American conifer species. Adelges tsugae, hemlock woolly adelgid, was introduced from Japan and reproduces parthenogenetically in North America, where it has rapidly decimated Tsuga canadensis and Tsuga caroliniana (the eastern and Carolina hemlocks, respectively). Adelges abietis, eastern spruce gall adelgid, introduced from Europe, forms distinctive pineapple-shaped galls on several native spruce species. While not considered a major forest pest, it weakens trees and increases susceptibility to additional stressors. Broad-spectrum insecticides that are often used to control adelgid populations can have off-target impacts on beneficial insects. Whole genome sequencing was performed on both species to aid in development of targeted solutions that may minimize ecological impact. Adelges abietis was sequenced using Illumina Linked-Read technology from 30 pooled individuals, with Hi-C scaffolding performed using data from a single individual collected from the same host plant. Adelges tsugae used Oxford Nanopore long-read sequencing from pooled nymphs. The assembled A. tsugae and A. abietis genomes, pooled from several parthenogenetic females, are 220.75 Mbp and 253.16 Mbp, respectively. Each consists of eight autosomal chromosomes, as well as two sex chromosomes (X1/X2), supporting the XX-XO sex determination system. The genomes are over 96% complete based on BUSCO assessment. Genome annotation identified 11,424 and 12,060 protein-coding genes in A. tsugae and A. abietis, respectively. Comparative analysis of proteins across 29 hemipteran species and 14 arthropod outgroups identified 31,666 putative gene families. Gene family evolution analysis with CAFE revealed lineage-specific expansions in immune-related aminopeptidases (ERAP1) and juvenile hormone binding proteins (JHBP), contractions in juvenile hormone acid methyltransferases (JHAMT), and conservation of nicotinic acetylcholine receptors (nAChR). These genes were explored as candidate families towards a long-term objective of developing adelgid-selective insecticides. Structural comparisons of proteins across seven focal species (Adelges tsugae, Adelges abietis, Adelges cooleyi, Rhopalosiphum maidis, Apis mellifera, Danaus plexippus, and Drosophila melanogaster) revealed high conservation of nAChR and ERAP1, while JHAMT exhibited species-specific structural divergence. The potential of JHAMT as a lineage-specific target for pest control was explored through virtual drug and pesticide screening.

adelgids↗
PubMed2006

Rational design of macrolides by virtual screening of combinatorial libraries generated through in silico manipulation of polyketide synthases.

Bacterial secondary metabolites display diverse biological activities, thus having potential as pharmacological agents. Although most of these compounds are discovered by random screening, it is possible to predict and re-design their structures based on the information on their biosynthetic pathways. Biosynthesis of macrolides, governed by modular polyketide synthases (PKS), obeys certain rules, which can be simulated in silico. PKS mode of action theoretically allows for a huge number of macrolides to be produced upon combinatorial manipulation. Since engineering of all possible PKS variants is practically unfeasible, we created Biogenerator software, which simulates manipulation of PKS and generates virtual libraries of macrolides. These libraries can be screened by computer-aided prediction of biological activities, as exemplified by analysis of erythromycin and macrolactin libraries. This approach allows rational selection of macrolides with desired biological activities and provides instructions regarding the composition of the PKS gene clusters necessary for microbial production of such molecules.

Combinatorial Chemistry Techniques↗
PubMed2000

A novel approach for the virtual screening and rational design of anticancer compounds.

A topological substructural approach to molecular design (TOSS-MODE) has been introduced for the selection and design of anticancer compounds. A quantitative model that discriminates anticancer compounds from the inactive ones in a training series was obtained. This model permits the correct classification of 91.43% of compounds in an external prediction set with only 1.43% of false actives and 7. 14% of false inactives. The model developed is then used in a simulation of a virtual search for Ras FTase inhibitors; 87% of the Ras FTase inhibitors used in this simulated search were correctly classified, thus indicating the ability of the TOSS-MODE model of finding lead compounds with novel structures and mechanism of action. Finally, a series of carbonucleosides was designed, and the compounds were classified as active/inactive anticancer compounds by using the model developed here. From the compounds so-designed, 20 were synthesized and evaluated experimentally for their antitumor effects on the proliferation of murine leukemia cells (L1210/0) and human T-lymphocyte cells (Molt4/C8 and CEM/0); 80% of these compounds were well-classified, as active or inactive, and only two pairs of isomeric compounds were false actives. The chloropurine derivatives were the most active compounds, especially compounds 6c, d.

Alkyl and Aryl Transferases↗
PubMedDate not supplied

Pharmacophore identification, in silico screening, and virtual library design for inhibitors of the human factor Xa.

Factor Xa inhibitors are innovative anticoagulant agents that provide a better safety/efficacy profile compared to other anticoagulative drugs. A chemical feature-based modeling approach was applied to identify crucial pharmacophore patterns from 3D crystal structures of inhibitors bound to human factor Xa (Pdb entries 1fjs, 1kns, 1eqz) using the software LIGANDSCOUT and CATALYST. The complex structures were selected regarding the criteria of high inhibitory potency (i.e. all ligands show K(i) values against factor Xa in the subnanomolar range) and good resolution (i.e. at least 2.2 A) in order to generate selective and high quality pharmacophore models. The resulting chemical-feature based hypotheses were used for virtual screening of commercial molecular databases such as the WDI database. Furthermore, a ligand-based molecular modeling approach was performed to obtain common-feature hypotheses that represent the relevant chemical interactions between 10 bioactive factor Xa inhibitors and the protein, respectively. In a next step a virtual combinatorial library was designed in order to generate new compounds with similar chemical and spatial properties as known inhibitors. The software tool ILIB DIVERSE was used for this procedure in order to provide new scaffolds of this group of anticoagulants. Finally we present the combination of these two techniques, hence virtual screening was performed with selective pharmacophore models in a focused virtual combinatorial database. De novo derived molecular scaffolds that were able to adequately satisfy the pharmacophore criteria are revealed and are promising templates for candidates for further development.

Anticoagulants↗