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Results for “virtual screening”

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At least 631 records · Page 35Linked to original sources
PubMed2005

Cinanserin is an inhibitor of the 3C-like proteinase of severe acute respiratory syndrome coronavirus and strongly reduces virus replication in vitro.

The 3C-like proteinase (3CLpro) of severe acute respiratory syndrome-associated coronavirus (SARS-CoV) is one of the most promising targets for anti-SARS-CoV drugs due to its crucial role in the viral life cycle. In this study, a database containing structural information of more than 8,000 existing drugs was virtually screened by a docking approach to identify potential binding molecules of SARS-CoV 3CLpro. As a target for screening, both a homology model and the crystallographic structure of the binding pocket of the enzyme were used. Cinanserin (SQ 10,643), a well-characterized serotonin antagonist that has undergone preliminary clinical testing in humans in the 1960s, showed a high score in the screening and was chosen for further experimental evaluation. Binding of both cinanserin and its hydrochloride to bacterially expressed 3CLpro of SARS-CoV and the related human coronavirus 229E (HCoV-229E) was demonstrated by surface plasmon resonance technology. The catalytic activity of both enzymes was inhibited with 50% inhibitory concentration (IC50) values of 5 microM, as tested with a fluorogenic substrate. The antiviral activity of cinanserin was further evaluated in tissue culture assays, namely, a replicon system based on HCoV-229E and quantitative test assays with infectious SARS-CoV and HCoV-229E. All assays revealed a strong inhibition of coronavirus replication at nontoxic drug concentrations. The level of virus RNA and infectious particles was reduced by up to 4 log units, with IC50 values ranging from 19 to 34 microM. These findings demonstrate that the old drug cinanserin is an inhibitor of SARS-CoV replication, acting most likely via inhibition of the 3CL proteinase.

Animals↗
PubMed2002

Protein flexibility and computer-aided drug design.

Although computational techniques are increasingly being used in computer-aided drug design, the effects due to protein flexibility are still ignored in many applications. This review revisits rigorous statistical mechanical methods for predicting binding affinity, discusses some recent developments for improving their speed and reliability, and examines faster approximate models for facilitating virtual screening and lead optimization.

Computational Biology↗
PubMed2026

Comparative and Subtractive Genomics Analysis of Multidrug-Resistant Klebsiella pneumoniae Strains for Novel Target Identification and Drug Repurposing Strategies.

The rapid rise of multidrug-resistant (MDR) Klebsiella pneumoniae has created a major global health challenge due to the limited availability of conserved therapeutic targets effective across diverse resistant strains. In this study, an integrative computational target-discovery and drug-repurposing framework was applied to six clinically relevant K. pneumoniae strains. Comparative genomic analysis identified 3012 conserved genes, which were subsequently filtered to nine essential, non-host homologous proteins. Among these, three conserved cytoplasmic proteins (accD, cpxR, and mraZ) were prioritized for functional analysis, with acetyl-CoA carboxylase subunit beta (accD) emerging as the most promising therapeutic target based on sequence conservation, predicted essentiality, subcellular localization, and pathway association. Structural assessment supported the reliability of the predicted accD model, whereas consensus binding-site analysis identified key residues suitable for ligand interaction. Virtual screening of FDA-approved drugs followed by molecular docking identified several compounds with favorable binding profiles toward accD. Subsequent molecular dynamics simulations, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), hydrogen-bond occupancy, principal component analysis (PCA), and PCA-based free energy landscape (FEL) analyses, consistently identified tenapanor, micafungin, deferoxamine, and cobicistat as the most stable protein-ligand complexes, with tenapanor exhibiting the most favorable overall structural and thermodynamic stability profile. These findings identify accD as a promising therapeutic target in MDR K. pneumoniae and suggest several FDA-approved compounds as potential candidates for drug repurposing. Although experimental validation is needed to confirm their biological activity and therapeutic potential, this study demonstrates the potential of integrating comparative genomics with molecular dynamics analyses to support antimicrobial target identification and drug repurposing against MDR bacterial pathogens.

Klebsiella pneumoniae↗
PubMed2005

Alterations in the expression of the apurinic/apyrimidinic endonuclease-1/redox factor-1 (APE/Ref-1) in human melanoma and identification of the therapeutic potential of resveratrol as an APE/Ref-1 inhibitor.

Apurinic/apyrimidinic endonuclease-1/redox factor-1 (APE/Ref-1) is a multifunctional protein involved in DNA base excision repair and redox regulation of many transcription factors. In different melanoma cell lines, we found that both nucleus and cytoplasm exhibited higher levels of Ref-1 compared with normal melanocytes. Similar increases of Ref-1 expression, detected by immunohistofluorescence, were also evident in nevi and malignant melanoma biopsies compared with normal skin, which were predominantly localized in the nucleus. Using recombinant adenovirus Adref-1, encoding full-length Ref-1, we transiently overexpressed APE/Ref-1 in human melanocytes, which protected these cells from UVB-induced apoptosis and increased foci formation in culture. Ref-1 overexpression also protected melanoma cells from cisplatin- or H2O2-induced apoptosis, whereas increased apoptosis was observed with Ref-1 antisense construct infection. These observations suggested that intracellular Ref-1 levels played an important role in sensitization of melanoma cells to apoptosis. Electrophoretic mobility shift assay results showed that in both cultured primary and metastatic melanomas DNA-binding activities of activator protein-1 and nuclear factor-kappaB were significantly diminished or shifted when anti-APE/Ref-1 antibody was added to deplete APE/Ref-1 from the binding complexes. Induced nuclear factor-kappaB transcriptional activities were also evident after Ref-1 overexpression. Furthermore, using three-dimensional molecular structure modeling and virtual screening, we found that resveratrol, a natural compound found in fruits and vegetables, docks into a druggable pocket of Ref-1 protein. In vitro studies revealed that resveratrol inhibited, in a dose-dependent manner, Ref-1-activated activator protein-1 DNA-binding activities as well as Ref-1 endonuclease activities and rendered melanoma cells more sensitive to dacarbazine treatment.

DNA-(Apurinic or Apyrimidinic Site) Lyase↗
PubMed2005

Design of novel bioisosteres of beta-diketo acid inhibitors of HIV-1 integrase.

HIV-1 integrase (IN) is an attractive and validated target for the development of novel therapeutics against AIDS. Significant efforts have been devoted to the identification of IN inhibitors using various methods. In this context, through virtual screening of the NCI database and structure-based drug design strategies, we identified several pharmacophoric fragments and incorporated them on various aromatic or heteroaromatic rings. In addition, we designed and synthesized a series of 5-aryl(heteroaryl)-isoxazole-3-carboxylic acids as biological isosteric analogues of beta-diketo acid containing inhibitors of HIV-1 IN and their derivatives. Further computational docking studies were performed to investigate the mode of interactions of the most active ligands with the IN active site. Results suggested that some of the tested compounds could be considered as lead compounds and suitable for further optimization.

Acquired Immunodeficiency Syndrome↗
PubMed2005

Evolutionary trace analysis of eukaryotic DNA topoisomerase I superfamily: identification of novel antitumor drug binding site.

The studies of novel inhibitors of DNA topoisomerase I (Topo I) have already become very promising in cancer chemotherapy. Identifying the new drug-binding residues is playing an important role in the design and optimization of Topo I inhibitors. The designed compounds may have novel scaffolds, thus will be helpful to overcome the toxicities of current camptothecin (CPT) drugs and may provide a solution to cross resistance with these drugs. Multiple sequence alignments were performed on eukaryotic DNA topoisomerase I superfamily and thus the evolutionary tree was constructed. The Evolutionary Trace method was applied to identify functionally important residues of human Topo I. It has been demonstrated that class-specific hydrophobic residues Ala351, Met428, Pro431 are located around the 7,9-position of CPT, indicating suitable substitution of hydrophobic group on CPT will increase antitumor activity. The conservative residue Lys436 in the superfamily is of particular interest and new CPT derivatives designed based on this residue may greatly increase water solubility of such drugs. It has also been demonstrated that the residues Asn352 and Arg364 were conservative in the superfamily, whose mutation will render CPT resistance. As our molecular docking studies demonstrated they did not make any direct interaction with CPT, they are important drug-binding site residues for future design of novel non-camptothecin lead compounds. This work provided a strong basis for the design and synthesis of novel highly potent CPT derivatives and virtual screening for novel lead compounds.

Amino Acid Sequence↗
PubMed2004

Partitioning in binary-transformed chemical descriptor spaces.

Here we describe a statistically based partitioning method called median partitioning (MP), which involves the transformation of value distributions of molecular property descriptors into a binary classification scheme. The MP approach fundamentally differs from other partitioning approaches that involve dimension reduction of chemical spaces such as cell-based partitioning, since MP directly operates in original, albeit simplified, chemical space. Modified versions of the MP algorithm have been implemented and successfully applied in diversity selection, compound classification, and virtual screening. These findings have demonstrated that dimension reduction techniques, although elegant in their design, are not necessarily required for effective partitioning of molecular datasets. An attractive feature of statistical partitioning approaches such as decision tree methods or MP is their computational efficiency, which is becoming an important criterion for the analysis of compound databases containing millions of molecules.

Algorithms↗
PubMed2004

Comparison of methods based on diversity and similarity for molecule selection and the analysis of drug discovery data.

The concepts of diversity and similarity of molecules are widely used in quantitative methods for designing (selecting) a representative set of molecules and for analyzing the relationship between chemical structure and biological activity. We review methods and algorithms for design of a diverse set of molecules in the chemical space using clustering, cell-based partitioning, or other distance-based approaches. Analogous cell-based and clustering methods are described for analyzing drug-discovery data to predict activity in virtual screening. Some performance comparisons are made. The choice of descriptor variables to characterize chemical structure is also included in the comparative study. We find that the diversity of a selected set is quite sensitive to both the statistical selection method and the choice of molecular descriptors and that, for the dataset used in this study, random selection works surprisingly well in providing a set of data for analysis.

Algorithms↗
PubMed2004

Automated protein structure homology modeling: a progress report.

Understanding the molecular function of proteins is greatly enhanced by insights gained from their three-dimensional structures. Since experimental structures are only available for a small fraction of proteins, computational methods for protein structure modeling play an increasingly important role. Comparative protein structure modeling is currently the most accurate method, yielding models suitable for a wide spectrum of applications, such as structure-guided drug development or virtual screening. Stable and reliable automated prediction pipelines have been developed to apply large-scale comparative modeling to whole genomes or entire sequence databases. Model repositories give access to these annotated and evaluated models. In this review, we will discuss recent developments in automated comparative modeling and provide selected examples illustrating the use of homology models.

Animals↗
PubMed2002

Trends in virtual combinatorial library design.

Recent developments in combinatorial molecular design using virtual screening methods are summarised. These include similarity-based compound clustering techniques, structure-based docking and scoring, and fragment-based de novo design. Three major trends have been identified: i) the design of small target-focused compound libraries yielding activity-enriched sets of molecules; ii) advanced prediction methods for "drug-like" molecular properties complement activity predictions in the library design process, forming a multi-dimensional objective function; iii) "cherry picking" of selected products is increasingly used in lead generation and optimisation compared to purely educt-driven library design methods aiming at maximising structural diversity.

Algorithms↗
PubMed2006

In silico approaches to prediction of aqueous and DMSO solubility of drug-like compounds: trends, problems and solutions.

The solubility of drugs and drug-like compounds has been the subject of extensive studies aimed at finding a way to predict solubility from molecular structure. The aqueous solubility of a drug is an important factor that influences its absorption, distribution and elimination in the body. Poor aqueous solubility often causes a drug to appear inactive and may cause other biological problems. Compound solubility in DMSO represents another serious problem in early stages of drug discovery. An appreciation of the factors affecting a compound's DMSO solubility could help in predicting the storage conditions and appropriateness of compounds for primary bioscreening programs. In silico procedures for estimation of water and DMSO solubility represent extremely useful tools for the drug discovery practitioners. In this review, we provide a critical discussion of in silico models for the prediction of DMSO and water solubility of drug-like compounds used for virtual screening. We describe the main tendencies in the field, "booming" approaches and unsolved problems. A critical analysis of the accuracy and applicability of methods is provided.

Biological Availability↗
PubMed2006

Prediction oriented QSAR modelling of EGFR inhibition.

Epidermal Growth Factor Receptor (EGFR) is a high priority target in anticancer drug research. Thousands of very effective EGFR inhibitors have been developed in the last decade. The known inhibitors are originated from a very diverse chemical space but--without exception--all of them act at the Adenosine TriPhosphate (ATP) binding site of the enzyme. We have collected all of the diverse inhibitor structures and the relevant biological data obtained from comparable assays and built prediction oriented Quantitative Structure-Activity Relationship (QSAR) which models the ATP binding pocket's interactive surface from the ligand side. We describe a QSAR method with automatic Variable Subset Selection (VSS) by Genetic Algorithm (GA) and goodness-of-prediction driven QSAR model building, resulting an externally validated EGFR inhibitory model built from pIC50 values of a diverse structural set of 623 EGFR inhibitors. Repeated Trainings/Evaluations (RTE) were used to obtain model fitness values and the effectiveness of VSS is amplified by using predictive ability scores of descriptors. Numerous models were generated by different methods and viable models were collected. Then, intensive RTE were applied to identify ultimate models for external validations. Finally, suitable models were validated by statistical tests. Since we use calculated molecular descriptors in the modeling, these models are suitable for virtual screening for obtaining novel potential EGFR inhibitors.

Enzyme Inhibitors↗
PubMed2006

Strategies for efficient lead structure discovery from natural products.

This investigation aims to evaluate strategies for an efficient selection of bioactive compounds from the multitude and biodiversity of the plant kingdom. Statistics prove natural products (NPs) as a source leading most consistently to successful development of new drugs. However, there are several reasons why the interest in finding bioactive NPs has generally declined at several major pharmaceutical companies. Their substantial argument is that the research in this field is time-consuming, highly complex and ineffective. A more rational and economic search for new lead structures from nature must therefore be a priority in order to overcome these problems. In this paper, different strategies are described to exploit the molecular diversity of bioactive secondary metabolites, namely classical pharmacognostic approaches and computational methods. The latter include various data mining tools, like virtual screening filtering experiments using pharmacophore models, docking studies, and neural networks, which help to establish a relationship between chemical structure and biological activity. The strengths and weaknesses of these methods will be shown in this review. Focusing on selected targets within the arachidonic acid cascade (phospholipase A(2), 5-lipoxygenase, cyclooxygenase-1 and -2), several studies of successful discoveries in the field of anti-inflammatory NPs were scrutinized for the applied strategies. Both the compilation of relevant published data and recent studies supported by our own research clearly demonstrate the benefits of the synergistic effect of a hybridization of these strategies for an effective drug discovery from natural ingredients.

Anti-Inflammatory Agents↗
PubMed2004

Structural bioinformatic approaches to the discovery of new antimycobacterial drugs.

Integrated bioinformatic approaches to drug discovery exploit computational techniques to examine the flow of information from genome to structure to function. Informatics is being be used to accelerate and rationalize the process of antimycobacterial drug discovery and design, with the immediate goals to identify viable drug targets and produce a set of critically evaluated protein target models and corresponding set of probable lead compounds. Bioinformatic approaches are being successfully applied in the selection and prioritization of putative mycobacterial drug target genes; computational modelling and x-ray structure validation of protein targets with drug lead compounds; simulated docking and virtual screening of potential lead compounds; and lead validation and optimization using structure-activity and structure-function relationships. By identifying active sites, characterizing patterns of conserved residues and, where relevant, predicting catalytic residues, bioinformatics provides information to aid the design of selective and efficacious pharmacophores. In this review, we describe selected recent progress in antimycobacterial drug design, illustrating the strengths and limitations of current structural bioinformatic approaches as tools in the fight against tuberculosis.

Anti-Bacterial Agents↗
PubMed2005

Docking: successes and challenges.

The state of the art of various computational aspects of docking-based virtual screening of database of small molecules is presented. The review encompasses the different search algorithms and the scoring functions used in docking methods and their applications to protein and nucleic acid drug targets. Recent progress made in the development and application of methods to include target flexibility are summarized. The fundamental issues and challenges involved in comparing various docking methods are discussed. Limitations of current technologies as well as future prospects are presented.

Algorithms↗
PubMed2006

Quaternary structure, substrate selectivity and inhibitor design for SARS 3C-like proteinase.

The SARS coronavirus 3C-like proteinase is recognized as a potential drug design target for the treatment of severe acute respiratory syndrome. In the past few years, much work has been done to understand the catalytic mechanism of this target protein and to design its selective inhibitors. The protein exists as a dimer/monomer mixture in solution and the dimer was confirmed to be the active species for the enzyme reaction. Quantitative dissociation constants have been reported for the dimer by using analytic ultracentrifuge, gel filtration and enzyme assays. Though the enzyme is a cysteine protease with a chymotrypsin fold, SARS 3C-like proteinase follows the general base catalytic mechanism similar to chymotrypsin. As the enzyme can cut eleven different sites on the viral polyprotein, the substrate specificity has been studied by synthesized peptides corresponding or similar to the cleavage sites on the polyprotein. Predictive model was built for substrate structure and activity relationships and can be applied in inhibitor design. Due to the lack of potential drugs for the treatment of SARS, the discovery of inhibitors against SARS 3C-like proteinase, which can potentially be optimized as drugs appears to be highly desirable. Various groups have been working on inhibitor discovery by virtual screening, compound library screening, modification of existing compounds or natural products. High-throughput in vitro assays, auto-cleavage assays and viral replication assays have been developed for inhibition activity tests. Inhibitors with IC50 values as low as 60 nM have been reported.

Animals↗
PubMed2006

Scoring functions for protein-ligand docking.

Virtual screening by molecular docking has become established as a method for drug lead discovery and optimization. All docking algorithms make use of a scoring function in combination with a method of search. Two theoretical aspects of scoring function performance dominate operational performance. The first is the degree to which a scoring function has a global extremum within the ligand pose landscape at the proper location. The second is the degree to which the magnitude of the function at the extremum is accurate. Presuming adequate search strategies, a scoring function's location performance will dominate behavior with respect to docking accuracy: the degree to which a predicted pose of a ligand matches experimental observation. A scoring function's magnitude performance will dominate behavior with respect to screening utility: enrichment of true ligands over non-ligands. Magnitude estimation also controls pure scoring accuracy: the degree to which bona fide ligands of a particular protein may be correctly ranked. Approaches to the development of scoring functions have varied widely, with a number of functions yielding similarly high levels of performance relating to the location issue. However, even among functions performing equally well on location, widely varying performance is observed on the question of magnitude. In many cases, performance is good enough to yield high enrichments of true ligands versus non-ligands in screening across a wide variety of protein types. Generally, performance is not good enough to correctly rank among true ligands. Strategies for improvement are discussed.

Animals↗
PubMed2005

Computational chemistry as an integral component of lead generation.

From library shaping to ADME-Tox prediction via virtual screening, computational chemistry is an integral component of Lead Generation. It provides a series of tools that help focusing on compounds with a balanced pharmacodynamic and ADME-Tox profile together with a high potential to optimize potency and selectivity.

Cluster Analysis↗