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PubMed2003

Binding site characteristics in structure-based virtual screening: evaluation of current docking tools.

Two new docking programs FRED (OpenEye Scientific Software) and Glide (Schrödinger, Inc.) in combination with various scoring functions implemented in these programs have been evaluated against a variety of seven protein targets (cyclooxygenase-2, estrogen receptor, p38 MAP kinase, gyrase B, thrombin, gelatinase A, neuraminidase) in order to assess their accuracy in virtual screening. Sets of known inhibitors were added to and ranked relative to a random library of drug-like compounds. Performance was compared in terms of enrichment factors and CPU time consumption. Results and specific features of the two new tools are discussed and compared to previously published results using FlexX (Tripos, Inc.) as a docking engine. In addition, general criteria for the selection of docking algorithms and scoring functions based on binding-site characteristics of specific protein targets are proposed. Figure Enrichment factors obtained with FlexX, Glide and FRED docking engines in combination with different scoring functions for seven selected targets with highly variable binding sites

Algorithms↗

PubMed2006

Sulfonylureas and glinides exhibit peroxisome proliferator-activated receptor gamma activity: a combined virtual screening and biological assay approach.

Most drugs currently employed in the treatment of type 2 diabetes either target the sulfonylurea receptor stimulating insulin release (sulfonylureas, glinides), or target the peroxisome proliferator-activated receptor (PPARgamma) improving insulin resistance (thiazolidinediones). Our work shows that sulfonylureas and glinides additionally bind to PPARgamma and exhibit PPARgamma agonistic activity. This activity was predicted in silico by virtual screening and confirmed in vitro in a binding assay, a transactivation assay, and by measuring the expression of PPARgamma target genes. Among the measured compounds, gliquidone and glipizide (two sulfonylureas), as well as nateglinide (a glinide), exhibit PPARgamma agonistic activity at concentrations comparable with those reached under pharmacological treatment. The most active of these compounds, gliquidone, is shown to be as potent as pioglitazone at inducing PPARgamma target gene expression. This dual mode of action of sulfonylureas and glinides may open new perspectives for the molecular pharmacology of antidiabetic drugs, because it provides evidence that drugs can be designed that target both the sulfonylurea receptor and PPARgamma. Targeting both receptors could increase pancreatic insulin secretion and improve insulin resistance. Glinides, sulfonylureas, and other acidified sulfonamides may be promising leads in the development of new PPARgamma agonists. In addition, we provide a unified concept of the PPARgamma binding ability of seemingly disparate compound classes.

ATP-Binding Cassette Transporters↗

PubMed2004

Acetylcholinesterase inhibitory activity of scopolin and scopoletin discovered by virtual screening of natural products.

For the targeting selection of acetylcholinesterase (AChE) inhibitors from natural sources we generated a structure-based pharmacophore model utilizing an in silico filtering experiment for the discovery of promising candidates out of a 3D multiconformational database consisting of more than 110,000 natural products. In our study, scopoletin (1) and its glucoside scopolin (2) emerged as potential AChE inhibitors by the virtual screening procedure. They were isolated by different chromatographic methods from the medicinal plant Scopolia carniolica Jaqc. and tested in an enzyme assay using Ellman's reagent. They showed moderate, but significant, dose-dependent and long-lasting inhibitory activities. In the in vivo experiments (icv application of 2 micromol) 1 and 2 increased the extracellular acetylcholine (ACh) concentration in rat brain to about 170% and 300% compared to basal release, respectively. At the same concentration, the positive control galanthamine increased the ACh concentration to about the same level as 1. These are the first in vivo results indicating an effect of coumarins on brain ACh.

Acetylcholine↗

PubMed2005

Enhancing the effectiveness of similarity-based virtual screening using nearest-neighbor information.

We test the hypothesis that fusing the outputs of similarity searches based on a single bioactive reference structure and on its nearest neighbors (of unknown activity) is more effective (in terms of numbers of high-ranked active structures) than a similarity search involving just the reference structure. This turbo similarity searching approach provides a simple way to enhance the effectiveness of simulated virtual screening searches of the MDL Drug Data Report database.

Computing Methodologies↗

PubMed2005

Discovery of high-affinity ligands of sigma1 receptor, ERG2, and emopamil binding protein by pharmacophore modeling and virtual screening.

ERG2, emopamil binding protein (EBP), and sigma-1 receptor (sigma(1)) are enzymes of sterol metabolism and an enzyme-related protein, respectively, that share high affinity for various structurally diverse compounds. To discover novel high-affinity ligands, pharmacophore models were built with Catalyst based upon a series of 23 structurally diverse chemicals exhibiting K(i) values from 10 pM to 100 microM for all three proteins. In virtual screening experiments, we retrieved drugs that were previously reported to bind to one or several of these proteins and also tested 11 new hits experimentally, of which three, among them raloxifene, had affinities for sigma(1) or EBP of <60 nM. When used to search a database of 3525 biochemicals of intermediary metabolism, a slightly modified ERG2 pharmacophore model successfully retrieved 10 substrate candidates among the top 28 hits. Our results indicate that inhibitor-based pharmacophore models for sigma(1), ERG2, and EBP can be used to screen drug and metabolite databases for chemically diverse compounds and putative endogenous ligands.

Animals↗

PubMed2026

Structure-based virtual screening, multi-score docking, and molecular dynamics simulation of novel small molecules targeting the epidermal growth factor receptor for potential management of oral squamous cell carcinoma.

UNLABELLED: Oral squamous cell carcinoma (OSCC) is a major global health burden, with epidermal growth factor receptor (EGFR) serving as an important therapeutic target. However, resistance to currently available EGFR inhibitors limits the efficacy of long-term treatment. In this study, a structure-based virtual screening approach was employed using the Mcule database to identify novel small molecules with potential EGFR-inhibitory activity. The top-ranked compounds were subjected to consensus docking using multiple docking platforms and compared with established EGFR inhibitors. The most promising complexes were further evaluated using 500 ns molecular dynamics simulations to investigate their structural stability, conformational flexibility, and binding persistence. ADMET and pharmacokinetic analyses were performed to assess the drug-like and safety profiles. Five lead compounds (C1-C5) demonstrated significant binding affinities toward EGFR, ranging from - 9.9 to - 9.2 kcal/mol, while satisfying the major drug-likeness criteria. Molecular dynamics simulations suggested that C1 and C4 may form relatively stable EGFR-ligand complexes, as supported by stable RMSD convergence and persistent interactions with key active-site residues throughout the simulation period. Trajectory-based interaction analyses further indicated a sustained binding behavior. ADMET profiling predicted favorable oral bioavailability and low predicted toxicity for most compounds, particularly C3 and C5, although a potential risk of cytochrome P450-mediated drug-drug interactions was observed. Overall, the shortlisted compounds exhibited docking and dynamic stability profiles comparable to those of the reference inhibitor Lapatinib. These findings suggest the potential therapeutic relevance of structurally novel EGFR-targeting scaffolds in OSCC and provide a foundation for future experimental validation through in vitro and in vivo studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s40203-026-00722-4.

ADMET↗

PubMedDate not supplied

Virtual screening and scaffold hopping based on GRID molecular interaction fields.

In this study, a set of strategies for structure-based design using GRID molecular interaction fields (MIFs) to derive a pharmacophoric representation of a protein is reported. Thrombin, one of the key enzymes involved in the blood coagulation cascade, was chosen as the model system since abundant published experimental data are available related to both crystal structures and structurally diverse sets of inhibitors. First, a virtual screening methodology was developed either using a pharmacophore representation of the protein based on GRID MIFs or using GRID MIFs from the 3D structure of a set of chosen thrombin inhibitors. The search was done in a 3D multiconformation version of the Available Chemical Directory (ACD) database, which had been spiked with 262 known thrombin inhibitors (multiple conformers available per compound). The model managed to find 80% of the known thrombin inhibitors among the 74,291 conformers in the ACD by only searching 5% of the database; hence, a 15-fold enrichment of the library was achieved. Second, a scaffold hopping methodology was developed using GRID MIFs, giving the scaffold interaction pattern and the shape of the scaffold, together with the distance between the anchor points. The scaffolds reported by Dolle in the Journal of Combinatorial Chemistry summaries (2000 and 2001) and scaffolds built or derived from ligands cocomplexed with the thrombin enzyme were parameterized using a new set of descriptors and saved into a searchable database. The scaffold representation from the database was then compared to a template scaffold (from a thrombin crystal structure), and the thrombin-derived scaffolds included in the database were found among the top solutions. To validate the usefulness of the methodology to replace the template scaffold, the entire molecule was built (scaffold and side chains) and the resulting compounds were docked into the active site of thrombin. The docking solutions showed the same binding pattern as the cocomplexed compound, hence, showing that this method can be a valuable tool for medicinal chemists to select interchangeable core structures (scaffolds) in an easy manner and retaining the binding properties from the original ligand.

Binding Sites↗

PubMed2004

Successful virtual screening for a submicromolar antagonist of the neurokinin-1 receptor based on a ligand-supported homology model.

The neurokinin-1 (NK1) receptor belongs to the family of G-protein-coupled receptors (GPCRs), which represents one of the most relevant target families in small-molecule drug design. In this paper, we describe a homology modeling of the NK1 receptor based on the high-resolution X-ray structure of rhodopsin and the successful virtual screening based on this protein model. The NK1 receptor model has been generated using our new MOBILE (modeling binding sites including ligand information explicitly) approach. Starting with preliminary homology models, it generates improved models of the protein binding pocket together with bound ligands. Ligand information is used as an integral part in the homology modeling process. For the construction of the NK1 receptor, antagonist CP-96345 was used to restrain the modeling. The quality of the obtained model was validated by probing its ability to accommodate additional known NK1 antagonists from structurally diverse classes. On the basis of the generated model and on the analysis of known NK1 antagonists, a pharmacophore model was deduced, which subsequently guided the 2D and 3D database search with UNITY. As a following step, the remaining hits were docked into the modeled binding pocket of the NK1 receptor. Finally, seven compounds were selected for biochemical testing, from which one showed affinity in the submicromolar range. Our results suggest that ligand-supported homology models of GPCRs may be used as effective platforms for structure-based drug design.

Amino Acid Sequence↗

PubMed2004

Virtual screening of human 5-aminoimidazole-4-carboxamide ribonucleotide transformylase against the NCI diversity set by use of AutoDock to identify novel nonfolate inhibitors.

AICAR transformylase (5-aminoimidazole-4-carboxamide ribonucleotide transformylase) is a folate-dependent activity of the bifunctional protein ATIC (AICAR transformylase and IMP cyclohydrolase) and is responsible for catalyzing the penultimate step of the de novo purine biosynthetic pathway. As such, AICAR transformylase has been proposed as a potential target for antineoplastic drug design. Virtual screening of the human AICAR transformylase active site by use of AutoDock against the NCI diversity set, a library of compounds with nonredundant pharmacophore profiles, has revealed 44 potential inhibitor candidates. In vitro inhibition assay of 16 soluble compounds from this list revealed that eight compounds with novel scaffolds, relative to the general folate template, had micromolar inhibition. Subsequent extension of docking trials on compounds with similar scaffolds from the entire NCI-3D database has unveiled 11 additional inhibitors that were confirmed by the in vitro inhibition assay. In particular, one compound, NSC30171, had nanomolar inhibition (K(i) = 154 nM, IC(50) = 600 nM) against AICAR transformylase. These 19 inhibitors serve as novel templates/scaffolds for development of more potent and specific non-folate-based AICAR transformylase inhibitors.

Binding Sites↗

PubMedDate not supplied

Influenza virus neuraminidase inhibitors: generation and comparison of structure-based and common feature pharmacophore hypotheses and their application in virtual screening.

X-ray crystallographic data of the influenza virus neuraminidase in complex with different inhibitors were used to generate chemical feature-based pharmacophore models of the binding site of this enzyme. The models were built using the software package Catalyst. Pharmacophore hypotheses derived from the 3-D structure of ligands cocrystallized with the enzyme were then compared with automatically generated common feature pharmacophore hypotheses for neuraminidase inhibitors. The latter models were found to contain fewer features and exhibited lower selectivity in virtual screening experiments. Some functions of the inhibitors obviously participate in more than one mode of interaction with the enzyme (charge-charge interaction and hydrogen bond) or form hydrogen bonds to several amino acids. Since such multiple interactions of one chemical function cannot be included into the Catalyst data format, strategies are presented to overcome these limitations. Finally, the results of 3-D database searching experiments using these hypotheses are described.

Crystallography, X-Ray↗

PubMedDate not supplied

Structure-based pharmacophore design and virtual screening for novel angiotensin converting enzyme 2 inhibitors.

The metallopeptidase Angiotensin Converting Enzyme (ACE) is an important drug target for the treatment of hypertension, heart, kidney, and lung disease. Recently, a close and unique human ACE homologue termed ACE2 has been identified and found to be an interesting new cardiorenal disease target. With the recently resolved inhibitor-bound ACE2 crystal structure available, we have attempted a structure-based approach to identify novel potent and selective inhibitors. Computational approaches focus on pharmacophore-based virtual screening of large compound databases. Selectivity was ensured by initial screening for ACE inhibitors within an internal database and the Derwent World Drug Index, which could be reduced to zero false positives and 0.1% hit rate, respectively. An average hit reduction of 0.44% was achieved with a five feature hypothesis, searching approximately 3.8 million compounds from various commercial databases. Seventeen compounds were selected based on high fit values as well as diverse structure and subjected to experimental validation in a bioassay. We show that all compounds displayed an inhibitory effect on ACE2 activity, the six most promising candidates exhibiting IC50 values in the range of 62-179 microM.

Angiotensin-Converting Enzyme 2↗

PubMed2006

Non-stochastic quadratic fingerprints and LDA-based QSAR models in hit and lead generation through virtual screening: theoretical and experimental assessment of a promising method for the discovery of new antimalarial compounds.

In order to explore the ability of non-stochastic quadratic indices to encode chemical information in antimalarials, four quantitative models for the discrimination of compounds having this property were generated and statistically compared. Accuracies of 90.2% and 83.3% for the training and test sets, respectively, were observed for the best of all the models, which included non-stochastic quadratic fingerprints weighted with Pauling electronegativities. With a comparative purpose and as a second validation experiment, an exercise of virtual screening of 65 already-reported antimalarials was carried out. Finally, 17 new compounds were classified as either active/inactive ones and experimentally evaluated for their potential antimalarial properties on the ferriprotoporphyrin (FP) IX biocrystallization inhibition test (FBIT). The theoretical predictions were in agreement with the experimental results. In the assayed test compound C5 resulted more active than chloroquine. The current result illustrates the usefulness of the TOMOCOMD-CARDD strategy in rational antimalarial-drug design, at the time that it introduces a new family of organic compounds as starting point for the development of promising antimalarials.

Algorithms↗

PubMed2006

Protein structures in virtual screening: a case study with CDK2.

The influence of protein structure on the successful reproduction of known ligand poses by high-throughput docking programs is rarely discussed. Two commonly used programs, Glide and GOLD, were used to dock a set of CDK2 inhibitors of known bound pose into 20 different CDK2 structures. The numbers of docked poses that reproduced the known pose are reported. Depending on the program and protein structure, 0.3%-96.2% of the ligands docked with the correct pose. Although it is not possible to say that any one structure is "the best" for virtual screening, there are some structures that are clearly better than others. The main determinants of this are the volume of the binding site into which the ligands are docked and the exact orientation of the residues forming the binding site.

Binding Sites↗

PubMed2001

Detailed analysis of scoring functions for virtual screening.

We present a comprehensive study of the performance of fast scoring functions for library docking using the program FlexX as the docking engine. Four scoring functions, among them two recently developed knowledge-based potentials, are evaluated on seven target proteins whose binding sites represent a wide range of size, form, and polarity. The results of these calculations give valuable insight into strengths and weaknesses of current scoring functions. Furthermore, it is shown that a well-chosen combination of two of the tested scoring functions leads to a new, robust scoring scheme with superior performance in virtual screening.

Databases, Factual↗

PubMed2005

Integration of virtual screening with high-throughput flow cytometry to identify novel small molecule formylpeptide receptor antagonists.

The formylpeptide receptor (FPR) family of G-protein-coupled receptors contributes to the localization and activation of tissue-damaging leukocytes at sites of chronic inflammation. We developed a FPR homology model and pharmacophore (based on the bovine rhodopsin crystal structure and known FPR ligands, respectively) for in silico screening of approximately 480,000 drug-like small molecules. A subset of 4324 compounds that matched the pharmacophore was then physically screened with the HyperCyt flow cytometry platform in high-throughput, no-wash assays that directly measure human FPR binding, with samples (each approximately 2500 cells in 2 microl) analyzed at 40/min. From 52 confirmed hits (1.2% hit rate), we identified 30 potential lead compounds (inhibition constant, Ki= 1-32 microM) representing nine distinct chemical families. Four compounds in one family were weak partial agonists. All others were antagonists. This virtual screening approach improved the physical screening hit rate by 12-fold (versus 0.1% hit-rate in a random compound collection), providing an efficient process for identifying small molecule antagonists.

Amino Acid Sequence↗

PubMed2004

Comparative evaluation of eight docking tools for docking and virtual screening accuracy.

Eight docking programs (DOCK, FLEXX, FRED, GLIDE, GOLD, SLIDE, SURFLEX, and QXP) that can be used for either single-ligand docking or database screening have been compared for their propensity to recover the X-ray pose of 100 small-molecular-weight ligands, and for their capacity to discriminate known inhibitors of an enzyme (thymidine kinase) from randomly chosen "drug-like" molecules. Interestingly, both properties are found to be correlated, since the tools showing the best docking accuracy (GLIDE, GOLD, and SURFLEX) are also the most successful in ranking known inhibitors in a virtual screening experiment. Moreover, the current study pinpoints some physicochemical descriptors of either the ligand or its cognate protein-binding site that generally lead to docking/scoring inaccuracies.

Computer Graphics↗

PubMedDate not supplied

Similarity search profiling reveals effects of fingerprint scaling in virtual screening.

Fingerprint scaling is a method to increase the performance of similarity search calculations. It is based on the detection of bit patterns in keyed fingerprints that are signatures of specific compound classes. Application of scaling factors to consensus bits that are mostly set on emphasizes signature bit patterns during similarity searching and has been shown to improve search results for different fingerprints. Similarity search profiling has recently been introduced as a method to analyze similarity search calculations. Profiles separately monitor correctly identified hits and other detected database compounds as a function of similarity threshold values and make it possible to estimate whether virtual screening calculations can be successful or to evaluate why they fail. This similarity search profile technique has been applied here to study fingerprint scaling in detail and better understand effects that are responsible for its performance. In particular, we have focused on the qualitative and quantitative analysis of similarity search profiles under scaling conditions. Therefore, we have carried out systematic similarity search calculations for 23 biological activity classes under scaling conditions over a wide range of scaling factors in a compound database containing approximately 1.3 million molecules and monitored these calculations in similarity search profiles. Analysis of these profiles confirmed increases in hit rates as a consequence of scaling and revealed that scaling influences similarity search calculations in different ways. Based on scaled similarity search profiles, compound sets could be divided into different categories. In a number of cases, increases in search performance under scaling conditions were due to a more significant relative increase in correctly identified hits than detected false-positives. This was also consistent with the finding that preferred similarity threshold values increased due to fingerprint scaling, which was well illustrated by similarity search profiling.

Journal Article↗

PubMed2003

Virtual screening for submicromolar leads of tRNA-guanine transglycosylase based on a new unexpected binding mode detected by crystal structure analysis.

Eubacterial tRNA-guanine transglycosylase (TGT) is involved in the hypermodification of cognate tRNAs, leading to the exchange of G34 by preQ1 at the wobble position in the anticodon loop. Mutation of the tgt gene in Shigella flexneri results in a significant loss of pathogenicity of the bacterium due to inefficient translation of a virulence protein mRNA. Herein, we describe the discovery of a ligand with an unexpected binding mode. On the basis of this binding mode, three slightly deviating pharmacophore hypotheses have been derived. Virtual screening based on this composite pharmacophore model retrieved a set of potential TGT inhibitors belonging to several compound classes. All nine tested inhibitors being representatives of these classes showed activity in the micromolar range, two of them even in the submicromolar range.

Binding Sites↗