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PubMedDate not supplied

Impact of scoring functions on enrichment in docking-based virtual screening: an application study on renin inhibitors.

The docking program LigandFit/Cerius(2) has been used to perform shape-based virtual screening of databases against the aspartic protease renin, a target of determined three-dimensional structure. The protein structure was used in the induced fit binding conformation that occurs when renin is bound to the highly active renin inhibitor 1 (IC(50) = 2 nM). The scoring was calculated using several different scoring functions in order to get insight into the predictability of the magnitude of binding interactions. A database of 1000 diverse and druglike compounds, comprised of 990 members of a virtual database generated by using the iLib diverse software and 10 known active renin inhibitors, was docked flexibly and scored to determine appropriate scoring functions. All seven scoring functions used (LigScore1, LigScore2, PLP1, PLP2, JAIN, PMF, LUDI) were able to retrieve at least 50% of the active compounds within the first 20% (200 molecules) of the entire test database. A hit rate of 90% in the top 1.4% resulted using the quadruple consensus scoring of LigScore2, PLP1, PLP2, and JAIN. Additionally, a focused database was created with the iLib diverse software and used for the same procedure as the test database. Docking and scoring of the 990 focused compounds and the 10 known actives were performed. A hit rate of 100% in the top 8.4% resulted with use of the triple consensus scoring of PLP1, PLP2, and PMF. As expected, a ranking of the known active compounds within the focused database compared to the test database was observed. Adequate virtual screening conditions were derived empirically. They can be used for proximate docking and scoring application of compounds with putative renin inhibiting potency.

Ligands↗
PubMed2002

Virtual screening and fast automated docking methods.

Recent advances in high-throughput protein structure determination and in computational chemistry have refocused attention on virtual screening and fast automated docking methods. This review provides a brief introduction to the basic ideas and outlines computational tools currently used. We also provide several examples of where virtual screening has proved successful, highlighting the usefulness of the approach.

Combinatorial Chemistry Techniques↗
PubMed2004

Virtual screening in structure-based drug discovery.

Recent advances in structure determination and computational methods have encouraged the development of structure-based virtual screening. Here we survey progress in the field and review the most recent methods, validation experiments and real applications, including an in-house example of hit identification for the oncology target Hsp90. These results provide a basis for discussing the current state of structure-based virtual screening and to outline the developments that are expected to have a major impact in the near future.

Combinatorial Chemistry Techniques↗
PubMedDate not supplied

Virtual screening using protein-ligand docking: avoiding artificial enrichment.

This study addresses a number of topical issues around the use of protein-ligand docking in virtual screening. We show that, for the validation of such methods, it is key to use focused libraries (containing compounds with one-dimensional properties, similar to the actives), rather than "random" or "drug-like" libraries to test the actives against. We also show that, to obtain good enrichments, the docking program needs to produce reliable binding modes. We demonstrate how pharmacophores can be used to guide the dockings and improve enrichments, and we compare the performance of three consensus-ranking protocols against ranking based on individual scoring functions. Finally, we show that protein-ligand docking can be an effective aid in the screening for weak, fragment-like binders, which has rapidly become a popular strategy for hit identification. All results presented are based on carefully constructed virtual screening experiments against four targets, using the protein-ligand docking program GOLD.

Ligands↗
PubMed2003

Analysis and optimization of structure-based virtual screening protocols. (3). New methods and old problems in scoring function design.

Scoring function research remains a primary focus of current structure-based virtual screening (SVS) technology development. Here, we present an alternative method for scoring function design that attempts to combine crystallographic structural information with data derived from directly within SVS calculations. The technique utilizes a genetic algorithm (GA) to optimize functions based on binding property data derived from multiple virtual screening calculations. These calculations are undertaken on protein data bank (PDB) complex active sites using ligands of known binding mode in conjunction with "noise" compounds. The advantages of such an approach are that the function does not rely on assay data and that it can potentially use the "noise" binding data to recognize the sub-optimal docking interactions inherent in SVS calculations. Initial efforts in technique exploration using DOCK are presented, with comparisons made to existing DOCK scoring functions. An analysis of the problems inherent to scoring function development is also made, including issues in dataset creation and limitations in descriptor utility when viewed from the perspective of docking mode resolution. The future directions such studies might take are also discussed in detail.

Algorithms↗
PubMed2006

Discovering severe acute respiratory syndrome coronavirus 3CL protease inhibitors: virtual screening, surface plasmon resonance, and fluorescence resonance energy transfer assays.

An integrated system has been developed for discovering potent inhibitors of severe acute respiratory syndrome coronavirus 3C-like protease (SARS-CoV 3CL(pro)) by virtual screening correlating with surface plasmon resonance (SPR) and fluorescence resonance energy transfer (FRET) technologies-based assays. The authors screened 81,287 small molecular compounds against SPECS database by virtual screening; 256 compounds were subsequently selected for biological evaluation. Through SPR technology-based assay, 52 from these 256 compounds were discovered to show binding to SARS-CoV 3CL(pro). The enzymatic inhibition activities of these 52 SARS-CoV 3CL(pro) binders were further applied to FRET-based assay, and IC(50) values were determined. Based on this integrated assay platform, 8 new SARS-CoV 3CL(pro) inhibitors were discovered. The fact that the obtained IC(50) values for the inhibitors are in good accordance with the discovered dissociation equilibrium constants (K(D)s) assayed by SPR implied the reliability of this platform. Our current work is hoped to supply a powerful approach in the discovery of potent SARS-CoV 3CL(pro) inhibitors, and the determined inhibitors could be used as possible lead compounds for further research.

Binding Sites↗
PubMed2006

Finding a needle in a haystack: development of a combinatorial virtual screening approach for identifying high specificity heparin/heparan sulfate sequence(s).

We describe a combinatorial virtual screening approach for predicting high specificity heparin/heparan sulfate sequences using the well-studied antithrombin-heparin interaction as a test case. Heparan sulfate hexasaccharides were simulated in the 'average backbone' conformation, wherein the inter-glycosidic bond angles were held constant at the mean of the known solution values, irrespective of their sequence. Molecular docking utilized GOLD with restrained inter-glycosidic torsions and intra-ring conformations, but flexible substituents at the 2-, 3-, and 6-positions and explicit incorporation of conformational variability of the iduronate residues. The approach reproduces the binding geometry of the sequence-specific heparin pentasaccharide to within 2.5 A. Screening of a combinatorial virtual library of 6,859 heparin hexasaccharides using a dual filter strategy, in which predicted antithrombin affinity was the first filter and self-consistency of docking was the second, resulted in only 10 sequences. Of these, nine were found to bind antithrombin in a manner identical to the natural pentasaccharide, while a novel hexasaccharide bound the inhibitor in a unique but dramatically different geometry and orientation. This work presents the first approach on combinatorial library screening for heparin/heparan sulfate GAGs to determine high specificity sequences and opens up huge opportunities to investigate numerous other physiologically relevant GAG-protein interactions.

Antithrombins↗
PubMed2005

Interaction profiles of protein kinase-inhibitor complexes and their application to virtual screening.

A major challenge facing structure-based drug discovery efforts is how to leverage the massive amount of experimental (X-ray and NMR) and virtual structural information generated from drug discovery projects. Many important drug targets have large numbers of protein-inhibitor complexes, necessitating tools to compare and contrast their similarities and differences. This information would be valuable for understanding potency and selectivity of inhibitors and could be used to define target constraints to assist virtual screening. We describe a profile-based approach that enables us to capture the conservation of interactions between a set of protein-ligand receptor complexes. The use of profiles provides a sensitive means to compare multiple inhibitors binding to a drug target. We demonstrate the utility of profile-based analysis of small molecule complexes from the protein-kinase family to identify similarities and differences in binding of ATP, p38, and CDK2 compounds to kinases and how these profiles can be applied to differentiate the selectivity of these inhibitors. Importantly, our virtual screening results demonstrate superior enrichment of kinase inhibitors using profile-based methods relative to traditional scoring functions. Interaction-based analysis should provide a valuable tool for understanding inhibitor binding to other important drug targets.

Adenosine Triphosphate↗
PubMed2004

Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures.

This paper reports a detailed comparison of a range of different types of 2D fingerprints when used for similarity-based virtual screening with multiple reference structures. Experiments with the MDL Drug Data Report database demonstrate the effectiveness of fingerprints that encode circular substructure descriptors generated using the Morgan algorithm. These fingerprints are notably more effective than fingerprints based on a fragment dictionary, on hashing and on topological pharmacophores. The combination of these fingerprints with data fusion based on similarity scores provides both an effective and an efficient approach to virtual screening in lead-discovery programmes.

Algorithms↗
PubMed2002

Effectiveness of graph-based and fingerprint-based similarity measures for virtual screening of 2D chemical structure databases.

This paper reports an evaluation of both graph-based and fingerprint-based measures of structural similarity, when used for virtual screening of sets of 2D molecules drawn from the MDDR and ID Alert databases. The graph-based measures employ a new maximum common edge subgraph isomorphism algorithm, called RASCAL, with several similarity coefficients described previously for quantifying the similarity between pairs of graphs. The effectiveness of these graph-based searches is compared with that resulting from similarity searches using BCI, Daylight and Unity 2D fingerprints. Our results suggest that graph-based approaches provide an effective complement to existing fingerprint-based approaches to virtual screening.

Algorithms↗
PubMed2006

Discovery of a novel family of SARS-CoV protease inhibitors by virtual screening and 3D-QSAR studies.

The severe acute respiratory syndrome-associated coronavirus (SARS-CoV) 3C-like protease (3CL(pro) or M(pro)) is an attractive target for the development of anti-SARS drugs because of its crucial role in the viral life cycle. In this study, a compound database was screened by the structure-based virtual screening approach to identify initial hits as inhibitors of SARS-CoV 3CL(pro). Out of the 59,363 compounds docked, 93 were selected for the inhibition assay, and 21 showed inhibition against SARS-CoV 3CL(pro) (IC(50) <or= 30 microM), with three of them having common substructures. Furthermore, a search for analogues with common substructure in the Maybridge, ChemBridge, and SPECS_SC databases led to the identification of another 25 compounds that exhibited inhibition against SARS-CoV 3CL(pro) (IC(50) = 3-1,000 microM). These compounds, 28 in total, were subjected to 3D-QSAR studies to elucidate the pharmacophore of SARS-CoV 3CL(pro).

Binding Sites↗
PubMed2003

Structure-based discovery of potassium channel blockers from natural products: virtual screening and electrophysiological assay testing.

Potassium ion (K(+)) channels are attractive targets for rational drug design. Based upon a three-dimensional model of the eukaryotic K(+) channels, the docking virtual screening approach was employed to search the China Natural Products Database. Compounds were ranked according to the relative binding energy, favorable shape complementarity, and potential of forming hydrogen bonds with the K(+) channel. Four candidate compounds found by virtual screening were investigated by using the whole-cell voltage-clamp recording in rat dissociated hippocampal neurons. When applied extracellularly, compound 1 markedly depressed the delayed rectifier K(+) current (I(K)) and fast transient K(+) current (I(A)), whereas compounds 2, 3, and 4 exerted a more potent and selective inhibitory effect on I(K). Intracellular application of the four compounds had no effect on both the K(+) currents.

Animals↗
PubMed2000

Library design and virtual screening using multiple 4-point pharmacophore fingerprints.

The use of multiple potential 4-point three-dimensional (3-D) pharmacophores for the design of combinatorial libraries and for virtual screening is discussed. These 3-D pharmacophoric fingerprints can be calculated from both ligands and complementary to a protein site, with a common frame of reference, and can be very rapidly searched to identify common and different 4-point pharmacophoric shapes in compounds and protein sites. A new extension to the method for structure-based design is reported that uses the shape of the target site as an additional constraint. This enables the docking process, for example in library design and virtual screening, to be quantified in terms of how many, and which, pharmacophoric hypotheses can be matched by a compound or a library of compounds.

Combinatorial Chemistry Techniques↗
PubMed2005

Design of ligand binding to an engineered protein cavity using virtual screening and thermal up-shift evaluation.

Proteins could be used to carry and deliver small compounds. As a tool for designing ligand binding sites in protein cores, a three-step virtual screening method is presented that has been optimised using existing data on T4 lysozyme complexes and tested in a newly engineered cavity in flavodoxin. The method can pinpoint, in large databases, ligands of specific protein cavities. In the first step, physico-chemical filters are used to screen the library and discard a majority of compounds. In the second step, a flexible, fast docking procedure is used to score and select a smaller number of compounds as potential binders. In the third step, a finer method is used to dock promising molecules of the hit list into the protein cavity, and an optimised free energy function allows discarding the few false positives by calculating the affinity of the modelled complexes. To demonstrate the portability of the method, several cavities have been designed and engineered in the flavodoxin from Anabaena PCC 7119, and the W66F/L44A double mutant has been selected as a suitable host protein. The NCI database has then been screened for potential binders, and the binding to the engineered cavity of five promising compounds and three tentative non-binders has been experimentally tested by thermal up-shift assays and spectroscopic titrations. The five tentative binders (some apolar and some polar), unlike the three tentative non-binders, are shown to bind to the host mutant and, importantly, not to bind to the wild type protein. The three-step virtual screening method developed can thus be used to identify ligands of buried protein cavities. We anticipate that the method could also be used, in a reverse manner, to identify natural or engineerable protein cavities for the hosting of ligands of interest.

Bacteriophage T4↗
PubMedDate not supplied

A knowledge-based weighting approach to ligand-based virtual screening.

On the basis of the recently introduced reduced graph concept of ErG (extending reduced graphs), a straightforward weighting approach to include additional (e.g., structural or SAR) knowledge into similarity searching procedures for virtual screening (wErG) is proposed. This simple procedure is exemplified with three data sets, for which interaction patterns available from X-ray structures of native or peptidomimetic ligands with their target protein are used to significantly improve retrieval rates of known actives from the MDL Drug Report database. The results are compared to those of other virtual screening techniques such as Daylight fingerprints, FTrees, UNITY, and various FlexX docking protocols. Here, it is shown that wErG exhibits a very good and stable performance independent of the target structure. On the basis of this (and the fact that ErG retrieves structurally more dissimilar compounds due to its potential to perform scaffold-hopping), the combination of wErG and FlexX is successfully explored. Overall, wErG is not only an easily applicable weighting procedure that efficiently identifies actives in large data sets but it is also straightforward to understand for both medicinal and computational chemists and can, therefore, be driven by several aspects of project-related knowledge (e.g., X-ray, NMR, SAR, and site-directed mutagenesis) in a very early stage of the hit identification process.

Computational Biology↗
PubMed2007

Evaluation of machine-learning methods for ligand-based virtual screening.

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed.

Artificial Intelligence↗
PubMedDate not supplied

Strategy for discovering chemical inhibitors of human cyclophilin a: focused library design, virtual screening, chemical synthesis and bioassay.

The discovery of cyclophilin A (CypA) inhibitor is now of special interest in the treatment of immunological disorders. In this work, using a strategy integrating focused combinatorial library design, virtual screening, chemical synthesis, and bioassay, a series of novel small molecular CypA inhibitors have been discovered. First, using the fragments taken from our previously discovered CypA inhibitors (Bioorg. Med. Chem. 2006, 14, 2209-2224) as building blocks, we designed a focused combinatorial library containing 255 molecules employing the LD1.0 program (J. Comb. Chem. 2005, 7, 398-406) developed by us. Sixteen compounds (1a-e, 2a-b, 3a-b, and 4a-g) were selected by using virtual screening against the X-ray crystal structure of CypA as well as druglike analysis for further synthesis and bioassay. All these sixteen molecules are CypA binders with binding affinities (K(D) values) ranging from 0.076 to 41.0 microM, and five of them (4a, 4c, and 4e-g) are potent CypA inhibitors with PPIase inhibitory activities (IC(50) values) of 0.25-6.43 microM. The hit rates for binders and inhibitors are as high as 100% and 31.25%, respectively. Remarkably, both the binding affinity and inhibitory activity of the most potent compound increase approximately 10 times than that of the most active compound discovered previously. The high hit rate and the high potency of the new CypA inhibitors demonstrated the efficiency of the strategy for focused library design and screening. In addition, the novel chemical entities reported in this study could be leads for discovering new therapies against the CypA pathway.

Binding Sites↗
PubMed2006

Identification of previously unrecognized antiestrogenic chemicals using a novel virtual screening approach.

The physiological roles of estrogen in sexual differentiation and development, female and male reproductive processes, and bone health are complex and diverse. Numerous natural and synthetic chemical compounds, commonly known as endocrine disrupting chemicals (EDCs), have been shown to alter the physiological effects of estrogen in humans and wildlife. As such, these EDCs may cause unanticipated and even undesirable effects. Large-scale in vitro and in vivo screening of chemicals to assess their estrogenic activity would demand a prodigious investment of time, labor, and money and would require animal testing on an unprecedented scale. Approaches in silico are increasingly recognized as playing a vital role in screening and prioritizing chemicals to extend limited resources available for experimental testing. Here, we evaluated a multistep procedure that is suitable for in silico (virtual) screening of large chemical databases to identify compounds exhibiting estrogenic activity. This procedure incorporates Shape Signatures, a novel computational tool that rapidly compares molecules on the basis of similarity in shape, polarity, and other bio-relevant properties. Using 4-hydroxy tamoxifen (4-OH TAM) and diethylstilbestrol (DES) as input queries, we employed this scheme to search a sample database of approximately 200,000 commercially available organic chemicals for matches (hits). Of the eight compounds identified computationally as potentially (anti)estrogenic, biological evaluation confirmed two as heretofore unknown estrogen antagonists. Subsequent radioligand binding assays confirmed that two of these three compounds exhibit antiestrogenic activities comparable to 4-OH TAM. Molecular modeling studies of these ligands docked inside the binding pocket of estrogen receptor alpha (ERalpha) elucidated key ligand-receptor interactions that corroborate these experimental findings. The present study demonstrates the utility of our computational scheme for this and related applications in drug discovery, predictive toxicology, and virtual screening.

Binding, Competitive↗