Discovery of novel cathepsin S inhibitors by pharmacophore-based virtual high-throughput screening

The cysteine protease cathepsin S (CatS) is involved in the pathogenesis of autoimmune disorders, atherosclerosis, and obesity. Therefore, it represents a promising pharmacological target for drug development. We generated ligand-based and structure-based pharmacophore models for noncovalent and covalent CatS inhibitors to perform virtual high-throughput screening of chemical databases in order to discover novel scaffolds for CatS inhibitors. An in vitro evaluation of the resulting 15 structures revealed seven CatS inhibitors with kinetic constants in the low micromolar range. These compounds can be subjected to further chemical modifications to obtain drugs for the treatment of autoimmune disorders and atherosclerosis.     

Receptor-based virtual ligand screening for the identification of novel CDC25 phosphatase inhibitors

CDC25 phosphatases play critical roles in cell cycle regulation and are attractive targets for anticancer therapies. Several small non-peptide molecules are known to inhibit CDC25, but many of them appear to form a covalent bond with the enzyme or act through oxidation of the thiolate group of the catalytic cysteine. Structure-based virtual ligand screening computations were performed with FRED, Surflex, and LigandFit, a compound collection of over 310,000 druglike molecules and the crystal structure of CDC25B in order to identify novel classes of ligands. In vitro experiments carried out on a selected list of 1500 molecules led to the discovery of 99 compounds able to inhibit CDC25B activity at 100 microM. Further docking computations were applied, allowing us to propose a binding mode for the most potent molecule (IC50 = 13 microM). Our best compounds represent promising new classes of CDC25 inhibitors that also exhibit antiproliferative properties.     

Comparison of ranking methods for virtual screening in lead-discovery programs

This paper discusses the use of several rank-based virtual screening methods for prioritizing compounds in lead-discovery programs, given a training set for which both structural and bioactivity data are available. Structures from the NCI AIDS data set and from the Syngenta corporate database were represented by two types of fragment bit-string and by sets of high-level molecular features. These representations were processed using binary kernel discrimination, similarity searching, substructural analysis, support vector machine, and trend vector analysis, with the effectiveness of the methods being judged by the extent to which active test set molecules were clustered toward the top of the resultant rankings. The binary kernel discrimination approach yielded consistently superior rankings and would appear to have considerable potential for chemical screening applications.     

Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures

Fingerprint-based similarity searching is widely used for virtual screening when only a single bioactive reference structure is available. This paper reviews three distinct ways of carrying out such searches when multiple bioactive reference structures are available: merging the individual fingerprints into a single combined fingerprint; applying data fusion to the similarity rankings resulting from individual similarity searches; and approximations to substructural analysis. Extended searches on the MDL Drug Data Report database suggest that fusing similarity scores is the most effective general approach, with the best individual results coming from the binary kernel discrimination technique.     

Identification of selective inhibitors for the glycosyltransferase MurG via high-throughput screening

Nucleotide-glycosyltransferases (NDP-Gtfs) play key roles in a wide range of biological processes. It is difficult to probe the roles of individual glycosyltransferases or their products because, with few exceptions, selective glycosyltransferase inhibitors do not exist. Here, we investigate a high-throughput approach to identify glycosyltransferase inhibitors based on a fluorescent donor displacement assay. We have applied the screen to E. coli MurG, an enzyme that is both a potential antibiotic target and a paradigm for a large family of glycosyltransferases. We show that the compounds identified in the donor-displacement screen of MurG are selective for MurG over other enzymes that use similar or identical substrates, including structurally related enzymes. The donor displacement assay described here should be adaptable to many other NDP-Gtfs and represents a new strategy to identify selective NDP-Gtf inhibitors.     

Toward the discovery of functional transthyretin amyloid inhibitors: application of virtual screening methods

Inhibition of amyloid fibril formation by stabilization of the native form of the protein transthyretin (TTR) is a viable approach for the treatment of familial amyloid polyneuropathy that has been gaining momentum in the field of amyloid research. The TTR stabilizer molecules discovered to date have shown efficacy at inhibiting fibrilization in vitro but display impairing issues of solubility, affinity for TTR in the blood plasma and/or adverse effects. In this study we present a benchmark of four protein- and ligand-based virtual screening (VS) methods for identifying novel TTR stabilizers: (i) two-dimensional (2D) similarity searches with chemical hashed, pharmacophore, and UNITY fingerprints, (ii) 3D searches based on shape, chemical, and electrostatic similarity, (iii) LigMatch, a new ligand-based method which uses multiple templates and combines 3D geometric hashing with a 2D preselection process, and (iv) molecular docking to consensus X-ray crystal structures of TTR. We illustrate the potential of the best-performing VS protocols to retrieve promising new leads by ranking a tailored library of 2.3 million commercially available compounds. Our predictions show that the top-scoring molecules possess distinctive features from the known TTR binders, holding better solubility, fraction of halogen atoms, and binding affinity profiles. To the best of our knowledge, this is the first attempt to rationalize the utilization of a large battery of in silico screening techniques toward the identification of a new generation of TTR amyloid inhibitors.     

Accelerating high-throughput virtual screening through molecular pool-based active learning

Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in excess of 10⁸ molecules), so too do the resources necessary to conduct exhaustive virtual screening campaigns on these libraries. However, Bayesian optimization techniques, previously employed in other scientific discovery problems, can aid in their exploration: a surrogate structure–property relationship model trained on the predicted affinities of a subset of the library can be applied to the remaining library members, allowing the least promising compounds to be excluded from evaluation. In this study, we explore the application of these techniques to computational docking datasets and assess the impact of surrogate model architecture, acquisition function, and acquisition batch size on optimization performance. We observe significant reductions in computational costs; for example, using a directed-message passing neural network we can identify 94.8% or 89.3% of the top-50 000 ligands in a 100M member library after testing only 2.4% of candidate ligands using an upper confidence bound or greedy acquisition strategy, respectively. Such model-guided searches mitigate the increasing computational costs of screening increasingly large virtual libraries and can accelerate high-throughput virtual screening campaigns with applications beyond docking.

Bayesian optimization can accelerate structure-based virtual screening campaigns by minimizing the total number of simulations performed while still identifying the vast majority of computational hits.     

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.     

Comparison of topological, shape, and docking methods in virtual screening

Virtual screening benchmarking studies were carried out on 11 targets to evaluate the performance of three commonly used approaches: 2D ligand similarity (Daylight, TOPOSIM), 3D ligand similarity (SQW, ROCS), and protein structure-based docking (FLOG, FRED, Glide). Active and decoy compound sets were assembled from both the MDDR and the Merck compound databases. Averaged over multiple targets, ligand-based methods outperformed docking algorithms. This was true for 3D ligand-based methods only when chemical typing was included. Using mean enrichment factor as a performance metric, Glide appears to be the best docking method among the three with FRED a close second. Results for all virtual screening methods are database dependent and can vary greatly for particular targets.     

Hit identification against peptidyl-prolyl isomerase of Theileria annulata by combined virtual high-throughput screening and molecular dynamics simulation approach

Theileria annulata secretes peptidyl prolyl isomerase enzyme (TaPIN1) to manipulate the host cell oncogenic signaling pathway by disrupting the tumor suppressor F-box and WD repeat domain-containing 7 (FBW7) protein level leading to an increased level of c-Jun proto-oncogene. Buparvaquone is a hydroxynaphthoquinone anti-theilerial drug and has been used to treat theileriosis. However, TaPIN1 contains the A53 P mutation that causes drug resistance. In this study, potential TaPIN1 inhibitors were investigated using a library of naphthoquinone derivatives. Comparative models of mutant (m) and wild type (wt) TaPIN1 were predicted and energy minimization was followed by structure validation. A naphthoquinone (hydroxynaphthalene-1,2-dione, hydroxynaphthalene-1,4-dione) and hydroxynaphthalene-2,3-dione library was screened by Schrödinger Glide HTVS, SP and XP docking methodologies and the docked compounds were ranked by the Glide XP scoring function. The two highest ranked docked compounds Compound 1 (4-hydroxy-3-[3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxynaphthalene-1,2-dione) and Compound 2 (6-acetyl-1,4,5,7,8-pentahydroxynaphthalene-2,3-dione) were used for further molecular dynamics (MD) simulation studies. The MD results showed that ligand Compound 1 was located in the active site of both mTaPIN1 and wtTaPIN1 and could be proposed as a potential inhibitor by acting as a substrate antagonist. However, ligand Compound 2 was displaced away from the binding pocket of wtTaPIN1 but was located near the active site binding pocket of mTaPIN1 suggesting that could be selectively evaluated as a potential inhibitor against the mTaPIN1. Compound 1 and Compound 2 ligands are potential inhibitors but Compound 2 is suggested as a better inhibitor for mTaPIN1. These ligands could also further evaluated as potential inhibitors against human peptidyl prolyl isomerase which causes cancer in humans by using the same mechanism as TaPIN1.     

Fluorescence resonance energy transfer assay for high-throughput screening of ADAMTS1 inhibitors

A disintegrin and metalloprotease with thrombospondin type I motifs-1 (ADAMTS1) plays a crucial role in inflammatory joint diseases and its inhibitors are potential candidates for anti-arthritis drugs. For the purposes of drug discovery, we reported the development and validation of fluorescence resonance energy transfer (FRET) assay for high-throughput screening (HTS) of the ADAMTS1 inhibitors. A FRET substrate was designed for a quantitative assay of ADAMTS1 activity and enzyme kinetics studies. The assay was developed into a 50-μL, 384-well assay format for high throughput screening of ADAMTS1 inhibitors with an overall Z' factor of 0.89. ADAMTS1 inhibitors were screened against a diverse library of 40,960 total compounds with the established HTS system. Four structurally related hits, naturally occurring compounds, kuwanon P, kuwanon X, albafuran C and mulberrofuran J, extracted from the Chinese herb Morus alba L., were identified for further investigation. The results suggest that this FRET assay is an excellent tool, not only for measurement of ADAMTS1 activity but also for discovery of novel ADAMTS1 inhibitors with HTS.     

Kinetic assay for high-throughput screening of in vitro transthyretin amyloid fibrillogenesis inhibitors

Stabilization of tetrameric transthyretin (TTR) by binding of small ligands is a current strategy aimed at inhibiting amyloid fibrillogenesis in transthyretin-associated pathologies, such as senile systemic amyloidosis (SSA) and familial amyloidotic polyneuropathy (FAP). A kinetic assay is developed for rapid evaluation of compounds as potential in vitro inhibitors in a high-throughput screening format. It is based on monitoring the time-dependent increase of absorbance due to turbidity occurring by acid-induced protein aggregation. The method uses the highly amyloidogenic Y78F mutant of human transthyretin (heterogously expressed in Escherichia coli cells). Initial rates of protein aggregation at different inhibitor concentrations follow a monoexponential dose-response curve from which inhibition parameters are calculated. For the assay development, thyroid hormones and nonsteroidal antiinflamatory drugs were chosen among other reference compounds. Some of them are already known to be in vitro inhibitors of TTR amyloidogenesis. Analysis time is optimized to last 1.5 h, and the method is implemented in microtiter plates for screening of libraries of potential fibrillogenesis inhibitors.     

The identification of new ATAD2 bromodomain inhibitors: the application of combined ligand and structure-based virtual screening

Ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) approaches were used to identify new inhibitors for ATAD2 bromodomain. The LBVS approach was used to search 23,129,083 clean compounds to identify compounds similar to an active compound with reported pIC₅₀ equal to 7.2. Based on LBVS results, 19 compounds were selected. To perform SBVS, by applying nine filters on 23,129,083 clean compounds, 1,057,060 compounds were selected. After performing SBVS on these selected compounds with idock software, 16 compounds with the lowest binding energies were selected. More accurate molecular docking analysis was performed on these 35 selected compounds by using iGEMDOCK software and six of them with the lowest binding energies were selected as hit compounds. These compounds were zinc36647229, zinc77969074, zinc13637358, zinc77971540, zinc12991296 and zinc19374204.     

Prediction of new chromene-based inhibitors of tubulin using structure-based virtual screening and molecular dynamics simulation methods

Multidrug resistance (MDR) is one of the serious problems in cancer research that causes failure in chemotherapy. Chromene-based compounds have been proven to be the novel anti-MDR agents for inhibiting proliferation of tumor cells through tubulin polymerization inhibition of by binding at the colchicine binding site. In this study, we screened a chromene-based database of small molecules using physicochemical, ADMET properties and molecular docking to identify potential hit compounds. In order to validate our hit compounds, molecular dynamics simulations and related analysis were carried out and the results suggest that our hit compounds (PubChem CIDs: 16814409, 17594471, 57367244 and 69899719) can prove to be potential inhibitors of tubulin. The in silico results show that the present hits, like colchicine, effectively suppressed the dynamic instability of microtubules and induced microtubule-depolymerization and cell cycle arrest.     

Identification of exosite-targeting inhibitors of anthrax lethal factor by high-throughput screening

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Highlights? Development of an assay for LF protease using a full-length protein substrate ? Lichen depsidones identified as exosite-targeting LF inhibitors ? Exosite inhibitors protect macrophages from LF cytotoxicity     

Discovery of novel human acrosin inhibitors by virtual screening

Human acrosin is an attractive target for the discovery of male contraceptive drugs. For the first time, structure-based drug design was applied to discover structurally diverse human acrosin inhibitors. A parallel virtual screening strategy in combination with pharmacophore-based and docking-based techniques was used to screen the SPECS database. From 16 compounds selected by virtual screening, a total of 10 compounds were found to be human acrosin inhibitors. Compound 2 was found to be the most potent hit (IC₅₀ = 14 μM) and its binding mode was investigated by molecular dynamics simulations. The hit interacted with human acrosin mainly through hydrophobic and hydrogen-bonding interactions, which provided a good starting structure for further optimization studies.