Comparative analysis of pharmacophore screening tools

The pharmacophore concept is of central importance in computer-aided drug design (CADD) mainly because of its successful application in medicinal chemistry and, in particular, high-throughput virtual screening (HTVS). The simplicity of the pharmacophore definition enables the complexity of molecular interactions between ligand and receptor to be reduced to a handful set of features. With many pharmacophore screening softwares available, it is of the utmost interest to explore the behavior of these tools when applied to different biological systems. In this work, we present a comparative analysis of eight pharmacophore screening algorithms (Catalyst, Unity, LigandScout, Phase, Pharao, MOE, Pharmer, and POT) for their use in typical HTVS campaigns against four different biological targets by using default settings. The results herein presented show how the performance of each pharmacophore screening tool might be specifically related to factors such as the characteristics of the binding pocket, the use of specific pharmacophore features, and the use of these techniques in specific steps/contexts of the drug discovery pipeline. Algorithms with rmsd-based scoring functions are able to predict more compound poses correctly as overlay-based scoring functions. However, the ratio of correctly predicted compound poses versus incorrectly predicted poses is better for overlay-based scoring functions that also ensure better performances in compound library enrichments. While the ensemble of these observations can be used to choose the most appropriate class of algorithm for specific virtual screening projects, we remarked that pharmacophore algorithms are often equally good, and in this respect, we also analyzed how pharmacophore algorithms can be combined together in order to increase the success of hit compound identification. This study provides a valuable benchmark set for further developments in the field of pharmacophore search algorithms, e.g., by using pose predictions and compound library enrichment criteria.     

Identification of Novel β3-Adrenoceptor Agonists Using Energetic Analysis, Structure Based Pharmacophores and Virtual Screening

β3 Adrenergic receptor (β3-AR), is a potential therapeutic target for the treatment of type II diabetes and obesity. We report the identification of novel compounds as β3-AR agonists by integrating different approaches of energetic analysis, structure based pharmacophore designing and virtual screening. In a step wise filtering protocol, structure based virtual screening of 2,33,450 compounds was done. These molecules were docked into the active site of the receptor utilizing three levels of accuracy; ligands passing the HTVS (high throughput virtual screening) step were subsequently analyzed in Glide SP (Standard Precision) and finally in Glide XP (Extra Precision) to estimate the receptor ligand binding affinities. In the second step a total of 300 pharmacophore hypotheses were generated from a set of known and diverse β3-AR agonists. The best hypothesis showed six features: three hydrogen bond acceptors, one positively charged group, and two aromatic rings. To cross validate, pharmacophore filtering was done on the set of shortlisted compounds from structure based VS (virtual screening). The different screening techniques employed were validated using enrichment factor calculations. The energetic based Pharmacophore performed fairly well at distinguishing active from the inactive compounds and yielded a greater diversity of active molecules whereas the number of actives retrieved in the case of structure based screening was the highest.     

Predicting hiCE inhibitors based upon pharmacophore models derived from the receptor and its ligands

Human intestinal carboxyl esterase (hiCE) is a drug target for ameliorating irinotecan-induced diarrhea. By reducing irinotecan-induced diarrhea, hiCE inhibitors can improve the anti-cancer efficacy of irinotecan. To find effective hiCE inhibitors, a new virtual screening protocol that combines pharmacophore models derived from the hiCE structure and its ligands has been proposed. The hiCE structure has been constructed through homology techniques using hCES1’s crystal structure. The hiCE structure was optimized via molecular dynamics simulations with the most known active hiCE inhibitors docked into the structure. An optimized pharmacophore, derived from the receptor, was then generated. A ligand-based pharmacophore was also generated from a larger set of known hiCE inhibitors. The final hiCE inhibitor predictions were based upon the virtual screening hits from both ligand-based and receptor-based pharmacophore models. The hit rates from the ligand-based and receptor-based pharmacophore models are 88% and 86%, respectively. The final hit rate is 94%. The two models are highly consistent with one another (85%). This proves that both models are reliable.     

Comparative virtual screening and novelty detection for NMDA-GlycineB antagonists

Identification of novel compound classes for a drug target is a challenging task for cheminformatics and drug design when considerable research has already been undertaken and many potent lead structures have been identified, which leaves limited unclaimed chemical space for innovation. We validated and successfully applied different state-of-the-art techniques for virtual screening (Bayesian machine learning, automated molecular docking, pharmacophore search, pharmacophore QSAR and shape analysis) of 4.6 million unique and readily available chemical structures to identify promising new and competitive antagonists of the strychnine-insensitive Glycine binding site (Glycine_B site) of the NMDA receptor. The novelty of the identified virtual hits was assessed by scaffold analysis, putting a strong emphasis on novelty detection. The resulting hits were tested in vitro and several novel, active compounds were identified. While the majority of the computational methods tested were able to partially discriminate actives from structurally similar decoy molecules, the methods differed substantially in their prospective applicability in terms of novelty detection. The results demonstrate that although there is no single best computational method, it is most worthwhile to follow this concept of focused compound library design and screening, as there still can new bioactive compounds be found that possess hitherto unexplored scaffolds and interesting variations of known chemotypes.     

Ensemble pharmacophore meets ensemble docking: a novel screening strategy for the identification of RIPK1 inhibitors

Programmed cell death has been a fascinating area of research since it throws new challenges and questions in spite of the tremendous ongoing research in this field. Recently, necroptosis, a programmed form of necrotic cell death, has been implicated in many diseases including neurological disorders. Receptor interacting serine/threonine protein kinase 1 (RIPK1) is an important regulatory protein involved in the necroptosis and inhibition of this protein is essential to stop necroptotic process and eventually cell death. Current structure-based virtual screening methods involve a wide range of strategies and recently, considering the multiple protein structures for pharmacophore extraction has been emphasized as a way to improve the outcome. However, using the pharmacophoric information completely during docking is very important. Further, in such methods, using the appropriate protein structures for docking is desirable. If not, potential compound hits, obtained through pharmacophore-based screening, may not have correct ranks and scores after docking. Therefore, a comprehensive integration of different ensemble methods is essential, which may provide better virtual screening results. In this study, dual ensemble screening, a novel computational strategy was used to identify diverse and potent inhibitors against RIPK1. All the pharmacophore features present in the binding site were captured using both the apo and holo protein structures and an ensemble pharmacophore was built by combining these features. This ensemble pharmacophore was employed in pharmacophore-based screening of ZINC database. The compound hits, thus obtained, were subjected to ensemble docking. The leads acquired through docking were further validated through feature evaluation and molecular dynamics simulation.     

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.     

Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling

Database screening using receptor-based pharmacophores is a computer-aided drug design technique that uses the structure of the target molecule (i.e. protein) to identify novel ligands that may bind to the target. Typically receptor-based pharmacophore modeling methods only consider a single or limited number of receptor conformations and map out the favorable binding patterns in vacuum or with a limited representation of the aqueous solvent environment, such that they may suffer from neglect of protein flexibility and desolvation effects. Site-Identification by Ligand Competitive Saturation (SILCS) is an approach that takes into account these, as well as other, properties to determine 3-dimensional maps of the functional group-binding patterns on a target receptor (i.e. FragMaps). In this study, a method to use the FragMaps to automatically generate receptor-based pharmacophore models is presented. It converts the FragMaps into SILCS pharmacophore features including aromatic, aliphatic, hydrogen-bond donor and acceptor chemical functionalities. The method generates multiple pharmacophore hypotheses that are then quantitatively ranked using SILCS grid free energies. The pharmacophore model generation protocol is validated using three different protein targets, including using the resulting models in virtual screening. Improved performance and efficiency of the SILCS derived pharmacophore models as compared to published docking studies, as well as a recently developed receptor-based pharmacophore modeling method is shown, indicating the potential utility of the approach in rational drug design.     

基于虚拟筛选策略发现新型潜在JAK3小分子抑制剂

利用已知活性的分子采用基于配体的策略构建药效团模型,通过基于类药规则、药效团模型、多种精度的分子对接算法、MM/GBSA结合能预测以及ADMET筛选手段对含约250万个分子的数据库进行虚拟筛选。发现5种JAK3抑制剂的新型骨架,其中6个以1-苯基咪唑烷-2-酮为骨架的分子在与JAK3激酶的结合能以及分子的ADMET性质评价方面均表现优异,具备高JAK3抑制剂潜力,被认为是虚拟筛选的命中分子。     

Parallel screening and activity profiling with HIV protease inhibitor pharmacophore models

Parallel Screening has been introduced as an in silico method to predict the potential biological activities of compounds by screening them with a multitude of pharmacophore models. This study presents an early application example employing a Pipeline Pilot-based screening platform for automatic large-scale virtual activity profiling. An extensive set of HIV protease inhibitor pharmacophore models was used to screen a selection of active and inactive compounds. Furthermore, we aimed to address the usually critically eyed point, whether it is possible in a parallel screening system to differentiate between similar molecules/molecules acting on closely related proteins, and therefore we incorporated a collection of other protease inhibitors including aspartic protease inhibitors. The results of the screening experiments show a clear trend toward most extensive retrieval of known active ligands, followed by the general protease inhibitors and lowest recovery of inactive compounds.     

Identification of potential Tpx inhibitors against pathogen-host interactions

Yersinia organisms cause many infectious diseases by invading human cells and delivering their virulence factors via the type three secretion system (T3SS). One alternative strategy in the fight against these pathogenic organisms is to interfere with their T3SS. Previous studies demonstrated that thiol peroxidase, Tpx is functional in the assembly of T3SS and its inhibition by salicylidene acylhydrazides prevents the secretion of pathogenic effectors. In this study, the aim was to identify potential inhibitors of Tpx using an integrated approach starting with high throughput virtual screening and ending with molecular dynamics simulations of selected ligands. Virtual screening of ZINC database of 500,000 compounds via ligand-based and structure-based pharmacophore models retrieved 10,000 hits. The structure-based pharmacophore model was validated using high-throughput virtual screening (HTVS). After multistep docking (SP and XP), common scaffolds were used to find common substructures and the ligand binding poses were optimized using induced fit docking. The stability of the protein–ligand complex was examined with molecular dynamics simulations and the binding free energy of the complex was calculated. As a final outcome eight compounds with different chemotypes were proposed as potential inhibitors for Tpx. The eight ligands identified by a detailed virtual screening protocol can serve as leads in future drug design efforts against the destructive actions of pathogenic bacteria.     

Virtual screening of B-Raf kinase inhibitors: A combination of pharmacophore modelling,molecular docking, 3D-QSAR model and binding free energy calculation studies

B-Raf kinase has been identified as an important target in recent cancer treatment. In order to discover structurally diverse and novel B-Raf inhibitors (BRIs), a virtual screening of BRIs against ZINC database was performed by using a combination of pharmacophore modelling, molecular docking, 3D-QSAR model and binding free energy (ΔG_bind) calculation studies in this work. After the virtual screening, six promising hit compounds were obtained, which were then tested for inhibitory activities of A375 cell lines. In the result, five hit compounds show good biological activities (IC₅₀ < 50 μM). The present method of virtual screening can be applied to find structurally diverse inhibitors, and the obtained five structurally diverse compounds are expected to develop novel BRIs.     

Greedy 3-Point Search (G3PS)—A Novel Algorithm for Pharmacophore Alignment

Chemical features of small molecules can be abstracted to 3D pharmacophore models, which are easy to generate, interpret, and adapt by medicinal chemists. Three-dimensional pharmacophores can be used to efficiently match and align molecules according to their chemical feature pattern, which facilitates the virtual screening of even large compound databases. Existing alignment methods, used in computational drug discovery and bio-activity prediction, are often not suitable for finding matches between pharmacophores accurately as they purely aim to minimize RMSD or maximize volume overlap, when the actual goal is to match as many features as possible within the positional tolerances of the pharmacophore features. As a consequence, the obtained alignment results are often suboptimal in terms of the number of geometrically matched feature pairs, which increases the false-negative rate, thus negatively affecting the outcome of virtual screening experiments. We addressed this issue by introducing a new alignment algorithm, Greedy 3-Point Search (G3PS), which aims at finding optimal alignments by using a matching-feature-pair maximizing search strategy while at the same time being faster than competing methods.