Enhancing specificity and sensitivity of pharmacophore-based virtual screening by incorporating chemical and shape features--a case study of HIV protease inhibitors

Virtual screening (VS), if applied appropriately, could significantly shorten the hit identification and hit-to-lead processes in drug discovery. Recently, the version of VS that is based upon similarity to a pharmacophore has received increased attention. This is due to two major factors: first, the public availability of the ZINC1 conformational database has provided a large selection pool with high-quality and purchasable small molecules; second, new technology has enabled a more accurate and flexible definition of pharmacophore models coupled with an efficient search speed. The major goal of this study was to achieve improved specificity and sensitivity of pharmacophore-based VS by optimizing the variables used to generate conformations of small molecules and those used to construct pharmacophore models from known inhibitors or from inhibitor-protein complex structures. By using human immunodeficiency virus protease and its inhibitors (PIs) as a case study, the impact of the key variables, including the selection of chemical features, involvement of excluded volumes (EV), the tolerance radius of excluded volumes, energy windows, and the maximum number of conformers in conformation generation, was explored. Protein flexibility was simulated by adjusting the sizes of EV. Our best pharmacophore model, combining both chemical features and excluded volumes, was able to correctly identify 60 out of 75 structurally diverse known PIs, while misclassifying only 5 out of 75 similar compounds that are not inhibitors. To evaluate the specificity of the model, 1193 oral drugs on the market were screened, and 25 original hits were identified, including 5 out of 6 known PI drugs.     

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。     

In Silico Strategy for Targeting the mTOR Kinase at Rapamycin Binding Site by Small Molecules

Computer aided drug-design methods proved to be powerful tools for the identification of new therapeutic agents. We employed a structure-based workflow to identify new inhibitors targeting mTOR kinase at rapamycin binding site. By combining molecular dynamics (MD) simulation and pharmacophore modelling, a simplified structure-based pharmacophore hypothesis was built starting from the FKBP12-rapamycin-FRB ternary complex retrieved from RCSB Protein Data Bank (PDB code 1FAP). Then, the obtained model was used as filter to screen the ZINC biogenic compounds library, containing molecules derived from natural sources or natural-inspired compounds. The resulting hits were clustered according to their similarity; moreover, compounds showing the highest pharmacophore fit-score were chosen from each cluster. The selected molecules were subjected to docking studies to clarify their putative binding mode. The binding free energy of the obtained complexes was calculated by MM/GBSA method and the hits characterized by the lowest ΔG_bind values were identified as potential mTOR inhibitors. Furthermore, the stability of the resulting complexes was studied by means of MD simulation which revealed that the selected compounds were able to form a stable ternary complex with FKBP12 and FRB domain, thus underlining their potential ability to inhibit mTOR with a rapamycin-like mechanism.     

Identification of novel PfDHODH inhibitors as antimalarial agents via pharmacophore-based virtual screening followed by molecular docking and in vivo antimalarial activity

Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) catalyses the fourth reaction of de novo pyrimidine biosynthesis in parasites, and represents an important target for the treatment of malaria. In this study, we describe pharmacophore-based virtual screening combined with docking study and biological evaluation as a rational strategy for identification of novel hits as antimalarial agents. Pharmacophore models were established from known PfDHODH inhibitors using the GALAHAD module with IC₅₀ values ranging from 0.033 μM to 142 μM. The best pharmacophore model consisted of three hydrogen bond acceptor, one hydrogen bond donor and one hydrophobic features. The pharmacophore models were validated through receiver operating characteristic and Günere–Henry scoring methods. The best pharmacophore model as a 3D search query was searched against the IBS database. Several compounds with different structures (scaffolds) were retrieved as hit molecules. Among these compounds, those with a Q_FIT value of more than 81 were docked in the PfDHODH enzyme to further explore the binding modes of these compounds. In silico pharmacokinetic and toxicities were predicted for the best docked molecules. Finally, the identified hits were evaluated in vivo for their antimalarial activity in a parasite inhibition assay. The hits reported here showed good potential to become novel antimalarial agents.     

Discovery and design of new PI3K inhibitors through pharmacophore-based virtual screening,molecular docking,and binding free energy analysis

Phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/the mammalian target of rapamycin (mTOR) signaling pathway is one of the sought after therapeutic target for treating human cancers. This pathway is often hyper activated in cancers. In the present study, pharmacophore-based virtual screening, molecular docking, and binding free energy calculations were performed on a series of quinoline derivatives which were reported to be effective against PI3Kα. A five-point pharmacophore hypothesis with one hydrogen bond acceptor (A), one hydrogen bond donor (D), one hydrophobic group (H), and two aromatic rings (R) was developed with acceptable R² and Q² values of 0.93 and 0.60 respectively. Eventually, common pharmacophore hypothesis-based screening was conducted against TOSLab, CPP, and ASINEX macrocylce databases, and potential hits were identified which were further subjected to rigorous docking process in order to screen out drug like molecules having crucial interactions with the target PI3Kα. Finally, binding free energy analysis was carried out for the top hits obtained from docking process. We also designed new 1, 3, 4-oxadiazole-based cyclic peptides by incorporating the structural features of the hits obtained from the above databases. Among the designed cyclic peptides, the cyclic peptide with tryptophan moiety showed good interactions and free binding energy values. On the whole, this study helped us in identifying new promising molecules as PI3Kα inhibitors which can be explored further to generate greater number of compounds with better pharmacokinetic properties.     

Molecular modeling studies to discover novel mIDH2 inhibitors with high selectivity for the primary and secondary mutants

Mutant isocitrate dehydrogenase 2 (mIDH2) is an emerging target for the treatment of cancer. AG-221 is the first mIDH2 inhibitor approved by the FDA for acute myeloid leukemia treatment, but its acquired resistance has recently been observed, necessitating the development of new inhibitor. In this study, a multi-step virtual screening protocol was employed for the analysis of a large database of compounds to identify potential mIDH2 inhibitors. To this end, we firstly utilized molecular dynamics (MD) simulations and binding free energy calculations to elucidate the key factors affecting ligand binding and drug resistance. Based on these findings, the receptor-ligand interaction-based pharmacophore (IBP) model and hierarchical docking-based virtual screening were sequentially carried out to assess 212,736 compounds from the Specs database. The resulting hits were finally ranked by PAINS filter and ADME prediction and the top compounds were obtained. Among them, six molecules were identified as mIDH2 putative inhibitors with high selectivity by interacting with the capping residue Asp312. Furthermore, subsequent docking and MD experiments demonstrated that compound V2 might have potential inhibitory activity against the AG-221-resistant mutants, thereby making it a promising lead for the development of novel mIDH2 inhibitors.     

New serotonin 5-HT(6) ligands from common feature pharmacophore hypotheses

Serotonin 5-HT6 receptor antagonists are thought to play an important role in the treatment of psychiatry, Alzheimer's disease, and probably obesity. To find novel and potent 5-HT6 antagonists and to provide a new idea for drug design, we used a ligand-based pharmacophore to perform the virtual screening of a commercially available database. A three-dimensional common feature pharmacophore model was developed by using the HipHop program provided in Catalyst software and was used as a query for screening the database. A recursive partitioning (RP) model which can separate active and inactive compounds was used as a filtering system. Finally a sequential virtual screening procedure (SQSP) was conducted, wherein both the common feature pharmacophore and the RP model were used in succession to improve the results. Some of the hits were selected based on druglikeness, ADME properties, structural diversity, and synthetic accessibility for real biological evaluation. The best hit compound showed a significant IC50 value of 9.6 nM and can be used as a lead for further drug development.     

Virtual screening studies to design potent CDK2-cyclin A inhibitors

The cell division cycle is controlled by cyclin-dependent kinases (CDK), which consist of a catalytic subunit (CDK1-CDK8) and a regulatory subunit (cyclin A-H). Pharmacophore analysis indicates that the best inhibitor model consists of (1) two hydrogen bond acceptors, (2) one hydrogen bond donor, and (3) one hydrophobic feature. The HypoRefine pharmacophore model gave an enrichment factor of 1.31 and goodness of fit score of 0.76. Docking studies were carried out to explore the structural requirements for the CDK2-cyclin A inhibitors and to construct highly predictive models for the design of new inhibitors. Docking studies demonstrate the important role of hydrogen bond and hydrophobic interactions in determining the inhibitor-receptor binding affinity. The validated pharmacophore model is further used for retrieving the most active hits/lead from a virtual library of molecules. Subsequently, docking studies were performed on the hits, and novel series of potent leads were suggested based on the interaction energy between CDK2-cyclin A and the putative inhibitors.     

Use of catalyst pharmacophore models for screening of large combinatorial libraries

Using a data set comprised of literature compounds and structure-activity data for cyclin dependent kinase 2, several pharmacophore hypotheses were generated using Catalyst and evaluated using several criteria. The two best were used in retrospective searches of 10 three-dimensional databases containing over 1,000,000 proprietary compounds. The results were then analyzed for the efficiency with which the hypotheses performed in the areas of compound prioritization, library prioritization, and library design. First as a test of their compound prioritization capabilities, the pharmacophore models were used to search combinatorial libraries that were known to contain CDK active compounds to see if the pharmacophore models could selectively choose the active compounds over the inactive compounds. Second as a test of their utility in library design again the pharmacophore models were used to search the active combinatorial libraries to see if the key synthons were over represented in the hits from the pharmacophore searches. Finally as a test of their ability to prioritize combinatorial libraries, several inactive libraries were searched in addition to the active libraries in order to see if the active libraries produced significantly more hits than the inactive libraries. For this study the pharmacophore models showed potential in all three areas. For compound prioritization, one of the models selected active compounds at a rate nearly 11 times that of random compound selection though in other cases models missed the active compounds entirely. For library design, most of the key fragments were over represented in the hits from at least one of the searches though again some key fragments were missed. Finally, for library prioritization, the two active libraries both produced a significant number of hits with both pharmacophore models, whereas none of the eight inactive libraries produced a significant number of hits for both models.     

Virtual Screening,Synthesis and Biological Evaluation of Streptococcus mutans Mediated Biofilm Inhibitors

Dental caries, a global oral health concern, is a biofilm-mediated disease. Streptococcus mutans, the most prevalent oral microbiota, produces extracellular enzymes, including glycosyltransferases responsible for sucrose polymerization. In bacterial communities, the biofilm matrix confers resistance to host immune responses and antibiotics. Thus, in cases of chronic dental caries, inhibiting bacterial biofilm assembly should prevent demineralization of tooth enamel, thereby preventing tooth decay. A high throughput screening was performed in the present study to identify small molecule inhibitors of S. mutans glycosyltransferases. Multiple pharmacophore models were developed, validated with multiple datasets, and used for virtual screening against large chemical databases. Over 3000 drug-like hits were obtained that were analyzed to explore their binding mode. Finally, six compounds that showed good binding affinities were further analyzed for ADME (absorption, distribution, metabolism, and excretion) properties. The obtained in silico hits were evaluated for in vitro biofilm formation. The compounds displayed excellent antibiofilm activities with minimum inhibitory concentration (MIC) values of 15.26–250 µg/mL.     

6-氮杂吲唑类Pim-1激酶抑制剂的3D-QSAR、药效团模型研究和分子设计

Pim-1激酶通过作用于多种信号通路或靶点影响肿瘤的发生发展,近年来被认为是肿瘤治疗的良好靶标。本文采用SYBYL-X2.1.1软件中的Topomer CoMFA、GALAHAD模块建立计算机模型,研究39个基于6-氮杂吲唑环的Pim-1激酶抑制剂的三维定量构效关系及药效团特征元素。结果显示,Topomer CoMFA建模所得交叉验证系数(q²)和相关系数(r²)分别为0.756和0.951,结合外部验证表明此3D-QSAR模型具有较高预测能力及较好的统计学稳定性,同时,用等势图描述了R1、R2基团处立体场、静电场对活性的具体影响。药效团研究结果表明,含氢键受体的芳香杂环母核结构,以及侧链取代基中含有芳香杂环结构对化合物的活性贡献较大。最后根据上述模型信息新设计了15个Pim-1激酶抑制剂分子并完成活性预测及分子对接模式研究,其中4个分子的预测pIC₅₀高于建模分子中活性最好的化合物17,Surflex-Dock分析显示新设计分子均与Pim-1激酶形成较强氢键相互作用。基于6-氮杂吲唑环的Pim-1激酶抑制剂的3D-QS...     

Molecular docking and pharmacophore filtering in the discovery of dual-inhibitors for human leukotriene A4 hydrolase and leukotriene C4 synthase

Combination of drugs for multiple targets has been a standard treatment in treating various diseases. A single chemical entity that acts upon multiple targets is emerging nowadays because of their predictable pharmacokinetic and pharmacodynamic properties. We have employed a computer-aided methodology combining molecular docking and pharmacophore filtering to identify chemical compounds that can simultaneously inhibit the human leukotriene hydrolase (hLTA4H) and the human leukotriene C4 synthase (hLTC4S) enzymes. These enzymes are the members of arachidonic acid pathway and act upon the same substrate, LTA4, producing different inflammatory products. A huge set of 4966 druglike compounds from the Maybridge database were docked into the active site of hLTA4H using the GOLD program. Common feature pharmacophore models were developed from the known inhibitors of both the targets using Accelrys Discovery Studio 2.5. The hits from the hLTA4H docking were filtered to match the chemical features of both the pharmacophore models. The compounds that resulted from the pharmacophore filtering were docked into the active site of hLTC4S and the hits those bind well at both the active sites and matched the pharmacophore models were identified as possible dual inhibitors for hLTA4H and hLTC4S enzymes. Reverse validation was performed to ensure the results of the study.     

Pharmacophore modelling,molecular docking and virtual screening for EGFR (HER 1) tyrosine kinase inhibitors

A pharmacophore model has been developed using diverse classes of epidermal growth factor receptor (EGFR) tyrosine kinase (TK) inhibitors useful in the treatment of human tumours. Among the top 10 generated hypotheses, the second hypothesis, with one hydrogen bond acceptor, one ring aromatic and three hydrophobic features, was found to be the best on the basis of Cat Scramble validation as well as test set prediction (r _training?=?0.89, r _test?=?0.82). The model also maps well to the external test set molecules as well as clinically active molecules and corroborates the docking studies. Finally, 10 hits were identified as potential leads after virtual screening of ZINC database for EGFR TK inhibition. The study may facilitate the designing and discovery of novel EGFR TK inhibitors.     

Molecular dynamics simulation study of PTP1B with allosteric inhibitor and its application in receptor based pharmacophore modeling

Allosteric inhibition of protein tyrosine phosphatase 1B (PTP1B), has paved a new path to design specific inhibitors for PTP1B, which is an important drug target for the treatment of type II diabetes and obesity. The PTP1B_1–282-allosteric inhibitor complex crystal structure lacks α7 (287–298) and moreover there is no available 3D structure of PTP1B_1–298 in open form. As the interaction between α7 and α6–α3 helices plays a crucial role in allosteric inhibition, α7 was modeled to the PTP1B_1–282 in open form complexed with an allosteric inhibitor (compound-2) and a 5 ns MD simulation was performed to investigate the relative orientation of the α7–α6–α3 helices. The simulation conformational space was statistically sampled by clustering analyses. This approach was helpful to reveal certain clues on PTP1B allosteric inhibition. The simulation was also utilized in the generation of receptor based pharmacophore models to include the conformational flexibility of the protein-inhibitor complex. Three cluster representative structures of the highly populated clusters were selected for pharmacophore model generation. The three pharmacophore models were subsequently utilized for screening databases to retrieve molecules containing the features that complement the allosteric site. The retrieved hits were filtered based on certain drug-like properties and molecular docking simulations were performed in two different conformations of protein. Thus, performing MD simulation with α7 to investigate the changes at the allosteric site, then developing receptor based pharmacophore models and finally docking the retrieved hits into two distinct conformations will be a reliable methodology in identifying PTP1B allosteric inhibitors. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Discovery of novel Pim-1 kinase inhibitors by a hierarchical multistage virtual screening approach based on SVM model, pharmacophore, and molecular docking

In this investigation, we describe the discovery of novel potent Pim-1 inhibitors by employing a proposed hierarchical multistage virtual screening (VS) approach, which is based on support vector machine-based (SVM-based VS or SB-VS), pharmacophore-based VS (PB-VS), and docking-based VS (DB-VS) methods. In this approach, the three VS methods are applied in an increasing order of complexity so that the first filter (SB-VS) is fast and simple, while successive ones (PB-VS and DB-VS) are more time-consuming but are applied only to a small subset of the entire database. Evaluation of this approach indicates that it can be used to screen a large chemical library rapidly with a high hit rate and a high enrichment factor. This approach was then applied to screen several large chemical libraries, including PubChem, Specs, and Enamine as well as an in-house database. From the final hits, 47 compounds were selected for further in vitro Pim-1 inhibitory assay, and 15 compounds show nanomolar level or low micromolar inhibition potency against Pim-1. In particular, four of them were found to have new scaffolds which have potential for the chemical development of Pim-1 inhibitors.     

An integrated approach to knowledge-driven structure-based virtual screening

In many practical applications of structure-based virtual screening (VS) ligands are already known. This circumstance requires that the obtained hits need to satisfy initial made expectations i.e., they have to fulfill a predefined binding pattern and/or lie within a predefined physico-chemical property range. Based on the RApid Index-based Screening Engine (RAISE) approach, we introduce cRAISE—a user-controllable structure-based VS method. It efficiently realizes pharmacophore-guided protein-ligand docking to assess the library content but thereby concentrates only on molecules that have a chance to fulfill the given binding pattern. In order to focus only on hits satisfying given molecular properties, library profiles can be utilized to simultaneously filter compounds. cRAISE was evaluated on a range of strict to rather relaxed hypotheses with respect to its capability to guide binding-mode predictions and VS runs. The results reveal insights into a guided VS process. If a pharmacophore model is chosen appropriately, a binding mode below 2 Å is successfully reproduced for 85 % of well-prepared structures, enrichment is increased up to median AUC of 73 %, and the selectivity of the screening process is significantly enhanced leading up to seven times accelerated runtimes. In general, cRAISE supports a versatile structure-based VS approach allowing to assess hypotheses about putative ligands on a large scale.     

Structure-based design of oxygen-linked macrocyclic kinase inhibitors: discovery of SB1518 and SB1578, potent inhibitors of Janus kinase 2 (JAK2) and Fms-like tyrosine kinase-3 (FLT3)

Macrocycles from our Aurora project were screened in a kinase panel and were found to be active on other kinase targets, mainly JAKs, FLT3 and CDKs. Subsequently these compounds became leads in our JAK2 project. Macrocycles with a basic nitrogen in the linker form a salt bridge with Asp86 in CDK2 and Asp698 in FLT3. This residue is conserved in most CDKs resulting in potent pan CDK inhibition. One of the main project objectives was to achieve JAK2 potency with 100-fold selectivity against CDKs. Macrocycles with an ether linker have potent JAK2 activity with the ether oxygen forming a hydrogen bond to Ser936. A hydrogen bond to the equivalent residues of JAK3 and most CDKs cannot be formed resulting in good selectivity for JAK2 over JAK3 and CDKs. Further optimization of the macrocyclic linker and side chain increased JAK2 and FLT3 activity as well as improving DMPK properties. The selective JAK2/FLT3 inhibitor 11 (Pacritinib, SB1518) has successfully finished phase 2 clinical trials for myelofibrosis and lymphoma. Another selective JAK2/FLT3 inhibitor, 33 (SB1578), has entered phase 1 clinical development for the non-oncology indication rheumatoid arthritis.     

On the value of homology models for virtual screening: discovering hCXCR3 antagonists by pharmacophore-based and structure-based approaches

Human chemokine receptor CXCR3 (hCXCR3) antagonists have potential therapeutic applications as antivirus, antitumor, and anti-inflammatory agents. A novel virtual screening protocol, which combines pharmacophore-based and structure-based approaches, was proposed. A three-dimensional QSAR pharmacophore model and a structure-based docking model were built to virtually screen for hCXCR3 antagonists. The hCXCR3 antagonist binding site was constructed by homology modeling and molecular dynamics (MD) simulation. By combining the structure-based and ligand-based screenings results, 95% of the compounds satisfied either pharmacophore or docking score criteria and would be chosen as hits if the union of the two searches was taken. The false negative rates were 15% for the pharmacophore model, 14% for the homology model, and 5% for the combined model. Therefore, the consistency of the pharmacophore model and the structural binding model is 219/273 = 80%. The hit rate for the virtual screening protocol is 273/286 = 95%. This work demonstrated that the quality of both the pharmacophore model and homology model can be measured by the consistency of the two models, and the false negatives in virtual screening can be reduced by combining two virtual screening approaches.     

Ligand-Based Pharmacophore Screening Strategy: a Pragmatic Approach for Targeting HER Proteins

Targeting ErbB family of receptors is an important therapeutic option, because of its essential role in the broad spectrum of human cancers, including non-small cell lung cancer (NSCLC). Therefore, in the present work, considerable effort has been made to develop an inhibitor against HER family proteins, by combining the use of pharmacophore modelling, docking scoring functions, and ADME property analysis. Initially, a five-point pharmacophore model was developed using known HER family inhibitors. The generated model was then used as a query to screen a total of 468,880 compounds of three databases namely ZINC, ASINEX, and DrugBank. Subsequently, docking analysis was carried out to obtain hit molecules that could inhibit the HER receptors. Further, analysis of GLIDE scores and ADME properties resulted in one hit namely BAS01025917 with higher glide scores, increased CNS involvement, and good pharmaceutically relevant properties than reference ligand, afatinib. Furthermore, the inhibitory activity of the lead compounds was validated by performing molecular dynamic simulations. Of note, BAS01025917 was found to possess scaffolds with a broad spectrum of antitumor activity. We believe that this novel hit molecule can be further exploited for the development of a pan-HER inhibitor with low toxicity and greater potential.