药效团技术在抗感染新药研究中的应用

药效团（pharmacophore）是指药物分子中对活性起重要作用的“药效特征元素”及其空间排列形式。药效团技术作为1种发展迅速的计算机辅助药物设计方法,已广泛应用于虚拟筛选、全新药物设计、先导化合物优化和ADMET预测等新药的设计和研发过程中,可提高药物研发的成功率,降低研发成本,缩短研发周期。近年来,禽流感、甲流感及超级细菌等新型传染性疾病的出现,为抗感染新药研发提出了新的挑战,为此,综述近年来国内、外发表的有关药效团模建技术的研究进展及其在抗感染新药研发中应用的文献以促进计算机辅助药物设计手段在抗感染新药研发中的应用。     

Pharmacophore‐Based Virtual Screening to Aid in the Identification of Unknown Protein Function

A pharmacophore‐based virtual screening method was developed and validated for use in predicting the function of a novel protein in terms of small metabolite binding. Five test cases were used for the validation study which spanned two different folds, four superfamilies, and three enzyme classes. Binding sites were predicted using a combination of two methods (CASTp and THEMATICS). The binding site was mapped with chemical probes representing hydrogen‐bond donor, acceptor, negative ionizable, positive ionizable, and hydrophobe. The interaction maps were converted to three or four feature pharmacophore models and used to search a database containing 80 018 tautomers/protomers/conformers of 10 535 metabolites. The pharmacophore‐based virtual screening eliminated >92% of the database as potential substrates and retrieved specific hits, which were ranked using a physics‐based scoring function. The known substrate or product was ranked within the top 0.7% and substrate‐like compounds within the top 1% of the metabolite database for all of the five test cases. The results suggest that using this pharmacophore‐based virtual screening is a time‐efficient strategy that can be applied to screen large databases to help predict the function of small metabolite binding proteins.     

Identification of Novel HCV RNA‐dependent RNA polymerase Inhibitors Using Pharmacophore‐Guided Virtual Screening

We performed pharmacophore‐guided virtual screening experiments using FlexX‐Pharm to identify novel inhibitors of hepatitis C virus RNA‐dependent RNA polymerase. Pharmacophore model generated from our previous analysis of the binding modes as well as structure‐based three‐dimensional quantitative structure–activity relationship studies of aryl diketoacid analogues was used. In pharmacophore‐guided virtual screening study, among 37 447 compounds in LeadQuest chemical library, 40 compounds were selected as novel candidates of hepatitis C virus RNA‐dependent RNA polymerase inhibitors, and their biological activities were evaluated. Especially, T29 was chosen for further development.     

虚拟筛选辅助新药发现方法研究进展

在新药发现过程中, 虚拟筛选的应用可以富集活性化合物, 降低筛选成本, 提高药物筛选的可行性, 因此已成为新药发现的重要方法。虚拟筛选与生物活性筛选的结合, 可以优势互补, 有效地促进新药的发现。本文介绍了非类药化合物排除、假阳性化合物排除、药效团搜索、分子对接计算以及分子相似性分析等几种方法在药物发现中的应用及其发展趋势, 以期更好地应用虚拟筛选方法, 促进新药的快速发现。     

Recent advances in pharmacophore modeling and its application to anti-influenza drug discovery

Introduction: The emergence of the highly pathogenic avian influenza (HPAI) H5N1 virus and the recent global circulation of H1N1 swine-origin influenza virus in 2009 have highlighted the need for new anti-influenza therapies. This has been made all the more important with the emergence of antiviral-resistant strains. Recent progress in achieving three-dimensional (3D) crystal structures of influenza viral proteins and efficient tools available for pharmacophore-based virtual screening are aiding us in the discovery and design of new antiviral compounds.

Areas covered: This review discusses pharmacophore modeling as a potential cost-effective and time-saving technology for new drug discovery as an alternative to high-throughput screening. Based on this technical platform, the authors discuss current progress and future prospects for developing novel influenza antivirals against pre-existing or emerging novel targets.

Expert opinion: Although it might be at an infant stage of development, the availability of the 3D crystal structures of influenza viral proteins is expected to accelerate the application of structure-based drug design (SBDD) and pharmacophore modeling. Furthermore, the neuraminidase inhibitor, one of the most successful examples of a SBDD, still receives great attention because of its superb antiviral activities and the resistance of influenza strains to oseltamivir. However, despite much success, pharmacophore-based virtual screening exhibits limited predictive power in hit identification. Further improvements in pharmacophore detection algorithms, proper combinations of in silico methods as well as judicious choosing of compounds are expected to improve the hit rate. With the help of these technologies, the discovery of anti-influenza agents will be accelerated.     

Discovery of novel acyl coenzyme a: cholesterol acyltransferase inhibitors: pharmacophore-based virtual screening, synthesis and pharmacology

The present study describes ligand‐based pharmacophore modeling of a series of structurally diverse acyl coenzyme A cholesterol acyltransferase inhibitors. Quantitative pharmacophore models were generated using HypoGen module of Discovery Studio 2.1, whereby the best pharmacophore model possessing two hydrophobic, one ring aromatic, and one hydrogen bond acceptor feature for inhibition of acyl coenzyme A cholesterol acyltransferase showed a very good correlation coefficient (r = 0.942) along with satisfactory cost analysis. Hypo1 was also validated by test set and cross‐validation methods. Developed models were found to be predictive as indicated by low error values for test set molecules. Virtual screening against Maybridge database using Hypo1 was performed. The two most potent compounds ( 47 and 48 ; predicted IC₅₀ = 1 nm ) of the retrieved hits were synthesized and biologically evaluated. These compounds showed 86% and 88% inhibition of acyl coenzyme A cholesterol acyltransferase (at 10 μg/mL) with IC₅₀ value of 3.6 and 2.5 nm , respectively. As evident from the close proximity of biological data to the predicted values, it can be concluded that the generated model (Hypo1) is a reliable and useful tool for lead optimization of novel acyl coenzyme A cholesterol acyltransferase inhibitors.     

Identification of promising DNA GyrB inhibitors for Tuberculosis using pharmacophore‐based virtual screening,molecular docking and molecular dynamics studies

In this study, we searched for potential DNA GyrB inhibitors using pharmacophore‐based virtual screening followed by molecular docking and molecular dynamics simulation approaches. For this purpose, a set of 248 DNA GyrB inhibitors was collected from the literature and a well‐validated pharmacophore model was generated. The best pharmacophore model explained that two each of hydrogen bond acceptors and hydrophobicity regions were critical for inhibition of DNA GyrB. Good statistical results of the pharmacophore model indicated that the model was robust in nature. Virtual screening of molecular databases revealed three molecules as potential antimycobacterial agents. The final screened promising compounds were evaluated in molecular docking and molecular dynamics simulation studies. In the molecular dynamics studies, RMSD and RMSF values undoubtedly explained that the screened compounds formed stable complexes with DNA GyrB. Therefore, it can be concluded that the compounds identified may have potential for the treatment of TB.     

Enrichment Assessment of Multiple Virtual Screening Strategies for Toll‐Like Receptor 8 Agonists Based on a Maximal Unbiased Benchmarking Data Set

Toll‐like receptor 8 agonists, which activate adaptive immune responses by inducing robust production of T‐helper 1‐polarizing cytokines, are promising candidates for vaccine adjuvants. As the binding site of toll‐like receptor 8 is large and highly flexible, virtual screening by individual method has inevitable limitations; thus, a comprehensive comparison of different methods may provide insights into seeking effective strategy for the discovery of novel toll‐like receptor 8 agonists. In this study, the performance of knowledge‐based pharmacophore, shape‐based 3D screening, and combined strategies was assessed against a maximum unbiased benchmarking data set containing 13 actives and 1302 decoys specialized for toll‐like receptor 8 agonists. Prior structure–activity relationship knowledge was involved in knowledge‐based pharmacophore generation, and a set of antagonists was innovatively used to verify the selectivity of the selected knowledge‐based pharmacophore. The benchmarking data set was generated from our recently developed ‘mubd‐decoymaker ’ protocol. The enrichment assessment demonstrated a considerable performance through our selected three‐layer virtual screening strategy: knowledge‐based pharmacophore ( Phar1 ) screening, shape‐based 3D similarity search ( Q4_combo ), and then a Gold docking screening. This virtual screening strategy could be further employed to perform large‐scale database screening and to discover novel toll‐like receptor 8 agonists.     

The discovery of novel β-secretase inhibitors: pharmacophore modeling, virtual screening, and docking studies

This article describes the identification of two small molecular inhibitors for β-secretase by integrating virtual screening with fluorescence resonance energy transfer bioassay. A ligand-based pharmacophore model was developed, and the sequential virtual screening of ZINC database was performed using the acquired pharmacophore model and molecular docking. Biological evaluation of 10 virtual hits led to the identification of two novel inhibitors with IC(50) values of 4.76 and 0.31 μm, respectively. These two moderate inhibitors could represent new potentials for the development of anti-Alzheimer's disease agents.     

Pharmacophore and QSAR studies to design novel histone deacetylase 2 inhibitors

One pharmacophore model and three quantitative structure-activity relationship models were developed on a series of benzimidazole and imidazole inhibitors of histone deacetylase 2. The goodness of hit score value of the best pharmacophore model was 0.756, which indicated that it is reliable to be used for virtual screening. The built pharmacophore model was used to search the NCI database. The hit compounds were subjected to molecular docking. The results showed that 25 compounds had high scores and strong interactions with histone deacetylase 2. In three-dimensional quantitative structure-activity relationship studies, good predictive models were obtained using comparative molecular field analysis, comparative molecular similarity indices analysis, and Topomer comparative molecular field analysis. Some putative active compounds were proposed based on compound no. 41. Twenty-six compounds had high scores and good interactions when they were docking into histone deacetylase 2.     

Design of a High Fragment Efficiency Library by Molecular Graph Theory

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采用药效团模型和分子对接方法筛选新型的组蛋白甲基转移酶G9a抑制剂

目的利用药效团模型和分子对接方法对商业化合物库ChemDiv中的G9afocused-libraries进行筛选,希望发现新骨架结构的G9a抑制剂。方法首先,使用Discovery studio 3.1软件分别构建基于配体的药效团模型和基于配体-受体复合物的药效团模型,并根据构建的2个模型再重新定义2个新的药效团模型。然后,构建测试集并测试药效团模型的预测能力。最后,选取最优药效团模型对G9afocused-libraries进行筛选,对筛选出的化合物使用CDOCKER分子对接进行分析与评价。结果测试结果显示,所构建的药效团模型具有一定的预测能力,通过该药效团筛选得到了2个结构新颖的潜在的G9a抑制剂。结论所构建的药效团模型具有一定的可靠性,虚拟筛选发现的G9a抑制剂还需进一步的实验证明。     

作用于FTase和Raf-1激酶的新型抗肿瘤药物的设计

Ras/Raf/MEK/ERK信号通路,在肿瘤细胞的增殖、分化、凋亡、转移、代谢等过程中起着重要的作用,本文着眼于Ras/Raf/MEK/ERK信号通路中的关键靶点,以法尼基转移酶(FTase)为主靶点,以Raf-1激酶为次靶点,借助计算机辅助药物设计(CADD),构建法尼基转移酶抑制剂(FTIs)和Raf-1激酶抑制剂的药效团模型,设计出同时与这两个靶点相匹配的多靶点配体药物。所设计的多靶点配体药物以氨甲基苯甲酸为母核结构,具有较好的预测活性。     

药物分子设计的策略：论药效团和骨架迁越

药物分子是由药效团和结构骨架构成的，药效团是由不连续的离散的原子、基团或片断所构成，但需结合在分子骨架上，形成具体的分子。骨架具有连续性，相同的药效团附着在不同的分子骨架上，构成了作用于同一靶标而结构多样的化合物。骨架依据受体的柔性和可塑性形成了“杂乱性”的空间。显示了受体结合部位的杂乱性。杂乱性越大，可容纳的配体分子的结构多样性就越多，意味着结构修饰与变换的余地大，成药的机会多。由苗头化合物演化成先导物，进而优化成候选药物，这由化合物变革成安全、有效、稳定、可控的药物过程就是保持药效团、变换分子骨架、修饰基团和边链的过程。结构骨架的变化可分为3个层次：以电子等排原理变换骨架结构；以优势结构为导向变换骨架结构；以结构一活性演化的方式变换骨架结构，即骨架迁越。骨架迁越的目的是改善分子的物化、药代、稳定性和赋予分子的结构新颖性。该文以实例阐述了骨架变换的方法与技巧。     

Structure-guided identification of a selective sulfonamide-based inhibitor targeting the human carbonic anhydrase VA isoform

In recent years, multistep hybrid computational protocols have attracted attention for their application in the drug discovery of enzyme inhibitors. So far, there are large collections of human carbonic anhydrase (hCA) inhibitors, but only a few of them selectively inhibit the mitochondrial isoforms hCA VA and VB as potential therapeutics in obesity treatment. Most sulfonamide-based inhibitors show poor selectivity for inhibiting isoforms of therapeutic interest over ubiquitous hCA I and hCA II. Herein, we propose a combination of ligand- and structure-based approaches to generate pharmacophore models for hCA VA inhibitors. Then, we performed a virtual screening (VS) campaign on a database of commercially available sulfonamides. Finally, the in silico screening followed by docking studies suggested several “hit compounds” that demonstrated to inhibit hCA VA at a low nanomolar concentration in a stopped-flow CO₂ hydrase assay. Notably, the best candidate, 2-(3,4-dihydro-2H-quinolin-1-yl)-N-(4-sulfamoylphenyl)acetamide (code name VAME- 28 ) proved to be a potent hCA VA inhibitor (K_i value of 54.8 nM) and a more selective agent over hCA II when compared to the reference compound topiramate.     

Identification of novel antitubulin agents by using a virtual screening approach based on a 7-point pharmacophore model of the tubulin colchi-site

Tubulin inhibition represents an established target in the field of anticancer research, and over the last 20 years, an intensive search for new antimicrotubule agents has occurred. Indeed, in silico models have been presented that might aid the discovery of novel agents. Among these, a 7-point pharmacophore model has been recently proposed. As a formal proof of this model, we carried out a ligand-based virtual screening on the colchicine-binding site. In vitro testing demonstrated that two compounds displayed a cytotoxic profile on neuroblastoma cancer cells (SH-SY5H) and one had an antitubulinic profile.     

Pharmacophore modeling technique applied for the discovery of proteasome inhibitors

Introduction: The 26S proteasome has many important roles in the biological functions of the cells, and proteasome inhibitors have multiple and complex activities on cells. These compounds can be natural or synthesized. Most synthetic derivatives have been rationally designed, synthesized and optimized to obtain the best selectivity and increase the activity. The design of chemical entities with desired molecular identification, which plays an important role in biological systems, is provided by pharmacophore modeling. Indeed, pharmacophore models can be established either in a ligand-based manner or in a receptor-based manner.

Areas covered: The authors discuss the application of pharmacophore modeling techniques to proteasome inhibitors development. Furthermore, the article reviews the classification of the currently discovered proteasome inhibitors where the principal mechanism of action and clinical application are represented.

Expert opinion: In the era of new drug development, database of compounds should be thoroughly evaluated with a combination of methods that consider both pharmacophore- and ligand-based virtual screening. The concept of pharmacophore helps to discover new active compounds and to evaluate their activity. The nature of proteasome inhibitor pharmacophore affects the secondary active-site specificity; indeed, increasing specificity decreases the cytotoxicity of the proteasome inhibitors. It is hypothesized that the balanced simultaneous modulation of a few druggable targets may have superior efficacy and fewer side effects than single-target or combination therapies for the treatment of human cancers. The discovery of new compounds should aim to find more active compounds that improve the compliance of patients.     

Identification of novel inhibitors of dipeptidylcarboxypeptidase of Leishmania donovani via ligand-based virtual screening and biological evaluation

Current treatment of leishmaniasis is based on chemotherapy, which relies on a handful of drugs with serious limitations, such as high cost, toxicity, and lack of efficacy in endemic regions. Therefore, development of new, effective, and affordable anti-leishmanial drugs is a global health priority. Dipeptidylcarboxypeptidase has been characterized and established as a drug target for antileishmanial drug discovery. We virtually screened a large chemical library of 15 452 compounds against a 3D model of dipeptidylcarboxypeptidase to identify novel inhibitors. The initial virtual screening using a ligand-based pharmacophore model identified 103 compounds. Forty-six compounds were shortlisted based on the docking scores and other scoring functions. Further, these compounds were subjected to biological assay, and four of them belonging to two chemical classes were identified as the lead compounds. Identification of these novel and chemically diverse inhibitors should provide leads to be optimized into candidates to treat these protozoan infections.     

First pharmacophore model of CCR3 receptor antagonists and its homology model-assisted, stepwise virtual screening

CCR3, a G protein-coupled receptor, plays a central role in allergic inflammation and is an important drug target for inflammatory diseases. To understand the structure-function relationship of CCR3 receptor, different computational techniques were employed, which mainly include: (i) homology modeling of CCR3 receptor, (ii) 3D-quantitative pharmacophore model of CCR3 antagonists, (iii) virtual screening of small compound databases, and (iv) finally, molecular docking at the binding site of the CCR3 receptor homology model. Pharmacophore model was developed for the first time, on a training data set of 22 CCR3 antagonists, using CATALYST HypoRefine program. Best hypothesis (Hypo1) has three different chemical features: two hydrogen-bond acceptors, one hydrophobic, and one ring aromatic. Hypo1 model was further validated using (i) 87 test set CCR3 antagonists, (ii) Cat Scramble randomization technique, and (iii) Decoy data set. Molecular docking studies were performed on modeled CCR3 receptor using 303 virtually screened hits, obtained from small compound database virtual screening. Finally, five hits were identified as potential leads against CCR3 receptor, which exhibited good estimated activities, favorable binding interactions, and high docking scores. These studies provided useful information on the structurally vital residues of CCR3 receptor involved in the antagonist binding, and their unexplored potential for the future development of potent CCR3 receptor antagonists.