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。     

基于药效团的BRD4靶向小分子抑制剂虚拟筛选

BRD4靶点和多种肿瘤密切相关,是具有良好成药性的热门靶点.本文选取活性较好且结构差异较大的BRD4小分子抑制剂作为训练集分子,基于配体小分子共同特征(HipHop)方法使用Discovery Studio 3.0分子模拟软件构建了药效团.药效团通过测试集验证、ROC曲线验证(SE(sensitivity)=0.937...     

基于药效团模型的DHODH抑制剂构效关系研究

利用药效团模型研究二氢乳清酸脱氢酶(Dihydroorotate dehydrogenase,DHODH)抑制剂的构效关系,为DHODH抑制剂的虚拟筛选提供新的方法.以31个具有DHODH抑制活性的化合物为训练集化合物,半数抑制浓度(IC50)范围为7~63000 nmol/L,利用Catalyst/HypoGen算法构建DHODH抑制剂药效团模型,通过对训练集化合物多个构象进行叠合,提取药效团特征及三维空间限制构建药效团模型.利用基于CatScramble的交叉验证方法及评价模型对已知活性化合物的活性预测能力,确定较优药效团模型.模型包含1个氢键受体、3个疏水中心,表征了受体配体相互作用时可能发生的氢键相互作用、疏水相互作用和π-π相互作用,4个药效特征在三维空间的排列概括了DHODH抑制剂产生活性的结构特点.所得较优模型对训练集化合物及测试集化合物的计算活性值与实验活性值的相关系数分别为0.8405和0.8788.利用药效团模型对来源于微生物的系列化合物进行虚拟筛选,筛选出59个预测活性较好的化合物,可作为进一步药物研发的候选化合物.     

基于虚拟筛选方法从天然产物中发现TNF-α抑制剂

肿瘤坏死因子α(TNF-α)是治疗类风湿性疾病、克罗恩肠炎、银屑病等自身免疫性疾病的重要靶点.TNF-α与TNF-1受体(TNFR1)相互作用参与了这些疾病的病理过程.本次实验基于TNF-α活性位点结构特征构建药效团,利用该药效团对天然产物数据库进行筛选,通过Lipinski's规则和Veber规则判断化合物类药性,应...     

基于结构虚拟筛选发现蛋白质精氨酸甲基转移酶5抑制剂

蛋白质精氨酸甲基转移酶5(PRMT5)是蛋白质甲基转移酶家族(PRMTs)的重要一员,其主要生理功能是催化精氨酸单对称二甲基化。PRMT5的上调发生在不同类型的肿瘤中,并与不良预后密切相关,已被视为肿瘤治疗中的潜在靶点。近年来,已有多种PRMT5抑制剂进入临床试验,但目前尚未有药物获批上市。本研究基于Glide对接的虚拟筛选和生物活性实验,发现化合物8018-1271对PRMT5酶的抑制活性IC₅₀值为13.56±0.86μmol/L,并通过分子动力学揭示其与PRMT5蛋白结构域的相互作用模式。本研究所得化合物8018-1271可作为进一步改造的先导化合物,为新型PRMT5抑制剂的发现提供参考。     

基于虚拟筛选和分子动力学模拟发现新型ULK1抑制剂

Unc-51样自噬激活激酶1（unc-51-like autophagy activating kinase 1,ULK1）作为自噬启动的重要调控因子,是肿瘤治疗的关键靶点之一。首先,以已知ULK1抑制剂为基础构建药效团模型,通过药效团模型筛选、分子对接以及分子力学广义波恩表面积（Molecular Mechanics/Generalized Born Surface Area,MM/GBSA）结合自由能计算等方法,对含有52万多个类药性小分子的数据库进行虚拟筛选,得到具有较高理论亲和力的化合物。随后,50ns的分子动力学模拟验证了蛋白质-配体复合物结合的稳定性,最后10ns的平均结合自由能的计算研究进一步验证了配体的结合能力。结果表明,6个化合物（F5258-0159、F3407-0428、F0529-1100、F0696-3531、F3222-5280、F6525-5596）具有骨架新颖、分子对接分数和结合自由能数值优异及与ULK1的结合状态稳定等特点,可以作为新型潜在的ULK1抑制剂用于肿瘤治疗的研究,也为新型ULK1抑制剂的设计和研发提供新的研究思路。     

Sulfonylurea类ALS/AHAS抑制剂作用方式的分子对接研究和新抑制剂的虚拟筛选

采用Dock5和Autodock3的组合, 从乙酰乳酸合成酶(ALS)的晶体结构出发, 对五个磺酰脲分子和三个类磺酰脲分子与ALS的相互作用方式进行了详细的分子对接研究, 并结合对ALS与氯嘧磺隆(类磺酰脲)共结晶的复合物晶体结构的分析得出了一个简化的药效团模型, 与前人利用其它手段得到的药效团模型一致. 结合此药效团模型并根据sulfonylurea类分子与ALS的作用机理, 我们对425个具有不同除草和杀虫作用的已知农药和ALS进行了分子对接研究和筛选, 从中发现了一些可能对ALS有抑制作用的农药分子. 此结果可以很好地解释这类农药的结构和活性的关系, 对设计、开发新ALS抑制剂的先导化合物提供依据和指导.     

药效团检索设计新的HIV-1蛋白酶抑制剂

通过对自建的未开发化合物三维结构库进行药效团检索,得到了4个对HIV-1蛋白酶抑制活化的化合物,通过构象分析发现包含药效团的构象处于优势构象,而且4个结构都含有带两个邻位羟基的苯环和一个间位羰基的药效团以及公共子结构。通过计算发现它们的疏水参数都很小。在考虑满足包含药效团的结构特征和有适中的疏水参数两个因素的前提下,设计出了新的具有潜在抑制HIV-1蛋白酶活性的化合物。它们的结构都比检索得到的四个化合物更为简单,因此易于合成。     

PI3Kα抑制剂的虚拟筛选、分子动力学模拟及活性验证

磷脂酰肌醇-3激酶信号通路的异常激活与肿瘤的发生与发展密切相关,被视为开发癌症治疗药物的关键靶点之一。本次实验采用基于靶点的虚拟筛选方法,根据Lipinski’s规则和PAINS规则对ZINC15化合物数据库进行类药性筛选并从中过滤出来500万个化合物,使用分子对接软件Autodock-vina将这批化合物对接到PI3Kα蛋白激酶(PDB ID:2RD0)的活性口袋中,通过聚类分析去除结构相似度高的化合物,应用ADMETlab评价化合物的成药性,随后对精筛出来的化合物做进一步的体外活性测试。采用分子动力学模拟和MM-GBSA方法验证复合物体系的稳定性,最终获得4个潜在的PI3Kα靶向小分子抑制剂。化合物ZINC33127329的生物活性在微摩尔级别,其IC₅₀值为8.79±2.58μmol/L。研究活性化合物与PI3Kα蛋白激酶的互作模式,揭示该化合物的构效关系,为设计选择性高,效力强的靶向PI3Kα蛋白激酶抑制剂提供了一定的理论指导与设计思路。     

基于MOE软件的本科生专业综合实验项目设计——耐药性金黄色葡萄球菌FtsZ抑制剂的虚拟筛选与验证

以耐药性金黄色葡萄球菌细丝温度敏感蛋白Z(FtsZ)抑制剂的虚拟筛选为例,在食品类专业综合实验课程教学中借助MOE软件,基于分子对接原理方法从花椒活性小分子库中筛选细菌FtsZ蛋白靶向抑制剂。通过配体小分子数据库的建立、受体蛋白质处理、对接参数设置、筛选结果评价和活性验证等内容,帮助学生系统掌握基于分子对接的活性分子虚拟初筛和活性验证工作流程,降低学生理解分子抑菌机制的难度,增强对跨学科交叉研究新手段的认识,拓展研究方法和创新能力。     

Pharmacophore Modeling and Virtual Screening of Novel Inhibitors for c-Kit Kinase

The stem cell factor receptor (c‐Kit) has been known to play critical roles in regulating numerous aspects of cellular behavior including cell growth, differentiation, migration and metabolism. In this investigation, a three‐dimensional pharmacophore model of c‐Kit inhibitors has been established by using the HypoGen algorithms implemented in the catalyst software package. The best quantitative pharmacophore model, hypothesis 1, which has the highest correlation coefficient (0.989), consists of one hydrogen bond acceptor, two hydrogen bond donors and one hydrophobic feature. To our knowledge, this is the first report on the pharmacophore modeling study of c‐Kit inhibitors. The best hypothesis, hypothesis 1, was used to screen molecular structural databases, including Specs and China Natural Products Database for potential lead compounds. The hit compounds were subsequently subjected to filtering by Lipinski's rules and docking study to refine the retrieved hits and as a result to reduce the rate of false positive. Finally 28 compounds were purchased or synthesized for further in vitro assay against several human tumour cell lines including A549, MCF‐7, HepG2 and PC‐3, in which c‐Kit is overexpressed. Two compounds show very low micromolar inhibition potency against the PC‐3 and HepG2 cell lines respectively. And they were selected for further modification and testing.     

Identification of Novel Covalent XPO1 Inhibitors Based on a Hybrid Virtual Screening Strategy

Nuclear export protein 1 (XPO1), a member of the nuclear export protein-p (Karyopherin-P) superfamily, regulates the transport of “cargo” proteins. To facilitate this important process, which is essential for cellular homeostasis, XPO1 must first recognize and bind the cargo proteins. To inhibit this process, small molecule inhibitors have been designed that inhibit XPO1 activity through covalent binding. However, the scaffolds for these inhibitors are very limited. While virtual screening may be used to expand the diversity of the XPO1 inhibitor skeleton, enormous computational resources would be required to accomplish this using traditional screening methods. In the present study, we report the development of a hybrid virtual screening workflow and its application in XPO1 covalent inhibitor screening. After screening, several promising XPO1 covalent molecules were obtained. Of these, compound 8 performed well in both tumor cell proliferation assays and a nuclear export inhibition assay. In addition, molecular dynamics simulations were performed to provide information on the mode of interaction of compound 8 with XPO1. This research has identified a promising new scaffold for XPO1 inhibitors, and it demonstrates an effective and resource-saving workflow for identifying new covalent inhibitors.     

Virtual Screening of Natural Compounds as Potential PI3K-AKT1 Signaling Pathway Inhibitors and Experimental Validation

A computational screening for natural compounds suitable to bind the AKT protein has been performed after the generation of a pharmacophore model based on the experimental structure of AKT1 complexed with IQO, a well-known inhibitor. The compounds resulted as being most suitable from the screening have been further investigated by molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis and toxicity profiles. Two compounds selected at the end of the computational analysis, i.e., ZINC2429155 (also named STL1) and ZINC1447881 (also named AC1), have been tested in an experimental assay, together with IQO as a positive control and quercetin as a negative control. Only STL1 clearly inhibited AKT activation negatively modulating the PI₃K/AKT pathway.     

Novel Dual‐Site‐Binding Neuraminidase Inhibitor from Virtual Screening by Pharmacophore and Molecular Dynamics Methods

Neuraminidase is a significant anti‐influenza target that plays crucial role in virus replication cycle. The discovery of 150‐cavity in Group‐1 neuraminidase provides us a novel mentality of designing inhibitor which can bind with both conserved site and 150‐cavity. In order to discover novel dual‐site‐binding inhibitors, a 3D chemical‐feature‐based pharmacophore model was established to cover dual‐site in neuraminidase. The dual‐site‐binding model was consistent in predicting the binding conformation of Group‐1 neuraminidase inhibitor and applied for virtual screening of Specs database. Compound 4 (ZINC05790048) that aligned well to the model was selected after multiple filtrations for molecular dynamics simulations, indicating improved binding energy with neuraminidase. It can sever as the lead compound for a novel series of inhibitors.     

Pharmacophore Modeling and in Silico Screening Studies to Design Potential KDR Kinase Inhibitors

A novel ligand‐based pharmacophore model for KDR kinase was generated on the basis of chemical features of 30 KDR kinase inhibitors. This pharmacophore model consists of one hydrogen‐bond acceptor, one hydrogen‐bond donor and two hydrophobic groups. Several methods have been used to validate the model, suggesting that it can serve as a reliable tool for virtual screening to facilitate the discovery of novel KDR inhibitors. The model was then used as database search query from the National Cancer Institute (NCI) database for the rational design to identify new hit compound.     

Evaluation of Virtual Screening Strategies for the Identification of γ-Secretase Inhibitors and Modulators

γ-Secretase is an intramembrane aspartyl protease that is important in regulating normal cell physiology via cleavage of over 100 transmembrane proteins, including Amyloid Precursor Protein (APP) and Notch family receptors. However, aberrant proteolysis of substrates has implications in the progression of disease pathologies, including Alzheimer’s disease (AD), cancers, and skin disorders. While several γ-secretase inhibitors have been identified, there has been toxicity observed in clinical trials associated with non-selective enzyme inhibition. To address this, γ-secretase modulators have been identified and pursued as more selective agents. Recent structural evidence has provided an insight into how γ-secretase inhibitors and modulators are recognized by γ-secretase, providing a platform for rational drug design targeting this protease. In this study, docking- and pharmacophore-based screening approaches were evaluated for their ability to identify, from libraries of known inhibitors and modulators with decoys with similar physicochemical properties, γ-secretase inhibitors and modulators. Using these libraries, we defined strategies for identifying both γ-secretase inhibitors and modulators incorporating an initial pharmacophore-based screen followed by a docking-based screen, with each strategy employing distinct γ-secretase structures. Furthermore, known γ-secretase inhibitors and modulators were able to be identified from an external set of bioactive molecules following application of the derived screening strategies. The approaches described herein will inform the discovery of novel small molecules targeting γ-secretase.     

From Myricetin to the Discovery of Novel Natural Human ENPP1 Inhibitors: A Virtual Screening,Molecular Docking,Molecular Dynamics Simulation,and MM/GBSA Study

It was recently revealed that naturally occurring myricetin can inhibit ectonucleotidase ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), which, in turn, can treat ischemic cardiac injury. However, due to myricetin’s poor druggability, its further developments are relatively limited, which necessitates the discovery of novel ENPP1-inhibiting myricetin analogs as alternatives. In this study, the binding model of myricetin with ENPP1 was elucidated by molecular docking and molecular dynamics studies. Subsequently, virtual screening on the self-developed flavonoid natural product database (FNPD), led to the identification of two flavonoid glycosides (Cas No: 1397173-50-0 and 1169835-58-8), as potential ENPP1 inhibitors. Docking scores and MM/GBSA binding energies predicted that they might have higher inhibitory effects than myricetin. This study provides a strong foundation for the future development of ischemic cardiac injury drugs.     

Virtual Screening and Biological Activity Evaluation of New Potent Inhibitors Targeting Hexokinase-II

Hexokinase-II (HK-II), the rate-limiting step enzyme in the glycolysis pathway, expresses high levels of cancer cells compared with normal cells. Due to its pivotal role in the different aspects of cancer physiology including cellular proliferation, metastasis, and apoptosis, HK-II provides a new therapeutic target for cancer therapy. The structure-based virtual screening targeting HK-II was used to hit identifications from small molecule databases, and the select compounds were further evaluated in biological assays. Forty-seven compounds with the lowest binding energies were identified as potential HK-II inhibitors. Among them, nine compounds displayed the highest cytotoxicity to three different cancer cells. Based on the mechanism study, compounds 4244-3659 and K611-0094 showed an obvious inhibitory effect on the HK-II enzyme. This study identified two potential inhibitors of HK-II and can be helpful for developing potential drugs targeting HK-II in tumor therapy.