基于CCR5受体的中药化合物抑制剂的虚拟筛选及活性评价

目的:以C-C趋化因子受体5(C-C chemokine receptor type 5,CCR5)为靶点,利用计算机辅助药物设计技术中的虚拟筛选技术、体外抗病毒活性实验、细胞毒性测试以及分子动力学实验筛选出低毒性、高抗艾滋病病毒(HIV)活性的中药化合物抑制剂,并探究其内在结合机制.方法:接受者操作特征曲线(rece...     

海洋细菌中活性化合物的筛选策略

目的 从700多株海洋沉积物分离得到的细菌中筛选安莎类抗生素和6-脱氧己糖(6DOH)糖基化修饰的次级代谢产物产生菌.方法 以3-氨基-5-羟基苯甲酸(AHBA)合酶基因和dTDP-葡萄糖-4,6-脱水酶基因为靶标,分别进行安莎类抗生素和6DOH糖基化修饰的次级代谢产物产生菌的分子筛选.对AHBA合酶及dTDP-葡萄糖-4,6-脱水酶基因阳性的菌株发酵液及提取物进行抗菌、抗肿瘤、抗病毒活性分析.采用利福霉素抗性及氢氧化钠显色初步鉴定安莎类化合物;采用α-萘酚硫酸显色初步鉴定6DOH糖基化修饰的化合物.利用16S rRNA序列对部分阳性菌株进行系统发育分析.结果 共获得39株AHBA合酶基因阳性和10株dTDP-葡萄糖-4,6-脱水酶基因阳性菌株.阳性菌株中,78%具有不同程度的生物活性.化学初步鉴定结果表明:49%的AHBA合酶基因阳性菌株产生安莎类化合物:50%的dTDP-葡萄糖-4,6-脱水酶基因阳性菌株产生6DOH糖基化修饰的化合物.系统发育分析表明,大多数阳性菌株属于放线菌中的链霉菌属.结论 基因探针筛选可作为一种合理、有效的方法从海洋细菌中发现活性代谢产物.     

以非洲猪瘟病毒DNA聚合酶X为靶点的海洋药物计算机虚拟筛选

摘 要：目的 从天然产物中高效快速的筛选出一批有效针对非洲猪瘟病毒的药物苗头化合物,为寻找能抑制猪瘟蔓延的生物饲料提供线索。方法 利用计算机辅助药物设计技术,以非洲猪瘟病毒。DNA聚合酶X的DNA结合区为靶点,进行虚拟筛选,在陆生和海洋天然产物中,化合物通过文献搜索和数据库查询明确部分天然产物的来源。结果 从陆生天然产物库中筛选出16个苗头化合物,其中有5个源自中国;在海洋化合物数据库MarineChem3D中得到了59个苗头化合物,其中有8个源自中国。结论 我国境内分布有13种潜在的具抑制非洲猪瘟效果的天然产物,其中大部分来源于海洋生物。     

以c-MET为抗癌靶点的海洋小分子虚拟筛选

目的：运用计算机虚拟筛选技术和分子动力学模拟寻找海洋小分子库(SWMD)中抗癌靶点c-MET的小分子抑制剂。方法：利用schrodinger中Ligand Docking模块对海藻代谢物数据库(SWMD)和PDB网站检索的c-MET蛋白（PDB：2rfs）进行基于受体的分子对接筛选,采用对接得分前十的结果,利用Swiss ADME网站进行成药性分析。最后将最好的结果用Gromacs进行分子动力学模拟。结果：分子对接结果显示打分前十的化合物都能与蛋白有较好的结合效果和对接姿势,蛋白与化合物之间的相互作用主要以氢键作用为主。分子动力学结果显示配体能在受体的结合口袋中稳定存在,同时具备较为稳定的对接构象。结论：基于分子对接技术和分子动力学虚拟筛选潜在的抗癌靶点c-MET的小分子抑制剂,为研发抗癌海洋药物提供科学指导与理论依据。     

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。     

当归黄芪超滤物通过调控ROS/GSH/GPx4轴抑制铁死亡改善放射性大鼠心肌纤维化

目的 探讨ROS/GSH/GPx4轴介导的心肌铁死亡在放射性心肌纤维化中的作用机制及当归黄芪超滤物的干预作用。方法 50只大鼠随机分为5组。除空白组外,其余各组大鼠均接受X射线单次局部胸部照射建立放射性心肌纤维化模型。辐射后,当归黄芪超滤物各组大鼠分别灌胃给药30 d后,用小动物超声评价心功能;比色法检测SOD、GSH、MDA及Fe²⁺活性;免疫荧光检测ROS表达;HE、Masson染色观察病理变化及纤维化;透射电镜观察心肌超微结构;Western blot检测铁死亡及纤维化蛋白表达。结果 与空白组比较,模型组LVEF和LVFS值降低;Fe²⁺、MDA、ROS水平上升,SOD、GSH水平下降;HE及Masson显示有炎性细胞聚集及胶原纤维沉积;透射电镜显示受损线粒体数量增多、排列紊乱,形态不规则、变小、嵴紊乱;Western blot显示α-SMA、Collagen I、NOX1蛋白表达升高,GPx4、FTH1蛋白表达降低;与模型组比较,当归黄芪超滤物组大鼠心功能改善,ROS、抗氧化指标恢复,Fe²⁺水平降低;线粒体结构...     

虚拟筛选辅助新药发现的研究进展

近年来从中药和天然药物中发现新药已成为药物研究领域的热点。虽然从中药和天然药物中能够分离得到大量新化合物,但通常这些新化合物的作用靶点难于确定,而且由于其收率较低,限制了进一步的药理学机制研究。因此,应用基于蛋白质结构的虚拟筛选方法,结合蛋白质生物化学和药理学加以验证,能够大大缩短研究时间并降低对化合物产量的需求。综述基于蛋白质结构-分子对接的天然产物活性筛选方法、虚拟筛选常用软件、天然产物数据库、结构生物学基本方法、蛋白质结构数据库（PDB）、筛选结果的实验性验证方法,以及虚拟筛选方法发现天然产物为先导化合物的成功应用实例,以期为天然药物活性成分的快速发现提供参考。

     

基于药效团模型的DNA错配修复化合物筛选

目的:找到一种具有DNA错配修复功能的化合物.方法:通过构建大肠埃希菌MutS蛋白受体与内源性配体之间的药效团模型,构建并筛选化合物数据库,使用分子对接和分子动力学模拟的方法,确认具有潜在DNA错配修复功能的先导化合物,并对其与受体蛋白之间的结合作用模式进行分析.结果:筛选得到了化合物52,分子动力学模拟显示该化合物与...     

基于计算机虚拟筛选技术寻找NDM-1抑制剂

β-内酰胺类抗生素的广泛使用,使得越来越多的细菌产生由β-内酰胺酶介导的耐药性,针对丝氨酸β-内酰胺酶,目前已有克拉维酸、舒巴坦等抑制剂与临床常用抗生素配伍使用,但尚无金属β-内酰胺酶的有效抑制剂,因此,寻找金属β-内酰胺酶尤其是目前最受瞩目的新德里金属β-内酰胺酶-1[NDM-1(B1类)]的抑制剂是遏制"超级细菌"引起的感染最迫切的要求。虚拟筛选作为发现新的先导化合物、寻找新药物的有力手段,大大缩小了人工进行配体活性筛选研究范围。我们通过计算机虚拟筛选技术,利用Discovery Studio 2.5和GOLD 3.0平台,基于NDM-1晶体结构(PDB:3Q6X),从一个含有2059个天然产物分子的化合物库里筛选得到6个可能具有金属β-内酰胺酶NDM-1(B1类)抑制活性的化合物结构。     

盐皮质激素受体拮抗剂的虚拟筛选

目的:采用分子对接和分子动力学方法进行盐皮质激素受体拮抗剂(mineralocorticoid receptor antagonists,MRAs)的虚拟筛选,以期发现全新结构的具有潜在治疗糖尿病肾病的先导化合物。方法:收集已知活性的MRAs及其诱饵分子,构建基于结构的盐皮质激素受体虚拟筛选模型,利用该模型对中药成分数据库(Traditional Chinese Medicines Integrated Database,TCMID)进行虚拟筛选,综合对接得分和结合模式挑选2个与阳性药类似的中药成分进行分子动力学模拟,以期发现潜在MRAs。结果:诱饵分子结果表明构建的虚拟筛选模型可用于MRAs化合物库的虚拟筛选,通过对中药成分数据库的虚拟筛选发现30个候选MRAs化合物,其中分子动力学模拟结果表明,水飞蓟莫林的结合自由能略差于已知阳性化合物,可作为MRAs的先导化合物。结论:分子对接结合分子动力学模拟方法可用于从中药成分中筛选新型盐皮质激素受体拮抗剂,为糖尿病肾病药物开发的相关实验研究提供设计思路。     

ENRI: A tool for selecting structure‐based virtual screening target conformations

Finding pharmaceutically relevant target conformations from an arbitrary set of protein conformations remains a challenge in structure‐based virtual screening (SBVS). The growth in the number of available conformations, either experimentally determined or computationally derived, obscures the situation further. While the inflated conformation space potentially contains viable druggable targets, the increase of conformational complexity, as a consequence, poses a selection problem. To address this challenge, we took advantage of machine learning methods, namely an over‐sampling and a binary classification procedure, and present a novel method to select druggable receptor conformations. Specifically, we trained a binary classifier on a set of nuclear receptor conformations, wherein each conformation was labeled with an enrichment measure for a corresponding SBVS. The classifier enabled us to formulate suggestions and identify enriching SBVS targets for six of seven nuclear receptors. Further, the classifier can be extended to other proteins of interest simply by feeding new training data sets to the classifier. Our work, thus, provides a methodology to identify pharmaceutically interesting receptor conformations for nuclear receptors and other drug targets.     

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.     

DrugRep: an automatic virtual screening server for drug repurposing

Computationally identifying new targets for existing drugs has drawn much attention in drug repurposing due to its advantages over de novo drugs, including low risk, low costs, and rapid pace. To facilitate the drug repurposing computation, we constructed an automated and parameter-free virtual screening server, namely DrugRep, which performed molecular 3D structure construction, binding pocket prediction, docking, similarity comparison and binding affinity screening in a fully automatic manner. DrugRep repurposed drugs not only by receptor-based screening but also by ligand-based screening. The former automatically detected possible binding pockets of the receptor with our cavity detection approach, and then performed batch docking over drugs with a widespread docking program, AutoDock Vina. The latter explored drugs using seven well-established similarity measuring tools, including our recently developed ligand-similarity-based methods LigMate and FitDock. DrugRep utilized easy-to-use graphic interfaces for the user operation, and offered interactive predictions with state-of-the-art accuracy. We expect that this freely available online drug repurposing tool could be beneficial to the drug discovery community. The web site is http://cao.labshare.cn/drugrep/.     

Pharmacophore-based virtual screening and density functional theory approach to identifying novel butyrylcholinesterase inhibitors

Aim:

To identify the critical chemical features, with reliable geometric constraints, that contributes to the inhibition of butyrylcholinesterase (BChE) function.

Methods:

Ligand-based pharmacophore modeling was used to identify the critical chemical features of BChE inhibitors. The generated pharmacophore model was validated using various techniques, such as Fischer''s randomization method, test set, and decoy set. The best pharmacophore model was used as a query in virtual screening to identify novel scaffolds that inhibit BChE. Compounds selected by the best hypothesis in the virtual screening were tested for drug-like properties, and molecular docking study was applied to determine the optimal orientation of the hit compounds in the BChE active site. To find the reactivity of the hit compounds, frontier orbital analysis was carried out using density functional theory.

Results:

Based on its correlation coefficient (0.96), root mean square (RMS) deviation (1.01), and total cost (105.72), the quantitative hypothesis Hypo1 consisting of 2 HBA, 1 Hy-Ali, and 1 Hy-Ar was selected as the best hypothesis. Thus, Hypo1 was used as a 3D query in virtual screening of the Maybridge and Chembridge databases. The hit compounds were filtered using ADMET, Lipinski''s Rule of Five, and molecular docking to reduce the number of false positive results. Finally, 33 compounds were selected based on their critical interactions with the significant amino acids in BChE''s active site. To confirm the inhibitors'' potencies, the orbital energies, such as HOMO and LUMO, of the hit compounds and 7 training set compounds were calculated. Among the 33 hit compounds, 10 compounds with the highest HOMO values were selected, and this set was further culled to 5 compounds based on their energy gaps important for stability and energy transfer. From the overall results, 5 hit compounds were confirmed to be potential BChE inhibitors that satisfied all the pharmacophoric features in Hypo1.

Conclusion:

This study pinpoints important chemical features with geometric constraints that contribute to the inhibition of BChE activity. Five compounds are selected as the best hit BchE-inhibitory compounds.     

基于分子对接技术筛选抗严重急性呼吸综合征冠状病毒2中药小分子化合物

目的:以SARS-CoV-S/ACE2复合蛋白和严重急性呼吸综合征冠状病毒2(severe acute respiratory syndrome coronavirus 2,SARS-CoV-2) Mpro水解酶为靶标,从TCMSP数据库中筛选SARS-CoV-2-ACE2结合阻断剂和SARS-CoV-2 Mpro水解酶抑制剂,指导以中药小分子为前体的抗SARS-CoV-2新药的研发。方法:根据文献报道,确定SARS-CoV-S/ACE2复合蛋白和SARS-CoV-2 Mpro水解酶晶体结构模型上的活性位点,利用LibDock分子对接技术虚拟筛选TCMSP数据库中的小分子化合物,结合打分值以及化合物与靶蛋白受体的相互作用模式优化筛选结果,获得具有抗SARS-CoV-2潜在活性的中药小分子化合物。结果:确定SARS-CoV-S/ACE2复合蛋白结构中位于结合表面上的ASP38,GLN42,GLN325,GLU329,TYR436和TYR491以及SARS-CoV-2 Mpro水解酶结构中的THR24,THR25,THR26,LEU27,ASN28和ASN119为本次分子对接的关键氨基酸;...     

Discovery of potential integrin VLA-4 antagonists using pharmacophore modeling, virtual screening and molecular docking studies

Very late antigen-4 (VLA-4) is an integrin protein, and its antagonists are useful as anti-inflammatory drugs. The aim of this study is to discover novel virtual lead compounds to use them in designing potent VLA-4 antagonists. A best pharmacophore model was generated with correlation coefficient of 0.935, large cost difference of 114.078, comprising two hydrogen bond acceptors and three hydrophobic features. It was further validated and used in database screening for potential VLA-4 antagonists. A homology model of VLA-4 was built and employed in molecular docking of screened hit compounds. Finally, two compounds were identified as potential virtual leads to be deployed in the designing of novel potent VLA-4 antagonists.     

Molecular docking-based virtual screening and dynamics simulation study of novel and potential SIRT7 inhibitors

In cancer cells, short for sirtuin (SIRT7) stabilizes the transformed state via its nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase activity. Epigenetic factor SIRT7 plays important roles in cancer biology, reversing cancer phenotypes and suppressing tumor growth when inactive. In the present study, we got the SIRT7 protein structure from Alpha Fold2 Database and performed structure-based virtual screening to develop specific SIRT7 inhibitors using the SIRT7 inhibitor 97,491 interaction mechanism. As candidates for specific SIRT7 inhibitors, compounds with high affinities to SIRT7 were chosen. ZINC000001910616 and ZINC000014708529, two of our leading compounds, showed strong interactions with SIRT7. Our MD simulation results also revealed that the 5-hydroxy-4H-thioxen-4-one group and terminal carboxyl group were critical groups responsible for interaction of small molecules with SIRT7. In our study, we demonstrated that targeting SIRT7 may offer novel therapeutic options for cancer treatment. Compounds ZINC000001910616 and ZINC000014708529 can serve as chemical probes to investigate SIRT7 biological functions and provide starting points for the development of novel therapeutics against cancers.     

Modifications of the scoring function in FlexX for virtual screening applications

A modification of the hydrogen bond score in the docking program FlexX is presented. Hydrogen bonds formed in inaccessible regions of protein cavities thereby gain larger weight than others formed at the protein surface. The modified scoring function is tested with thrombin as a target. Secondly, a recently published knowledge-based scoring function is comparedto the FlexX scoring function in several database ranking experiments. This revised version was published online in June 2006 with corrections to the Cover Date.     

Ligand-based virtual screening and molecular docking studies to identify the critical chemical features of potent cathepsin D inhibitors

Cathepsin D is a major component of lysosomes and plays a major role in catabolism and degenerative diseases. The quantitative structure-activity relationship study was used to explore the critical chemical features of cathepsin D inhibitors. Top 10 hypotheses were built based on 36 known cathepsin D inhibitors using HypoGen/Discovery Studio v2.5. The best hypothesis Hypo1 consists of three hydrophobic, one hydrogen bond acceptor lipid, and one hydrogen bond acceptor features. The selected Hypo1 model was cross-validated using Fischer's randomization method to identify the strong correlation between experimental and predicted activity value as well as the test set and decoy sets used to validate its predictability. Moreover, the best hypothesis was used as a 3D query in virtual screening of Scaffold database. Subsequently, the screened hit molecules were filtered by applying Lipinski's rule of five, absorption, distribution, metabolism, and toxicity, and molecular docking studies. Finally, 49 compounds were obtained as potent cathepsin D inhibitors based on the consensus scoring values, critical interactions with protein active site residues, and predicted activity values. Thus, we suggest that the application of Hypo1 could assist in the selection of potent cathepsin D leads from various databases. Hence, this model was used as a valuable tool to design new candidate for cathepsin D inhibitors.