基于虚拟筛选技术对氟康唑细胞毒性的机制评价

目的 探讨氟康唑对细胞是否具有毒性,能否用于防治细胞污染。方法 虚拟筛选与分子对接技术筛选潜在的毒性靶点。使用人肝癌SMMC-7721细胞、人胚肝HHL-5细胞与人胚肾HEK-293细胞进行体外实验。CCK8实验验证氟康唑对细胞活性的影响。Hoechst 33342/PI双染实验进行形态学观察。qRT-PCR实验进一步验证氟康唑对虚拟筛选所获的靶点是否有影响,明确对细胞影响的机制。结果 虚拟筛选前6个核心毒性靶点是IL-6、TP53、TNF、IL-1β、VEGFA以及EGFR。CCK8实验与Hoechst 33342/PI双染实验显示氟康唑对SMMC-7721细胞和HHL-5细胞具有抑制作用,对HEK-293细胞具有促增殖作用。qRT-PCR实验表明氟康唑导致细胞毒性的核心靶点是IL-6、IL-1β和EGFR。结论 氟康唑不适合广泛应用于预防细胞污染。     

虚拟筛选与新药发现

虚拟筛选是创新药物研究的新方法和新技术,近年来引起了研究机构和制药公司的高度重视,并且已经成为一种与高通量筛选互补的实用化工具,加入到了创新药物研究的工作流程(pipeline)中。本文介绍国际上虚拟筛选及其在创新药物发现中应用的研究进展,特别介绍了我国这方面研究的状况。     

新型冠状病毒相关靶点小分子抑制剂虚拟筛选

为确定治疗新型冠状病毒(SARS-CoV-2)感染的候选药物,开展了针对SARS-CoV-2的药物虚拟筛选研究。以SARS-CoV-2的刺突蛋白(S蛋白)和3CL蛋白酶(主蛋白酶)作为药物靶点,以美国食品药品监督管理局(FDA)批准上市的2 726个小分子药物作为候选,通过分子对接方法,筛选出了3种(阿巴瑞克(Abarelix)、西曲瑞克(Cetrorelix)、鞣酸(Tannic acid))与S蛋白具有较强结合能力的小分子药物,1种(戈舍瑞林(Goserelin))与3CL蛋白酶具有较好结合能力的小分子药物,它们理论上都具有抑制新型冠状病毒复制的效果。将靶向3CL蛋白酶的候选药物与辉瑞公司开发的药物Paxlovid进行比较,发现其作用位点均集中于3CL蛋白酶的第130-200位的残基周围,具有相似的结合位点与相互作用。此外也对候选药物的物理与化学性质及与基因相互作用进行了分析。本研究可为开发新型冠状病毒感染的治疗药物提供参考。     

Autodock Vina与Discovery Studio在虚拟筛选耐药蛋白抑制剂中的比较

随着大量与细菌耐药相关的基因的发现和其表达蛋白结构的成功测定,从已有的化合物中通过计算机模拟方法筛选对耐药蛋白靶点有作用的候选化合物,成为了药物发现的一个标准途径。虚拟筛选在耐药基因抑制剂的发现中可以提高效率、降低实验成本。本文介绍了Autodock Vina和Discovery Studio在基于分子对接法的虚拟筛选中的使用,并对比分析其对β-内酰胺酶活性位点的筛选结果。希望通过这种比较促进虚拟筛选在药物设计领域中的应用,提高耐药基因抑制剂的发现速度。     

面向虚拟筛选的GPU加速的分子对接方法

虚拟筛选是在计算机上对化合物分子进行模拟预筛选,找出容易和药物靶标结合的小分子(配体),从而降低实际实验测试次数,提高药物先导化合物的发现效率。常用的分子对接软件可以用于基于结构的虚拟筛选,寻找配体与靶标的最佳的作用模式和结合构象,并通过打分函数来筛选出潜在的配体。现有的对接软件如AutoDock Vina等在分子对接过程中需要耗费大量时间和计算资源,特别是面对大规模分子对接时,过长的筛选时间不能满足应用需求,因此,本文在最高效的QVina2对接软件基础上,提出一种基于GPU的QVina 2并行化方法QVina2-GPU,利用GPU硬件高度并行体系加速分子对接。具体包括增加初始化分子构象数量,以扩展蒙特卡罗的迭代局部搜索中线程的并行规模,增加蒙特卡罗的迭代搜索的广度以减少每次蒙特卡罗迭代搜索深度,并利用Wolfe-Powell准则改进局部搜索算法,提高了对接精度,进一步减少蒙特卡罗迭代搜索深度,最后,在NVIDIA Geforce RTX 3090平台上在公开的配体数据库上验证了QVina2-GPU的性能,实验表明在保证分子对接精度的基础上,我们提出的QVina2-GPU对Qvina2的平均加速比达到5.18倍,最大加速比达到12.28倍。     

红藤化学成分中端粒酶TERT基因激动剂的虚拟筛选

目的:为了发现红藤中能够激活端粒酶逆转录酶(TERT)的化学成分.方法:检索文献和数据库,确定红藤中的小分子化合物(配体);使用AutodockVina软件与受体TERT(PDB ID:5CQG)进行分子对接,根据结合能(打分值)确定潜在的激活TERT的化合物.结果:红藤中126种化合物与5CQG的结合能与化合物的结构...     

辐射增敏剂增敏活性的分子连接性研究

计算４２个辐射增敏剂的各阶分子连接性指数ｍＸｔ及△ｍＸｔ,并对其中３８个非对称性化合物的分子连接性指数与其增敏活性进行定量构效关系（ＱｕａｎｔｉｔａｔｉｒｅＳｔｒｕｃｔｕｒｅＡｃｔｉｖｉｔｙＲｅｌａｔｉｏｎｓｈｉｐ,ＱＳＡＲ）的研究,得到相关方程。并分析了影响增敏活性的１ｇＰ、ＥＳ及σ在一定程度上都可通过分子连接性指数表达出来。对这些增敏剂进行分子对称性的研究,发现对称性会降低分子的极性,进而降低其增敏活性。另外,还发现在这些增敏剂中存在亚类现象,并对各亚类的反应机理进行了探讨。     

系统比较氨基酸描述子在多肽定量构效关系中的应用

氨基酸描述子是采用多元统计方法从大量氨基酸性质参数中提取得到的少数关键信息成分,目前已被广泛应用于多肽生物活性预测及蛋白质功能判别等领域。鉴于近年来氨基酸描述子种类的极速扩增,收集了目前国内外发表的27种氨基酸描述子,并将其用于8组经典多肽集的结构表征及定量构效关系研究。通过系统比较这些描述子对同一肽集与不同肽集的统计建模结果,我们认为物理化学描述子的建模效果优于拓扑描述子,拓扑描述子的建模效果优于三维结构描述子,且已有诸多氨基酸描述子已经达到性能限度,如未考虑肽链内部各氨基酸残基的交互影响,肽配基与相应靶标蛋白的相互结合,因此不再建议按照传统思路进一步提出新型描述子种类。     

计算机辅助筛选核酸适配体技术

计算机信息技术已经渗透到我们生活的方方面面,不仅可以辅助药物的筛选,也能够模拟药物的作用.目前已有研究使用计算机辅助技术筛选适配体,对筛选效率的提升和筛选高亲和力的适配体有着重要的指导作用.文中将主要对计算机通过序列评估、结构分析、分子对接3个方面辅助适配体筛选的方法进行综述.     

细胞分子生物学技术在靶向药物筛选中的应用

随着细胞及分子生物学的发展,新技术方法越来越多地用于新靶点建立和药物筛选研究,为药物设计、靶点的选择和用药方案的确定提供理论依据,同时使药物筛选有了更高的特异性,对药物筛选和药理学研究起到了极大的促进作用. 论述了功能基因的筛选、高通量细胞筛选和高内涵筛选技术、反义核酸技术、转基因/基因敲除技术、基因芯片、蛋白质芯片、组织芯片、酶免疫分析、荧光免疫分析、流式细胞技术等方法在靶向药物筛选中的应用.     

3D QSAR pharmacophore-based virtual screening for the identification of potential inhibitors of tyrosinase

Abstract

Tyrosinase plays an important role in melanin biosynthesis and protects skin against ultraviolet radiations. Functional deficiency of tyrosinase results in serious dermatological diseases. Tyrosinase also participates in neuromelanin formation in the human brain, which leads to neurodegeneration resulting in Parkinson’s disease. In fruits and vegetables, tyrosinase plays a critical role in senescence, causing undesired browning that results in faster deterioration and shorter shelf lines. The only commercially available tyrosinase is mushroom tyrosinase and it shows the highest homology to the mammalian tyrosinase. Although kojic acid is currently used as a tyrosinase inhibitor, they have serious side effects such as dermatitis, carcinogenesis and hepatotoxicity. Therefore, in order to develop a more active and safer tyrosinase inhibitor, 3D QSAR pharmacophore models were generated based on experimentally known inhibitors. The pharmacophore model, Hypo1, was developed with a large cost difference, high correlation coefficient and low RMS deviation. Hypo1 showed a good spatial arrangement; consisting of five-point features including two hydrogen bond acceptor, one hydrogen bond donor and two hydrophobic features. Hypo1 was further validated by cost analysis, test set and Fisher’s randomisation method. Hypo1 was used as a 3D query for screening the in-house drug-like databases, and the hits were further selected by applying ADMET, Lipinski’s rule of five and fit value criteria. To identify binding conformations, the obtained hits were subjected to molecular docking. Finally, molecular dynamics simulations revealed the appropriate binding modes of hit compounds. To conclude, we propose the final three hit compounds with new structural scaffolds as a virtual candidate as tyrosinase inhibitors.

Communicated by Ramaswamy H. Sarma     

Homology modeling,docking and structure-based virtual screening for new inhibitor identification of Klebsiella pneumoniae heptosyltransferase-III

Abstract

Klebsiella pneumoniae (K. pneumoniae) is a Gram-negative opportunistic pathogen commonly associated with hospital-acquired infections that are often resistant even to antibiotics. Heptosyltransferase (HEP) belongs to the family of glycosyltransferase-B (GT-B) and plays an important in the synthesis of lipopolysaccharides (LPS) essential for the formation of bacterial cell membrane. HEP-III participates in the transfer of heptose sugar to the outer surface of bacteria to synthesize LPS. LPS truncation increases the bacterial sensitivity to hydrophobic antibiotics and detergents, making the HEP as a novel drug target. In the present study, we report the 3D homology model of K. pneumoniae HEP-III and its structure validation. Active site was identified based on similarities with known structures using Dali server, and structure-based pharmacophore model was developed for the active site substrate ADP. The generated pharmacophore model was used as a 3D search query for virtual screening of the ASINEX database. The hit compounds were further filtered based on fit value, molecular docking, docking scores, molecular dynamics (MD) simulations of HEP-III complexed with hit molecules, followed by binding free energy calculations using Molecular Mechanics-Poisson–Boltzmann Surface Area (MM-PBSA). The insights obtained in this work provide the rationale for design of novel inhibitors targeting K. pneumoniae HEP-III and the mechanistic aspects of their binding.

Communicated by Ramaswamy H. Sarma     

Recovering the true targets of specific ligands by virtual screening of the protein data bank

The Protein Data Bank (PDB) has been processed to extract a screening protein library (sc-PDB) of 2148 entries. A knowledge-based detection algorithm has been applied to 18,000 PDB files to find regular expressions corresponding to either protein, ions, co-factors, solvent, or ligand atoms. The sc-PDB database comprises high-resolution X-ray structures of proteins for which (i) a well-defined active site exists, (ii) the bound-ligand is a small molecular weight molecule. The database has been screened by an inverse docking tool derived from the GOLD program to recover the known target of four unrelated ligands. Both the database and the inverse screening procedures are accurate enough to rank the true target of the four investigated ligands among the top 1% scorers, with 70-100 fold enrichment with respect to random screening. Applying the proposed screening procedure to a small-sized generic ligand was much less accurate suggesting that inverse screening shall be reserved to rather selective compounds.     

Docking, virtual high throughput screening and in silico fragment-based drug design

The drug discovery process has been profoundly changed recently by the adoption of computational methods helping the design of new drug candidates more rapidly and at lower costs. In silico drug design consists of a collection of tools helping to make rational decisions at the different steps of the drug discovery process, such as the identification of a biomolecular target of therapeutical interest, the selection or the design of new lead compounds and their modification to obtain better affinities, as well as pharmacokinetic and pharmacodynamic properties. Among the different tools available, a particular emphasis is placed in this review on molecular docking, virtual high-throughput screening and fragment-based ligand design.     

New potential inhibitors of mTOR: a computational investigation integrating molecular docking,virtual screening and molecular dynamics simulation

The mTOR (mammalian or mechanistic Target Of Rapamycin), a complex metabolic pathway that involves multiple steps and regulators, is a major human metabolic pathway responsible for cell growth control in response to multiple factors and that is dysregulated in various types of cancer. The classical inhibition of the mTOR pathway is performed by rapamycin and its analogs (rapalogs). Considering that rapamycin binds to an allosteric site and performs a crucial role in the inhibition of the mTOR complex without causing the deleterious side effects common to ATP-competitive inhibitors, we employ ligand-based drug design strategies, such as virtual screening methodology, computational determination of ADME/Tox properties of selected molecules, and molecular dynamics in order to select molecules with the potential to become non-ATP-competitive inhibitors of the mTOR enzymatic complex. Our findings suggest five novel potential mTOR inhibitors, with similar or better properties than the classic inhibitor complex, rapamycin.     

Shape-based virtual screening,docking, and molecular dynamics simulations to identify Mtb-ASADH inhibitors

Aspartate β-semialdehyde dehydrogenase (ASADH) is a key enzyme for the biosynthesis of essential amino acids and several important metabolites in microbes. Inhibition of ASADH enzyme is a promising drug target strategy against Mycobacterium tuberculosis (Mtb). In this work, in silico approach was used to identify potent inhibitors of Mtb-ASADH. Aspartyl β-difluorophosphonate (β-AFP), a known lead compound, was used to understand the molecular recognition interactions (using molecular docking and molecular dynamics analysis). This analysis helped in validating the computational protocol and established the participation of Arg99, Glu224, Cys130, Arg249, and His256 amino acids as the key amino acids in stabilizing ligand–enzyme interactions for effective binding, an essential feature is H-bonding interactions with the two arginyl residues at the two ends of the ligand. Best binding conformation of β-AFP was selected as a template for shape-based virtual screening (ZINC and NCI databases) to identify compounds that competitively inhibit the Mtb-ASADH. The top rank hits were further subjected to ADME and toxicity filters. Final filter was based on molecular docking analysis. Each screened molecule carries the characteristics of the highly electronegative groups on both sides separated by an average distance of 6?Å. Finally, the best predicted 20 compounds exhibited minimum three H-bonding interactions with Arg99 and Arg249. These identified hits can be further used for designing the more potent inhibitors against ASADH family. MD simulations were also performed on two selected compounds (NSC4862 and ZINC02534243) for further validation. During the MD simulations, both compounds showed same H-bonding interactions and remained bound to key active residues of Mtb-ASADH.     

The importance of ARG513 as a hydrogen bond anchor to discover COX-2 inhibitors in a virtual screening campaign

Structure-based virtual screening (SBVS) protocols were developed to find cyclooxygenase-2 (COX-2) inhibitors using the Protein-Ligand ANT System (PLANTS) docking software. The directory of useful decoys (DUD) dataset for COX-2 was used to retrospectively validate the protocols; the DUD consists of 426 known inhibitors in 13289 decoys. Based on criteria used in the article describing DUD datasets, the default protocol showed poor results. However, having ARG513 as a hydrogen bond anchor increased the quality of the SBVS protocol. The modified protocol showed results that could be well considered, with a maximum enrichment factor (EF(max)) value of 32.2.     

Comparative docking of dual conformations in human fatty acid synthase thioesterase domain reveals potential binding cavity for virtual screening of ligands

Human fatty acid synthase (hFASN), a homo dimeric lipogenic enzyme with seven catalytic domains, is an important clinical target in cancer, metabolic syndrome and infections. Here, molecular modelling and docking methods were implemented to examine the inter-molecular interactions of thioesterase (TE) domain in hFASN with its physiological substrate, and to identify potential chemical inhibitors. TE catalyses the hydrolysis of thioester bond between palmitate and the 4’ phosphopantetheine of acyl carrier protein, releasing 16-carbon palmitate. The crystal structure of hFASN TE in two inhibitory conformations (A and B) were geometry-optimized and used for molecular docking with palmitate, orlistat (a known FASN inhibitor) and virtual screening against compounds from National Cancer Institute (NCI) database. Relatively, low binding affinity was observed during the complex formation of palmitate with A (?.164 kcal/mol) and B (?.332 kcal/mol) forms of TE, when compared with orlistat-docked TE (A form: ?5.872 kcal/mol and B form: ?5.484 kcal/mol), clearly indicating that the native inhibited conformation (crystal structure) was unfavourable for substrate binding. We used these orlistat dual binding modes as positive controls for prioritizing the ligands during virtual screening. From 2, 31,617 molecules in the NCI database, 916 high-scoring compounds (hit ligands) were obtained for A-form and 4582 for B-form of the TE-domain, which were then ranked according to glide docking score, XP H bond score, absorption, distribution, metabolism and excretion and binding free energy (Prime/MM-GBSA). Consequently, two top scoring ligands (NSC: 319661 and NSC: 153166) emerged as promising drug candidates that may be tested in FASN-over-expressing diseases.     

Discover potential inhibitors for PFKFB3 using 3D-QSAR,virtual screening,molecular docking and molecular dynamics simulation

Abstract

The 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3) is a master regulator of glycolysis in cancer cells by synthesizing fructose-2,6-bisphosphate (F-2,6-BP), a potent allosteric activator of phosphofructokinase-1 (PFK-1), which is a rate-limiting enzyme of glycolysis. PFKFB3 is an attractive target for cancer treatment. It is valuable to discover promising inhibitors by using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking and molecular dynamics simulation. Twenty molecules with known activity were used to build 3D-QSAR pharmacophore models. The best pharmacophore model was ADHR called Hypo1, which had the highest correlation value of 0.98 and the lowest RMSD of 0.82. Then, the Hypo1 was validated by cost value method, test set method and decoy set validation method. Next, the Hypo1 combined with Lipinski's rule of five and ADMET properties were employed to screen databases including Asinex and Specs, total of 1,048,159 molecules. The hits retrieved from screening were docked into protein by different procedures including HTVS, SP and XP. Finally, nine molecules were picked out as potential PFKFB3 inhibitors. The stability of PFKFB3-lead complexes was verified by 40?ns molecular dynamics simulation. The binding free energy and the energy contribution of per residue to the binding energy were calculated by MM-PBSA based on molecular dynamics simulation.