Pharmacophore modeling,virtual screening,docking and in silico ADMET analysis of protein kinase B (PKB β) inhibitors

Protein kinase B (PKB) is a key mediator of proliferation and survival pathways that are critical for cancer growth. Therefore, inhibitors of PKB are useful agents for the treatment of cancer. Herein, we describe pharmacophore-based virtual screening combined with docking study as a rational strategy for identification of novel hits or leads. Pharmacophore models of PKB β inhibitors were established using the DISCOtech and refined with GASP from compounds with IC₅₀ values ranging from 2.2 to 246 nM. The best pharmacophore model consists of one hydrogen bond acceptor (HBA), one hydrogen bond donor (HBD) site and two hydrophobic (HY) features. The pharmacophore models were validated through receiver operating characteristic (ROC) and Güner-Henry (GH) scoring methods indicated that the model-3 was statistically valuable and reliable in identifying PKB β inhibitors. Pharmacophore model as a 3D search query was searched against NCI database. Several compounds with different structures (scaffolds) were retrieved as hits. Molecules with a Q_fit value of more than 95 and three other known inhibitors were docked in the active site of PKB to further explore the binding mode of these compounds. Finally in silico pharmacokinetic and toxicities were predicted for active hit molecules. The hits reported here showed good potential to be PKB β inhibitors.     

变精度覆盖粗糙集模型的比较

介绍覆盖粗糙集和Ziarko变精度粗糙集模型,将Ziarko变精度粗糙近似算子应用于覆盖近似空间,借助引入的误差参数β (0 ≤β<0.5),给出2种变精度覆盖粗糙集模型的β上近似、β下近似、β边界和β负域的定义。讨论2种模型中β上、下近似算子的基本性质、2种模型之间的关系以及变精度覆盖粗糙集模型与其他粗糙集模型的关系。     

Binding free energy calculations using MMPB/GBSA approaches for PAMAM-G4-drug complexes at neutral,basic and acid pH conditions

Dendrimers are synthetic macromolecules with a highly-branched structure and high concentration of surface groups. Among dendrimers, Poly(amidoamine) (PAMAM) has received substantial attention as a novel drug carrier and delivery system. Depending on the generation and type of terminal groups, dendrimer toxicity could change and include cytotoxicity. Although PAMAM is water soluble, molecular modeling of the dendrimer-drug complex is considered challenging for exploring the conformational mobility of dendrimers and atomic specific interactions during the dendrimer-drug association. However, conventional protocols for predicting binding affinities have been designed for small protein molecules or protein-protein complexes that can be applied to study the dendrimer-drug association. In this work, we performed docking calculations for a set of 94 previously reported compounds on PAMAM of fourth generation (G4-PAMAM) to select six compounds, cromoglicic acid (CRO) − a mast cell stabilizer, Fusidic acid (FUS) − a bacteriostatic antibiotic, and Methotrexate (MTX) − a chemotherapy agent and immune system suppressant, which have the highest affinities for G4-PAMAM, and Lidocaine (LDC) − used to numb tissue in a specific area and for ventricular tachycardia treatment, Metoprolol (MET) − a β₁ receptor blocker, and Pindolol (PIN) − a β blocker, which have the lowest affinities for the G4-PAMAM dendrimer, to perform MD simulations combined with the molecular mechanics generalized/Poisson-Boltzmann surface area MMGBSA/MMPBSA approach to investigate the interactions of generating 4 charge-neutral, charge-basic and charge-acid G4-PAMAM dendrimers. In addition, to validate these theoretical G4-PAMAM-drug complexes, the complexes were experimentally conjugated to determine their stability in aqueous solubility studies immediately and over one year. Our results show that among the different commercial drugs, both charged and neutral PAMAM have the most favorable binding free energies for CRO, MTX, and FUS, which appears to be due to a complex counterbalance of electrostatics and van der Waals interactions. These theoretical and aqueous solubility studies supported the high affinity of methotrexate for the G4-PAMAM-drug due to its carboxyl and aryl moieties that favor its accommodation by noncovalent interactions.     

Applying linear interaction energy method for binding affinity calculations of podophyllotoxin analogues with tubulin using continuum solvent model and prediction of cytotoxic activity

Podophyllotoxin and its analogues have important therapeutic value in the treatment of cancer, due to their ability to induce apoptosis in cancer cells in a proliferation-independent manner. These ligands bind to colchicine binding site of tubulin near the α- and β-tubulin interface and interfere with tubulin polymerization. The binding free energies of podophyllotoxin-based inhibitors of tubulin were computed using a linear interaction energy (LIE) method with a surface generalized Born (SGB) continuum solvation model. A training set of 76 podophyllotoxin analogues was used to build a binding affinity model for estimating the free energy of binding for 36 inhibitors (test set) with diverse structural modifications. The average root mean square error (RMSE) between the experimental and predicted binding free energy values was 0.56 kcal/mol which is comparable to the level of accuracy achieved by the most accurate methods, such as free energy perturbation (FEP) or thermodynamic integration (TI). The squared correlation coefficient between experimental and SGB–LIE estimates for the free energy for the test set compounds is also significant (R² = 0.733). On the basis of the analysis of the binding energy, we propose that the three-dimensional conformation of the A, B, C and D rings is important for interaction with tubulin. On the basis of this insight, 12 analogues of varying ring modification were taken, tested with LIE methodology and then validated with their experimental potencies of tubulin polymerization inhibition. Low levels of RMSE for the majority of inhibitors establish the structure-based LIE method as an efficient tool for generating more potent and specific inhibitors of tubulin by testing rationally designed lead compounds based on podophyllotoxin derivatization.     

New design of nucleotide excision repair (NER) inhibitors for combination cancer therapy

Many cancer chemotherapy agents act by targeting the DNA of cancer cells, causing substantial damage within their genome and causing them to undergo apoptosis. An effective DNA repair pathway in cancer cells can act in a reverse way by removing these drug-induced DNA lesions, allowing cancer cells to survive, grow and proliferate. In this context, DNA repair inhibitors opened a new avenue in cancer treatment, by blocking the DNA repair mechanisms from removing the chemotherapy-mediated DNA damage. In particular, the nucleotide excision repair (NER) involves more than thirty protein–protein interactions and removes DNA adducts caused by platinum-based chemotherapy. The excision repair cross-complementation group 1 (ERCC1)-xeroderma pigmentosum, complementation group A (XPA) protein (XPA–ERCC1) complex seems to be one of the most promising targets in this pathway. ERCC1 is over expressed in cancer cells and the only known cellular function so far for XPA is to recruit ERCC1 to the damaged point. Here, we build upon our recent advances in identifying inhibitors for this interaction and continue our efforts to rationally design more effective and potent regulators for the NER pathway. We employed in silico drug design techniques to: (1) identify compounds similar to the recently discovered inhibitors, but more effective at inhibiting the XPA–ERCC1 interactions, and (2) identify different scaffolds to develop novel lead compounds. Two known inhibitor structures have been used as starting points for two ligand/structure-hybrid virtual screening approaches. The findings described here form a milestone in discovering novel inhibitors for the NER pathway aiming at improving the efficacy of current platinum-based therapy, by modulating the XPA–ERCC1 interaction.     

Development of a receptor model for efficient in silico screening of HIV-1 integrase inhibitors

Integrase (IN) is a key viral enzyme for the replication of the type-1 human immunodeficiency virus (HIV-1), and as such constitutes a relevant therapeutic target for the development of anti-HIV agents. However, the lack of crystallographic data of HIV IN complexed with the corresponding viral DNA has historically hindered the application of modern structure-based drug design techniques to the discovery of new potent IN inhibitors (INIs). Consequently, the development and validation of reliable HIV IN structural models that may be useful for the screening of large databases of chemical compounds is of particular interest. In this study, four HIV-1 IN homology models were evaluated respect to their capability to predict the inhibition potency of a training set comprising 36 previously reported INIs with IC₅₀ values in the low nanomolar to the high micromolar range. Also, 9 inactive structurally related compounds were included in this training set. In addition, a crystallographic structure of the IN-DNA complex corresponding to the prototype foamy virus (PFV) was also evaluated as structural model for the screening of inhibitors. The applicability of high throughput screening techniques, such as blind and ligand-guided exhaustive rigid docking was assessed. The receptor models were also refined by molecular dynamics and clustering techniques to assess protein sidechain flexibility and solvent effect on inhibitor binding. Among the studied models, we conclude that the one derived from the X-ray structure of the PFV integrase exhibited the best performance to rank the potencies of the compounds in the training set, with the predictive power being further improved by explicitly modeling five water molecules within the catalytic side of IN. Also, accounting for protein sidechain flexibility enhanced the prediction of inhibition potencies among the studied compounds. Finally, an interaction fingerprint pattern was established for the fast identification of potent IN inhibitors. In conclusion, we report an exhaustively validated receptor model if IN that is useful for the efficient screening of large chemical compounds databases in the search of potent HIV-1 IN inhibitors.     

Miniaturized devices for isothermal DNA amplification addressing DNA diagnostics

Microfluidics is an emerging technology enabling the development of lab-on-a-chip systems for clinical diagnostics, drug discovery and screening, food safety and environmental analysis. Currently, available nucleic acid diagnostic tests take advantage of polymerase chain reaction that allows exponential amplification of portions of nucleic acid sequences that can be used as indicators for the identification of various diseases. At the same time, isothermal methods for DNA amplification are being developed and are preferred for their simplified protocols and the elimination of thermocycling. Here, we present a low-cost and fast DNA amplification device for isothermal helicase dependent amplification implemented in the detection of mutations related to breast cancer as well as the detection of Salmonella pathogens. The device is fabricated by mass production amenable technologies on printed circuit board substrates, where copper facilitates the incorporation of on-chip microheaters, defining the thermal zone necessary for isothermal amplification methods.     

Antiviral drug acyclovir exhibits antitumor activity via targeting βTrCP1: Molecular docking and dynamics simulation study

The critical role of βTrCP1 in cancer development makes it a discerning target for the development of small drug like molecules. Currently, no inhibitor exists that is able to target its substrate binding site. Through molecular docking and dynamics simulation assays, we explored the comparative binding pattern of βTrCP1-WD40 domain with ACV and its phospho-derivatives (ACVMP, ACVDP and ACVTP). Consequently, through principal component analysis, βTrCP1-ACVTP was found to be more stable complex by obscuring a reduced conformational space than other systems. Thus based on the residual contribution and hydrogen bonding pattern, ACVTP was considered as a noteworthy inhibitor which demarcated binding in the cleft formed by βTrCP1-WD40 specific β-propeller. The outcomes of this study may provide a platform for rational design of specific and potent inhibitor against βTrCP1, with special emphasis on anticancer activity.     

Drug/nondrug classification using Support Vector Machines with various feature selection strategies

In conjunction with the advance in computer technology, virtual screening of small molecules has been started to use in drug discovery. Since there are thousands of compounds in early-phase of drug discovery, a fast classification method, which can distinguish between active and inactive molecules, can be used for screening large compound collections. In this study, we used Support Vector Machines (SVM) for this type of classification task. SVM is a powerful classification tool that is becoming increasingly popular in various machine-learning applications. The data sets consist of 631 compounds for training set and 216 compounds for a separate test set. In data pre-processing step, the Pearson's correlation coefficient used as a filter to eliminate redundant features. After application of the correlation filter, a single SVM has been applied to this reduced data set. Moreover, we have investigated the performance of SVM with different feature selection strategies, including SVM–Recursive Feature Elimination, Wrapper Method and Subset Selection. All feature selection methods generally represent better performance than a single SVM while Subset Selection outperforms other feature selection methods. We have tested SVM as a classification tool in a real-life drug discovery problem and our results revealed that it could be a useful method for classification task in early-phase of drug discovery.     

Multi-generational pharmacophore modeling for ligands to the cholane steroid-recognition site in the β1 modulatory subunit of the BKCa channel

Large conductance, voltage- and Ca²⁺-gated K⁺ (BK_Ca) channels play a critical role in smooth muscle contractility and thus represent an emerging therapeutic target for drug development to treat vascular disease, gastrointestinal, bladder and uterine disorders. Several compounds are known to target the ubiquitously expressed BK_Ca channel-forming α subunit. In contrast, just a few are known to target the BK_Ca modulatory β₁ subunit, which is highly expressed in smooth muscle and scarce in most other tissues. Lack of available high-resolution structural data makes structure-based pharmacophore modeling of β₁ subunit-dependent BK_Ca channel activators a major challenge. Following recent discoveries of novel BK_Ca channel activators that act via β₁ subunit recognition, we performed ligand-based pharmacophore modeling that led to the successful creation and fine-tuning of a pharmacophore over several generations. Initial models were developed using physiologically active cholane steroids (bile acids) as template. However, as more compounds that act on BK_Ca β₁ have been discovered, our model has been refined to improve accuracy. Database searching with our best-performing model has uncovered several novel compounds as candidate BK_Ca β₁ subunit ligands. Eight of the identified compounds were experimentally screened and two proved to be activators of recombinant BK_Ca β₁ complexes. One of these activators, sobetirome, differs substantially in structure from any previously reported activator.     

IBM microcomputer programs that analyze DNA sequences for tRNA genes

A set of four computer programs that search DNA sequence data files for transfer RNA genes have been written in IBM (Microsoft) BASIC for the IBM personal computer. These programs locate and plot predicted secondary structures of tRNA genes in the cloverleaf conformation. The set of programs are applicable to eukaryotic tRNA genes, including those containing intervening sequences, and to prokaryotic and mitochondrial tRNA genes. In addition, two of the programs search up to 150 residues downstream of tRNA gene sequences for possible eukaryotic RNA polymerase III termination sites comprised of at least four consecutive T residues. Molecular biologists studying a variety of gene sequence and flanking regions can use these programs to search for the additional presence of tRNA genes. Furthermore, investigators studying tRNA gene structure-to-function relationships would not need to do extensive restriction mapping to locate tRNA gene sequences within their cloned DNA fragments.     

Nonadditive entropies of order 1 and type β and of order α and type β; cascaded channels and the equivocation inequality

Using non-additive entropy of order 1 and type β and non-additive entropy of order α and type β, the variation of the equivocation and the average mutual information is studied for cascade channels. The equivocation inequality is shown to be satisfied by non-additive entropy of order 1 and type β (β,β > 0) for all channels and all probability distributions. It is shown that non-additive entropy of order α and type β satisfies this inequality for binary symmetric channels for certain probability distributions.     

一种创新的抗乳腺癌药物分子特征筛选模型

据不完全统计显示,2020年全球乳腺癌新增人数达到226万,女性乳腺癌是最常见的癌症类型,死亡率高居第五,因此对乳腺癌的治疗研究变得愈发重要。对雌激素受体α亚型(ERα)的研究显示其在乳腺发育过程中扮演重要角色。本文收集作用于ERα的化合物及其生物活性数据,并以一系列分子结构描述符作为自变量和以化合物的生物活性值作为因变量,通过随机森林与梯度提升树并融合专家知识来构建分子筛选模型,筛选出前20个对生物活性最具有显著影响的分子描述符。这些分子描述符对指导已有活性化合物的结构优化和药物研究具有重要意义。     

Wideband high gain conical dielectric resonator antenna: An experimental study of superstrate and reflector

A physically feasible new hybrid geometry for gain improvement with wideband characteristics is designed and validated experimentally in a conical shaped dielectric resonator antenna (DRA). It comprises of one superstrate and one reflector, with simple slot coupling technique for excitation. The reflector beneath the ground plane mainly accountable for significant gain improvement (～109%) by reducing the back radiation, whereas the superstrate dedicates for maintaining wideband (12.65%). The demonstrated result shows S ₁₁ < ?10 dB band between 7.4 GHz and 8.4 GHz, with 11.25 dBi peak gain, which are well matched with their simulated counter parts. It also gives high co‐pol to cross‐pol difference (～40 dB) in broadside direction. This new geometry can be eligible for X‐band applications as well can usher the DRA researchers for further innovations.     

基于VOC检测的光离子化检测器的实验研究

采用光离子化检测器(PID)传感器对挥发性有机化合物(vOC)气体进行检测,通过对PID的研究,自行设计适合本系统的PID检测器结构,并搭建了一套完整的检测系统.通过实验证明:所搭建的以PID检测器为核心的VOC气体检测系统已能够实现对VOC气体的定量检测.确定实验流量为160～170 mL/min,且在(0～5) ×10-6内传感器响应呈线性关系.     

Development of predictive in silico model for cyclosporine- and aureobasidin-based P-glycoprotein inhibitors employing receptor surface analysis

P-glycoprotein (Pgp) is implicated in multiple drug resistance (MDR) exhibited by several types of cancer against a multitude of anticancer chemotherapeutic agents. This problem prompted several research groups to search for effective P-gp inhibitors. Cyclosporine A (CsA), aureobasidin A (AbA) and related analogues were reported to possess potent inhibitory actions against Pgp. In this work we employed receptor surface analysis (RSA) to construct two satisfactory receptor surface models (RSMs) for cyclosporine- and aureobasidin-based Pgp inhibitors. These pseudoreceptors were combined to achieve satisfactory three-dimensional quantitative structure activity relationship (3D-QSAR) for 68 different cyclosporine and aureobasidin derivatives. Upon validation against an external set of 16 randomly selected Pgp inhibitors, the optimal 3D-QSAR was found to be self-consistent and predictive (r(LOO)(2)=0.673, r(PRESS)(2)=0.600). The resulting 3D-QSAR was employed to probe the structural factors that control the inhibitory activities of cyclosporine and aureobasidin analogues against Pgp.