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.     

Strategies for generating less toxic P-selectin inhibitors: pharmacophore modeling, virtual screening and counter pharmacophore screening to remove toxic hits

This paper describes the generation of ligand-based as well as structure-based models and virtual screening of less toxic P-selectin receptor inhibitors. Ligand-based model, 3D-pharmacophore was generated using 27 quinoline salicylic acid compounds and is used to retrieve the actives of P-selectin. This model contains three hydrogen bond acceptors (HBA), two ring aromatics (RA) and one hydrophobic feature (HY). To remove the toxic hits from the screened molecules, a counter pharmacophore model was generated using inhibitors of dihydrooratate dehydrogenase (DHOD), an important enzyme involved in nucleic acid synthesis, whose inhibition leads to toxic effects. Structure-based models were generated by docking and scoring of inhibitors against P-selectin receptor, to remove the false positives committed by pharmacophore screening. The combination of these ligand-based and structure-based virtual screening models were used to screen a commercial database containing 538,000 compounds.     

Multi-step virtual screening to develop selective DYRK1A inhibitors

Developing selective inhibitors for a particular kinase remains a major challenge in kinase-targeted drug discovery. Here we performed a multi-step virtual screening for dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) inhibitors by focusing on the selectivity for DYRK1A over cyclin-dependent kinase 5 (CDK5). To examine the key factors contributing to the selectivity, we constructed logistic regression models to discriminate between actives and inactives for DYRK1A and CDK5, respectively, using residue-based binding free energies. The residue-based parameters were calculated by molecular mechanics-generalized Born surface area (MM-GBSA) decomposition methods for kinase–ligand complexes modeled by computer ligand docking. Based on the findings from the logistic regression models, we built a three-dimensional (3D) pharmacophore model and chose filter criteria for the multi-step virtual screening. The virtual hit compounds obtained from the screening were assessed for their inhibitory activities against DYRK1A and CDK5 by in vitro assay. Our screening identified two novel selective DYRK1A inhibitors with IC₅₀ values of several μM for DYRK1A and >100 μM for CDK5, which can be further optimized to develop more potent selective DYRK1A inhibitors.     

乙酰胆碱酯酶抑制剂虚拟筛选方法研究

在虚拟筛选过程中，虚拟筛选策略和方法是获取结果的基础，但需要通过实验数据来检验。获得虚拟筛选合理的限制因素，将为大规模虚拟筛选提供方法依据。通过建立乙酰胆碱酯酶抑制剂的活性检测方法，检测了4000多个化合物的生物活性，并发现了34个活性较高的化合物，同时将设计的6个不同的虚拟筛选方法分别用于2个乙酰胆碱醋酶抑制剂虚拟筛选模型。将所预测的可能活性化合物及两模型共有的可能活性化合物分别与实验所得的活性化合物对照，综合分析讨论虚拟筛选乙酰胆碱酯酶抑制剂的重要因素和进一步富集活性化合物的方法，为大规模的虚拟筛选乙酰胆碱醋酶抑制剂提供可靠依据，并为其他基于蛋白酶结构的虚拟筛选提供参考。     

In silico screening and study of novel ERK2 inhibitors using 3D QSAR,docking and molecular dynamics

ERK2 is a dual specificity protein kinase, part of the Ras/Raf/MEK/ERK signal transduction cascade. It forms an interesting target for inhibition based on its relationship with cell proliferation and oncogenesis. A 3D QSAR pharmacophore model (Hypo1) with high correlation (r = 0.938) was developed for ERK2 ATP site on the basis of experimentally known inhibitors. The model included three hydrogen bonds, and one hydrophobic site. Assessment of Hypo1 through Fisher randomization, cost analysis, leave one out method and decoy test suggested that the model can reliably detect ERK2 inhibitors. Hypo1 has been used for virtual screening of potential inhibitors from ZINC, Drug Bank, NCI, Maybridge and Chembank databases. Using Hypo1 as a query, databases have been interrogated for compounds who meet the pharmacophore features. The resulting hit compounds were subject to docking and analysis. Docking and molecular dynamics analysis showed that in order to achieve a higher potency compounds have to interact with catalytic site, glycine rich loop, Hinge region, Gatekeeper region and ATP site entrance residues. We also identified catalytic site and Glycine rich loop as important regions to bind by molecules for better potency and selectivity.     

A systematic methodology for large scale compound screening: A case study on the discovery of novel S1PL inhibitors

Decrease in sphingosine 1-phosphate (S1P) concentration induces migration of pathogenic T cells to the blood stream, disrupts the CNS and it is implicated in multiple sclerosis (MS), a progressive inflammatory disorder of the central nervous system (CNS), and Alzheimer’s disease (AD). A promising treatment alternative for MS and AD is inhibition of the activity of the microsomal enzyme sphingosine 1-phosphate lyase (S1PL), which degrades intracellular S1P. This report describes an integrated systematic approach comprising virtual screening, molecular docking, substructure search and molecular dynamics simulation to discover novel S1PL inhibitors. Virtual screening of the ZINC database via ligand-based and structure-based pharmacophore models yielded 10000 hits. After molecular docking, common substructures of the top ranking hits were identified. The ligand binding poses were optimized by induced fit docking. MD simulations were performed on the complex structures to determine the stability of the S1PL-ligand complex and to calculate the binding free energy. Selectivity of the selected molecules was examined by docking them to hERG and cytochrome P450 receptors. As a final outcome, 15 compounds from different chemotypes were proposed as potential S1PL inhibitors. These molecules may guide future medicinal chemistry efforts in the discovery of new compounds against the destructive action of pathogenic T cells.     

基于分子参数的CYP2C9抑制剂的预测模型

细胞色素P4502C9(cytochrome P4502C9,CYP2C9)是肝脏重要的一种异物质代谢酶,许多药物或化学物质均可抑制和干扰其活性,在某种药物发现早期,预测基于CYP2C9抑制的药-药相互作用对筛选及发现新药具有重要意义。本文旨在建立CYP2C9抑制剂的预测模型,并确定抑制剂和非抑制剂显著不同的参数。选择81个化合物作为数据集,随机选其中64个为训练集,其余为验证集;选取250个分子参数给化合物数字化。采用逐步判别分析法(stepwise discriminant analysis method)和K-均值聚类分析法(K-Means cluster analysis method)模拟,建立数学模型,并用验证集检验模型的预测能力。结果表明:训练集的抑制剂正确率为96.4%,非抑制剂为97.2%;验证集的抑制剂正确率为85.7%,非抑制剂为90.0%。而采用K-均值聚类法时,抑制剂和非抑制剂的正确率也分别达到了82.9%和86.9%。对结果的深入分析找出对该模型贡献较大的参数为分子中氨基、烯基基团电拓扑状态指数、碳环数量以及疏水性参数,那些参数对区分抑制剂和非抑制剂两种结构差异、帮助指导CYP2C9抑制剂的筛选和发现具有重要意义。     

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.     

An effective HIV-1 integrase inhibitor screening platform: Rationality validation of drug screening,conformational mobility and molecular recognition analysis for PFV integrase complex with viral DNA

As an important target for the development of novel anti-AIDS drugs, HIV-1 integrase (IN) has been widely concerned. However, the lack of a complete accurate crystal structure of HIV-1 IN greatly blocks the discovery of novel inhibitors. In this work, an effective HIV-1 IN inhibitor screening platform, namely PFV IN, was filtered from all species of INs. Next, the 40.8% similarity with HIV-1 IN, as well as the high efficiency of virtual screening and the good agreement between calculated binding free energies and experimental ones all proved PFV IN is a promising screening platform for HIV-1 IN inhibitors. Then, the molecular recognition mechanism of PFV IN by its substrate viral DNA and six naphthyridine derivatives (NRDs) inhibitors was investigated through molecular docking, molecular dynamics simulations and water-mediated interactions analyses. The functional partition of NRDs IN inhibitors could be divided into hydrophobic and hydrophilic ones, and the Mg²⁺ ions, water molecules and conserved DDE motif residues all interacted with the hydrophilic partition, while the bases in viral DNA and residues like Tyr212, Pro214 interacted with the hydrophobic one. Finally, the free energy landscape (FEL) and cluster analyses were performed to explore the molecular motion of PFV IN-DNA system. It is found that the association with NRDs inhibitors would obviously decrease the motion amplitude of PFV IN-DNA, which may be one of the most potential mechanisms of IN inhibitors. This work will provide a theoretical basis for the inhibitor design based on the structure of HIV-1 IN.     

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.     

Virtual screening against Mycobacterium tuberculosis dihydrofolate reductase: suggested workflow for compound prioritization using structure interaction fingerprints

In this study, we suggest a new workflow for the identification and prioritization of potential compounds targeted against Mycobacterium tuberculosis dihydrofolate reductase, an important folate cycle enzyme and a validated target for the development of anti-tubercular agents. First, we have performed an integrated pharmacophore and structure-based virtual screening using Maybridge small molecule database, subsequently interaction patterns from known actives to the receptor were applied for scoring and ranking the virtual screening hits using structure interaction fingerprint (SIFt)-based similarity approach. In addition, agglomerative hierarchical clustering of the structure interaction fingerprints permits the easy separation of active from inactive binding modes. Using this approach we screened 59275 Maybridge compounds and 20 compounds were prioritized as promising virtual screening hits. Though using a receptor interaction scoring approach, the results were not biased toward the chemical classes of the known actives and the proposed compounds were structurally diverse with low molecular weights and structural complexities. Our results suggest that structure-based virtual screening coupled with the SIFt should be a valuable tool for prioritization of virtual screening hits.     

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.     

机器学习预测ACEI的比较与分析

血管紧张素转换酶抑制剂（ACEI）对高血压的治疗具有重要意义。基于从结构复杂的化合物数据库中构建的候选小分子数据集,采用分子对接技术从数据集中筛选出样本构建分类模型。分别采用支持向量机、[K]近邻、决策树、随机森林和贝叶斯方法建立血管紧张素转换酶潜在抑制剂和非抑制剂的分类模型。经结果对比,支持向量机相比于其他方法有更高的预测率,其中模型总体预测率和相关系数分别为82.4%和0.653。研究表明,支持向量机方法对于虚拟筛选血管紧张素转换酶抑制剂具有良好的效果。     

基于结构的组蛋白去乙酰化酶抑制剂虚拟筛选

组蛋白去乙酰化酶是抗肿瘤作用的新靶点,基于该酶复合物的三维结构,首先对具有分子多样性的数据库进行了虚拟筛选;然后根据已知HDAC抑制剂的结构特征和筛选的结果,以及与生物大分子互补性,选择合理的构建单元,组建靶向的虚拟组合库;最后进行数据库虚拟筛选,对分子对接的结果进行评分,选择出理论上与HDAC有较好结合能力的化合物,设计了酰胺类、脲类和酰肼类全新结构类型的HDAC抑制剂,初步生物活性评价结果表明,预期有生物活性的化合物显示出一定的HDAC酶抑制活性。     

Identification of p38α MAP kinase inhibitors by pharmacophore based virtual screening

The p38α mitogen-activated protein (MAP) kinase plays a vital role in treating many inflammatory diseases. In the present study, a combined ligand and structure based pharmacophore model was developed to identify potential DFG-in selective p38 MAP kinase inhibitors. Conformations of co-crystallised inhibitors were used in the development and validation of ligand and structure based pharmacophore modeling approached. The validated pharmacophore was utilized in database screening to identify potential hits. After Lipinski's rule of five filter and molecular docking analysis, nineteen hits were purchased and selected for in vitro analysis. The virtual hits exhibited promising activity against tumor necrosis factor-α (TNF-α) with 23–98% inhibition at 10 μM concentration. Out of these seven compounds has shown potent inhibitory activity against p38 MAP kinase with IC₅₀ values ranging from 12.97 to 223.5 nM. In addition, the toxicity study against HepG2 cells was also carried out to confirm the safety profile of identified virtual hits.     

Relaxed complex scheme suggests novel inhibitors for the lyase activity of DNA polymerase beta

DNA polymerase beta (pol β), the error-prone polymerase of base excision repair, plays a significant role in chemotherapeutic agent resistance. Its over expression reduces the efficacy of anticancer drug therapies including ionizing radiation, bleomycin, monofunctional alkylating agents and cisplatin. Small-scale studies on different types of cancer showed that pol β is mutated in approximately 30% of tumors. These mutations further lower pol β fidelity in DNA synthesis exposing the genome to serious mutations. These findings suggested pol β as a promising therapeutic target for cancer treatment. More than 60 pol β-inhibitors have been identified so far, however, most of them are either not potent or specific enough to become a drug. Here, we applied the relaxed complex scheme virtual screening (RCSVS) to allow for the full receptor flexibility in filtering the NCI diversity set, DrugBank compounds and a library of ～ 9000 fragmental compounds for novel pol β inhibitors. In this procedure we screened the set of ～ 12,500 compounds against an ensemble of 11 dominant-receptor structures representing the essential backbone dynamics of the 8 kDa domain of pol β. Our results predicted new compounds that can bind with higher affinity to the lyase active site compared to pamoic acid (PA), a well-known inhibitor of DNA pol β.     

基于JPPF的异构分布式MTH1虚拟筛选系统

随着癌症发病率日益升高,寻找治疗癌症的新靶点已成为世界范围内的研究热点。最新研究指出MTH1蛋白在癌细胞中生存是必须的,而在正常细胞中是非必需的,设计有选择性的MTH1抑制剂将对癌症的治疗有着重要的意义。MTH1的筛选需要大规模的高性能计算资源,但目前缺少具体成型的集筛选模拟于一体、跨平台的分布式异构软件以用于快速地从大规模数据库中获得潜在的候选药物小分子。本文基于JPPF分布式并行框架和Autodock Vina设计一种具有良好兼容性和跨平台性的肿瘤药物虚拟筛选计算系统,通过对100万目标分子集进行虚拟筛选,筛选结果直接靶向了MTH1的药物分子。该系统的实现为快速构建大规模药物分子虚拟筛选技术提供了解决方案和新思路。     

Combined in silico approaches for the identification of novel inhibitors of human islet amyloid polypeptide (hIAPP) fibrillation

Human islet amyloid polypeptide (hIAPP) is a natively unfolded polypeptide hormone of glucose metabolism, which is co-secreted with insulin by the β-cells of the pancreas. In patients with type 2 diabetes, IAPP forms amyloid fibrils because of diabetes-associated β-cells dysfunction and increasing fibrillation, in turn, lead to failure of secretory function of β-cells. This provides a target for the discovery of small organic molecules against protein aggregation diseases. However, the binding mechanism of these molecules with monomers, oligomers and fibrils to inhibit fibrillation is still an open question. In this work, ligand and structure-based in silico approaches were used to identify novel fibrillation inhibitors and/or fibril binding compounds. The best pharmacophore model was used as a 3D search query for virtual screening of a compound database to identify novel molecules having the potential to be therapeutic agents against protein aggregation diseases. Docking and molecular dynamics simulation studies were used to explore the interaction pattern and mechanism of the identified novel small molecules with predicted hIAPP structure, its aggregation prone conformation and fibril forming segments. We show that catechins with galloyl group and molecules having two to three planar apolar rings bind to hIAPP structures and fibril forming segments with greater affinity. The differences in binding affinities of different compounds against several fibril forming segments of the peptide suggest that a mixture of active compounds may be required for treatment of aggregation diseases.