α-Synuclein as a Target for Metallo-Anti-Neurodegenerative Agents

The unique thermodynamic and kinetic coordination chemistry of ruthenium allows it to modulate key adverse aggregation and membrane interactions of α-synuclein (α-syn) associated with Parkinson's disease. We show that the low-toxic Ru^III complex trans-[ImH][RuCl₄(Me₂SO)(Im)] (NAMI-A) has dual inhibitory effects on both aggregation and membrane interactions of α-syn with submicromolar affinity, and disassembles pre-formed fibrils. NAMI-A abolishes the cytotoxicity of α-syn towards neuronal cells and mitigates neurodegeneration and motor impairments in a rat model of Parkinson's. Multinuclear NMR and MS analyses show that NAMI-A binds to residues involved in protein aggregation and membrane binding. NMR studies reveal the key steps in pro-drug activation and the effect of activated NAMI-A species on protein folding. Our findings provide a new basis for designing ruthenium complexes which could mitigate α-syn-induced Parkinson's pathology differently from organic agents.     

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.     

Receptor-Based Pharmacophore Modeling in the Search for Natural Products for COVID-19 Mpro

Considering the urgency of the COVID-19 pandemic, we developed a receptor-based pharmacophore model for identifying FDA-approved drugs and hits from natural products. The COVID-19 main protease (M^pro) was selected for the development of the pharmacophore model. The model consisted of a hydrogen bond acceptor, donor, and hydrophobic features. These features demonstrated good corroboration with a previously reported model that was used to validate the present model, showing an RMSD value of 0.32. The virtual screening was carried out using the ZINC database. A set of 208,000 hits was extracted and filtered using the ligand pharmacophore mapping, applying the lead-like properties. Lipinski’s filter and the fit value filter were used to minimize hits to the top 2000. Simultaneous docking was carried out for 200 hits for natural drugs belonging to the FDA-approved drug database. The top 28 hits from these experiments, with promising predicted pharmacodynamic and pharmacokinetic properties, are reported here. To optimize these hits as M^pro inhibitors and potential treatment options for COVID-19, bench work investigations are needed.     

In Silico Strategy for Targeting the mTOR Kinase at Rapamycin Binding Site by Small Molecules

Computer aided drug-design methods proved to be powerful tools for the identification of new therapeutic agents. We employed a structure-based workflow to identify new inhibitors targeting mTOR kinase at rapamycin binding site. By combining molecular dynamics (MD) simulation and pharmacophore modelling, a simplified structure-based pharmacophore hypothesis was built starting from the FKBP12-rapamycin-FRB ternary complex retrieved from RCSB Protein Data Bank (PDB code 1FAP). Then, the obtained model was used as filter to screen the ZINC biogenic compounds library, containing molecules derived from natural sources or natural-inspired compounds. The resulting hits were clustered according to their similarity; moreover, compounds showing the highest pharmacophore fit-score were chosen from each cluster. The selected molecules were subjected to docking studies to clarify their putative binding mode. The binding free energy of the obtained complexes was calculated by MM/GBSA method and the hits characterized by the lowest ΔG_bind values were identified as potential mTOR inhibitors. Furthermore, the stability of the resulting complexes was studied by means of MD simulation which revealed that the selected compounds were able to form a stable ternary complex with FKBP12 and FRB domain, thus underlining their potential ability to inhibit mTOR with a rapamycin-like mechanism.     

Pharmacophore Mode lingand Virtual Screening to Design the Potential Influenza Virus Endonuclease Inhibitors

Influenza virus endonuclease is an attractive target for antiviral therapy in the treatment of influenza infection. The purpos e of this study is to design a novel antiviral agent with improved biological activities against the influenza virus endonuclease. In this study, chemical feature‐based 3D pharmacophore models were developed from 41 known influenza virus endonuclease inhibitors. The best quantitative pharmacohore model (Hypo 1), which consists of two hydrogen‐bond acceptors and two hydrophobic features, yields the highest correlation coefficient (R = 0.886). Hypo 1 was further validated by the cross validation method and the test set compounds. The application of this model for predicting the activities of 11 known influenza virus endonuclease inhibitors in the test set shows great success. The correlation coefficient of 0.942 and a cross validation of 95;% confidence level prove that this model is reliable in identifying structurally diverse compounds for influenza virus endonuclease inhibition. The most active compound (compound 1) from the training set was docked into the active site of the influenza virus endonuclease as an additional verification that the pharmacophore model is accurate. The docked conformation showed important hydrogen bond interactions between the compound and two amino acids, Lys 134 and Lys 137. After validation, this model was used to screen the NCI chemical database to identify new influenza virus endonuclease inhibitors. Our study shows that the to pranking compound out of the 10 newly identified compounds using fit value ranking has an estimated activity of 0.049 μM. These newly identified lead compounds can be further experimentally validated using in vitro techniques.     

Identification of potential HIV-1 integrase strand transfer inhibitors: In silico virtual screening and QM/MM docking studies

HIV-1 integrase (IN) is a retroviral enzyme that catalyses integration of the reverse-transcribed viral DNA into the host genome, which is necessary for efficient viral replication. In this study, we have performed an in silico virtual screening for the identification of potential HIV-1 IN strand transfer (ST) inhibitors. Pharmacophore modelling and atom-based 3D-QSAR studies were carried out for a series of compounds belonging to 3-Hydroxypyrimidine-2,4-diones. Based on the ligand-based pharmacophore model, we obtained a five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), one hydrophobic group (H) and one aromatic ring (R) as pharmacophoric features. The pharmacophore hypothesis AADHR was used as a 3D query in a sequential virtual screening study to filter small molecule databases Maybridge, ChemBridge and Asinex. Hits matching with pharmacophore hypothesis AADHR were retrieved and passed progressively through Lipinski’s rule of five filtering, molecular docking and hierarchical clustering. The five compounds with best hits with novel and diverse chemotypes were subjected to QM/MM docking, which showed improved docking accuracy. We further performed molecular dynamics simulation and found three compounds that form stable interactions with key residues. These compounds could be used as a leads for further drug development and rational design of HIV-1 IN 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.     

Pharmacophore modeling and in silico screening for new P450 19 (aromatase) inhibitors

Cytochrome P450 19 (P450 19, aromatase) constitutes a successful target for the treatment of breast cancer. This study analyzes chemical features common to P450 19 inhibitors to develop ligand-based, selective pharmacophore models for this enzyme. The HipHop and HypoRefine algorithms implemented in the Catalyst software package were employed to create both common feature and quantitative models. The common feature model for P450 19 includes two ring aromatic features in its core and two hydrogen bond acceptors at the ends. The models were used as database search queries to identify active compounds from the NCI database.     

Molecular modelling studies on adamantane-based Ebola virus GP-1 inhibitors using docking,pharmacophore and 3D-QSAR

The pathogenic Ebola virus (EBOV) causes a potential health risk and global spread. To date, few drugs are available for the treatment of Ebola virus disease (EVD) that allow researchers to use computational methods for designing potential drugs. The developed PHASE-based common six-point pharmacophore hypothesis (AADHPR_1) showed the necessity of two hydrogen bond acceptor features, one hydrogen bond donor feature, one hydrophobic group feature, one positively ionizable and one aromatic ring feature for further designing. We developed best 3D-QSAR models with high regression coefficients for the training (r²>0.82) and test (Q²>0.5) sets for both atoms-based and field-based 3D-QSAR models. The molecule 1A-4 (docking score = –4.711 kcal/mol) was obtained as best docked (SP mode) on Ebola virus envelope glycoprotein (PDB ID-3CSY) as compared with the standards oseltamivir (docking score = –4.39 kcal/mol) and zanamivir (docking score = –3.392 kcal/mol). The obtained ZINC hit ZINC58935541 showed a good docking score of –4.892 kcal/mol. The ZINC58935541 molecule also showed a strong binding affinity towards the receptor cavity of Ebola virus envelope glycoprotein when simulated for 1.2 ns. The good QikProp parameters reflect the fact that this molecule, upon optimization into a lead, might become a good candidate for the treatment of EVD.     

Development of predictive pharmacophore model for in silico screening,and 3D QSAR CoMFA and CoMSIA studies for lead optimization,for designing of potent tumor necrosis factor alpha converting enzyme inhibitors

A chemical feature-based pharmacophore model was developed for Tumor Necrosis Factor-α converting enzyme (TACE) inhibitors. A five point pharmacophore model having two hydrogen bond acceptors (A), one hydrogen bond donor (D) and two aromatic rings (R) with discrete geometries as pharmacophoric features was developed. The pharmacophore model so generated was then utilized for in silico screening of a database. The pharmacophore model so developed was validated by using four compounds having proven TACE inhibitory activity which were grafted into the database. These compounds mapped well onto the five listed pharmacophoric features. This validated pharmacophore model was also used for alignment of molecules in CoMFA and CoMSIA analysis. The contour maps of the CoMFA/CoMSIA models were utilized to provide structural insight for activity improvement of potential novel TACE inhibitors. The pharmacophore model so developed could be used for in silico screening of any commercial/in house database for identification of TACE inhibiting lead compounds, and the leads so identified could be optimized using the developed CoMSIA model. The present work highlights the tremendous potential of the two mutually complementary ligand-based drug designing techniques (i.e. pharmacophore mapping and 3D-QSAR analysis) using TACE inhibitors as prototype biologically active molecules.     

Design,synthesis, biological evaluation and molecular dynamics of LAR inhibitors

In this study, firstly, the pharmacophore model was established based on LAR inhibitors. ZINC database and drug-like database were screened by Hypo-1-LAR model, and the embryonic compound ZINC71414996 was obtained. Based on this compound, we designed 9 compounds. Secondly, the synthetic route of the compound was determined by consulting Reaxys and Scifinder databases, and 9 compounds (1a-1i) were synthesized by nucleophilic substitution, Suzuki reaction and so on. Meanwhile, their structures were confirmed by ¹H NMR and ¹³C NMR. Thirdly, the Enzymatic assays was carried out, the biological evaluation of compounds 1a-1i led to the identification of a novel PTP-LAR inhibitor 1c, which displayed an IC₅₀ value of 4.8 μM. At last, molecular dynamics simulation showed that compounds could interact strongly with the key amino acids LYS1350, LYS1352, ARG1354, TYR1355, LYS1433, ASP1435, TRP1488, ASP1490, VAL1493, SER1523, ARG1528, ARG1561, GLN1570, LYS1681, thereby inhibiting the protein activity. This study constructed the pharmacophore model of LAR protein, designed small-molecule inhibitors, conducted compound synthesis and enzyme activity screening, so as to provide a basis for searching for drug-capable lead compounds.     

Ligand-Based Pharmacophore Screening Strategy: a Pragmatic Approach for Targeting HER Proteins

Targeting ErbB family of receptors is an important therapeutic option, because of its essential role in the broad spectrum of human cancers, including non-small cell lung cancer (NSCLC). Therefore, in the present work, considerable effort has been made to develop an inhibitor against HER family proteins, by combining the use of pharmacophore modelling, docking scoring functions, and ADME property analysis. Initially, a five-point pharmacophore model was developed using known HER family inhibitors. The generated model was then used as a query to screen a total of 468,880 compounds of three databases namely ZINC, ASINEX, and DrugBank. Subsequently, docking analysis was carried out to obtain hit molecules that could inhibit the HER receptors. Further, analysis of GLIDE scores and ADME properties resulted in one hit namely BAS01025917 with higher glide scores, increased CNS involvement, and good pharmaceutically relevant properties than reference ligand, afatinib. Furthermore, the inhibitory activity of the lead compounds was validated by performing molecular dynamic simulations. Of note, BAS01025917 was found to possess scaffolds with a broad spectrum of antitumor activity. We believe that this novel hit molecule can be further exploited for the development of a pan-HER inhibitor with low toxicity and greater potential.     

Natural compounds as potential Hsp90 inhibitors for breast cancer-Pharmacophore guided molecular modelling studies

Breast cancer is one of the major impediments affecting women globally. The ATP-dependant heat shock protein 90 (Hsp90) forms the central component of molecular chaperone machinery that predominantly governs the folding of newly synthesized peptides and their conformational maturation. It regulates the stability and function of numerous client proteins that are frequently upregulated and/or mutated in cancer cells, therefore, making Hsp90 inhibition a promising therapeutic strategy for the development of new efficacious drugs to treat breast cancer. In the present in silico investigation, a structure-based pharmacophore model was generated with hydrogen bond donor, hydrogen bond acceptor and hydrophobic features complementary to crucial residues Ala55, Lys58, Asp93, Ile96, Met98 and Thr184 directed at inhibiting the ATP-binding activity of Hsp90. Subsequently, the phytochemical dataset of 3210 natural compounds was screened to retrieve the prospective inhibitors after rigorous validation of the model pharmacophore. The retrieved 135 phytocompounds were further filtered by drug-likeness parameters including Lipinski’s rule of five and ADMET properties, then investigated via molecular docking-based scoring. Molecular interactions were assessed using Genetic Optimisation for Ligand Docking program for 95 drug-like natural compounds against Hsp90 along with two clinical drugs as reference compounds – Geldanamycin and Radicicol. Docking studies revealed three phytochemicals are better than the investigated clinical drugs. The reference and hit compounds with dock scores of 48.27 (Geldanamycin), 40.90 (Radicicol), 73.04 (Hit1), 72.92 (Hit2) and 68.12 (Hit3) were further validated for their binding stability through molecular dynamics simulations. We propose that the non-macrocyclic scaffolds of three identified phytochemicals might aid in the development of novel therapeutic candidates against Hsp90-driven cancers.     

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...     

Computational Investigations about the Effects of Hetero‐molecular Aggregation on Bioactivities: a Case of Neonicotinoids and Water

Molecular aggregation state of bioactive compounds plays a key role in bio‐interactive procedure. Diverse aggregation states of bioactive compounds contribute to different biological or chemical properties. Water‐bridge, as the simple hetero‐molecular aggregation, has been found bridging the binding between many bioactive compounds and their targets through hydrogen bonding network, e.g. in the recognition of neonicotinoids with insect nAChRs. To better understanding the roles of water‐bridge on bioactivities of compounds, an approach of hetero‐dimeric aggregation with water was proposed. Quantitative structure‐activity relationship (QSAR) and pharmacophore modeling investigations were applied on 19 neonicotinoids, as well as their aggregates with water. The aggregate‐based CoMSIA, PHASE and linear QSAR models presented better statistical significance and predictabilities than the monomer ones, which indicated that the bioactivities correlated with the aggregate properties and water bridged hydrogen bond of the active site. All results revealed the essential roles of water‐bridge in ligand recognition, which should be considered in future ligand design and optimization.     

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.     

表皮生长因子受体酪氨酸激酶抑制剂的药效团研究

根据一系列表皮生长因子受体酪氨酸激酶抑制剂的三维定量构效关系研究，得 到了该类抑制剂的药效团，研究结果与Novartis的药效团模型相当类似．药效团包 括一个氢键受体，一个氢键给体，一个疏水区和一个带有氯或溴原子药效团对于研 究表皮生长因子受体酪氨酸激酶抑制剂结构与活性的关系具有重要的意义．通过三 维数据库搜索可能会得到新的先导化合物．     

Molecular dynamics and pharmacophore modelling studies of different subtype (ALK and EGFR (T790M)) inhibitors in NSCLC

Extensively validated 3D pharmacophore models for ALK (anaplastic lymphoma kinase) and EGFR (T790M) (epithelial growth factor receptor with acquired secondary mutation) were developed. The pharmacophore model for ALK (r² = 0.96, q² = 0.692) suggested that two hydrogen bond acceptors and three hydrophobic groups arranged in 3-D space are essential for the binding affinity of ALK inhibitors. Similarly, the pharmacophore model for EGFR (T790M) (r² = 0.92, q² = 0.72) suggested that the presence of a hydrogen bond acceptor, two hydrogen bond donors and a hydrophobic group plays vital role in binding of an inhibitor of EGFR (T790M). These pharmacophore models allowed searches for novel ALK and EGFR (T790M) dual inhibitors from multiconformer 3D databases (Asinex, Chembridge and Maybridge). Finally, the eight best hits were selected for molecular dynamics simulation, to study the stability of their complexes with both proteins and final binding orientations of these molecules. After molecular dynamics simulations, one hit has been predicted to possess good binding affinity for both ALK and EGFR (T790M), which can be further investigated for its experimental in-vitro/in-vivo activities.     

3D QSAR Pharmacophore Based Virtual Screening for Identification of Potential Inhibitors for CDC25B

Owing to its fundamental roles in cell cycle phases, the cell division cycle 25B (CDC25B) was broadly considered as potent clinical drug target for cancers. In this study, 3D QSAR pharmacophore models for CDC25B inhibitors were developed by the module of Hypogen. Three methods (cost analysis, test set prediction, and Fisher’s test) were applied to validate that the models could be used to predict the biological activities of compounds. Subsequently, 26 compounds satisfied Lipinski’s rule of five were obtained by the virtual screening of the Hypo-1-CDC25B against ZINC databases. It was then discovered that 9 identified molecules had better binding affinity than a known CDC25B inhibitors-compound 1 using docking studies. The molecular dynamics simulations showed that the compound had favorable conformations for binding to the CDC25B. Thus, our findings here would be helpful to discover potent lead compounds for the treatment of cancers.