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.     

Identification of novel topoisomerase II alpha inhibitors by virtual screening,molecular docking,and bioassay

Breast cancer is one of the most common tumors, and its treatment still leaves room for improvement. Topoisomerase II alpha is a potential target for the treatment of human diseases such as breast cancer. In this article, we attempted to discover a novel anticancer drug. We have used the topoisomerase II alpha protein-Homo sapiens (Human) to hierarchically screen the Maybridge database. Based on their docking score, the top hit compounds have been assayed for inhibition in a topoisomerase II pBR322 DNA relaxation assay in vitro. Candidate compound 6 (CP6) was found to have the best inhibitory effect for topoisomerase II among the 20 tested compounds. In addition, CP6 had potent cytotoxicity against eight tested tumor cell lines. At the same time, CP6 was shown to have potential anti-multidrug resistance capabilities. This study identifies CP6, which can contribute to the development of new topoisomerase II inhibitors as anticancer agents.     

Pharmacophore‐based virtual screening,molecular docking,molecular dynamics simulation,and biological evaluation for the discovery of novel BRD4 inhibitors

Bromodomain is a recognition module in the signal transduction of acetylated histone. BRD4, one of the bromodomain members, is emerging as an attractive therapeutic target for several types of cancer. Therefore, in this study, an attempt has been made to screen compounds from an integrated database containing 5.5 million compounds for BRD4 inhibitors using pharmacophore‐based virtual screening, molecular docking, and molecular dynamics simulations. As a result, two molecules of twelve hits were found to be active in bioactivity tests. Among the molecules, compound 5 exhibited potent anticancer activity, and the IC₅₀ values against human cancer cell lines MV4‐11, A375, and HeLa were 4.2, 7.1, and 11.6 μm , respectively. After that, colony formation assay, cell cycle, apoptosis analysis, wound‐healing migration assay, and Western blotting were carried out to learn the bioactivity of compound 5 .     

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.     

The discovery of novel vascular endothelial growth factor receptor tyrosine kinases inhibitors: pharmacophore modeling, virtual screening and docking studies

We have applied pharmacophore generation, database searching and docking methodologies to discover new structures for the design of vascular endothelial growth factor receptors, the tyrosine kinase insert domain-containing receptor kinase inhibitors. The chemical function based pharmacophore models were built for kinase insert domain-containing receptor kinase inhibitors from a set of 10 known inhibitors using the algorithm HipHop, which is implemented in the CATALYST software. The highest scoring HipHop model consists of four features: one hydrophobic, one hydrogen bond acceptor, one hydrogen bond donor and one ring aromatic function. Using the algorithm CatShape within CATALYST, the bound conformation of 4-amino-furo [2, 3-d] pyrimidine binding to kinase insert domain-containing receptor kinase was used to generate a shape query. A merged shape and hypothesis query that is in an appropriate alignment was then built. The combined shape and hypothesis model was used as a query to search Maybridge database for other potential lead compounds. A total of 39 compounds were retrieved as hits. The hits obtained were docked into kinase insert domain-containing receptor kinase active site. One novel potential lead was proposed based on CATALYST fit value, LigandFit docking scores, and examination of how the hit retain key interactions known to be required for kinase binding. This compound inhibited vascular endothelial growth factor stimulated kinase insert domain-containing receptor phosphorylation in human umbilical vein endothelial cells.     

Histamine H3 receptor ligands by hybrid virtual screening,docking, molecular dynamics simulations,and investigation of their biological effects

Histamine H₃ receptors (H₃R), belonging to G‐protein coupled receptors (GPCR) class A superfamily, are responsible for modulating the release of histamine as well as of other neurotransmitters by a negative feedback mechanism mainly in the central nervous system (CNS). These receptors have gained increased attention as therapeutic target for several CNS related neurological diseases. In the current study, we aimed to identify novel H₃R ligands using in silico virtual screening methods. To this end, a combination of ligand‐ and structure‐based approaches was utilized for screening of ZINC database on the homology model of human H₃R. Structural similarity‐ and pharmacophore‐based approaches were employed to generate compound libraries. Various molecular modeling methodologies such as molecular docking and dynamics simulation along with different drug likeness filtering criteria were applied to select anti‐H₃R ligands as promising candidate molecules based on different known parent lead compounds. In vitro binding assays of the selected molecules demonstrated three of them being active within the micromolar and submicromolar K_i range. The current integrated computational and experimental methods used in this work can provide new general insights for systematic hit identification for novel anti‐H₃R agents from large compound libraries.     

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.     

Identification of novel polo-like kinase 1 inhibitors by a hybrid virtual screening

Polo-like kinase 1 is an important and attractive oncological target that plays a key role in mitosis and cytokinesis. A combined pharmacophore- and docking-based virtual screening was performed to identify novel polo-like kinase 1 inhibitors. A total of 34 hit compounds were selected and tested in vitro, and some compounds showed inhibition of polo-like kinase 1 and human tumor cell growth. The most potent compound (66) inhibited polo-like kinase 1 with an IC(50) value of 6.99 μm. The docked binding models of two hit compounds were discussed in detail. These compounds contained novel chemical scaffolds and may be used as foundations for the development of novel classes of polo-like kinase 1 inhibitors.     

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。     

采用药效团模型和分子对接方法筛选新型的组蛋白甲基转移酶G9a抑制剂

目的利用药效团模型和分子对接方法对商业化合物库ChemDiv中的G9afocused-libraries进行筛选,希望发现新骨架结构的G9a抑制剂。方法首先,使用Discovery studio 3.1软件分别构建基于配体的药效团模型和基于配体-受体复合物的药效团模型,并根据构建的2个模型再重新定义2个新的药效团模型。然后,构建测试集并测试药效团模型的预测能力。最后,选取最优药效团模型对G9afocused-libraries进行筛选,对筛选出的化合物使用CDOCKER分子对接进行分析与评价。结果测试结果显示,所构建的药效团模型具有一定的预测能力,通过该药效团筛选得到了2个结构新颖的潜在的G9a抑制剂。结论所构建的药效团模型具有一定的可靠性,虚拟筛选发现的G9a抑制剂还需进一步的实验证明。     

基于分子对接、定量构效关系和分子动力学研究筛选小分子SIRT1抑制剂

为了寻找和发现靶向SIRT1的治疗AML的新型先导化合物,本研究利用分子对接与MM-GBSA结合自由能计算进行虚拟筛选,从231 511个天然小分子类药分子库中筛选出8个潜在的SIRT1抑制剂,通过对已有的SIRT1抑制剂分子作为训练集和测试集进行QSAR建模,对筛选出的潜在SIRT1抑制剂分子进行活性预测,随后进行分...     

Combined comparative molecular field analysis,comparative molecular similarity indices analysis,molecular docking and molecular dynamics studies of histone deacetylase 6 inhibitors

Human histone deacetylase isoform 6 (HDAC6) has been shown to have an immense role in cell motility and aggresome formation and is being an attractive selective target for the treatment of multiple tumour types and neurodegenerative conditions. The discovery of selective HDAC6 inhibitors with new chemical functionalities is therefore of utmost interest to researchers. In order to examine the structural requirements for HDAC6‐specific inhibitors and to derive predictive model which can be used for designing new selective HDAC6 inhibitors, a three‐dimensional quantitative structure–activity relationship study was carried out on a diverse set of ligands using common feature‐based pharmacophore alignment followed by employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. The models displayed high correlation of 0.978 and 0.991 for best CoMFA and CoMSIA models, respectively, and a good statistical significance. The model could be used for predicting activities of the test set compounds as well as for deriving useful information regarding steric, electrostatic, hydrophobic properties of the molecules used in this study. Further, the training and test set molecules were docked into the HDAC6 binding site and molecular dynamics was carried out to suggest structural requirements for design of new inhibitors.     

Structure‐based virtual screening toward the discovery of novel inhibitors of the DNA repair activity of the human apurinic/apyrimidinic endonuclease 1

The DNA repair activity of human apurinic/apyrimidinic endonuclease 1 (APE1) has been recognized as a promising target for the development of small‐molecule inhibitors to be used in combination with anticancer agents. In an attempt to identify novel inhibitors of APE1, we present a structure‐based virtual screening (SBVS) study based on molecular docking analysis of the compounds of NCI database using the GOLD 5.1.0 (Genetic Optimization for Ligand Docking) suite of programs. Compounds selected in this screening were tested with a fluorescence‐based APE1 endonuclease activity assay. Two compounds ( 37 and 41 ) were able to inhibit the multifunctional enzyme APE1 in the micromolar range, while compound 22 showed inhibitory effects at nanomolar concentrations. These results were confirmed by a plasmid DNA nicking assay. In addition, the potential APE1 inhibitors did not affect the cell viability of non‐tumor MCF10A cells. Overall, compounds 22 , 37, and 41 appear to be important scaffolds for the design of novel APE1 inhibitors and this study highlights the relevance of in silico‐based approaches as valuable tools in drug discovery.     

Similarity versus docking in 3D virtual screening

The development of similarity methods for fast flexible ligand superposition has recently received considerable attention. These efforts have brought similarity methods to a level of performance comparable to the well established protein-ligand docking methods for binding mode assessment and molecular database screening. However, the strengths and intrinsic limitations of both methodologies have been also stressed out extensively. As the number of resolved ligand-bound protein structures increases, combining ligand-based and receptor-based approaches emerges as a consensus strategy to maximally exploit the structural information available and improve the results obtained with either of the methods alone. This revised version was published online in June 2006 with corrections to the Cover Date.     

First pharmacophore model of CCR3 receptor antagonists and its homology model-assisted, stepwise virtual screening

CCR3, a G protein-coupled receptor, plays a central role in allergic inflammation and is an important drug target for inflammatory diseases. To understand the structure-function relationship of CCR3 receptor, different computational techniques were employed, which mainly include: (i) homology modeling of CCR3 receptor, (ii) 3D-quantitative pharmacophore model of CCR3 antagonists, (iii) virtual screening of small compound databases, and (iv) finally, molecular docking at the binding site of the CCR3 receptor homology model. Pharmacophore model was developed for the first time, on a training data set of 22 CCR3 antagonists, using CATALYST HypoRefine program. Best hypothesis (Hypo1) has three different chemical features: two hydrogen-bond acceptors, one hydrophobic, and one ring aromatic. Hypo1 model was further validated using (i) 87 test set CCR3 antagonists, (ii) Cat Scramble randomization technique, and (iii) Decoy data set. Molecular docking studies were performed on modeled CCR3 receptor using 303 virtually screened hits, obtained from small compound database virtual screening. Finally, five hits were identified as potential leads against CCR3 receptor, which exhibited good estimated activities, favorable binding interactions, and high docking scores. These studies provided useful information on the structurally vital residues of CCR3 receptor involved in the antagonist binding, and their unexplored potential for the future development of potent CCR3 receptor antagonists.     

Identification of novel inhibitors of dipeptidylcarboxypeptidase of Leishmania donovani via ligand-based virtual screening and biological evaluation

Current treatment of leishmaniasis is based on chemotherapy, which relies on a handful of drugs with serious limitations, such as high cost, toxicity, and lack of efficacy in endemic regions. Therefore, development of new, effective, and affordable anti-leishmanial drugs is a global health priority. Dipeptidylcarboxypeptidase has been characterized and established as a drug target for antileishmanial drug discovery. We virtually screened a large chemical library of 15 452 compounds against a 3D model of dipeptidylcarboxypeptidase to identify novel inhibitors. The initial virtual screening using a ligand-based pharmacophore model identified 103 compounds. Forty-six compounds were shortlisted based on the docking scores and other scoring functions. Further, these compounds were subjected to biological assay, and four of them belonging to two chemical classes were identified as the lead compounds. Identification of these novel and chemically diverse inhibitors should provide leads to be optimized into candidates to treat these protozoan infections.     

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.     

Identification of novel antilipogenic agents targeting fatty acid biosynthesis through structure‐based virtual screening

An Asinex Gold Platinium chemical library subset of 12 055 compounds was screened employing docking simulations in the active site of the human FAS KS domain. Among them, 13 compounds were further evaluated for their ability to inhibit fatty acid biosynthesis. Four compounds were found to be active in particular ASN05064661 and ASN05374526 with IC50 values of 6.6 and 10.5 μm , respectively. A binding mode study was further conducted with these two compounds structurally related to benzene sulfonamide and aromatic polyamide. This study showed that they fit tightly with the active site with several interactions, notably with the key residues Cys161, His293, and His331.