QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking,molecular dynamics simulation and MMGBSA calculation approaches

Congruous coronavirus drug targets and analogous lead molecules must be identified as quickly as possible to produce antiviral therapeutics against human coronavirus (HCoV SARS 3CLpro) infections. In the present communication, we bear recognized a HIT candidate for HCoV SARS 3CLpro inhibition. Four Parametric GA-MLR primarily based QSAR model (R2:0.84, R2adj:0.82, Q2loo: 0.78) was once promoted using a dataset over 37 structurally diverse molecules along QSAR based virtual screening (QSAR-VS), molecular docking (MD) then molecular dynamic simulation (MDS) analysis and MMGBSA calculations. The QSAR-based virtual screening was utilized to find novel lead molecules from an in-house database of 100 molecules. The QSAR-vS successfully offered a hit molecule with an improved _PEC₅₀ value from 5.88 to 6.08. The benzene ring, phenyl ring, amide oxygen and nitrogen, and other important pharmacophoric sites are revealed via MD and MDS studies. Ile164, Pro188, Leu190, Thr25, His41, Asn46, Thr47, Ser49, Asn189, Gln191, Thr47, and Asn141 are among the key amino acid residues in the S1 and S2 pocket. A stable complex of a lead molecule with the HCoV SARS 3CLpro was discovered using MDS. MM-GBSA calculations resulted from MD simulation results well supported with the binding energies calculated from the docking results. The results of this study can be exploited to develop a novel antiviral target, such as an HCoV SARS 3CLpro Inhibitor.     

Virtual screening of organic molecule databases. Design of focused libraries of potential ligands of NMDA and AMPA receptors

A system of virtual screening of organic molecule databases is designed, which permits preprocessing of databases, molecular docking to a three-dimensional model of receptor, and post-processing of the results obtained. Using this screening system, it is possible to reproduce positions of the known ligands in the glutamate sites of the NMDA and AMPA receptors and in the glycine site of the NMDA receptor, to substantially enrich the database with potentially active compounds, and to distinguish between the agonistic and antagonistic character of the action of these compounds in the case of docking to the open and closed forms of the binding sites. Based on the results of screening of a database of low-molecular-weight organic compounds (total of 135,000 structures) using models of the open and closed forms of the glutamate and glycine sites of the NMDA receptor and of the glutamate site of the AMPA receptor, focused libraries of potential agonists and antagonists of these sites were designed.     

Virtual screening of novel noncovalent inhibitors for SARS-CoV 3C-like proteinase

The SARS coronavirus 3C-like proteinase is considered as a potential drug design target for the treatment of severe acute respiratory syndrome (SARS). Owing to the lack of available drugs for the treatment of SARS, the discovery of inhibitors for SARS coronavirus 3C-like proteinase that can potentially be optimized as drugs appears to be highly desirable. We have built a "flexible" three-dimensional model for SARS 3C-like proteinase by homology modeling and multicanonical molecular dynamics method and used the model for virtual screening of chemical databases. After Dock procedures, strategies including pharmocophore model, consensus scoring, and "drug-like" filters were applied in order to accelerate the process and improve the success rate of virtual docking screening hit lists. Forty compounds were purchased and tested by HPLC and colorimetric assay against SARS 3C-like proteinase. Three of them including calmidazolium, a well-known antagonist of calmodulin, were found to inhibit the enzyme with an apparent K(i) from 61 to 178 microM. These active compounds and their binding modes provide useful information for understanding the binding sites and for further selective drug design against SARS and other coronavirus.     

Computational Approaches to Discover Novel Natural Compounds for SARS-CoV-2 Therapeutics

Scientists all over the world are facing a challenging task of finding effective therapeutics for the coronavirus disease (COVID-19). One of the fastest ways of finding putative drug candidates is the use of computational drug discovery approaches. The purpose of the current study is to retrieve natural compounds that have obeyed to drug-like properties as potential inhibitors. Computational molecular modelling techniques were employed to discover compounds with potential SARS-CoV-2 inhibition properties. Accordingly, the InterBioScreen (IBS) database was obtained and was prepared by minimizing the compounds. To the resultant compounds, the absorption, distribution, metabolism, excretion and toxicity (ADMET) and Lipinski's Rule of Five was applied to yield drug-like compounds. The obtained compounds were subjected to molecular dynamics simulation studies to evaluate their stabilities. In the current article, we have employed the docking based virtual screening method using InterBioScreen (IBS) natural compound database yielding two compounds has potential hits. These compounds have demonstrated higher binding affinity scores than the reference compound together with good pharmacokinetic properties. Additionally, the identified hits have displayed stable interaction results inferred by molecular dynamics simulation results. Taken together, we advocate the use of two natural compounds, STOCK1N-71493 and STOCK1N-45683 as SARS-CoV-2 treatment regime.     

Computational validation of the importance of absolute stereochemistry in virtual screening

Consideration of stereochemistry early in the identification and optimization of lead compounds can improve the efficiency and efficacy of the drug discovery process and reduce the time spent on subsequent drug development. These improvements can result by focusing on specific enantiomers that have the desired potential therapeutic effect (eutomers), while removing from consideration enantiomers that may have no, or even undesirable, effects (distomers). A virtual screening campaign that correctly takes stereochemical information into account can, in theory, be utilized to provide information about the relative binding affinities of enantiomers. Thus, the proper enumeration of the relevant stereoisomers in general, and enantiomeric pairs in particular, of chiral compounds is crucial if one is to use virtual screening as an effective drug discovery tool. As is obvious, in cases where no stereochemical information is provided for chiral compounds in a 2D chemical database, then each possible stereoisomer should be generated for construction of the subsequent 3D database to be used for virtual screening. However, acute problems can arise in 3D database construction when relative stereochemistry is encoded in a 2D database for a chiral compound containing multiple stereogenic atoms but absolute stereochemistry is not implied. In this case, we report that generation of enantiomeric pairs is imperative in database development if one is to obtain accurate docking results. A study is described on the impact of the neglect of enantiomeric pairs on virtual screening using the human homolog of murine double minute 2 (MDM2) protein, the product of a proto-oncogene, as the target. Docking in MDM2 with GLIDE 4.0 was performed using the NCI Diversity Set 3D database and, for comparison, a set of enantiomers we created corresponding to mirror image structures of the single enantiomers of chiral compounds present in the NCI Diversity Set. Our results demonstrate that potential lead candidates may be overlooked when databases contain 3D structures representing only a single enantiomer of racemic chiral compounds.     

GPCR structure-based virtual screening approach for CB2 antagonist search

The potential for therapeutic specificity in regulating diseases has made cannabinoid (CB) receptors one of the most important G-protein-coupled receptor (GPCR) targets in search for new drugs. Considering the lack of related 3D experimental structures, we have established a structure-based virtual screening protocol to search for CB2 bioactive antagonists based on the 3D CB2 homology structure model. However, the existing homology-predicted 3D models often deviate from the native structure and therefore may incorrectly bias the in silico design. To overcome this problem, we have developed a 3D testing database query algorithm to examine the constructed 3D CB2 receptor structure model as well as the predicted binding pocket. In the present study, an antagonist-bound CB2 receptor complex model was initially generated using flexible docking simulation and then further optimized by molecular dynamic and mechanical (MD/MM) calculations. The refined 3D structural model of the CB2-ligand complex was then inspected by exploring the interactions between the receptor and ligands in order to predict the potential CB2 binding pocket for its antagonist. The ligand-receptor complex model and the predicted antagonist binding pockets were further processed and validated by FlexX-Pharm docking against a testing compound database that contains known antagonists. Furthermore, a consensus scoring (CScore) function algorithm was established to rank the binding interaction modes of a ligand on the CB2 receptor. Our results indicated that the known antagonists seeded in the testing database can be distinguished from a significant amount of randomly chosen molecules. Our studies demonstrated that the established GPCR structure-based virtual screening approach provided a new strategy with a high potential for in silico identifying novel CB2 antagonist leads based on the homology-generated 3D CB2 structure model.     

Ensemble pharmacophore meets ensemble docking: a novel screening strategy for the identification of RIPK1 inhibitors

Programmed cell death has been a fascinating area of research since it throws new challenges and questions in spite of the tremendous ongoing research in this field. Recently, necroptosis, a programmed form of necrotic cell death, has been implicated in many diseases including neurological disorders. Receptor interacting serine/threonine protein kinase 1 (RIPK1) is an important regulatory protein involved in the necroptosis and inhibition of this protein is essential to stop necroptotic process and eventually cell death. Current structure-based virtual screening methods involve a wide range of strategies and recently, considering the multiple protein structures for pharmacophore extraction has been emphasized as a way to improve the outcome. However, using the pharmacophoric information completely during docking is very important. Further, in such methods, using the appropriate protein structures for docking is desirable. If not, potential compound hits, obtained through pharmacophore-based screening, may not have correct ranks and scores after docking. Therefore, a comprehensive integration of different ensemble methods is essential, which may provide better virtual screening results. In this study, dual ensemble screening, a novel computational strategy was used to identify diverse and potent inhibitors against RIPK1. All the pharmacophore features present in the binding site were captured using both the apo and holo protein structures and an ensemble pharmacophore was built by combining these features. This ensemble pharmacophore was employed in pharmacophore-based screening of ZINC database. The compound hits, thus obtained, were subjected to ensemble docking. The leads acquired through docking were further validated through feature evaluation and molecular dynamics simulation.     

Investigation of MM-PBSA rescoring of docking poses

Target-based virtual screening is increasingly used to generate leads for targets for which high quality three-dimensional (3D) structures are available. To allow large molecular databases to be screened rapidly, a tiered scoring scheme is often employed whereby a simple scoring function is used as a fast filter of the entire database and a more rigorous and time-consuming scoring function is used to rescore the top hits to produce the final list of ranked compounds. Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) approaches are currently thought to be quite effective at incorporating implicit solvation into the estimation of ligand binding free energies. In this paper, the ability of a high-throughput MM-PBSA rescoring function to discriminate between correct and incorrect docking poses is investigated in detail. Various initial scoring functions are used to generate docked poses for a subset of the CCDC/Astex test set and to dock one set of actives/inactives from the DUD data set. The effectiveness of each of these initial scoring functions is discussed. Overall, the ability of the MM-PBSA rescoring function to (i) regenerate the set of X-ray complexes when docking the bound conformation of the ligand, (ii) regenerate the X-ray complexes when docking conformationally expanded databases for each ligand which include "conformation decoys" of the ligand, and (iii) enrich known actives in a virtual screen for the mineralocorticoid receptor in the presence of "ligand decoys" is assessed. While a pharmacophore-based molecular docking approach, PhDock, is used to carry out the docking, the results are expected to be general to use with any docking method.     

High-throughput screening identifies inhibitors of the SARS coronavirus main proteinase

The causative agent of severe acute respiratory syndrome (SARS) has been identified as a novel coronavirus, SARS-CoV. The main proteinase of SARS-CoV, 3CLpro, is an attractive target for therapeutics against SARS owing to its fundamental role in viral replication. We sought to identify novel inhibitors of 3CLpro to advance the development of appropriate therapies in the treatment of SARS. 3CLpro was cloned, expressed, and purified from the Tor2 isolate. A quenched fluorescence resonance energy transfer assay was developed for 3CLpro to screen the proteinase against 50,000 drug-like small molecules on a fully automated system. The primary screen identified 572 hits; through a series of virtual and experimental filters, this number was reduced to five novel small molecules that show potent inhibitory activity (IC50 = 0.5-7 microM) toward SARS-CoV 3CLpro.     

整合药效团及共价对接用于虚拟筛选JAK3抑制剂

大量研究表明JAK3与炎症疾病的发生、发展具有密切的关系,使得JAK3成为一个极具潜力的药物靶点。其中,JAK3共价抑制剂因其选择性高、活性强的特点受到广泛关注。但是,JAK3与其家族的其他成员同源性高,使得开发JAK3选择性抑制剂充满挑战。计算机虚拟筛选方法可以在分子水平对JAK3的结构特征进行针对性筛选,但是传统的共价对接方法效率较低、准确度欠佳,因此本文提出了一种结合药效团和共价对接的虚拟筛选策略。该联用方法从DrugBank数据库成功地筛选出已报道的JAK3临床抑制剂,表明了这种虚拟筛选不仅具有较高的效率,同时具备了较强的筛选准确性,为JAK3共价抑制剂的虚拟筛选提供一定的指导作用。     

Identification of potential Tpx inhibitors against pathogen-host interactions

Yersinia organisms cause many infectious diseases by invading human cells and delivering their virulence factors via the type three secretion system (T3SS). One alternative strategy in the fight against these pathogenic organisms is to interfere with their T3SS. Previous studies demonstrated that thiol peroxidase, Tpx is functional in the assembly of T3SS and its inhibition by salicylidene acylhydrazides prevents the secretion of pathogenic effectors. In this study, the aim was to identify potential inhibitors of Tpx using an integrated approach starting with high throughput virtual screening and ending with molecular dynamics simulations of selected ligands. Virtual screening of ZINC database of 500,000 compounds via ligand-based and structure-based pharmacophore models retrieved 10,000 hits. The structure-based pharmacophore model was validated using high-throughput virtual screening (HTVS). After multistep docking (SP and XP), common scaffolds were used to find common substructures and the ligand binding poses were optimized using induced fit docking. The stability of the protein–ligand complex was examined with molecular dynamics simulations and the binding free energy of the complex was calculated. As a final outcome eight compounds with different chemotypes were proposed as potential inhibitors for Tpx. The eight ligands identified by a detailed virtual screening protocol can serve as leads in future drug design efforts against the destructive actions of pathogenic bacteria.     

新型芳磺酰基色氨酸酯以及芳磺酰基谷氨酸二酯类化合物的合成与生物活性研究

在乙酰羟酸合成酶(AHAS)与磺酰脲类化合物复合物晶体结构的基础上, 利用分子对接法进行MDL/ACD三维数据库虚拟筛选, 得到了部分结合能较低的小分子化合物结构. 对其中的芳磺酰基色氨酸酯以及芳磺酰基谷氨酸二酯类化合物进行了合成, 共合成22个具有潜在活性的新衍生化合物, 其结构通过核磁、质谱、红外及元素分析验证, 并对所有新化合物进行了体内、体外活性测试. 初步的体外生物活性测试结果表明, 两类化合物对AHAS具有较低的抑制性; 体内生物活性测试结果表明, 化合物4d, 4g和4k具有一定的除草活性, 在100 μg/mL的浓度下, 它们对油菜根长的抑制率分别为70.8%, 52.4%和50.2%.     

SARS-CoV protease inhibitors design using virtual screening method from natural products libraries

Two natural products databases, the marine natural products database (MNPD) and the traditional Chinese medicines database (TCMD), were used to find novel structures of potent SARS-CoV protease inhibitors through virtual screening. Before the procedure, the databases were filtered by Lipinski's ROF and Xu's extension rules. The results were analyzed by statistic methods to eliminate the bias in target-based database screening toward higher molecular weight compounds for enhancing the hit rate. Eighteen lead compounds were recommended by the screening procedure. They were useful for experimental scientists in prioritizing drug candidates and studying the interaction mechanism. The binding mechanism was also analyzed between the best screening compound and the SARS protein.     

Comparison of topological, shape, and docking methods in virtual screening

Virtual screening benchmarking studies were carried out on 11 targets to evaluate the performance of three commonly used approaches: 2D ligand similarity (Daylight, TOPOSIM), 3D ligand similarity (SQW, ROCS), and protein structure-based docking (FLOG, FRED, Glide). Active and decoy compound sets were assembled from both the MDDR and the Merck compound databases. Averaged over multiple targets, ligand-based methods outperformed docking algorithms. This was true for 3D ligand-based methods only when chemical typing was included. Using mean enrichment factor as a performance metric, Glide appears to be the best docking method among the three with FRED a close second. Results for all virtual screening methods are database dependent and can vary greatly for particular targets.     

Discovery and design of new PI3K inhibitors through pharmacophore-based virtual screening,molecular docking,and binding free energy analysis

Phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/the mammalian target of rapamycin (mTOR) signaling pathway is one of the sought after therapeutic target for treating human cancers. This pathway is often hyper activated in cancers. In the present study, pharmacophore-based virtual screening, molecular docking, and binding free energy calculations were performed on a series of quinoline derivatives which were reported to be effective against PI3Kα. A five-point pharmacophore hypothesis with one hydrogen bond acceptor (A), one hydrogen bond donor (D), one hydrophobic group (H), and two aromatic rings (R) was developed with acceptable R² and Q² values of 0.93 and 0.60 respectively. Eventually, common pharmacophore hypothesis-based screening was conducted against TOSLab, CPP, and ASINEX macrocylce databases, and potential hits were identified which were further subjected to rigorous docking process in order to screen out drug like molecules having crucial interactions with the target PI3Kα. Finally, binding free energy analysis was carried out for the top hits obtained from docking process. We also designed new 1, 3, 4-oxadiazole-based cyclic peptides by incorporating the structural features of the hits obtained from the above databases. Among the designed cyclic peptides, the cyclic peptide with tryptophan moiety showed good interactions and free binding energy values. On the whole, this study helped us in identifying new promising molecules as PI3Kα inhibitors which can be explored further to generate greater number of compounds with better pharmacokinetic properties.     

Chromenone-based GSK-3β inhibitors as potential therapeutic targets for cardiovascular diseases: In silico study,molecular dynamics,and ADMET profiles

Glycogen synthase kinase-3 beta (GSK-3β) regulates glycogen metabolism and many different cellulars, including apoptosis, signaling, and neural. It is a crucial therapeutic receptor in heart disease, type 2 diabetes, and Alzheimer's. In this study, using computational methods, flavonoid compounds were investigated for potential inhibitors against GSK-3β. Virtual screening was utilized to investigate flavonoid compounds obtained from the PubChem database. Structure of human heart mitochondria of GSK-3β receptor constructed by homology modeling. Best binding poses were discovered via in silico molecular docking simulation. We surveyed noncovalent interactions among amino acid residues involved in the active site of the modeled Protein and compounds via molecular docking and molecular dynamics (MD).Moreover, ADMET characteristics of best docking conformers have been investigated. The obtained results revealed that compound 1 containing chromenone moiety with binding energy H-bond ?11.4 kcal/mol inhibited effectively binding pocket of the GSK-3β receptor. Moreover, MD simulation analysis (RMSD and radius of gyration indicated complex of the compound and GSK-3β receptor remained stable throughout 100 ns MD simulation, and also analysis of ADMET profiles revealed that selected compounds had good drug-likeness and pharmacokinetic properties. Hence, it was suggested that compounds with chromenone scaffold could potentially inhibit GSK-3β. Structural modification of the chromenone derivatives may result in the discovery of promising candidates for identifying novel drugs as GSK-3β inhibitors.     

Identification of Novel TRPC5 Inhibitors by Pharmacophore-Based and Structure-Based Approaches

Canonical transient receptor potential-5 (TRPC5), which belongs to the subfamily of transient receptor potential (TRP) channels, is a non-selective cation channel mainly expressed in the central nervous system and shows more restricted expression in the periphery. TRPC5 plays a crucial role in human physiology and pathology, for instance, anxiety, depression, epilepsy, pain, memory and chronic kidney disease (CKD). However, due to lack of the effective and selective inhibitors, its physiological and pathological mechanism remains so far unknown. It is therefore pivotal to identify potential TRPC5 inhibitors. We have applied ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) methods. The pharmacophore models of TRPC5 antagonists generated by using the HypoGen and HipHop algorithms were used as a query model for the screening of potential inhibitors against the Specs database. The resultant hits from LBVS were further screened by SBVS. SBVS was carried out based on the homology model generation of human TRPC5, binding site identification, molecular dynamics optimization and molecular docking studies. In our systematic screening approaches, we have identified 7 hits compounds with comparable dock score after Lipinski and Veber rules, ADMET, PAINS analysis, cluster analysis, and similarity analysis. In conclusion, the current research provides novel backbones for the new-generation of TRPC5 inhibitors.