Evolutionary chemical binding similarity approach integrated with 3D-QSAR method for effective virtual screening

BackgroundThe virtual screening (VS) of lead compounds using molecular docking and pharmacophore detection is now an important tool in drug discovery. VS tasks typically require a combination of several software tools and a molecular graphics system. Thus, the integration of all the requisite tools in a single operating environment could reduce the complexity of running VS experiments. However, only a few freely available integrated software platforms have been developed.ResultsA free open-source platform, IVSPlat 1.0, was developed in this study for the management and automation of VS tasks. We integrated several VS-related programs into a molecular graphics system to provide a comprehensive platform for the solution of VS tasks based on molecular docking, pharmacophore detection, and a combination of both methods. This tool can be used to visualize intermediate and final results of the VS execution, while also providing a clustering tool for the analysis of VS results. A case study was conducted to demonstrate the applicability of this platform.ConclusionsIVSPlat 1.0 provides a plug-in-based solution for the management, automation, and visualization of VS tasks. IVSPlat 1.0 is an open framework that allows the integration of extra software to extend its functionality and modified versions can be freely distributed. The open source code and documentation are available at http://kyc.nenu.edu.cn/IVSPlat/.

Aim: This work aims to understand potential inhibitory structural requirements and identify lead compounds for non-small cell lung cancer through 3D-QSAR pharmacophore-based virtual screening, molecular docking, CoMSIA and CoMFA QSAR modelling. Materials and Methods: QSAR pharmacophore models were developed by HypoGen Module and validated by test data set, Fischer’s randomization and Guner-Henry equation. The well-validated pharmacophore model was employed to perform virtual screening to identify potent hits from ZINC database. The retrieved hits were subsequently subjected to filtering using ADMET descriptors and Lipinski’s Rule of Five. CoMSIA and CoMFA were then utilized to produce QSAR models on phenylpyrimidine derivatives also. Results: Validations on 3D-QSAR pharmacophore model indicate that the enrichment factor is 6.34, GH is 0.517 and a correlation coefficient is 0.83, implying its highly predictive ability. Top three hits: ZINC29356266, ZINC06589615, and ZINC03375633 were identified as promising potent inhibitory candidates with IC50 value of about 0.54 µM and fitness value of about 59.4. Interestingly, the top three hits indicate dual inhibitory activity targeting EGFR and PD-L1 from structure-based docking. Two developed QSAR models from CoMSIA and CoMFA modelling indicate a potential predictive ability (q2=0.67, and 0.71 respectively). The designed compound C indicates a more potential (dual) inhibitory activity (pIC50=7.39) targeting EGFR (fitness=59.78) and CTLA-4 (fitness =47.90). Conclusion: Validations indicate that the developed 3D-QSAR pharmacophore model is highly predictive. Top three hits were identified as promising potent inhibitory candidates and indicated dual inhibitory activity targeting EGFR and PD-L1. The designed compound C indicates a more potential (dual) inhibitory activity targeting EGFR and CTLA‐4. These important 3D-QSAR and molecular docking bioinformatics results achieved from this work should be valuable in designing more promising potent inhibitory candidates and developing novel lead compounds against advanced NSCLC in future.

The intuitive nature of the pharmacophore concept has made it widely accepted by the medicinal chemistry community, evidenced by the past 3 decades of development and application of computerized pharmacophore modeling tools. On the other hand, shape complementarity has been recognized as a critical factor in molecular recognition between drugs and their receptors. Recent development of fast and accurate shape comparison tools has facilitated the wide spread use of shape matching technologies in drug discovery. However, pharmacophore and shape technologies, if used separately, often lead to high false positive rate. Thus, integrating pharmacophore matching and shape matching technologies into one program has the potential to reduce the false positive rates in virtual screening. Other issues of current pharmacophore technologies include sometimes high false negative rate and non-quantitative prediction. In this article, we first focus on a recently implemented method (Shape4) that combines receptor based shape matching and pharmacophore comparison in a single algorithm to create shape pharmacophore models for virtual screening. We also examine a recent example that utilizes multi-complex information to develop receptor-based pharmacophore models that promises to reduce false negative rate. Finally, we review several methods that employ receptor-based pharmacophore map and pharmacophore key descriptors for QSAR modeling. We conclude by emphasizing the concept of receptor-based shape pharmacophore and its roles in future drug discovery.

Identification of dietary compounds that interact with the circadian clock machinery: Molecular docking and structural similarity analysis

Database screening using receptor-based pharmacophores is a computer-aided drug design technique that uses the structure of the target molecule (i.e. protein) to identify novel ligands that may bind to the target. Typically receptor-based pharmacophore modeling methods only consider a single or limited number of receptor conformations and map out the favorable binding patterns in vacuum or with a limited representation of the aqueous solvent environment, such that they may suffer from neglect of protein flexibility and desolvation effects. Site-Identification by Ligand Competitive Saturation (SILCS) is an approach that takes into account these, as well as other, properties to determine 3-dimensional maps of the functional group-binding patterns on a target receptor (i.e. FragMaps). In this study, a method to use the FragMaps to automatically generate receptor-based pharmacophore models is presented. It converts the FragMaps into SILCS pharmacophore features including aromatic, aliphatic, hydrogen-bond donor and acceptor chemical functionalities. The method generates multiple pharmacophore hypotheses that are then quantitatively ranked using SILCS grid free energies. The pharmacophore model generation protocol is validated using three different protein targets, including using the resulting models in virtual screening. Improved performance and efficiency of the SILCS derived pharmacophore models as compared to published docking studies, as well as a recently developed receptor-based pharmacophore modeling method is shown, indicating the potential utility of the approach in rational drug design.

The virtual screening problems associated with acetylcholinesterase (AChE) inhibitors were explored using multiple shape, and structure-based modeling strategies. The employed strategies include molecular docking, similarity search, and pharmacophore modeling. A subset from directory of useful decoys (DUD) related to AChE inhibitors was considered, which consists of 105 known inhibitors and 3732 decoys. Statistical quality of the models was evaluated by enrichment factor (EF) metrics and receiver operating curve (ROC) analysis. The results revealed that electrostatic similarity search protocol using EON (ET_combo) outperformed all other protocols with outstanding enrichment of >95% in top 1% and 2% of the dataset with an AUC of 0.958. Satisfactory performance was also observed for shape-based similarity search protocol using ROCS and PHASE. In contrast, the molecular docking protocol performed poorly with enrichment factors <30% in all cases. The shape- and electrostatic-based similarity search protocol emerged as a plausible solution for virtual screening of AChE inhibitors.

Aims: The research work aims to apply the current virtual screening approaches for rapid screening of available compounds as inhibitors of the novel coronavirus (COVID-19). Background: The worldwide pandemic, uncontrolled spread, and lack of effective therapeutics demand novel SARS-CoV-2 inhibitory anti-viral agents. Objective: The major objectives of the present work are – i) effective utilization of open-source computer- aided drug design (CADD) tools; ii) to prepare a database according to chemical structure similarity to the reported anti-viral drug, Favipiravir; and iii) to investigate potential inhibitors of the novel coronavirus. Methods: The dataset was prepared based on the chemical structure similarity feature of ChemSpider. The virtual screening was carried out using molecular docking and ADMET properties. For performing molecular docking studies, the standard docking protocol of iGEMDOCK was used. Result: Based on chemical structure similarity search to Favipiravir, a small library of 40 compounds was designed. The docking score and ADMET properties were analyzed to prioritize the compounds. Conclusion: The virtual screening resulted in the identification of potential anti-viral compounds. Among the designed library of compounds based on structural similarity to Favipiravir, 70% of compounds were found to possess docking scores more than that of Favipiravir. The amino acid residues involved in binding at the RNA dependent RNA polymerase (RdRp) were identified. The compounds have shown acceptable ADME properties and are potentially non-toxic. Other: The study has successfully applied the open source CADD tools to investigate novel SARS-CoV-2 polymerase inhibitors.

We have previously examined the binding patterns of various substrates to human cytochrome P450 2D6 (CYP2D6) using a series of molecular modeling methods. In this study, we further explored the binding modes of various types of inhibitors to CYP2D6 using a combination of ligand- and protein-based modeling approaches. Firstly, we developed and validated a pharmacophore model for CYP2D6 inhibitors, which consisted of two hydrophobic features and one hydrogen bond acceptor feature. Secondly, we constructed and validated a quantitative structure-activity relationship (QSAR) model for CYP2D6 inhibitors which gave a poor to moderate prediction accuracy. Thirdly, a panel of CYP2D6 inhibitors were subject to molecular docking into the active site of wild-type and mutated CYP2D6 enzyme. We demonstrated that 8 residues in the active site (Leu213, Glu216, Ser217, Gln244, Asp301, Ser304, Ala305, and Phe483) played an important role in the binding to the inhibitors via hydrogen bond formation and/or π-π stacking interaction. Apparent changes in the binding modes of the inhibitors have been observed with Phe120Ile, Glu216Asp, Asp301Glu mutations in CYP2D6. Finally, we screened for potential binders/inhibitors from the Chinese herbal medicine Scutellaria baicalensis (Huangqin, Baikal Skullcap) using the established pharmacophore model for CYP2D6 inhibitors and molecular docking approach. Overall, 18 out of 40 compounds from S. baicalensis were mapped to the pharmacophore model of CYP2D6 inhibitors and most herbal compounds from S. baicalensis could be docked into the active site of CYP2D6. Our study has provided insights into the molecular mechanisms of interaction of synthetic and herbal compounds with human CYP2D6 and further benchmarking studies are needed to validate our modeling and virtual screening results.

Zika virus is a mosquito borne pathogen with a single strand RNA genome. Human infection with this virus is usually asymptomatic, however outbreaks reported in both Pacific region and Latin America have been associated with increase in frequency of microcephaly in newborns and fetuses of infected mothers. Also, the incidence of Guillain-Barré syndrome had also increased among adults with Zika virus infection. Currently, neither vaccine nor antiviral drug has been developed against Zika virus. Structure based virtual screening can be employed, through drug repurposing strategy, to accelerate the identification of potential anti-Zika virus candidates. As such, virtual screening of approved drugs against Zika virus NS2B/NS3 protease can help to recognize new hits capable of hindering viral ability to replicate and evade immune system of the host. In this computational study, we have screened 1615 FDA approved drugs against NS2B/NS3 protease enzyme of Zika virus by using both molecular docking and dynamics simulation. Our virtual screening results indicate that the anti-muscarinic agent Darifenacin and the anti-diarrheal agent Loperamide may have a promising capacity to inhibit Zika virus NS2B/NS3 protease. According to molecular docking and dynamics simulation, these two approved drugs have good binding capacity to NS2B/NS3 as reported by docking energy of binding and MM-PBSA binding energy. In addition, both Darifenacin and Loperamide were able to maintain close proximity to protease crystal throughout simulation period. However, invitro evaluation of these two drugs against Zika virus NS2B/NS3 protease is required to confirm these computational results.

A novel software (VSDMIP) for the virtual screening (VS) of chemical libraries integrated within a MySQL relational database is presented. Two main features make VSDMIP clearly distinguishable from other existing computational tools: (i) its database, which stores not only ligand information but also the results from every step in the VS process, and (ii) its modular and pluggable architecture, which allows customization of the VS stages (such as the programs used for conformer generation or docking), through the definition of a detailed workflow employing user-configurable XML files. VSDMIP, therefore, facilitates the storage and retrieval of VS results, easily adapts to the specific requirements of each method and tool used in the experiments, and allows the comparison of different VS methodologies. To validate the usefulness of VSDMIP as an automated tool for carrying out VS several experiments were run on six protein targets (acetylcholinesterase, cyclin-dependent kinase 2, coagulation factor Xa, estrogen receptor alpha, p38 MAP kinase, and neuraminidase) using nine binary (actives/inactive) test sets. The performance of several VS configurations was evaluated by means of enrichment factors and receiver operating characteristic plots.

Purified vitexin compound 1 (VB1), a novel lignanoid isolated from the seeds of the Chinese herb Vitex negundo, has strong antioxidant abilities and broad antitumor activities. However, little is known about its anti-photoaging effect on the skin and the underlying mechanism. Here, we demonstrated that VB1 significantly attenuates ultraviolet A (UVA)-induced senescence in human dermal fibroblasts (HDFs), as evidenced by senescence-associated β-gal staining, MTT assays, and western blot analysis of the expression of p16 and matrix metalloproteinase-1 (MMP-1). Furthermore, mass spectrometry revealed that VB1 could directly bind to Mitogen-Activated Protein Kinase 1 (MAPK1). Molecular docking and molecular dynamics simulation methods confirmed the mass spectroscopy results and predicted six possible binding amino acids of MAPK1 that most likely interacted with VB1. Subsequent immunoprecipitation analysis, including different MAPK1 mutants, revealed that VB1 directly interacted with the residues, glutamic acid 58 (E58) and arginine 65 (R65) of MAPK1, leading to the partial reversal of UVA-induced senescence in HEK293T cells. Finally, we demonstrated that the topical application of VB1 to the skin of mice significantly reduced photoaging phenotypes in vivo. Collectively, these data demonstrated that VB1 reduces UVA-induced senescence by targeting MAPK1 and alleviates skin photoaging in mice, suggesting that VB1 may be applicable for the prevention and treatment of skin photoaging.

Viral hepatitis B is caused by the hepatitis B virus, which is characterized by liver lesions. Bupleuri Radix and Scutellariae Radix are the main traditional medicine pairs with remarkable efficacy in hepatitis B. However, their molecular mechanisms are incompletely understood. The main active components of Bupleuri Radix and Scutellariae Radix, as well as therapeutic targets for the treatment of hepatitis B, were identified through network pharmacology techniques. We identified viral hepatitis B targets using the GeneCards, online mendelian inheritance in man, and therapeutic target databases. We discovered the active components of Bupleuri Radix and Scutellariae Radix as well as therapeutic targets using the encyclopedia of traditional Chinese medicine, HERB, traditional Chinese medicine systems pharmacology database, and a bioinformatics analysis tool for molecular mechanism of traditional Chinese medicine databases. VENNY obtained the intersections. Cytoscape and STRING were used to create the "active ingredient-potential target" network and protein interaction network. The DAVID database was used to enrich GO and KEGG pathways. The results were confirmed using the molecular docking method. There were 1827 viral hepatitis B targets, and 37 active ingredients for Bupleuri and Scutellariae Radix, with the main components being quercetin, wogonin, baicalein, and kaempferol. Tumor necrosis factor (TNF), mitogen-activated protein kinase 3 (MAPK3), interleukin-6 (IL-6), vascular endothelial growth factor A, cysteinyl aspartate specific proteinase 3, transcription factor AP-1 (JUN), RAC-alpha serine/threonine-protein kinase, and cellular tumor antigen p53 are among the 78 common targets of Bupleuri Radix and Scutellariae Radix intervention in viral hepatitis B. KEGG enrichment resulted in 107 pathways, including cancer, hepatitis B, and TNF signaling pathways. According to the molecular docking technique, quercetin, wogonin, baicalein, and kaempferol had strong binding activities with TNF, MAPK3, and IL-6. In this study, we initially identified various molecular targets and multiple pathways involved in hepatitis B treatment with Bupleuri Radix and Scutellariae Radix.

Molecular modelling is widely used in support of medicinal chemistry programs, with several theoretical approaches used in attempts to expedite drug discovery. In this study, three methods – molecular docking (Glide), shape similarity (ROCS), and pharmacophore modelling (Phase) – were evaluated for their ability to reproduce experimentally determined binding modes of 25 PDE4 inhibitors, identified by X-ray crystallography. Molecular docking was able to provide a good approximation (RMSD less than 2 Å) in 59% of cases, when considering the top binding pose. The pairwise comparisons, using molecular shape similarity, gave good matches in 42% of cases. Pharmacophore models were unable to predict good binding modes for a series of PDE4 inhibitors.

Human intestinal carboxyl esterase (hiCE) is a drug target for ameliorating irinotecan-induced diarrhea. By reducing irinotecan-induced diarrhea, hiCE inhibitors can improve the anti-cancer efficacy of irinotecan. To find effective hiCE inhibitors, a new virtual screening protocol that combines pharmacophore models derived from the hiCE structure and its ligands has been proposed. The hiCE structure has been constructed through homology techniques using hCES1’s crystal structure. The hiCE structure was optimized via molecular dynamics simulations with the most known active hiCE inhibitors docked into the structure. An optimized pharmacophore, derived from the receptor, was then generated. A ligand-based pharmacophore was also generated from a larger set of known hiCE inhibitors. The final hiCE inhibitor predictions were based upon the virtual screening hits from both ligand-based and receptor-based pharmacophore models. The hit rates from the ligand-based and receptor-based pharmacophore models are 88% and 86%, respectively. The final hit rate is 94%. The two models are highly consistent with one another (85%). This proves that both models are reliable.

Single-coordinate-driving method for molecular docking: application to modeling of guest inclusion in cyclodextrin