Pharmacophore modeling, virtual screening, molecular docking studies and density functional theory approaches to identify novel ketohexokinase (KHK) inhibitors

Computational investigation of Moringa oleifera phytochemicals targeting EGFR: molecular docking, molecular dynamics simulation and density functional theory studies

Epidermal growth factor receptor (EGFR) is a prominent target for anticancer therapy due to its role in activating several cell signaling cascades. Clinically approved EGFR inhibitors are reported to show treatment resistance and toxicity, this study, therefore, investigates Moringa oleifera phytochemicals to find potent and safe anti-EGFR compounds. For that, phytochemicals were screened based on drug-likeness and molecular docking analysis followed by molecular dynamics simulation, density functional theory analysis and ADMET analysis to identify the effective inhibitors of EGFR tyrosine kinase (EGFR-TK) domain. Known EGFR-TK inhibitors (1-4 generations) were used as control. Among 146 phytochemicals, 136 compounds showed drug-likeness, of which Delta 7-Avenasterol was the most potential EGFR-TK inhibitor with a binding energy of -9.2 kcal/mol followed by 24-Methylenecholesterol (-9.1 kcal/mol), Campesterol (-9.0 kcal/mol) and Ellagic acid (-9.0 kcal/mol). In comparison, the highest binding affinity from control drugs was displayed by Rociletinib (-9.0 kcal/mol). The molecular dynamics simulation (100 ns) exhibited the structural stability of native EGFR-TK and protein-inhibitor complexes. Further, MM/PBSA computed the binding free energies of protein complex with Delta 7-Avenasterol, 24-Methylenecholesterol, Campesterol and Ellagic acid as -154.559 ± 18.591 kJ/mol, -139.176 ± 19.236 kJ/mol, -136.212 ± 17.598 kJ/mol and -139.513 ± 23.832 kJ/mol, respectively. Non-polar interactions were the major contributors to these energies. The density functional theory analysis also established the stability of these inhibitor compounds. ADMET analysis depicted acceptable outcomes for all top phytochemicals without displaying any toxicity. In conclusion, this report has identified promising EGFR-TK inhibitors to treat several cancers that can be further investigated through laboratory and clinical tests.

In Silico Inhibition of BACE-1 by Selective Phytochemicals as Novel Potential Inhibitors: Molecular Docking and DFT Studies

BACKGROUND

Alzheimer's Disease (AD) has become the most common age-dependent disease of dementia. The trademark pathologies of AD are the presence of amyloid aggregates in neurofibrils. Recently phytochemicals being considered as potential inhibitors against various neurodegenerative, antifungal, antibacterial and antiviral diseases in human beings.

OBJECTIVE

This study targets the inhibition of BACE-1 by phytochemicals using in silico drug discovery analysis.

METHODS

A total of 3150 phytochemicals were collected from almost 25 different plants through literature assessment. The ADMET studies, molecular docking and density functional theory (DFT) based analysis were performed to analyze the potential inhibitory properties of these phytochemicals.

RESULTS

The ADMET and docking results exposed seven compounds that have high potential as an inhibitory agent against BACE-1 and show binding affinity >8.0 kcal/mol against BACE-1. They show binding affinity greater than those of various previously reported inhibitors of BACE-1. Furthermore, DFT based analysis has shown high reactivity for these seven phytochemicals in the binding pocket of BACE- 1, based on ELUMO, EHOMO and Kohn-Sham energy gap. All seven phytochemicals were testified (as compared to experimental ones) as novel inhibitors against BACE-1.

CONCLUSION

Out of seven phytochemicals, four were obtained from plant Glycyrrhiza glabra i.e. Shinflavanone, Glabrolide, Glabrol and PrenyllicoflavoneA, one from Huperzia serrate i.e. Macleanine, one from Uncaria rhynchophylla i.e. 3a-dihydro-cadambine and another one was from VolvalerelactoneB from plant Valeriana-officinalis. It is concluded that these phytochemicals are suitable candidates for drug/inhibitor against BACE-1, and can be administered to humans after experimental validation through in vitro and in vivo trials.

Molecular modelling, synthesis and antimicrobial evaluation of benzimidazole nucleoside mimetics

A series of new N-1-(β-d-ribofuranosyl) benzimidazole derivatives has been designed using in silico methods and synthesized as probable antimicrobial agents. Further, the compounds were assessed for their antibacterial and antifungal activity. Antibacterial screening was done by employing broth micro-dilution method and compounds exhibited excellent inhibitory activity (MIC, 50-1.56 µg/mL) against different human pathogenic bacteria, viz. B. cerus, B. subtilis, S. aureus, E. coli and P. aeruginosa and drug resistant strain (DRS) of E. coli. A great synergistic effect was observed during evaluation of ∑FIC, where a combination study was performed using standard references, viz. chloramphenicol and kanamycin. The MIC data obtained from different methods of combination approach revealed 4-128 fold more potency compared to compounds tested alone. The results clearly indicated the possibility of these compounds as active ingredients of drug regimen used against MDR strains. Antifungal screening were also performed employing two different methods, viz. serial dilution method and zone inhibition method, clearly indicated that compounds were also potentially active against several species of pathogenic fungal strains, viz. A. flavus, A. niger, F. oxysporum and C. albicans. The assessment of structure activity relationship (SAR) clearly revealed that presence of less polar and more hydrophobic substituents positively favours the antibacterial activity, conversely, more polar and hydrophilic substituents favours the antifungal activities. Thus, the results positively endorsed the compounds as potent antibacterial and antifungal agents which could be developed as possible drug regimens.

Analysis of Inhibitor Binding Combined with Reactivity Studies to Discover the Potentially Inhibiting Phytochemicals Targeting Chikungunya Viral Replication

BACKGROUND

Chikungunya fever is a challenging threat to human health in various parts of the world nowadays. Many attempts have been made for developing an effective drug against this viral disease and no effective antiviral treatment has been developed to control the spread of the Chikungunya virus (CHIKV) in humans.

OBJECTIVE

This research is aimed at the discovery of potential inhibitors against this virus by employing computational techniques to study the interactions between non-structural proteins of Chikungunya virus and phytochemicals from plants.

METHODS

Four non-structural proteins were docked with 2035 phytochemicals from various plants. The ligands having binding energies ≥ -8.0 kcal/mol were considered as potential inhibitors for these proteins. ADMET studies were also performed to analyze different pharmacological properties of these docked compounds and to further analyze the reactivity of these phytochemicals against CHIKV, DFT analysis was carried out based on HOMO and LUMO energies.

RESULTS

By analyzing the binding energies, Ki, ADMET properties and band energy gaps, it was observed that 13 phytochemicals passed all the criteria to be a potent inhibitor against CHIKV in humans.

CONCLUSION

A total of 13 phytochemicals were identified as potent inhibiting candidates, which can be used against the Chikungunya virus.

Network pharmacology based high throughput screening for identification of multi targeted anti-diabetic compound from traditionally used plants

The incurable Type 2 diabetes mellitus (T2DM) has now been considered a pandemic with only supportive care in existence. Due to the adverse effects of available anti-diabetic drugs, there arises a great urgency to develop new drug molecules. One of the alternatives that can be considered for the treatment of T2DM are natural compounds from traditionally used herbal medicine. The present study undertakes, an integrated multidisciplinary concept of Network Pharmacology to evaluate the efficacy of potent anti-diabetic compound from traditionally used anti-diabetic plants of north east India and followed by DFT analysis. In the course of the study, 22 plant species were selected on the basis of their use in traditional medicine for the treatment of T2DM by various ethnic groups of the north eastern region of India. Initially, a library of 1053 compounds derived from these plants was generated. This was followed by network preparation between compounds and targets based on the docking result. The compounds having the best network property were considered for DFT analysis. We have identified that auraptene, a monoterpene coumarin for its activity in the management of Type 2 diabetes mellitus and deciphered its unexplored probable mechanisms. Molecular dynamics simulation of the ligand-protein complexes also reveals the stable binding of auraptene with the target proteins namely, Protein Kinase C θ, Glucocorticoid receptor, 11-β hydroxysteroid dehydrogenase 1 and Aldose Reductase, all of which form uniform interactions throughout the MD simulation trajectory. Therefore, this finding could provide new insights for the development of a new anti-diabetic drug.Communicated by Ramaswamy H. Sarma.

The Discovery of Novel BCR-ABL Tyrosine Kinase Inhibitors Using a Pharmacophore Modeling and Virtual Screening Approach

Chronic myelogenous leukemia (CML) typically results from a reciprocal translocation between chromosomes 9 and 22 to produce the bcr-abl oncogene that when translated, yields the p210 BCR-ABL protein in more than 90% of all CML patients. This protein has constitutive tyrosine kinase activity that activates numerous downstream pathways that ultimately produces uncontrolled myeloid proliferation. Although the use of the BCR-ABL tyrosine kinase inhibitors (TKIs), such as imatinib, nilotinib, dasatinib, bosutinib, and ponatinib have increased the overall survival of CML patients, their use is limited by drug resistance and severe adverse effects. Therefore, there is the need to develop novel compounds that can overcome these problems that limit the use of these drugs. Therefore, in this study, we sought to find novel compounds using Hypogen and Hiphip pharmacophore models based on the structures of clinically approved BCR-ABL TKIs. We also used optimal pharmacophore models such as three-dimensional queries to screen the ZINC database to search for potential BCR-ABL inhibitors. The hit compounds were further screened using Lipinski’s rule of five, ADMET and molecular docking, and the efficacy of the hit compounds was evaluated. Our in vitro results indicated that compound ZINC21710815 significantly inhibited the proliferation of K562, BaF3/WT, and BaF3/T315I leukemia cells by inducing cell cycle arrest. The compound ZINC21710815 decreased the expression of p-BCR-ABL, STAT5, and Crkl and produced apoptosis and autophagy. Our results suggest that ZINC21710815 may be a potential BCR-ABL inhibitor that should undergo in vivo evaluation.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Integrating pharmacophore mapping, virtual screening, density functional theory, molecular simulation towards the discovery of novel apolipoprotein (apoE ε4) inhibitors

AIM

An integrated protocol of virtual screening involving molecular docking, pharmacophore probing, and simulations was established to identify small novel molecules targeting crucial residues involved in the variant apoE ε4 to mimic its behavior as apoE2 thereby eliminating the amyloid plaque accumulation and facilitating its clearance.

MATERIALS AND METHODS

An excellent ligand-based and structure-based approach was made to identify common pharmacophoric features involving structure-based docking with respect to apoE ε4 leading to the development of apoE ε4 inhibitors possessing new scaffolds. An effort was made to design multiple-substituted triazine derivatives series bearing a novel scaffold. A structure-based pharmacophore mapping was developed to explore the binding sites of apoE ε4 which was taken into consideration. Subsequently, virtual screening, ADMET, DFT searches were at work to narrow down the proposed hits to be forwarded as a potential drug likes candidates. Further, the binding patterns of the best-proposed hits were studied and were forwarded for molecular dynamic simulations of 10 ns for its structural optimization.

RESULTS

Selectivity profile for the most promising candidates was studied, revealing significantly C13 and C15 to be the most potent compounds. The proposed hits can be forwarded for further study against apoE ε4 involved in neurological disorder Alzheimer's.

Prediction Methods of Herbal Compounds in Chinese Medicinal Herbs

Chinese herbal medicine has recently gained worldwide attention. The curative mechanism of Chinese herbal medicine is compared with that of western medicine at the molecular level. The treatment mechanism of most Chinese herbal medicines is still not clear. How do we integrate Chinese herbal medicine compounds with modern medicine? Chinese herbal medicine drug-like prediction method is particularly important. A growing number of Chinese herbal source compounds are now widely used as drug-like compound candidates. An important way for pharmaceutical companies to develop drugs is to discover potentially active compounds from related herbs in Chinese herbs. The methods for predicting the drug-like properties of Chinese herbal compounds include the virtual screening method, pharmacophore model method and machine learning method. In this paper, we focus on the prediction methods for the medicinal properties of Chinese herbal medicines. We analyze the advantages and disadvantages of the above three methods, and then introduce the specific steps of the virtual screening method. Finally, we present the prospect of the joint application of various methods.

Exploration for novel inhibitors showing back-to-front approach against VEGFR-2 kinase domain (4AG8) employing molecular docking mechanism and molecular dynamics simulations

Background

Angiogenesis is a process of formation of new blood vessels and is an important criteria demonstrated by cancer cells. Over a period of time, these cancer cells infect the other parts of the healthy body by a process called progression. The objective of the present article is to identify a drug molecule that inhibits angiogenesis and progression.

Methods

In this pursuit, ligand based pharmacophore virtual screening was employed, generating a pharmacophore model, Hypo1 consisting of four features. Furthermore, this Hypo1 was validated recruiting, Fischer’s randomization, test set method and decoy set method. Later, Hypo1 was allowed to screen databases such as Maybridge, Chembridge, Asinex and NCI and were further filtered by ADMET filters and Lipinski’s Rule of Five. A total of 699 molecules that passed the above criteria, were challenged against 4AG8, an angiogenic drug target employing GOLD v5.2.2.

Results

The results rendered by molecular docking, DFT and the MD simulations showed only one molecule (Hit) obeyed the back-to-front approach. This molecule displayed a dock score of 89.77, involving the amino acids, Glu885 and Cys919, Asp1046, respectively and additionally formed several important hydrophobic interactions. Furthermore, the identified lead molecule showed interactions with key residues when challenged with CDK2 protein, 1URW.

Conclusion

The lead candidate showed several interactions with the crucial residues of both the targets. Furthermore, we speculate that the residues Cys919 and Leu83 are important in the development of dual inhibitor. Therefore, the identified lead molecule can act as a potential inhibitor for angiogenesis and progression.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4050-1) contains supplementary material, which is available to authorized users.

Toward a hierarchical virtual screening and toxicity risk analysis for identifying novel CA XII inhibitors

Carbonic anhydrase isoform XII (CA XII) is a potential target for cancer treatment. In this study, pharmacophore modeling, hierarchical virtual screening, and toxicity risk analysis were performed for identifying novel CA XII inhibitors. A pharmacophore model of two classes of CA XII inhibitors was generated. The pharmacophore model indicated the important features of inhibitors for the binding with the CA XII. The model was then utilized to screen the ZINC and CoCoCo databases for retrieving potential hit compounds of CA XII. For accurate conclusions about the selectivity of inhibitors, the retrieved molecules which obey of Lipinski's rule of five (RO5) and have no toxicity risk were docked in a CA XII 3D structure by smina. Finally, on the basis of binding affinity and the binding mode of the molecules, twelve molecules were prioritized as promising hits. It should be noted that two of hits H5 and H6 were previously reported in the CHEMBL database. This hierarchical method is worthy of reducing the time and using almost all information available. The final hits may be used as a lead to discovery novel CA XII inhibitors.

3D pharmacophore-based virtual screening, docking and density functional theory approach towards the discovery of novel human epidermal growth factor receptor-2 (HER2) inhibitors

Human epidermal growth factor receptor 2 (HER2) is one of the four members of the epidermal growth factor receptor (EGFR) family and is expressed to facilitate cellular proliferation across various tissue types. Therapies targeting HER2, which is a transmembrane glycoprotein with tyrosine kinase activity, offer promising prospects especially in breast and gastric/gastroesophageal cancer patients. Persistence of both primary and acquired resistance to various routine drugs/antibodies is a disappointing outcome in the treatment of many HER2 positive cancer patients and is a challenge that requires formulation of new and improved strategies to overcome the same. Identification of novel HER2 inhibitors with improved therapeutics index was performed with a highly correlating (r=0.975) ligand-based pharmacophore model (Hypo1) in this study. Hypo1 was generated from a training set of 22 compounds with HER2 inhibitory activity and this well-validated hypothesis was subsequently used as a 3D query to screen compounds in a total of four databases of which two were natural product databases. Further, these compounds were analyzed for compliance with Veber's drug-likeness rule and optimum ADMET parameters. The selected compounds were then subjected to molecular docking and Density Functional Theory (DFT) analysis to discern their molecular interactions at the active site of HER2. The findings thus presented would be an important starting point towards the development of novel HER2 inhibitors using well-validated computational techniques.