Targeting natural compounds against HER2 kinase domain as potential anticancer drugs applying pharmacophore based molecular modelling approaches

In Silico Comparison of Bioactive Compounds Characterized from Azadirachta indica with an FDA-Approved Drug against Schistosomal Agents: New Insight into Schistosomiasis Treatment

The burden of human schistosomiasis, a known but neglected tropical disease in Sub-Saharan Africa, has been worrisome in recent years. It is becoming increasingly difficult to tackle schistosomiasis with praziquantel, a drug known to be effective against all Schistosoma species, due to reports of reduced efficacy and resistance. Therefore, this study seeks to investigate the antischistosomal potential of phytochemicals from Azadirachta indica against proteins that have been implicated as druggable targets for the treatment of schistosomiasis using computational techniques. In this study, sixty-three (63) previously isolated and characterized phytochemicals from A. indica were identified from the literature and retrieved from the PubChem database. In silico screening was conducted to assess the inhibitory potential of these phytochemicals against three receptors (Schistosoma mansoni Thioredoxin glutathione reductase, dihydroorotate dehydrogenase, and Arginase) that may serve as therapeutic targets for schistosomiasis treatment. Molecular docking, ADMET prediction, ligand interaction, MMGBSA, and molecular dynamics simulation of the hit compounds were conducted using the Schrodinger molecular drug discovery suite. The results show that Andrographolide possesses a satisfactory pharmacokinetic profile, does not violate the Lipinski rule of five, binds with favourable affinity with the receptors, and interacts with key amino acids at the active site. Importantly, its interaction with dihydroorotate dehydrogenase, an enzyme responsible for the catalysis of the de novo pyrimidine nucleotide biosynthetic pathway rate-limiting step, shows a glide score and MMGBSA of -10.19 and -45.75 Kcal/mol, respectively. In addition, the MD simulation shows its stability at the active site of the receptor. Overall, this study revealed that Andrographolide from Azadirachta indica could serve as a potential lead compound for the development of an anti-schistosomal drug.

Synthesis of Indole-Linked Thiadiazoles and their Anticancer Action against Triple-Negative Breast Cancer

With a lack of targeted therapy and significantly high metastasis, heterogeneity, and relapse rates, Triple-Negative Breast Cancer (TNBC) offers substantial treatment challenges and demands more chemotherapeutic interventions. In the present study, indole-endowed thiadiazole derivatives have been synthesized and screened for antiproliferative potency against the triple-negative breast cancer MDA-MB-231 cell line. Compound 4 h, possessing chlorophenyl moiety, displays the best anticancer potency (IC50: 0.43 μM) in the cell viability assay. The title compounds demonstrate substantial docking competency against the EGFR receptor (PDB ID: 3POZ), validating their in-vitro ant proliferative action. With a high docking score (-9.9 to -8.7 kcal/mol), the indole hybrids display significant binding propensity comparable to the co-crystallized ligand TAK-285 and occupy a similar strategic position in the active domain of the designated receptor. The quantum and electronic properties of the integrated templates are evaluated through DFT, and optimal values of the deduced global reactivity indices, such as energy gap, electronegativity, ionization potential, chemical potential, electrophilicity, etc., suggest their apt biochemical reactivity. The indole hybrids show near-appropriate pharmacokinetic efficacy and bioavailability in the in-silico studies, indicating their candidacy for potential drug usage. Promising in-vitro anticancer action and binding interfaces project indole conjugates as potential leads in addressing the TNBC dilemma.

Computational and Preclinical Prediction of the Antimicrobial Properties of an Agent Isolated from Monodora myristica: A Novel DNA Gyrase Inhibitor

The African nutmeg (Monodora myristica) is a medically useful plant. We, herein, aimed to critically examine whether bioactive compounds identified in the extracted oil of Monodora myristica could act as antimicrobial agents. To this end, we employed the Schrödinger platform as the computational tool to screen bioactive compounds identified in the oil of Monodora myristica. Our lead compound displayed the highest potency when compared with levofloxacin based on its binding affinity. The hit molecule was further subjected to an Absorption, Distribution, Metabolism, Excretion (ADME) prediction, and a Molecular Dynamics (MD) simulation was carried out on molecules with PubChem IDs 529885 and 175002 and on three standards (levofloxacin, cephalexin, and novobiocin). The MD analysis results demonstrated that two molecules are highly compact when compared to the native protein; thereby, this suggests that they could affect the protein on a structural and a functional level. The employed computational approach demonstrates that conformational changes occur in DNA gyrase after the binding of inhibitors; thereby, this resulted in structural and functional changes. These findings expand our knowledge on the inhibition of bacterial DNA gyrase and could pave the way for the discovery of new drugs for the treatment of multi-resistant bacterial infections.

Study on the molecular mechanism of anti-liver cancer effect of Evodiae fructus by network pharmacology and QSAR model

Introduction: Evodiae Fructus (EF) is the dried, near ripe fruit of Euodia rutaecarpa (Juss.) Benth in Rutaceae. Numerous studies have demonstrated its anti-liver cancer properties. However, the molecular mechanism of Evodiae fructus against liver cancer and its structure-activity connection still require clarification. Methods: We utilized network pharmacology and a QSAR (2- and 3-dimensional) model to study the anti-liver cancer effect of Evodiae fructus. First, by using network pharmacology to screen the active substances and targets of Evodiae fructus, we investigated the signaling pathways involved in the anti-liver cancer actions of Evodiae fructus. The 2D-QSAR pharmacophore model was then used to predict the pIC50 values of compounds. The hiphop method was used to create an ideal 3D-QSAR pharmacophore model for the prediction of Evodiae fructus compounds. Finally, molecular docking was used to validate the rationality of the pharmacophore, and molecular dynamics was used to disclose the stability of the compounds by assessing the trajectories in 10 ns using RMSD, RMSF, Rg, and hydrogen bonding metrics. Results: In total, 27 compounds were acquired from the TCMSP and TCM-ID databases, and 45 intersection targets were compiled using Venn diagrams. Network integration analysis was used in this study to identify SRC as a primary target. Key pathways were discovered by KEGG pathway analysis, including PD-L1 expression and PD-1 checkpoint pathway, EGFR tyrosine kinase inhibitor resistance, and ErbB signaling pathway. Using a 2D-QSAR pharmacophore model and the MLR approach to predict chemical activity, ten highly active compounds were found. Two hydrophobic features and one hydrogen bond acceptor feature in the 3D-QSAR pharmacophore model were validated by training set chemicals. The results of molecular docking revealed that 10 active compounds had better docking scores with SRC and were linked to residues via hydrogen and hydrophobic bonds. Molecular dynamics was used to show the structural stability of obacunone, beta-sitosterol, and sitosterol. Conclusion:Pharmacophore 01 has high selectivity and the ability to distinguish active and inactive compounds, which is the optimal model for this study. Obacunone has the optimal binding ability with SRC. The pharmacophore model proposed in this study provides theoretical support for further screening effective anti-cancer Chinese herbal compounds and optimizing the compound structure.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Screening, Docking, and Molecular Dynamics Study of Natural Compounds as an Anti-HER2 for the Management of Breast Cancer

Breast cancer (BC) is one of the most frequent types of cancer that affect women. Human epidermal growth factor receptor-2 (HER2) is responsible for 20% of all BC cases. The use of anti-HER2 natural compounds in the cure of BC that is HER2-positive patients has resulted in significant increases in survival in both early and advanced stages. The findings of in-silico research support the use of ligands as possible HER2 inhibitors, and molecules with high free energy of binding may have considerable anti-BC action, making them candidates for future drug development. The inhibitory activity of selected ligands like ZINC43069427 and ZINC95918662 against HER2 was found to be -11.0 and -8.50 kcal/mol, respectively. The amino acid residues Leu726, Val734, Ala751, Lys753, Thr798, Gly804, Arg849, Leu852, Thr862, and Asp863 were found in common interaction as compared to the control compound Lapatinib. Molecular dynamics study calculations of these selected potent inhibitors were conducted and found to be stable over the 50 ns simulation time in terms of root mean square deviation (RMSD), root-mean square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA). In addition, there are several parameters such as absorption, distribution, metabolism, and excretion toxicity (ADMET), physicochemical, and drug-likeness that were checked and found in good range to be potential lead-like molecules. Several drug-likeness rules like Lipinski, Ghose, Veber, Egan, and Muegge were checked and found to be positive for these rules. Based on these calculations and different parameters, these top two selected natural compounds can be used as potential candidates for anti-HER2 for the management of BC.

Network analysis and ligand-based pharmacophore modeling for discovery of small molecule against glioblastoma multiforme

Aim: This study uses network pharmacology to design a c-Src inhibitor followed by pharmacophore modeling to combat glioblastoma multiforme. These in silico approaches are suitable for designing and developing new molecules of interest. Materials & methods: The authors performed virtual screening, pharmacophore analysis and validation of results using various in silico tools and reliable data from different types of literature and databases. Results: The in silico pipeline the authors followed produced reliable chemical information to combat glioblastoma. The authors identified a chemical template against the c-Src protein, which was validated statistically and computationally. Conclusion: The authors have successfully identified a chemical template against c-Src, which will be developed into a promising inhibitor in future studies.

Nucleolar localization of the ErbB3 receptor as a new target in glioblastoma

Background

The nucleolus is a subnuclear, non-membrane bound domain that is the hub of ribosome biogenesis and a critical regulator of cell homeostasis. Rapid growth and division of cells in tumors are correlated with intensive nucleolar metabolism as a response to oncogenic factors overexpression. Several members of the Epidermal Growth Factor Receptor (EGFR) family, have been identified in the nucleus and nucleolus of many cancer cells, but their function in these compartments remains unexplored.

Results

We focused our research on the nucleolar function that a specific member of EGFR family, the ErbB3 receptor, plays in glioblastoma, a tumor without effective therapies. Here, Neuregulin 1 mediated proliferative stimuli, promotes ErbB3 relocalization from the nucleolus to the cytoplasm and increases pre-rRNA synthesis. Instead ErbB3 silencing or nucleolar stress reduce cell proliferation and affect cell cycle progression.

Conclusions

These data point to the existence of an ErbB3-mediated non canonical pathway that glioblastoma cells use to control ribosomes synthesis and cell proliferation. These results highlight the potential role for the nucleolar ErbB3 receptor, as a new target in glioblastoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12860-022-00411-y.

Targeting Peptidyl-prolyl Cis-trans Isomerase NIMA-interacting 1: A Structure-based Virtual Screening Approach to Find Novel Inhibitors

BACKGROUND

Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (Pin1) is an enzyme that isomerizes phosphorylated serine or threonine motifs adjacent to proline residues. Pin1 has important roles in several cellular signaling pathways, consequently impacting the development of multiple types of cancers.

METHODS

Based on the previously reported inhibitory activity of pentacyclic triterpenoids isolated from the gum resin of Boswellia genus against Pin1, we designed a computational experiment using molecular docking, pharmacophore filtering, and structural clustering allied to molecular dynamics (MD) simulations and binding free energy calculations to explore the inhibitory activity of new triterpenoids against Pin1 structure.

RESULTS

Here, we report different computational evidence that triterpenoids from neem (Azadirachta indica A. Juss), such as 6-deacetylnimbinene, 6-Oacetylnimbandiol, and nimbolide, replicate the binding mode of the Pin1 substrate peptide, interacting with high affinity with the binding site and thus destabilizing the Pin1 structure.

CONCLUSIONS

Our results are supported by experimental data, and provide interesting structural insights into their molecular mechanism of action, indicating that their structural scaffolds could be used as a start point to develop new inhibitors against Pin1.

Exploring the cause of the dual allosteric targeted inhibition attaching to allosteric sites enhancing SHP2 inhibition

SHP2 is a protein tyrosine phosphatase (PTP) that can regulate the tyrosine phosphorylation level. Overexpression of SHP2 will promote the development of cancer diseases, so SHP2 has become one of the popular targets for the treatment of cancer. Studies have reported that both SHP099 and SHP844 are inhibitors of SHP2 and bind to different allosteric sites 1 and 2, respectively. Studies have shown that combining SHP099 with SHP844 will enhance pharmacological pathway inhibition in cells. This study uses molecular dynamic simulations to explore the dual allosteric targeted inhibition mechanism. The result shows that the residues THR108-TRP112 (allosteric site 1) move to LEU236-GLN245 (αB-αC link loop in PTP domain) , the residues of GLN79-GLN87 (allosteric site 2) get close to LEU262-GLN269 (αA-αB link loop in PTP domain) and HIS458-ARG465 (P-loop) come near to ARG501-THR507 (Q-loop) in SHP2-SHP099-SHP844 system, which makes the "inactive conformation" more stable and prevents the substrate from entering the catalytic site. Meanwhile, residue GLU110 (allosteric site 1), ARG265 (allosteric site 2), and ARG501 (Q-loop) are speculated to be the key residues that causing the SHP2 protein in auto-inhibition conformation. It is hoped that this study will provide clues for the development of the dual allosteric targeted inhibition of SHP2.

Spirostanol Sapogenins and Saponins from Convallaria majalis L. Structural Characterization by 2D NMR, Theoretical GIAO DFT Calculations and Molecular Modeling

Two new spirostanol sapogenins (5β-spirost-25(27)-en-1β,2β,3β,5β-tetrol 3 and its 25,27-dihydro derivative, (25S)-spirostan-1β,2β,3β,5β-tetrol 4) and four new saponins were isolated from the roots and rhizomes of Convallaria majalis L. together with known sapogenins (isolated from Liliaceae): 5β-spirost-25(27)-en-1β,3β-diol 1, (25S)-spirostan-1β,3β-diol 2, 5β-spirost-25(27)-en-1β,3β,4β,5β-tetrol 5, (25S)-spirostan-1β,3β,4β,5β-tetrol 6, 5β-spirost-25(27)-en-1β,2β,3β,4β,5β-pentol 7 and (25S)-spirostan-1β,2β,3β,4β,5β-pentol 8. New steroidal saponins were found to be pentahydroxy 5-O-glycosides; 5β-spirost-25(27)-en-1β,2β,3β,4β,5β-pentol 5-O-β-galactopyranoside 9, 5β-spirost-25(27)-en-1β,2β,3β,4β,5β-pentol 5-O-β-arabinonoside 11, 5β-(25S)-spirostan-1β,2β,3β,4β,5β-pentol 5-O-galactoside 10 and 5β-(25S)-spirostan-1β,2β,3β,4β,5β-pentol 5-O-arabinoside 12 were isolated for the first time. The structures of those compounds were determined by NMR spectroscopy, including 2D COSY, HMBC, HSQC, NOESY, ROESY experiments, theoretical calculations of shielding constants by GIAO DFT, and mass spectrometry (FAB/LSI HR MS). An attempt was made to test biological activity, particularly as potential chemotherapeutic agents, using in silico methods. A set of 12 compounds was docked to the PDB structures of HER2 receptor and tubulin. The results indicated that diols have a higher affinity to the analyzed targets than tetrols and pentols. Two compounds (25S)-spirosten-1β,3β-diol 1 and 5β-spirost-25(27)-en-1β,2β,3β,4β,5β-pentol 5-O-galactoside 9 were selected for further evaluation of biological activity.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

The identification of novel inhibitors of human angiotensin-converting enzyme 2 and main protease of Sars-Cov-2: A combination of in silico methods for treatment of COVID-19

The angiotensin-converting enzyme 2 (ACE2) and main protease (MPro), are the putative drug candidates for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we performed 3D-QSAR pharmacophore modeling and screened 1,264,479 ligands gathered from Pubchem and Zinc databases. Following the calculation of the ADMET properties, molecular docking was carried out. Moreover, the de novo ligand design was performed. MD simulation was then applied to survey the behavior of the ligand-protein complexes. Furthermore, MMPBSA has utilized to re-estimate the binding affinities. Then, a free energy landscape was used to find the most stable conformation of the complexes. Finally, the hybrid QM-MM method was carried out for the precise calculation of the energies.

The Hypo1 pharmacophore model was selected as the best model. Our docking results indicate that the compounds ZINC12562757 and 112,260,215 were the best potential inhibitors of the ACE2 and MPro, respectively. Furthermore, the Evo_1 compound enjoys the highest docking energy among the designed de novo ligands. Results of RMSD, RMSF, H-bond, and DSSP analyses have demonstrated that the lead compounds preserve the stability of the complexes’ conformation during the MD simulation. MMPBSA data confirmed the molecular docking results. The results of QM-MM showed that Evo_1 has a stronger potential for specific inhibition of MPro, as compared to the 112,260,215 compound.

Molecular docking analysis of HER-2 inhibitor from the ZINC database as anticancer agents

The human epidermal growth factor (HER2) is a transmembrane receptor that is highly expressed in breast cancer and in different other cancers. Therefore, it is of interest to identify the new HER2 inhibitors from a selected 300 compounds in the ZINC database. The top two hit compounds (ZINC000014780728 (-11.0 kcal/mol) and ZINC000014762512 (-10.8 kcal/mol)) showed a high affinity with HER2 relative to the reference compound (lapatinib (-10.2 kcal/mol)) for further consideration.

Mitochondrial functional and structural impairment is involved in the antitumor activity of δ-tocotrienol in prostate cancer cells

The therapeutic options for castration-resistant prostate cancer (CRPC) are still limited. Natural bioactive compounds were shown to possess pro-death properties in different tumors. We previously reported that δ-tocotrienol (δ-TT) induces apoptosis, paraptosis and autophagy in CRPC cells. Here, we investigated whether δ-TT might exert its activity by impairing mitochondrial functions. We demonstrated that, in PC3 and DU145 cells, δ-TT impairs mitochondrial respiration and structural dynamics. In both cell lines, δ-TT triggers mitochondrial Ca²⁺ and ROS overload. In PC3 cells, both Ca²⁺ and ROS mediate the δ-TT-related anticancer activities (decrease of cell viability, apoptosis, paraptosis, autophagy and mitophagy). As expected, in autophagy-defective DU145 cells, Ca²⁺ overload was involved in δ-TT-induced pro-death effects but not in autophagy and mitophagy. In this cell line, we also demonstrated that ROS overload is not involved in the anticancer activities of δ-TT, supporting a low susceptibility of these cells to ROS-related oxidative stress. Taken together, these data demonstrate that, in CRPC cells, δ-TT triggers cell death by inducing mitochondrial functional and structural impairments, providing novel mechanistic insights in its antitumor activity.

Pharmacoinformatics approaches to identify potential hits against tetraacyldisaccharide 4'-kinase (LpxK) of Pseudomonas aeruginosa

Pseudomonas aeruginosa infection can cause pneumonia and urinary tract infection and the management of Pseudomonas aeruginosa infection is critical in multidrug resistance, hospital-acquired bacteremia and ventilator-associated pneumonia. The key enzymes of lipid A biosynthesis in Pseudomonas aeruginosa are promising drug targets. However, the enzyme tetraacyldisaccharide 4'-kinase (LpxK) has not been explored as a drug target so far. Several pharmacoinformatics tools such as comparative metabolic pathway analysis (Metacyc), data mining from a database of essential genes (DEG), homology modeling, molecular docking, pharmacophore based virtual screening, ADMET prediction and molecular dynamics simulation were used in identifying novel lead compounds against this target. The top virtual hits STOCK6S-33288, 43621, 39892, 37164 and 35740 may serve as the templates for the design and synthesis of potent LpxK inhibitors in the management of serious Pseudomonas aeruginosa infection.

Exploration and evaluation of bioactive phytocompounds against BRCA proteins by in silico approach

The proteins encoded by the two major breast cancer genes (BRCA1 and BRCA2), ensure the stability of DNA and prevent uncontrolled cell growth; mutation of these genes is linked to the development of hereditary breast cancers. Exploration of human breast cancer inhibitors plays a vital role in the drug discovery process. In the current work, in silico studies were performed which involves a computational approach for the identification of active phytocompounds from the diverse set of medicinal plant products against the BRCA receptor. The in silico study through pharmacokinetics and pharmacodynamics properties shown promising outcomes for these phytocompounds data set as breast cancer inhibitors. It was observed that the compounds conformed to the Lipinski's rule of five and had good bioavailability. The drug-likeness model score and ADMET profile of the designed ligands also established their potential as a drug candidate. The docking study provided useful insights on potential target-lead interactions and indicated that the newly designed leads had a good binding affinity for BRCA targets. A pharmacophore model was built to explore the scaffolds for BRCA inhibitory activity. An effort is made to screen an inhibitor against BRCA targets by combining the use of ADMET, docking score, and pharmacophore model.Communicated by Ramaswamy H. Sarma.

Molecular dynamics, MM/PBSA and in vitro validation of a novel quinazoline-based EGFR tyrosine kinase inhibitor identified using structure-based in silico screening

EGFR-TK has been a target strongly associated with the development of NSCLCs. A structure-based virtual screening campaign was launched against EGFR-TK by virtual screening a 3D library of 167 commercially available small molecules downloaded from ChemBridge Corporation. The virtual screen identified 12 virtual hit molecules, which were biologically evaluated against an EGFR-TK inhibitor-sensitive NSCLC cell line, A549. A quinazoline-based molecule 1, was most active and displayed ∼58% cytotoxicity at 20 μM single dose. The mode of cell death suggests molecule 1 induced apoptosis, which is characteristic of EGFR-TK pathway inhibition. A 50 ns MD simulation was conducted on three different systems: free EGFR-TK, molecule 1 complexed to EGFR-TK, and the positive control, lapatinib, complexed to EGFR-TK. The MD simulations showed increase in stabilisation of the EGFR-TK structure for the complexed systems, i.e., lower RMSDs and RMSFs for complexed EGFR-TK structures compared to the free EGFR-TK system. The binding affinities were estimated using MM/PBSA in the last 10 ns of the MD simulation that revealed comparable binding free energies between molecule 1 and lapatinib, ΔG_bind = -25.0 and -23.9 kcal/mol, respectively. Per residue binding free energy decomposition studies revealed non-polar interactions contributed mostly to the binding free energies. Residues Leu718, Arg841 and Phe856 were predicted to contribute most to the binding free energies for molecule 1.

Short communication for targeting natural compounds against HER2 kinase domain as potential anticancer drugs applying pharmacophore based molecular modelling approaches- part 2

Breast cancer is one of the common causes of death noticed in women globally. In order to find effective therapeutics, the current investigation has focussed on identifying candidate compounds for EGFR and HER2. Accordingly, the pharmacophore modelling approaches were adapted to identify two prospective compounds and were docked against the target 3RCD that is complexed with TAK-285 a known dual inhibitor. Focussing on the target 3RCD, our results have showed that the compounds have demonstrated a good binding affinity towards the target occupying the binding pocket. They have established key residue interactions with stable molecular dynamics simulation results. The Hit compounds have demonstrated a potential to penetrate the blood brain barrier thereby enriching their therapeutics towards breast cancer brain metastasis. Taken together, our findings propose two candidate compounds as EGFR/HER2 inhibitors that might serve as novel chemical spaces for designing and developing new inhibitors.

Construction of Quantitative Structure Activity Relationship (QSAR) Models to Predict Potency of Structurally Diversed Janus Kinase 2 Inhibitors

Janus kinase 2 (JAK2) inhibitors represent a promising therapeutic class of anticancer agents against many myeloproliferative disorders. Bioactivity data on pIC50 of 2229 JAK2 inhibitors were employed in the construction of quantitative structure-activity relationship (QSAR) models. The models were built from 100 data splits using decision tree (DT), support vector machine (SVM), deep neural network (DNN) and random forest (RF). The predictive power of RF models were assessed via 10-fold cross validation, which afforded excellent predictive performance with R2 and RMSE of 0.74 ± 0.05 and 0.63 ± 0.05, respectively. Moreover, test set has excellent performance of R2 (0.75 ± 0.03) and RMSE (0.62 ± 0.04). In addition, Y-scrambling was utilized to evaluate the possibility of chance correlation of the predictive model. A thorough analysis of the substructure fingerprint count was conducted to provide insights on the inhibitory properties of JAK2 inhibitors. Molecular cluster analysis revealed that pyrazine scaffolds have nanomolar potency against JAK2.

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.

Identification of Novel Scaffolds with Dual Role as Antiepileptic and Anti-Breast Cancer

Aromatase inhibitors with an $\mathrm{IC}_{50}$ IC 50 value ranging from 1.4 to 49.7 µM are known to act as antiepileptic drugs besides being potential breast cancer inhibitors. The aim of the present study is to identify novel antiepileptic aromatase inhibitors with higher activity exploiting the ligand-based pharmacophore approach utilizing the experimentally known inhibitors. The resultant Hypo1 consists of four features and was further validated by using three different strategies. Hypo1 was allowed to screen different databases to identify lead molecules and were further subjected to Lipinski's Rule of Five and ADMET to establish their drug-like properties. Consequently, the obtained 68-screened molecules were subjected to molecular docking by GOLD v5.2.2. Furthermore, the compounds with the highest dock scores were assessed for molecular interactions. Later, the MD simulation was applied to evaluate the protein backbone stabilities and binding energies adapting GROMACS v5.0.6 and MM/PBSA which was followed by the density functional theory (DFT), to analyze their orbital energies, and further the energy gap between them. Eventually, the number of Hit molecules was culled to three projecting Hit1, Hit2, and Hit3 as the potential lead compounds based on their highest dock scores, hydrogen bond interaction, lowest energy gap, and the least binding energies and stable MD results.

Discovery of Small Molecules that Target Vascular Endothelial Growth Factor Receptor-2 Signalling Pathway Employing Molecular Modelling Studies

Angiogenesis is defined as the formation of new blood vessels and is a key phenomenon manifested in a host of cancers during which tyrosine kinases play a crucial role. Vascular endothelial growth factor receptor-2 (VEGFR-2) is pivotal in cancer angiogenesis, which warrants the urgency of discovering new anti-angiogenic inhibitors that target the signalling pathways. To obtain this objective, a structure-based pharmacophore model was built from the drug target VEGFR-2 (PDB code: 4AG8), complexed with axitinib and was subsequently validated and employed as a 3D query to retrieve the candidate compounds with the key inhibitory features. The model was escalated to molecular docking studies resulting in seven candidate compounds. The molecular docking studies revealed that the seven compounds displayed a higher dock score than the reference-cocrystallised compound. The GROningen MAchine for Chemical Simulations (GROMACS) package guided molecular dynamics (MD) results determined their binding mode and affirmed stable root mean square deviation. Furthermore, these compounds have preserved their key interactions with the residues Glu885, Glu917, Cys919 and Asp1046. The obtained findings deem that the seven compounds could act as novel anti-angiogenic inhibitors and may further assist as the prototype in designing and developing new inhibitors.

In Silico Study Probes Potential Inhibitors of Human Dihydrofolate Reductase for Cancer Therapeutics

Dihydrofolate reductase (DHFR) is an essential cellular enzyme and thereby catalyzes thereduction of dihydrofolate to tetrahydrofolate (THF). In cancer medication, inhibition of humanDHFR (hDHFR) remains a promising strategy, as it depletes THF and slows DNA synthesis and cellproliferation. In the current study, ligand-based pharmacophore modeling identified and evaluatedthe critical chemical features of hDHFR inhibitors. A pharmacophore model (Hypo1) was generatedfrom known inhibitors of DHFR with a correlation coefficient (0.94), root mean square (RMS)deviation (0.99), and total cost value (125.28). Hypo1 was comprised of four chemical features,including two hydrogen bond donors (HDB), one hydrogen bond acceptor (HBA), and onehydrophobic (HYP). Hypo1 was validated using Fischer's randomization, test set, and decoy setvalidations, employed as a 3D query in a virtual screening at Maybridge, Chembridge, Asinex,National Cancer Institute (NCI), and Zinc databases. Hypo1-retrieved compounds were filtered byan absorption, distribution, metabolism, excretion, and toxicity (ADMET) assessment test andLipinski's rule of five, where the drug-like hit compounds were identified. The hit compounds weredocked in the active site of hDHFR and compounds with Goldfitness score was greater than 44.67(docking score for the reference compound), clustering analysis, and hydrogen bond interactionswere identified. Furthermore, molecular dynamics (MD) simulation identified three compounds asthe best inhibitors of hDHFR with the lowest root mean square deviation (1.2 Å to 1.8 Å), hydrogenbond interactions with hDHFR, and low binding free energy (-127 kJ/mol to -178 kJ/mol). Finally,the toxicity prediction by computer (TOPKAT) affirmed the safety of the novel inhibitors of hDHFRin human body. Overall, we recommend novel hit compounds of hDHFR for cancer and rheumatoidarthritis chemotherapeutics.

Molecular docking, pharmacophore based virtual screening and molecular dynamics studies towards the identification of potential leads for the management of H. pylori

The enzyme pantothenate synthetase panC is one of the potential new antimicrobial drug targets, but it is poorly characterized in H. pylori. H. pylori infection can cause gastric cancer and the management of H. pylori infection is crucial in various gastric ulcers and gastric cancer. The current study describes the use of innovative drug discovery and design approaches like comparative metabolic pathway analysis (Metacyc), exploration of database of essential genes (DEG), homology modelling, pharmacophore based virtual screening, ADMET studies and molecular dynamics simulations in identifying potential lead compounds for the H. pylori specific panC. The top ranked virtual hits STOCK1N-60270, STOCK1N-63040, STOCK1N-44424 and STOCK1N-63231 can act as templates for synthesis of new H. pylori inhibitors and they hold a promise in the management of gastric cancers caused by H. pylori.

Computational approaches such as pharmacophore modeling, virtual screening and MD simulations were explored to find the potential hits as H. pylori specific panC inhibitors for the management of gastric ulcers and gastric cancers.

Natural Compound Modulates the Cervical Cancer Microenvironment-A Pharmacophore Guided Molecular Modelling Approaches

Cervical cancer is regarded as one of the major burdens noticed in women next to breast cancer. Although, human papilloma viruses (HPVs) are regarded as the principal causative agents, they require certain other factors such as oestrogen hormone to induce cervical cancer. Aromatase is an enzyme that converts androgens into oestrogens and hindering this enzyme could subsequently hamper the formation of oestrogen thereby alleviating the disease. Accordingly, in the current investigation, a structure based pharmacophore was generated considering two proteins bearing the Protein Data Bank (PDB) codes 3EQM (pharm 1) and 3S7S (pharm 2), respectively. The two models were employed as the 3D query to screen the in-house built natural compounds database. The obtained 51 compounds were escalated to molecular docking studies to decipher on the binding affinities and to predict the quintessential binding modes which were affirmed by molecular dynamics (MD) simulations. The compound has induced dose-dependent down regulation of PP2B, Nitric oxide synthase-2 (NOS2), and Interleukin 6 (IL-6) genes in the HeLa cells and has modulated the expression of apoptotic genes such as Bax, Bcl2, and caspases-3 at different concentrations. These results guide us to comprehend that the identified aromatase inhibitor was effective against the cervical cancer cells and additionally could server as scaffolds in designing new drugs.

Computational Approaches Towards Kinases as Attractive Targets for Anticancer Drug Discovery and Development

BACKGROUND

One of the major goals of computational chemists is to determine and develop the pathways for anticancer drug discovery and development. In recent past, high performance computing systems elicited the desired results with little or no side effects. The aim of the current review is to evaluate the role of computational chemistry in ascertaining kinases as attractive targets for anticancer drug discovery and development.

METHODS

Research related to computational studies in the field of anticancer drug development is reviewed. Extensive literature on achievements of theorists in this regard has been compiled and presented with special emphasis on kinases being the attractive anticancer drug targets.

RESULTS

Different approaches to facilitate anticancer drug discovery include determination of actual targets, multi-targeted drug discovery, ligand-protein inverse docking, virtual screening of drug like compounds, formation of di-nuclear analogs of drugs, drug specific nano-carrier design, kinetic and trapping studies in drug design, multi-target QSAR (Quantitative Structure Activity Relationship) model, targeted co-delivery of anticancer drug and siRNA, formation of stable inclusion complex, determination of mechanism of drug resistance, and designing drug like libraries for the prediction of drug-like compounds. Protein kinases have gained enough popularity as attractive targets for anticancer drugs. These kinases are responsible for uncontrolled and deregulated differentiation, proliferation, and cell signaling of the malignant cells which result in cancer.

CONCLUSION

Interest in developing drugs through computational methods is a growing trend, which saves equally the cost and time. Kinases are the most popular targets among the other for anticancer drugs which demand attention. 3D-QSAR modelling, molecular docking, and other computational approaches have not only identified the target-inhibitor binding interactions for better anticancer drug discovery but are also designing and predicting new inhibitors, which serve as lead for the synthetic preparation of drugs. In light of computational studies made so far in this field, the current review highlights the importance of kinases as attractive targets for anticancer drug discovery and development.

Exploring the Therapeutic Ability of Fenugreek against Type 2 Diabetes and Breast Cancer Employing Molecular Docking and Molecular Dynamics Simulations

Fenugreek (Trigonella foenum-graecum) is used as a spice throughout the world. It is known for its medicinal properties such as antidiabetic, anticarcinogenic, and immunological activities. The present study shows the properties and the nutritional quality of fenugreek seed extract and focuses on screening of active compounds in drug designing for type 2 diabetes and breast cancer. Quantitative analysis was used to calculate the percentages of protein, carbohydrates moisture, fatty acid, galactomannan, oil, and amino acid. Phytochemical analysis revealed the presence of flavonoids, terpenoids, phenols, proteins, saponins, and tannins in fenugreek seed extracts. Molecular docking and molecular dynamics simulation-based computational drug discovery methods were employed to address the role of fenugreek seed constituents against type 2 diabetes and breast cancer. The computational results reveal that the compound galactomannan can be ascribed as potential drug candidate against breast cancer and type 2 diabetes rendered by higher molecular dock scores, stable molecular dynamics (MD) simulations results, and lower binding energy calculations.