Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets.

Computational modelling remains an indispensable technique in drug discovery. With myriad of high computing resources, and improved modelling algorithms, there has been a high-speed in the drug development cycle with promising success rate compared to the traditional route. For example, lapatinib; a well-known anticancer drug with clinical applications was discovered with computational drug design techniques. Similarly, molecular modelling has been applied to various disease areas ranging from cancer to neurodegenerative diseases. The techniques ranges from high-throughput virtual screening, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) to molecular dynamics simulation. This review focuses on the application of computational modelling tools in the identification of drug candidates for Breast cancer. First, we begin with a succinct overview of molecular modelling in the drug discovery process. Next, we take note of special efforts on the developments and applications of combining these techniques with particular emphasis on possible breast cancer therapeutic targets such as estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), vascular endothelial growth factor (VEGF), breast cancer gene 1 (BRCA1), and breast cancer gene 2 (BRCA2). Finally, we discussed the search for covalent inhibitors against these receptors using computational techniques, advances, pitfalls, possible solutions, and future perspectives.

Antidiabetes study of Spondias mombin (Linn) stem bark fractions in high-sucrose diet-induced diabetes in Drosophila melanogaster.

Objective: The onset of insulin resistant diabetes has been associated with a high-sucrose diet in vertebrates and invertebrates. However, various parts of Spondias mombin reportedly possess antidiabetic potential. However, the antidiabetic efficacy of S. mombin stem bark in high-sucrose diet-induced Drosophila melanogaster model has not been explored. In this study, the antidiabetic and antioxidant effects of the solvent fractions of S. mombin stem bark were evaluated using in vitro, in vivo, and in silico methods. Methods: Successive fractionation of S. mombin stem bark ethanol extract was performed; the resulting fractions were subjected to in vitro antioxidant and antidiabetic assays using standard protocols. The active compounds identified from the high-performance liquid chromatography (HPLC) study of the n-butanol fraction were docked against the active site of Drosophila α-amylase using AutoDoc Vina. The n-butanol and ethyl acetate fractions of the plant were incorporated into the diet of diabetic and nondiabetic flies to study the in vivo antidiabetic and antioxidant properties. Results: The results obtained revealed that n-butanol and ethyl acetate fractions had the highest in vitro anti-oxidant capacity by inhibiting 2,2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing antioxidant power, and hydroxyl radical followed by significant inhibition of α-amylase. HPLC analysis revealed the identification of eight compounds with quercetin having the highest peak followed by rutin, rhamnetin, chlorogenic acid, zeinoxanthin, lutin, isoquercetin, and rutinose showing the lowest peak. The fractions restored the glucose and antioxidant imbalance in diabetic flies, which is comparable with the standard drug (metformin). The fractions were also able to upregulate the mRNA expression of insulin-like peptide 2, insulin receptor, and ecdysone-inducible gene 2 in diabetic flies. The in silico studies revealed the inhibitory potential of active compounds against α-amylase with isoquercetin, rhamnetin, rutin, quercetin, and chlorogenic acid having higher binding affinity than the standard drug (acarbose). Conclusion: Overall, the butanol and ethyl acetate fractions of S. mombin stem bark ameliorate type 2 diabetes in Drosophila. However, further studies are needed in other animal models to confirm the antidiabetes effect of the plant.

Protein-protein interaction and interference of carcinogenesis by supramolecular modifications.

Protein-protein interactions (PPIs) are essential in normal biological processes, but they can become disrupted or imbalanced in cancer. Various technological advancements have led to an increase in the number of PPI inhibitors, which target hubs in cancer cell's protein networks. However, it remains difficult to develop PPI inhibitors with desired potency and specificity. Supramolecular chemistry has only lately become recognized as a promising method to modify protein activities. In this review, we highlight recent advances in the use of supramolecular modification approaches in cancer therapy. We make special note of efforts to apply supramolecular modifications, such as molecular tweezers, to targeting the nuclear export signal (NES), which can be used to attenuate signaling processes in carcinogenesis. Finally, we discuss the strengths and weaknesses of using supramolecular approaches to targeting PPIs.

Identification of HER2 inhibitors from curcumin derivatives using combination of in silico screening and molecular dynamics simulation.

Breast cancer remains a major world health challenge in women. Some Breast cancers are human epidermal growth factor receptor 2 (HER2) positive. Since this protein promotes the growth of cancer cells, it remains a therapeutic target for novel drugs. This study uses in silico model to predict HER2 inhibitors from curcumin derivatives via QSAR, e-pharmacophore, ADMET as well as structure-based virtual screening using Schrodinger suite. The molecular dynamics simulation of lead compounds, reference ligand and co-crystalized ligand was performed using GROMACS. At the end, eight active curcumin derivatives were predicted as inhibitors of HER2 with high binding affinity and better interaction compared with the reference drug (Neratinib) but lower binding affinity compared with the co-crystalized ligand (TAK-285). After prediction of the bioactivity of the molecules using AutoQSAR, the hit compounds showed appreciable inhibitory pIC50 compared with the reference and co-crystalized ligands against HER2. The pharmacokinetics profile predicted the eight hit compounds as drug-like and drug candidates. The MD simulation predicted the stability of the two top-scored compounds (10763284 and 78321412) in complex with HER2 for the final 80 ns of the trajectory period after initial equilibration with higher H-bond interactions in the protein-reference drug complex compared to the hit compounds-HER2 complexes. This study revealed that curcumin derivatives especially (1E,6E)-1,8-bis(4-hydroxy-3-methoxyphenyl)octa-1,6-diene-3,5-dione and (1E,6E)-4-ethyl-1,7-bis(4-hydroxy-3-methoxyphenyl)hepta-1,6-diene-3,5-dione were identified to demonstrate inhibitory activity against HER2 which is comparable to neratinib. Conclusively, the lead compounds require further in vitro and in vivo experimental validation for the discovery of new HER2 antagonists for breast cancer management.Communicated by Ramaswamy H. Sarma.

Discovery of putative inhibitors against main drivers of SARS-CoV-2 infection: Insight from quantum mechanical evaluation and molecular modeling.

SARS-CoV-2 triggered a worldwide medical crisis, affecting the world's social, emotional, physical, and economic equilibrium. However, treatment choices and targets for finding a solution to COVID-19's threat are becoming limited. A viable approach to combating the threat of COVID-19 is by unraveling newer pharmacological and therapeutic targets pertinent in the viral survival and adaptive mechanisms within the host biological milieu which in turn provides the opportunity to discover promising inhibitors against COVID-19. Therefore, using high-throughput virtual screening, manually curated compounds library from some medicinal plants were screened against four main drivers of SARS-CoV-2 (spike glycoprotein, PLpro, 3CLpro, and RdRp). In addition, molecular docking, Prime MM/GBSA (molecular mechanics/generalized Born surface area) analysis, molecular dynamics (MD) simulation, and drug-likeness screening were performed to identify potential phytodrugs candidates for COVID-19 treatment. In support of these approaches, we used a series of computational modeling approaches to develop therapeutic agents against COVID-19. Out of the screened compounds against the selected SARS-CoV-2 therapeutic targets, only compounds with no violations of Lipinski's rule of five and high binding affinity were considered as potential anti-COVID-19 drugs. However, lonchocarpol A, diplacol, and broussonol E (lead compounds) were recorded as the best compounds that satisfied this requirement, and they demonstrated their highest binding affinity against 3CLpro. Therefore, the 3CLpro target and the three lead compounds were selected for further analysis. Through protein-ligand mapping and interaction profiling, the three lead compounds formed essential interactions such as hydrogen bonds and hydrophobic interactions with amino acid residues at the binding pocket of 3CLpro. The key amino acid residues at the 3CLpro active site participating in the hydrophobic and polar inter/intra molecular interaction were TYR54, PRO52, CYS44, MET49, MET165, CYS145, HIS41, THR26, THR25, GLN189, and THR190. The compounds demonstrated stable protein-ligand complexes in the active site of the target (3CLpro) over a 100 ns simulation period with stable protein-ligand trajectories. Drug-likeness screening shows that the compounds are druggable molecules, and the toxicity descriptors established that the compounds demonstrated a good biosafety profile. Furthermore, the compounds were chemically reactive with promising molecular electron potential properties. Collectively, we propose that the discovered lead compounds may open the way for establishing phytodrugs to manage COVID-19 pandemics and new chemical libraries to prevent COVID-19 entry into the host based on the findings of this computational investigation.

Structure-based discovery of selective CYP17A1 inhibitors for Castration-resistant prostate cancer treatment.

Prostate cancer (PCa) is the most common malignancy found in men and the second leading cause of cancer-related death worldwide. Castration-resistant PCa (CRPC) is defined by PCa cells that stop responding to hormone therapy. Cytochrome P450 17α-hydroxylase/17,20-lyase (CYP17A1) plays a critical role in the biosynthesis of androgens in humans. Androgen signaling cascade is a principal survival pathway for PCa cells and androgen-deprivation therapy (ADT) remains the key treatment for patients marked with locally advanced and metastatic PCa cells. Available synthetic drugs have been reported for toxicity, drug resistance, and decreasing efficacy. Thus, the design of novel selective inhibitors of CYP17A1 lyase would help circumvent associated side effects and improve pharmacological activities. Therefore, we employed structural bioinformatics techniques via molecular docking; molecular mechanics generalized born surface area (MM-GBSA), molecular dynamics (MD) simulation, and pharmacokinetic study to identify putative CYP17A1 lyase inhibitors. The results of the computational investigation showed that the Prunus dulcis compounds exhibited higher binding energy than the clinically approved abiraterone acetate. The stability of the ligand with the highest binding affinity (quercetin-3-o-rutinoside) was observed during MD simulation for 10 ns. Quercetin-3-o-rutinoside was observed to be stable within the active site of CYP17A1Lyase throughout the simulation period. The result of the pharmacokinetic study revealed that these compounds are promising therapeutic agents. Collectively, this study proposed that bioactive compounds from P. dulcis may be potential selective inhibitors of CYP17A1Lyase in CRPC treatments.

Computational Evaluation of Bioactive Compounds from Colocasia affinis Schott as a Novel EGFR Inhibitor for Cancer Treatment.

INTRODUCTION: Epidermal growth factor receptor (EGFR) is a transmembrane protein that belongs to the ErbB/HER-family of tyrosine kinase receptors. Somatic mutations and overexpression of EGFR have been reported to play a vital role in cancer cell development and progression, including cell proliferation, differentiation, angiogenesis, apoptosis, and metastatic spread. Hence, EGFR is an important therapeutic target for the treatment of various types of epithelial cancers. Somatic mutations have led to resistance to clinically approved synthetic EGFR inhibitors. Furthermore, synthetic EGFR inhibitors have been associated with several side effects. Thus, there is a need to develop novel EGFR inhibitors with an acceptable biosafety profile and high efficacy. METHODS: Herein, we employed structural bioinformatics and theoretical chemistry techniques via molecular docking, molecular mechanics generalized Born surface area (MM-GBSA) calculation, density functional theory analysis (DFT), and pharmacokinetic study to identify novel EGFR inhibitors. RESULTS: The stringent molecular docking and MM-GBSA calculations identified MET 793, LYS 745, PHE 723, ASP 855, ARG 411, and THR 854 as principal amino acid residues for EGFR-ligands interactions. Furthermore, Colocasia affinis Schott compounds exhibited higher binding energy and more stable interactions than the reference compound (gefitinib). DFT analysis also ascertains better bioactivity and chemical reactivity of C. affinis Schott with favorable intramolecular charge transfer between electron-donor and electron acceptor groups. The pharmacokinetic profile of C. affinis Schott bioactive compounds satisfies Lipinski's rule of five assessment. CONCLUSION: Collectively, C. affinis Schott compounds demonstrated higher inhibitory potentials against EGFR and better pharmacological properties when compared with gefitinib. C. affinis Schott compounds are therefore suggested as promising therapeutic EGFR inhibitors for cancer treatment.

Identification of Terpenoids From Abrus precatorius Against Parkinson's Disease Proteins Using In Silico Approach.

Parkinson's disease (PD) is the second major neuro-degenrative disorder that causes morbidity and mortality among older populations. Terpenoids were reported as potential neuro-protective agents. Therefore, this study seeks to unlock the inhibitory potential of terpenoids from Abrus precatorius seeds against proteins involve in PD pathogenesis. In this study, in silico molecular docking of 5 terpenoids derived from high-performance liquid chromatography (HPLC) analysis of A. precatorius seeds against α-synuclein, catechol-o-methyltransferase, and monoamine oxidase B which are markers of PD was performed using Autodock vina. The absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) of the hits were done using Swiss ADME predictor and molecular dynamic (MD) simulation of the hit-protein complex was performed using Desmond Schrodinger software. Five out of 6 compounds satisfied the ADME/Tox parameters and showed varying degrees of binding affinities with selected proteins. Drimenin-α-synuclein complex showed the lowest binding energy of -9.1 kcal/mol followed by interaction with key amino acid residues necessary for α-synuclein inhibition. The selection of this complex was justified by its stability in MD simulation conducted for 10 ns and exhibited stable interaction in terms of root mean square deviation (RMSD) and root mean square deviation error fluctuation (RMSF) values.