Evaluation of novel Akt1 inhibitors as anticancer agents using virtual co-crystallized pharmacophore generation

Discovery of New HER2 Inhibitors via Computational Docking, Pharmacophore Modeling, and Machine Learning

The human epidermal growth factor receptor 2 (HER2) is a critical oncogene implicated in the development of various aggressive cancers, particularly breast cancer. Discovering novel HER2 inhibitors is crucial for expanding therapeutic options for HER2-related malignancies. In this study, we present a computational workflow that focuses on generating pharmacophores derived from docked poses of a selected list of 15 diverse, potent HER2 inhibitors, utilizing flexible docking. The resulting pharmacophores, along with other physicochemical molecular descriptors, were then evaluated in a machine learning-quantitative structure-activity relationship (ML-QSAR) analysis against 1,272 HER2 inhibitors. Several machine learning methods were assessed, and a genetic function algorithm (GFA) was employed for feature selection. Ultimately, GFA combined with Bagging and J48Graft classifiers produced the best self-consistent and predictive models. These models highlighted the significance of two pharmacophores, Hypo_1 and Hypo_2, in distinguishing potent from less active inhibitors. The successful ML-QSAR models and their associated pharmacophores were used to screen the National Cancer Institute (NCI) database for novel HER2 inhibitors. Three promising anti-HER2 leads were identified, with the top-performing lead demonstrating an experimental anti-HER2 IC50 value of 3.85 μM. Notably, the three inhibitors exhibited distinct chemical scaffolds compared to existing HER2 inhibitors, as indicated by principal component analysis.

Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes

Aurora-A (AURKA) is serine/threonine protein kinase involved in the regulation of numerous processes of cell division. Numerous studies have demonstrated strong association between AURKA and cancer. AURKA is overexpressed in many cancers, such as colon, breast and prostate cancers. Consequently, AURKA has emerged as promising target for therapeutic intervention in cancer management. Herein, we describe a computational workflow for the discovery of novel anti-AURKA inhibitory leads starting with ligand-based assessment of the pharmacophoric space of six diverse sets of inhibitors. Subsequently, machine learning/QSAR modeling was coupled with genetic function algorithm to search for the best possible combination of machine learner, ligand-based pharmacophore(s) and molecular descriptors capable of explaining variation in anti-AURKA bioactivities within a collected list of inhibitors. Two learners succeeded in achieving acceptable structure/activity correlations, namely, random forests and extreme gradient boosting (XGBoost). Three pharmacophores emerged in the successful ML models. These were then used as 3D search queries to mine the National Cancer Institute database for novel anti-AURKA leads. Top-ranking 38 hits were assessed in vitro for their anti-AURKA bioactivities. Among them, three compounds exhibited promising dose-response curves, demonstrating experimental IC50 values ranging from sub-micromolar to low micromolar values. Remarkably, two of these compounds are of novel chemotypes.

An in silico approach to identify novel and potential Akt1 (protein kinase B-alpha) inhibitors as anticancer drugs

Akt1 (protein kinase B) has become a major focus of attention due to its significant functionality in a variety of cellular processes and the inhibition of Akt1 could lead to a decrease in tumour growth effectively in cancer cells. In the present work, we discovered a set of novel Akt1 inhibitors by using multiple computational techniques, i.e. pharmacophore-based virtual screening, molecular docking, binding free energy calculations, and ADME properties. A five-point pharmacophore hypothesis was implemented and validated with AADRR38. The obtained R2 and Q2 values are in the acceptable region with the values of 0.90 and 0.64, respectively. The generated pharmacophore model was employed for virtual screening to find out the potential Akt1 inhibitors. Further, the selected hits were subjected to molecular docking, binding free energy analysis, and refined using ADME properties. Also, we designed a series of 6-methoxybenzo[b]oxazole analogues by comprising the structural characteristics of the hits acquired from the database. Molecules D1-D10 were found to have strong binding interactions and higher binding free energy values. In addition, Molecular dynamic simulation was performed to understand the conformational changes of protein-ligand complex.

Computational analysis of bevacizumab binding with protein receptors for its potential anticancer activity

Breast cancer poses a significant global challenge, prompting researchers to explore novel approaches for potential treatments. In this study, we investigated the binding free energy (ΔG) of bevacizumab, an anti-cancer therapy targeting angiogenesis through the inhibition of vascular endothelial growth factor (VEGF), with various proto-oncogenes including CDK4, EGFR, frizzled, IGFR, OmoMYC, and KIT. Our in-silico investigation revealed that hydrogen bonding is pivotal in inducing conformational changes within the DNA structure, impeding its replication and preventing cell death. Molecular docking results revealed the presence of crucial hydrogen bonds and supported the formation of stable bevacizumab complexes. The molecular docking scores for the tested complexes were CDK4 (Score = -7.2 kcal/mol), EGFR (Score = -8.5 kcal/mol), frizzled (Score = -6.9 kcal/mol), IGFR (Score = -7.8 kcal/mol), KIT (Score = -6.5 kcal/mol), and MYC (Score = -8.3 kcal/mol). The binding mode demonstrated vital hydrogen bonds correlated with the observed energy gap. Notably, the calculated binding free energies of the tested compounds are as follows: CDK4 (ΔG = 24275.195 ± 6411.293 kJ/mol), EGFR (ΔG = 363273.625 ± 8731.466 kJ/mol), frizzled (ΔG = 181751.990 ± 28438.515 kJ/mol), IGFR (ΔG = 162414.725 ± 10728.367 kJ/mol), KIT (ΔG = 40162.585 ± 4331.017 kJ/mol), and MYC (ΔG = 434783.463 ± 53989.676 kJ/mol). Furthermore, through extensive 100 ns MD simulations, we observed the formation of a stable bevacizumab complex structure. The simulations confirmed the stability of the bevacizumab complex with the proto-oncogenes. The results of this study highlight the potential of bevacizumab complex as a promising candidate for anticancer treatment. The identification of hydrogen bonding, along with the calculated binding free energies and molecular docking scores, provides valuable insights into the molecular interactions and stability of the bevacizumab complexes. These findings and the extensive MD simulations open new avenues for future research and development of bevacizumab as a targeted therapy for breast cancer and other related malignancies.Communicated by Ramaswamy H. Sarma.

In silico and in vitro study of bioactive compounds of Nigella sativa for targeting neuropilins in breast cancer

Introduction: Breast cancer poses a significant global challenge, prompting researchers to explore novel approaches for potential treatments. Material and Methods: For in vitro study we used thin layer chromatography (TAC) for phytochemical screening, total antioxidant capacity (TLC) assay for antioxidant capacity, and hemolytic activity test for toxicity of Neuropilins (NRPs). We performed bioinformatic analyses to predict protein structures, molecular docking, pharmacophore modeling, and virtual screening to reveal interactions with oncogenes. We conducted 200 ns Molecular Dynamics (MD) simulations and MMGBSA calculations to assess the complex dynamics and stability. Results: We identified phytochemical constituents in Nigella sativa leaves, including tannins, saponins, steroids, and cardiac glycosides, while phlobatannins and terpenoids were absent. The leaves contained 9.4% ± 0.04% alkaloids and 1.9% ± 0.05% saponins. Methanol extract exhibited the highest yield and antioxidant capacity, with Total Flavonoid Content at 127.51 ± 0.76 mg/100 g and Total Phenolic Content at 134.39 ± 0.589 mg GAE/100 g. Hemolysis testing showed varying degrees of hemolysis for different extracts. In-silico analysis indicated stable Neuropilin complexes with key signaling pathways relevant for anti-cancer therapy. Molecular docking scores at different possesses (0, C-50, C -80, C-120,C -150, C -200 ns) revealed strong hydrogen bonding in the complexes and showed -12.9, -11.6, and -11.2 binding Affinities (kcal/mol) to support their stability. Our MD simulations analysis at 200ns confirmed the stability of Neuropilin complexes with the signaling pathways protein PI3K. The calculated binding free energies using MMGBSA provided valuable quantitative information on ligand potency on different time steps. These findings highlight the potential health benefits of N. sativa leaves and their possible role in anti-cancer treatments targeting angiogenesis. Conclusion: Nigella sativa leaves have shown significant medical potential due to their bioactive compounds, which exhibit strong properties in supporting organogenic processes related to cancer. Furthermore, studies have highlighted the promising role of neuropilins in anticancer treatment, demonstrating stable interactions and potential as targeted therapy specifically for breast cancer.

Forecasting of potential anti-inflammatory targets of some immunomodulatory plants and their constituents using in vitro, molecular docking and network pharmacology-based analysis

Most synthetic immunomodulatory medications are extremely expensive, have many disadvantages and suffer from a lot of side effects. So that, introducing immunomodulatory reagents from natural sources will have great impact on drug discovery. Therefore, this study aimed to comprehend the mechanism of the immunomodulatory activity of some natural plants via network pharmacology together with molecular docking and in vitro testing. Apigenin, luteolin, diallyl trisulfide, silibinin and allicin had the highest percentage of C-T interactions while, AKT1, CASP3, PTGS2, NOS3, TP53 and MMP9 were found to be the most enriched genes. Moreover, the most enriched pathways were pathways in cancer, fluid shear stress and atherosclerosis, relaxin signaling pathway, IL-17 signaling pathway and FoxO signaling pathway. Additionally, Curcuma longa, Allium sativum, Oleu europea, Salvia officinalis, Glycyrrhiza glabra and Silybum marianum had the highest number of P-C-T-P interactions. Furthermore, molecular docking analysis of the top hit compounds against the most enriched genes revealed that silibinin had the most stabilized interactions with AKT1, CASP3 and TP53, whereas luteolin and apigenin exhibited the most stabilized interactions with AKT1, PTGS2 and TP53. In vitro anti-inflammatory and cytotoxicity testing of the highest scoring plants exhibited equivalent outcomes to those of piroxicam.

Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning

Lysine-specific histone demethylase 1 (LSD-1) is an epigenetic enzyme that oxidatively cleaves methyl groups from monomethyl and dimethyl Lys4 of histone H3 and is highly overexpressed in different types of cancer. Therefore, it has been widely recognized as a promising therapeutic target for cancer therapy. Towards this end, we employed various Computer Aided Drug Design (CADD) approaches including pharmacophore modelling and machine learning. Pharmacophores generated by structure-based (SB) (either crystallographic-based or docking-based) and ligand-based (LB) (either supervised or unsupervised) modelling methods were allowed to compete within the context of genetic algorithm/machine learning and were assessed by Shapley additive explanation values (SHAP) to end up with three successful pharmacophores that were used to screen the National Cancer Institute (NCI) database. Seventy-five NCI hits were tested for their LSD-1 inhibitory properties against neuroblastoma SH-SY5Y cells, pancreatic carcinoma Panc-1 cells, glioblastoma U-87 MG cells and in vitro enzymatic assay, culminating in 3 nanomolar LSD-1 inhibitors of novel chemotypes.

Structure-Based Discovery and Bioactivity Evaluation of Novel Aurora-A Kinase Inhibitors as Anticancer Agents via Docking-Based Comparative Intermolecular Contacts Analysis (dbCICA)

Aurora-A kinase plays a central role in mitosis, where aberrant activation contributes to cancer by promoting cell cycle progression, genomic instability, epithelial-mesenchymal transition, and cancer stemness. Aurora-A kinase inhibitors have shown encouraging results in clinical trials but have not gained Food and Drug Administration (FDA) approval. An innovative computational workflow named Docking-based Comparative Intermolecular Contacts Analysis (dbCICA) was applied—aiming to identify novel Aurora-A kinase inhibitors—using seventy-nine reported Aurora-A kinase inhibitors to specify the best possible docking settings needed to fit into the active-site binding pocket of Aurora-A kinase crystal structure, in a process that only potent ligands contact critical binding-site spots, distinct from those occupied by less-active ligands. Optimal dbCICA models were transformed into two corresponding pharmacophores. The optimal one, in capturing active hits and discarding inactive ones, validated by receiver operating characteristic analysis, was used as a virtual in-silico search query for screening new molecules from the National Cancer Institute database. A fluorescence resonance energy transfer (FRET)-based assay was used to assess the activity of captured molecules and five promising Aurora-A kinase inhibitors were identified. The activity was next validated using a cell culture anti-proliferative assay (MTT) and revealed a most potent lead 85(NCI 14040) molecule after 72 h of incubation, scoring IC₅₀ values of 3.5–11.0 μM against PANC1 (pancreas), PC-3 (prostate), T-47D and MDA-MB-231 (breast)cancer cells, and showing favorable safety profiles (27.5 μM IC₅₀ on fibroblasts). Our results provide new clues for further development of Aurora-A kinase inhibitors as anticancer molecules.

Investigation of Binding Characteristics of Phosphoinositide-dependent Kinase-1 (PDK1) Co-crystallized Ligands Through Virtual Pharmacophore Modeling Leading to Novel Anti-PDK1 Hits

BACKGROUND

3-Phosphoinositide Dependent Protein Kinase-1 (PDK1) is being lately considered as an attractive and forthcoming anticancer target. A Protein Data Bank (PDB) cocrystallized crystal provides not only rigid theoretical data but also a realistic molecular recognition data that can be explored and used to discover new hits.

OBJECTIVE

This incited us to investigate the co-crystallized ligands' contacts inside the PDK1 binding pocket via a structure-based receptor-ligand pharmacophore generation technique in Discovery Studio 4.5 (DS 4.5).

METHODS

Accordingly, 35 crystals for PDK1 were collected and studied. Every single receptorligand interaction was validated and the significant ones were converted into their corresponding pharmacophoric features. The generated pharmacophores were scored by the Receiver Operating Characteristic (ROC) curve analysis.

RESULTS

Consequently, 169 pharmacophores were generated and sorted, 11 pharmacophores acquired good ROC-AUC results of 0.8 and a selectivity value above 8. Pharmacophore 1UU3_2_01 was used in particular as a searching filter to screen NCI database because of its acceptable validity criteria and its distinctive positive ionizable feature. Several low micromolar PDK1 inhibitors were revealed. The most potent hit illustrated anti-PDK1 IC50 values of 200 nM with 70% inhibition against SW480 cell lines.

CONCLUSION

Eventually, the active hits were docked inside the PDK1 binding pocket and the recognition points between the active hits and the receptor were analyzed that led to the discovery of new scaffolds as potential PDK1 inhibitors.

Discovery of New Phosphoinositide 3-kinase Delta (PI3Kδ) Inhibitors via Virtual Screening using Crystallography-derived Pharmacophore Modelling and QSAR Analysis

BACKGROUND

PI3Kδ is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3Kδ inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3Kδ binding.

METHODS

Seventeen PI3Kδ crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors).

RESULTS

The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values.

CONCLUSION

Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3Kδ inhibitors.

Combining ligand- and structure-based in silico methods for the identification of natural product-based inhibitors of Akt1

The traditional method of drug discovery process has been surpassed by a rational approach where computer-aided drug designing plays a vital role in the identification of leads from large compound databases. Further, natural products have an important role in drug discovery as these have been the source of most active ingredients of medicines. Herein, in silico structure- and ligand-based approaches have been applied to screen in-house IIIM natural product repository for Akt1 (serine/threonine protein kinases) which is a well-known therapeutic target for cancer due to its overexpression and preventing the cells from undergoing apoptosis. Combined ligand-based and structure-based strategies were applied on to the existing library comprising of about 700 pure natural products, and the compounds identified from screening were biologically evaluated for Akt1 inhibition using Akt1 kinase activity assay. Fourteen promising compounds showed significant inhibition at 500 nM through in vitro screening, and from them, eight were new for Akt1 inhibition. Through the MD studies of Akt1 with the most active compound IN00145, it was inferred that Lys179, Glu191, Glu228, Ala230, Glu234 and Asp292 are the important amino acid residues which provide stability to the Akt1-IN00145 complex. Lead optimization studies were also performed around the actives to design better and selective inhibitors for Akt1. The results emphasized the successful application of virtual screening to identify new Akt1 inhibitor scaffolds that can be developed into a drug candidate in drug discovery programme.

Ligand-based modeling of Akt3 lead to potent dual Akt1/Akt3 inhibitor

Akt1 and Akt3 are important serine/threonine-specific protein kinases involved in G2 phase required by cancer cells to maintain cell cycle and to prevent cell death. Accordingly, inhibitors of these kinases should have potent anti-cancer properties. This prompted us to use pharmacophore/QSAR modeling to identify optimal binding models and physicochemical descriptors that explain bioactivity variation within a set of 74 diverse Akt3 inhibitors. Two successful orthogonal pharmacophores were identified and further validated using receiver operating characteristic (ROC) curve analyses. The pharmacophoric models and associated QSAR equation were applied to screen the national cancer institute (NCI) list of compounds for new Akt3 inhibitors. Six hits showed significant experimental anti-Akt3 IC₅₀ values, out of which one compound exhibited dual low micromolar anti-Akt1 and anti-Akt3 inhibitory profiles.