Molecular Modeling Study of c-KIT/PDGFRα Dual Inhibitors for the Treatment of Gastrointestinal Stromal Tumors

Design, synthesis, in-silico studies and apoptotic activity of novel amide enriched 2-(1H)- quinazolinone derivatives

Cancer is a broad classification of diseases that can affect any organ or body tissue due to aberrant cellular proliferation for unknown reasons. Many present chemotherapeutic drugs are highly toxic and have little selectivity. Additionally, they lead to the development of medication resistance. Therefore, developing tailored chemotherapeutic drugs with minimal side effects and good selectivity is crucial for cancer treatment. 2-(1H)-Quinazolinone is one of the vital scaffold and anticancer activity is one of the prominent biological activities of this class. Here we report the novel set of amide-enriched 2-(1H)-quinazolinone derivatives (7a-j) and their apoptotic activity with the help of MTT assay method against four human cancer cell lines: PC3 (prostate cancer), DU-145 (prostate cancer), A549 (lung cancer), and MCF7 (breast cancer). When compared to etoposide, every synthetic test compound (7a-j) exhibited moderate to excellent activity. The IC50 values of the new amide derivatives (7a-j) varied from 0.07 ± 0.0061 μM to 10.8 ± 0.69 μM. While the positive control, etoposide, exhibited 1.97 ± 0.45 μM to 3.08 ± 0.135 μM range. Among the novel amide derivatives (7a-j), in particular, 7i and 7j showed strong apoptotic activity against MCF7; 7h showed against PC3, and 7g showed against DU-145. Molecular docking studies of test compounds (7a-j) with the EGFR tyrosine kinase domain (PDB ID: 1M17) protein provided the significant docking scores for each test compound (7a-j) (-9.00 to -9.67 kcal/mol). Additionally, DFT investigations and MD simulations validated the predictions of molecular docking. According to the findings of the ADME analysis, oral absorption by humans is anticipated to be higher than 85 % for all test compounds.

Comprehensive analysis of crystal structure, spectroscopic properties, quantum chemical insights, and molecular docking studies of two pyrazolopyridine compounds: potential anticancer agents

In this study, two pyrazolo[3,4-b]pyridine derivatives (4a and 4b) were grown using a slow evaporation solution growth technique and characterized by FT-IR, HRMS, 1H/13C NMR spectroscopy, and X-ray crystallography. The 4a and 4b structures crystallized in monoclinic and triclinic systems with space groups P21/n and P1̄, respectively. Theoretical calculations were performed at the DFT/B3LYP level for the optimized geometries. The results were in excellent agreement with the experimental data (spectroscopic and XRD). This investigation encompasses molecular modeling studies including Hirshfeld surface analysis, energy framework calculations, and frontier molecular orbital analysis. Intermolecular interactions within the crystal structures of the compounds were explored through Hirshfeld surface analysis, which revealed the notable presence of hydrogen bonding and hydrophobic interactions. This insight provides valuable information on the structural stability and potential solubility characteristics of these compounds. The research was extended to docking analysis with eight distinct kinases (BRAF, HER2, CSF1R, MEK2, PDGFRA, JAK, AKT1, and AKT2). The results of this analysis demonstrate that both 4a and 4b interact effectively with the kinase-binding sites through a combination of hydrophobic interactions and hydrogen bonding. Compound 4a had the best affinity for proteins; this is related to the fact that the compound is not rigid and has a small size, allowing it to sit well at any binding site. This study contributes to the advancement of kinase inhibitor research and offers potential avenues for the development of new therapeutic agents for cancer treatment.

Affinity Purification and Molecular Characterization of Angiotensin-Converting Enzyme (ACE)-Inhibitory Peptides from Takifugu flavidus

An affinity chromatography filler of CNBr-activated Sepharose 4B-immobilized ACE was used to purify ACE-inhibitory peptides from Takifugu flavidus protein hydrolysate (<1 kDa). Twenty-four peptides with an average local confidence score (ALC) ≥ 80% from bounded components (eluted by 1 M NaCl) were identified by LC-MS/MS. Among them, a novel peptide, TLRFALHGME, with ACE-inhibitory activity (IC50 = 93.5 µmol·L-1) was selected. Molecular docking revealed that TLRFALHGME may interact with the active site of ACE through H-bond, hydrophobic, and electrostatic interactions. The total binding energy (ΔGbinding) of TLRFALHGME was estimated to be -82.7382 kJ·mol-1 by MD simulations, indicating the favorable binding of peptides with ACE. Furthermore, the binding affinity of TLRFALHGME to ACE was determined by surface plasmon resonance (SPR) with a Kd of 80.9 µmol, indicating that there was a direct molecular interaction between them. TLRFALHGME has great potential for the treatment of hypertension.

Screening and Analysis of Possible Drugs Binding to PDGFRα: A Molecular Modeling Study

The platelet-derived growth factor receptor (PDGFR) is a membrane tyrosine kinase receptor involved in several metabolic pathways, not only physiological but also pathological, as in tumor progression, immune-mediated diseases, and viral diseases. Considering this macromolecule as a druggable target for modulation/inhibition of these conditions, the aim of this work was to find new ligands or new information to design novel effective drugs. We performed an initial interaction screening with the human intracellular PDGFRα of about 7200 drugs and natural compounds contained in 5 independent databases/libraries implemented in the MTiOpenScreen web server. After the selection of 27 compounds, a structural analysis of the obtained complexes was performed. Three-dimensional quantitative structure-activity relationship (3D-QSAR) and absorption, distribution, metabolism, excretion, and toxicity (ADMET) analyses were also performed to understand the physicochemical properties of identified compounds to increase affinity and selectivity for PDGFRα. Among these 27 compounds, the drugs Bafetinib, Radotinib, Flumatinib, and Imatinib showed higher affinity for this tyrosine kinase receptor, lying in the nanomolar order, while the natural products included in this group, such as curcumin, luteolin, and epigallocatechin gallate (EGCG), showed sub-micromolar affinities. Although experimental studies are mandatory to fully understand the mechanisms behind PDGFRα inhibitors, the structural information obtained through this study could provide useful insight into the future development of more effective and targeted treatments for PDGFRα-related diseases, such as cancer and fibrosis.

Proteomic characterization of post-translational modifications in drug discovery

Protein post-translational modifications (PTMs), which are usually enzymatically catalyzed, are major regulators of protein activity and involved in almost all celluar processes. Dysregulation of PTMs is associated with various types of diseases. Therefore, PTM regulatory enzymes represent as an attractive and important class of targets in drug research and development. Inhibitors against kinases, methyltransferases, deacetyltransferases, ubiquitin ligases have achieved remarkable success in clinical application. Mass spectrometry-based proteomics technologies serve as a powerful approach for system-wide characterization of PTMs, which facilitates the identification of drug targets, elucidation of the mechanisms of action of drugs, and discovery of biomakers in personalized therapy. In this review, we summarize recent advances of proteomics-based studies on PTM targeting drugs and discuss how proteomics strategies facilicate drug target identification, mechanism elucidation, and new therapy development in precision medicine.

Binding Studies and Lead Generation of Pteridin-7(8H)-one Derivatives Targeting FLT3

Ligand modification by substituting chemical groups within the binding pocket is a popular strategy for kinase drug development. In this study, a series of pteridin-7(8H)-one derivatives targeting wild-type FMS-like tyrosine kinase-3 (FLT3) and its D835Y mutant (FL3D835Y) were studied using a combination of molecular modeling techniques, such as docking, molecular dynamics (MD), binding energy calculation, and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies. We determined the protein–ligand binding affinity by employing molecular mechanics Poisson–Boltzmann/generalized Born surface area (MM-PB/GBSA), fast pulling ligand (FPL) simulation, linear interaction energy (LIE), umbrella sampling (US), and free energy perturbation (FEP) scoring functions. The structure–activity relationship (SAR) study was conducted using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and the results were emphasized as a SAR scheme. In both the CoMFA and CoMSIA models, satisfactory correlation statistics were obtained between the observed and predicted inhibitory activity. The MD and SAR models were co-utilized to design several new compounds, and their inhibitory activities were anticipated using the CoMSIA model. The designed compounds with higher predicted pIC50 values than the most active compound were carried out for binding free energy evaluation to wild-type and mutant receptors using MM-PB/GBSA, LIE, and FEP methods.

Natural Products in Preventing Tumor Drug Resistance and Related Signaling Pathways

Drug resistance is still an obstacle in cancer therapy, leading to the failure of tumor treatment. The emergence of tumor drug resistance has always been a main concern of oncologists. Therefore, overcoming tumor drug resistance and looking for new strategies for tumor treatment is a major focus in the field of tumor research. Natural products serve as effective substances against drug resistance because of their diverse chemical structures and pharmacological effects. We reviewed the signaling pathways involved in the development of tumor drug resistance, including Epidermal growth factor receptor (EGFR), Renin-angiotensin system (Ras), Phosphatidylinositol-3-kinase/protein kinase B (PI3K/Akt), Wnt, Notch, Transforming growth factor-beta (TGF-β), and their specific signaling pathway inhibitors derived from natural products. This can provide new ideas for the prevention of drug resistance in cancer therapy.

Application of ultrasonography in predicting the biological risk of gastrointestinal stromal tumors

OBJECTIVES

To explore and establish a reliable and noninvasive ultrasound model for predicting the biological risk of gastrointestinal stromal tumors (GISTs).

MATERIALS AND METHODS

We retrospectively reviewed 266 patients with pathologically-confirmed GISTs and 191 patients were included. Data on patient sex, age, tumor location, biological risk classification, internal echo, echo homogeneity, boundary, shape, blood flow signals, presence of necrotic cystic degeneration, long diameter, and short/long (S/L) diameter ratio were collected. All patients were divided into low-, moderate-, and high-risk groups according to the modified NIH classification criteria. All indicators were analyzed by univariate analysis. The indicators with inter-group differences were used to establish regression and decision tree models to predict the biological risk of GISTs.

RESULTS

There were statistically significant differences in long diameter, S/L ratio, internal echo level, echo homogeneity, boundary, shape, necrotic cystic degeneration, and blood flow signals among the low-, moderate-, and high-risk groups (all p < .05). The logistic regression model based on the echo homogeneity, shape, necrotic cystic degeneration and blood flow signals had an accuracy rate of 76.96% for predicting the biological risk, which was higher than the 72.77% of the decision tree model (based on the long diameter, the location of tumor origin, echo homogeneity, shape, and internal echo) (p = .008). In the low-risk and high-risk groups, the predicting accuracy rates of the regression model reached 87.34 and 81.82%, respectively.

CONCLUSIONS

Transabdominal ultrasound is highly valuable in predicting the biological risk of GISTs. The logistic regression model has greater predictive value than the decision tree model.

Computer aided designing of novel pyrrolopyridine derivatives as JAK1 inhibitors

Janus kinases (JAKs) are a family of non-receptor kinases that play a key role in cytokine signaling and their aberrant activities are associated with the pathogenesis of various immune diseases. The JAK1 isoform plays an essential role in the types 1 and II interferon signaling and elicits signals from the interleukin-2, interleukin-4, gp130, and class 2 receptor families. It is ubiquitously expressed in humans and its overexpression has been linked with autoimmune diseases such as myeloproliferative neoplasm. Although JAK1 inhibitors such as Tofacitinib have been approved for medical use, the low potency and off-target effects of these inhibitors have limited their use and calls for the development of novel JAK1 inhibitors. In this study, we used computational methods on a series of pyrrolopyridine derivatives to design new JAK1 inhibitors. Molecular docking and molecular dynamics simulation methods were used to study the protein-inhibitor interactions. 3D-quantitative structure–activity relationship models were developed and were used to predict the activity of newly designed compounds. Free energy calculation methods were used to study the binding affinity of the inhibitors with JAK1. Of the designed compounds, seventeen of the compounds showed a higher binding energy value than the most active compound in the dataset and at least six of the compounds showed higher binding energy value than the pan JAK inhibitor Tofacitinib. The findings made in this study could be utilized for the further development of JAK1 inhibitors.

Novel dynamic residue network analysis approaches to study allosteric modulation: SARS-CoV-2 Mpro and its evolutionary mutations as a case study

The rational search for allosteric modulators and the allosteric mechanisms of these modulators in the presence of mutations is a relatively unexplored field. Here, we established novel in silico approaches and applied them to SARS-CoV-2 main protease (Mpro) as a case study. First, we identified six potential allosteric modulators. Then, we focused on understanding the allosteric effects of these modulators on each of its protomers. We introduced a new combinatorial approach and dynamic residue network (DRN) analysis algorithms to examine patterns of change and conservation of critical nodes, according to five independent criteria of network centrality. We observed highly conserved network hubs for each averaged DRN metric on the basis of their existence in both protomers in the absence and presence of all ligands (persistent hubs). We also detected ligand specific signal changes. Using eigencentrality (EC) persistent hubs and ligand introduced hubs we identified a residue communication path connecting the allosteric binding site to the catalytic site. Finally, we examined the effects of the mutations on the behavior of the protein in the presence of selected potential allosteric modulators and investigated the ligand stability. One crucial outcome was to show that EC centrality hubs form an allosteric communication path between the allosteric ligand binding site to the active site going through the interface residues of domains I and II; and this path was either weakened or lost in the presence of some of the mutations. Overall, the results revealed crucial aspects that need to be considered in rational computational drug discovery.

Molecular Modeling Studies of N-phenylpyrimidine-4-amine Derivatives for Inhibiting FMS-like Tyrosine Kinase-3

Overexpression and frequent mutations in FMS-like tyrosine kinase-3 (FLT3) are considered risk factors for severe acute myeloid leukemia (AML). Hyperactive FLT3 induces premature activation of multiple intracellular signaling pathways, resulting in cell proliferation and anti-apoptosis. We conducted the computational modeling studies of 40 pyrimidine-4,6-diamine-based compounds by integrating docking, molecular dynamics, and three-dimensional structure-activity relationship (3D-QSAR). Molecular docking showed that K644, C694, F691, E692, N701, D829, and F830 are critical residues for the binding of ligands at the hydrophobic active site. Molecular dynamics (MD), together with Molecular Mechanics Poison-Boltzmann/Generalized Born Surface Area, i.e., MM-PB(GB)SA, and linear interaction energy (LIE) estimation, provided critical information on the stability and binding affinity of the selected docked compounds. The MD study suggested that the mutation in the gatekeeper residue F691 exhibited a lower binding affinity to the ligand. Although, the mutation in D835 in the activation loop did not exhibit any significant change in the binding energy to the most active compound. We developed the ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models. CoMFA (q² = 0.802, r² = 0.983, and QF32 = 0.698) and CoMSIA (q² = 0.725, r² = 0.965 and QF32 = 0.668) established the structure-activity relationship (SAR) and showed a reasonable external predictive power. The contour maps from the CoMFA and CoMSIA models could explain valuable information about the favorable and unfavorable positions for chemical group substitution, which can increase or decrease the inhibitory activity of the compounds. In addition, we designed 30 novel compounds, and their predicted pIC₅₀ values were assessed with the CoMSIA model, followed by the assessment of their physicochemical properties, bioavailability, and free energy calculation. The overall outcome could provide valuable information for designing and synthesizing more potent FLT3 inhibitors.

MDM-TASK-web: MD-TASK and MODE-TASK web server for analyzing protein dynamics

The web server, MDM-TASK-web, combines the MD-TASK and MODE-TASK software suites, which are aimed at the coarse-grained analysis of static and all-atom MD-simulated proteins, using a variety of non-conventional approaches, such as dynamic residue network analysis, perturbation-response scanning, dynamic cross-correlation, essential dynamics and normal mode analysis. Altogether, these tools allow for the exploration of protein dynamics at various levels of detail, spanning single residue perturbations and weighted contact network representations, to global residue centrality measurements and the investigation of global protein motion. Typically, following molecular dynamic simulations designed to investigate intrinsic and extrinsic protein perturbations (for instance induced by allosteric and orthosteric ligands, protein binding, temperature, pH and mutations), this selection of tools can be used to further describe protein dynamics. This may lead to the discovery of key residues involved in biological processes, such as drug resistance. The server simplifies the set-up required for running these tools and visualizing their results. Several scripts from the tool suites were updated and new ones were also added and integrated with 2D/3D visualization via the web interface. An embedded work-flow, integrated documentation and visualization tools shorten the number of steps to follow, starting from calculations to result visualization. The Django-powered web server (available at https://mdmtaskweb.rubi.ru.ac.za/) is compatible with all major web browsers. All scripts implemented in the web platform are freely available at https://github.com/RUBi-ZA/MD-TASK/tree/mdm-task-web and https://github.com/RUBi-ZA/MODE-TASK/tree/mdm-task-web.

Recent Advances in the Development of Pyrazolopyridines as Anticancer Agents

Cancer, especially malignant tumor, is a serious threat to people's life and health. It is recognized as an enormous challenge in the 21st century. Continuous efforts are needed to overcome this problem. Pyrazolopyridine nucleus, similar in structure to purine, shows a variety of biological activities, which is mainly attributed to the antagonistic nature towards the natural purines in many biological processes. This has aroused enormous attention for many researchers. At present, a large number of new chemical entities containing pyrazolopyridine nucleus have been found as anticancer agents. In this review we summarize novel pyrazolopyridine-containing derivatives with biological activities. Furthermore, we outline the relationships between the structures of variously modified pyrazolopyridines and their anticancer activity.

Designing of the N-ethyl-4-(pyridin-4-yl)benzamide based potent ROCK1 inhibitors using docking, molecular dynamics, and 3D-QSAR

Rho-associated kinase-1 (ROCK1) has been recognized for its pivotal role in heart diseases, different types of malignancy, and many neurological disorders. Hyperactivity of ROCK phosphorylates the protein kinase-C (PKC), which ultimately induces smooth muscle cell contraction in the vascular system. Inhibition of ROCK1 has been shown to be a promising therapy for patients with cardiovascular disease. In this study, we have conducted molecular modeling techniques such as docking, molecular dynamics (MD), and 3-Dimensional structure-activity relationship (3D-QSAR) on a series of N-ethyl-4-(pyridin-4-yl)benzamide-based compounds. Docking and MD showed critical interactions and binding affinities between ROCK1 and its inhibitors. To establish the structure-activity relationship (SAR) of the compounds, 3D-QSAR techniques such as Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were used. The CoMFA (q ² = 0.774, r ² = 0.965, ONC = 6, and r p r e d 2 = 0.703) and CoMSIA (q ² = 0.676, r ² = 0.949, ONC = 6, and r p r e d 2 = 0.548) both models have shown reasonable external predictive activity, and contour maps revealed favorable and unfavorable substitutions for chemical group modifications. Based on the contour maps, we have designed forty new compounds, among which, seven compounds exhibited higher predictive activity (pIC₅₀). Further, we conducted the MD study, ADME/Tox, and SA score prediction using the seven newly designed compounds. The combination of docking, MD, and 3D-QSAR studies helps to understand the coherence modification of existing molecules. Our study may provide valuable insight into the development of more potent ROCK1 inhibitors.