In Silico Repurposing of a Novel Inhibitor (drug) of EGFR and VEGFR-2 Kinases of Cancer by Pharmacokinetics, Toxicity, Molecular Docking, and Molecular Dynamics Simulation.

Vascular endothelial growth factor is an angiogenic that promotes the development and metastasis of tumors (VEGF). The epidermal growth factor receptor, or EGFR, controls the division, growth, and death of cells via several signaling pathways. It has been found that most of the tyrosine kinase EGFR/VEGFR-2 inhibited by drugs that the FDA has approved are so far. The main objective of the present study was to identify an efficacious and selective dual inhibitor of VEGFR-2/EGFR for the treatment of cancer. Out of the 400 ligands tested against the kinases, 12 compounds demonstrated the best docking scores through molecular docking for the two kinases. Of these, only compound SCHEMBL2435814 inhibited the kinases with the highest score values when compared to a reference, vandetanib, as a dual inhibitor of EGFR/VEGFR-2 kinases through interaction with the identified active sites pocket. Following drug-likeness score toxicity and pharmacokinetic testing, the two compounds, SCHEMBL2435814 and vandetanib, were analyzed to determine how the two kinases interacted with each other. The results of calculations of π-cation interactions, hydrogen bonds, and hydrophobic interactions demonstrated a strong interaction between the two kinases and SCHEMBL2435814. Eventually, molecular dynamic modeling was used to assess the stability of complexes. This demonstrated many characteristics, including RMSF, RMSD, SASA, RG, and H-bond analysis, which demonstrated that SCHEMBL2435814 with the two kinases was more stable than vandetanib over a 100ns simulation period. By suppressing EGFR/VEGFR-2, chemical SCHEMBL2435814 may be able to postpone the signaling pathway of proteins that are essential to the advancement of cancer.

Targeting EGFR and VEGFR-2 Kinases With Nanoparticles: A Computational Approach for Cancer Therapy Advancement.

The study investigates titanium and zinc nanoparticles as inhibitors for the epidermal growth factor receptor (EGFR) and vascular endothelial growth factor receptor-2 (VEGFR-2), pivotal regulators of cell processes. VEGFR-2 activation fuels tumor angiogenesis in cancer cells, sustaining malignant tissue expansion. Molecular docking analysis illustrates the nanoparticles' binding to the active sites, inhibiting the phosphorylation of key proteins in downstream signaling. This inhibition offers a promising therapeutic approach to impede cancer-related signaling, potentially slowing down aberrant protein cascades controlled by EGFR and VEGFR-2. The findings propose a novel avenue for cancer treatment, targeting abnormal growth pathways using titanium and zinc nanoparticles.

'In silico' repurposing new inhibitors of EGFR and VEGFR-2 kinases via biophysical mechanisms.

Epidermal growth factor receptor (EGFR) controls cell growth, death, and proliferation through a variety of signaling mechanisms. The expression of vascular endothelial growth factor receptor-2 (VEGFR-2) by endothelial cells from malignant tissues triggers a series of signaling pathways that lead to tumor angiogenesis and increase cancer cell survival, proliferation, migration, and vascular permeability. The aim is to find novel inhibitors for EGFR and VEGFR-2 kinases by molecular docking drug-likeness models, pharmacokinetic, interaction analysis, and molecular dynamic simulation. Over 482 ligands were tested against the kinases, there are about 20 compounds that had the best docking scores for the 2 kinases but only compound 2C inhibited them with the highest score values by binding to active sites pocket established through molecular docking study. Secondly, the drug-likeness score of 2C was very good compared to the other compounds. The pharmacokinetics, physicochemical properties, and toxicity of 2C were much better than sorafenib and erlotinib as references. Analysis of interaction showed a strong interaction between 2C and active sites of EGFR and VEGFR-2 kinases illustrated by calculation of halogen bonds, π-Cation Interactions, Hydrogen Bonds, and Hydrophobic Interactions. Finally, the molecular dynamic simulation was also used to assess the stability of the EGFR and VEGFR-2 kinases-2C complexes. The complexes' stability was validated by RMSD, Rg, RMSF, SASA, and several hydrogen bonds analysis. 2C was shown to interact stably with pocket residues after MD simulation. Compound 2C may be a promising way to slow the signaling cascade of proteins that are significant contributors to the spread of cancer.Communicated by Ramaswamy H. Sarma.

A computational peptide model induces cancer cells' apoptosis by docking Kringle 5 to GRP78.

BACKGROUND: Cells can die through a process called apoptosis in both pathological and healthy conditions. Cancer development and progression may result from abnormal apoptosis. The 78-kDa glucose-regulated protein (GRP78) is increased on the surface of cancer cells. Kringle 5, a cell apoptosis agent, is bound to GRP78 to induce cancer cell apoptosis. Kringle 5 was docked to GRP78 using ClusPro 2.0. The interaction between Kringle 5 and GRP78 was investigated. RESULTS: The interacting amino acids were found to be localized in three areas of Kringle 5. The proposed peptide is made up of secondary structure amino acids that contain Kringle 5 interaction residues. The 3D structure of the peptide model amino acids was created using the PEP-FOLD3 web tool. CONCLUSIONS: The proposed peptide completely binds to the GRP78 binding site on the Kringle 5, signaling that it might be effective in the apoptosis of cancer cells.

Potential antiviral peptides targeting the SARS-CoV-2 spike protein.

BACKGROUND: The coronavirus disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection became an international pandemic and created a public health crisis. The binding of the viral Spike glycoprotein to the human cell receptor angiotensin-converting enzyme 2 (ACE2) initiates viral infection. The development of efficient treatments to combat coronavirus disease is considered essential. METHODS: An in silico approach was employed to design amino acid peptide inhibitor against the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. The designed inhibitor (SARS-CoV-2 PEP 49) consists of amino acids with the α1 helix and the ß4 - ß5 sheets of ACE2. The PEP-FOLD3 web tool was used to create the 3D structures of the peptide amino acids. Analyzing the interaction between ACE2 and the RBD of the Spike protein for three protein data bank entries (6M0J, 7C8D, and 7A95) indicated that the interacting amino acids were contained inside two regions of ACE2: the α1 helical protease domain (PD) and the ß4 - ß5 sheets. RESULTS: Molecular docking analysis of the designed inhibitor demonstrated that SARS-CoV-2 PEP 49 attaches directly to the ACE2 binding site of the Spike protein with a binding affinity greater than the ACE2, implying that the SARS-CoV-2 PEP 49 model may be useful as a potential RBD binding blocker.

Seeking antiviral drugs to inhibit SARS-CoV-2 RNA dependent RNA polymerase: A molecular docking analysis.

COVID-19 outbreak associated with the severe acute respiratory syndrome coronavirus (SARS-CoV-2) raised health concerns across the globe and has been considered highly transmissible between people. In attempts for finding therapeutic treatment for the new disease, this work has focused on examining the polymerase inhibitors against the SARS-CoV-2 nsp12 and co-factors nsp8 and nsp7. Several polymerase inhibitors were examined against PDB ID: 6M71 using computational analysis evaluating the ligand's binding affinity to replicating groove to the active site. The findings of this analysis showed Cytarabine of -5.65 Kcal/mol with the highest binding probability (70%) to replicating groove of 6M71. The complex stability was then examined over 19 ns molecular dynamics simulation suggesting that Cytarabine might be possible potent inhibitor for the SARS-CoV-2 RNA Dependent RNA Polymerase.

SARS-CoV-2 variant surge and vaccine breakthrough infection: A computational analysis.

Coronavirus Delta variant was first detected in India in October of 2020, and it led to a massive second wave of COVID-19 cases in the country. Since then, the highly infectious Delta strain has been spreading globally. The Delta variant and its sub-lineages showed an increased infection rate with a reduced effect of the potential antibody neutralization. The current work is a modeled computational analysis of the mutated receptor-binding domain (RBD) of the SARS-CoV-2 B.1.617 lineage binding with ACE2 and GRP78 to understand the increased strain transmissibility. The cell-surface Glucose Regulated Protein 78 (GRP78) attached to the mutated ACE2-SARS-CoV-2 Spike RBD complex is modeled. The results showed that GRP78 ß-substrate-binding domain weakly binds to the wild-type RBD combined with angiotensin-converting enzyme 2 (ACE2) within the SARS-CoV-2 Spike RBD-ACE2 complex. Both GRP78 and ACE2 bind approximately in the same region on the wild-type SARS-CoV-2 Spike RBD surface. On the other hand, GRP78 strongly binds to the mutated SARS-CoV-2 Spike RBD in the RBD-ACE2 complex through the α-substrate-binding domain instead of ß-substrate-binding domain in a different region from that of ACE2. The current findings suggest that blocking the main ACE2 pathway may not prevent the interactions between GRP78 and the mutated SARS-CoV-2 Spike RBD, which might introduce an additional avenue into the virus invasion for the host cell if the ACE2 pathway is blocked by the neutralized antibodies. Hence, the peptide satpdb10668 has been proposed as a potential inhibitor of SARS-CoV-2 attachment and virus invasion into the host cell.

In silico molecular docking analysis for repurposing approved antiviral drugs against SARS-CoV-2 main protease.

Developing a safe and effective antiviral treatment takes a decade, however, when it comes to the coronavirus disease (COVID-19), time is a sensitive matter to slow the spread of the pandemic. Screening approved antiviral drugs against COVID-19 would speed the process of finding therapeutic treatment. The current study examines commercially approved drugs to repurpose them against COVID-19 virus main protease using structure-based in-silico screening. The main protease of the coronavirus is essential in the viral replication and is involved in polyprotein cleavage and immune regulation, making it an effective target when developing the treatment. A Number of approved antiviral drugs were tested against COVID-19 virus using molecular docking analysis by calculating the free natural affinity of the binding ligand to the active site pocket and the catalytic residues without forcing the docking of the ligand to active site. COVID-19 virus protease solved structure (PDB ID: 6LU7) is targeted by repurposed drugs. The molecular docking analysis results have shown that the binding of Remdesivir and Mycophenolic acid acyl glucuronide with the protein drug target has optimal binding features supporting that Remdesivir and Mycophenolic acid acyl glucuronide can be used as potential anti-viral treatment against COVID-19 disease.

Development of a new Python-based cardiac phantom for myocardial SPECT imaging.

PURPOSE: The aim of this work was to develop a digital dynamic cardiac phantom able to mimic gated myocardial perfusion single photon emission computed tomography (SPECT) images. METHODS: A software code package was written to construct a cardiac digital phantom based on mathematical ellipsoidal model utilizing powerful numerical and mathematic libraries of python programing language. An ellipsoidal mathematical model was adopted to create the left ventricle geometrical volume including myocardial boundaries, left ventricular cavity, with incorporation of myocardial wall thickening and motion. Realistic myocardial count density from true patient studies was used to simulate statistical intensity variation during myocardial contraction. A combination of different levels of defect extent and severity were precisely modeled taking into consideration defect size variation during cardiac contraction. Wall thickening was also modeled taking into account the effect of partial volume. RESULTS: It has been successful to build a python-based software code that is able to model gated myocardial perfusion SPECT images with variable left ventricular volumes and ejection fraction. The recent flexibility of python programming enabled us to manipulate the shape and control the functional parameters in addition to creating variable sized-defects, extents and severities in different locations. Furthermore, the phantom code also provides different levels of image filtration mimicking those filters used in image reconstruction and their influence on image quality. Defect extent and severity were found to impact functional parameter estimation in consistence to clinical examinations. CONCLUSION: A python-based gated myocardial perfusion SPECT phantom has been successfully developed. The phantom proved to be reliable to assess cardiac software analysis tools in terms of perfusion and functional parameters. The software code is under further development and refinement so that more functionalities and features can be added.