In Silico Insights on GD2 : A Potential Target for Pediatric Neuroblastoma

Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer

Cervical cancer stands as a prevalent gynaecologic malignancy affecting women globally, often linked to persistent human papillomavirus infection. Biomarkers associated with cervical cancer, including VEGF-A, VEGF-B, VEGF-C, VEGF-D, and VEGF-E, show upregulation and are linked to angiogenesis and lymphangiogenesis. This research aims to employ in-silico methods to target tyrosine kinase receptor proteins-VEGFR-1, VEGFR-2, and VEGFR-3, and identify novel inhibitors for Vascular Endothelial Growth Factors receptors (VEGFRs). A comprehensive literary study was conducted which identified 26 established inhibitors for VEGFR-1, VEGFR-2, and VEGFR-3 receptor proteins. Compounds with high-affinity scores, including PubChem ID-25102847, 369976, and 208908 were chosen from pre-existing compounds for creating Deep Learning-based models. RD-Kit, a Deep learning algorithm, was used to generate 43 million compounds for VEGFR-1, VEGFR-2, and VEGFR-3 targets. Molecular docking studies were conducted on the top 10 molecules for each target to validate the receptor-ligand binding affinity. The results of Molecular Docking indicated that PubChem IDs-71465,645 and 11152946 exhibited strong affinity, designating them as the most efficient molecules. To further investigate their potential, a Molecular Dynamics Simulation was performed to assess conformational stability, and a pharmacophore analysis was also conducted for indoctrinating interactions.

Structure-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation of EGFR for the Clinical Treatment of Glioblastoma

Glioblastoma (GBM) is a WHO Grade IV tumor with poor visibility, a high risk of comorbidity, and exhibit limited treatment options. Resurfacing from second-rate glioma was originally classified as either mandatory or optional. Recent interest in personalized medicine has motivated research toward biomarker stratification-based individualized illness therapy. GBM biomarkers have been investigated for their potential utility in prognostic stratification, driving the development of targeted therapy and customizing therapeutic treatment. Due to the availability of a specific EGFRvIII mutational variation with a clear function in glioma-genesis, recent research suggests that EGFR has the potential to be a prognostic factor in GBM, while others have shown no clinical link between EGFR and survival. The pre-existing pharmaceutical lapatinib (PubChem ID: 208,908) with a higher affinity score is used for virtual screening. As a result, the current study revealed a newly screened chemical (PubChem CID: 59,671,768) with a higher affinity than the previously known molecule. When the two compounds are compared, the former has the lowest re-rank score. The time-resolved features of a virtually screened chemical and an established compound were investigated using molecular dynamics simulation. Both compounds are equivalent, according to the ADMET study. This report implies that the virtual screened chemical could be a promising Glioblastoma therapy.

Potential Benefits of Dietary Plant Compounds on Normal and Tumor Brain Cells in Humans: In Silico and In Vitro Approaches

Neuroblastoma can be accessed with compounds of larger sizes and wider polarities, which do not usually cross the blood-brain barrier. Clinical data indicate cases of spontaneous regression of neuroblastoma, suggesting a reversible point in the course of cell brain tumorigenesis. Dual specificity tyrosine-phosphorylation-regulated kinase2 (DYRK2) is a major molecular target in tumorigenesis, while curcumin was revealed to be a strong inhibitor of DYRK2 (PBD ID: 5ZTN). Methods: in silico studies by CLC Drug Discovery Workbench (CLC) and Molegro Virtual Docker (MVD) Software on 20 vegetal compounds from the human diet tested on 5ZTN against the native ligand curcumin, in comparison with anemonin. In vitro studies were conducted on two ethanolic extracts from Anemone nemorosa tested on normal and tumor human brain cell lines NHA and U87, compared with four phenolic acids (caffeic, ferulic, gentisic, and para-aminobenzoic/PABA). Conclusions: in silico studies revealed five dietary compounds (verbascoside, lariciresinol, pinoresinol, medioresinol, matairesinol) acting as stronger inhibitors of 5ZTN compared to the native ligand curcumin. In vitro studies indicated that caffeic acid has certain anti-proliferative effects on U87 and small benefits on NHA viability. A. nemorosa extracts indicated potential benefits on NHA viability, and likely dangerous effects on U87.

Integrated Transcriptome Profiling Identifies Prognostic Hub Genes as Therapeutic Targets of Glioblastoma: Evidenced by Bioinformatics Analysis

Glioblastoma (GBM) is the most devastating and frequent type of primary brain tumor with high morbidity and mortality. Despite the use of surgical resection followed by radio- and chemotherapy as standard therapy, the progression of GBM remains dismal with a median overall survival of <15 months. GBM embodies a populace of cancer stem cells (GSCs) that is associated with tumor initiation, invasion, therapeutic resistance, and post-treatment reoccurrence. However, understanding the potential mechanisms of stemness and their candidate biomarkers remains limited. Hence in this investigation, we aimed to illuminate potential candidate hub genes and key pathways associated with the pathogenesis of GSC in the development of GBM. The integrated analysis discovered differentially expressed genes (DEGs) between the brain cancer tissues (GBM and GSC) and normal brain tissues. Multiple approaches, including gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were employed to functionally annotate the DEGs and visualize them through the R program. The significant hub genes were identified through the protein-protein interaction network, Venn diagram analysis, and survival analysis. We observed that the upregulated DEGs were prominently involved in the ECM-receptor interaction pathway. The downregulated genes were mainly associated with the axon guidance pathway. Five significant hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) were screened out through multiple analyses. GO and KEGG analyses of hub genes uncovered that these genes were primarily enriched in disease-associated pathways such as the inhibition of apoptosis and the DNA damage repair mechanism, activation of the cell cycle, EMT (epithelial-mesenchymal transition), hormone AR (androgen receptor), hormone ER (estrogen receptor), PI3K/AKT (phosphatidylinositol 3-kinase and AKT), RTK (receptor tyrosine kinase), and TSC/mTOR (tuberous sclerosis complex and mammalian target of rapamycin). Consequently, the epigenetic regulatory network disclosed that hub genes played a vital role in the progression of GBM. Finally, candidate drugs were predicted that can be used as possible drugs to treat GBM patients. Overall, our investigation offered five hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) that could be used as precise diagnostic and prognostic candidate biomarkers of GBM and might be used as personalized therapeutic targets to obstruct gliomagenesis.

Structure-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation of VEGF inhibitors for the clinical treatment of Ovarian Cancer

Vascular endothelial growth factor (VEGF) and its receptor play an important role both in physiologic and pathologic angiogenesis, which is identified in ovarian cancer progression and metastasis development. The aim of the present investigation is to identify a potential vascular endothelial growth factor inhibitor which is playing a crucial role in stimulating the immunosuppressive microenvironment in tumor cells of the ovary and to examine the effectiveness of the identified inhibitor for the treatment of ovarian cancer using various in silico approaches. Twelve established VEGF inhibitors were collected from various literatures. The compound AEE788 displays great affinity towards the target protein as a result of docking study. AEE788 was further used for structure-based virtual screening in order to obtain a more structurally similar compound with high affinity. Among the 80 virtual screened compounds, CID 88265020 explicates much better affinity than the established compound AEE788. Based on molecular dynamics simulation, pharmacophore and comparative toxicity analysis of both the best established compound and the best virtual screened compound displayed a trivial variation in associated properties. The virtual screened compound CID 88265020 has a high affinity with the lowest re-rank score and holds a huge potential to inhibit the VGFR and can be implemented for prospective future investigations in ovarian cancer.

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

A Multi-target Drug Designing for BTK, MMP9, Proteasome and TAK1 for the Clinical Treatment of Mantle Cell Lymphoma

BACKGROUND

Mantle cell lymphoma (MCL) is a type of non-Hodgkin lymphoma characterized by the mutation and overexpression of the cyclin D1 protein by the reciprocal chromosomal translocation t(11;14)(q13:q32).

AIM

The present study aims to identify potential inhibition of MMP9, Proteasome, BTK, and TAK1 and determine the most suitable and effective protein target for the MCL.

METHODOLOGY

Nine known inhibitors for MMP9, 24 for proteasome, 15 for BTK and 14 for TAK1 were screened. SB-3CT (PubChem ID: 9883002), oprozomib (PubChem ID: 25067547), zanubrutinib (PubChem ID: 135565884) and TAK1 inhibitor (PubChem ID: 66760355) were recognized as drugs with high binding capacity with their respective protein receptors. 41, 72, 102 and 3 virtual screened compounds were obtained after the similarity search with compound (PubChem ID:102173753), PubChem compound SCHEMBL15569297 (PubChem ID:72374403), PubChem compound SCHEMBL17075298 (PubChem ID:136970120) and compound CID: 71814473 with best virtual screened compounds.

RESULT

MMP9 inhibitors show commendable affinity and good interaction profile of compound holding PubChem ID:102173753 over the most effective established inhibitor SB-3CT. The pharmacophore study of the best virtual screened compound reveals its high efficacy based on various interactions. The virtual screened compound's better affinity with the target MMP9 protein was deduced using toxicity and integration profile studies.

CONCLUSION

Based on the ADMET profile, the compound (PubChem ID: 102173753) could be a potent drug for MCL treatment. Similar to the established SB-3CT, the compound was non-toxic with LD50 values for both the compounds lying in the same range.

Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery

Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short period. The present review is an overview based on some applications of Machine Learning based tools, such as GOLD, Deep PVP, LIB SVM, etc. and the algorithms involved such as support vector machine (SVM), random forest (RF), decision tree and Artificial Neural Network (ANN), etc. at various stages of drug designing and development. These techniques can be employed in SNP discoveries, drug repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure- based Virtual Screening (SBVS), Lead identification, quantitative structure-activity relationship (QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance in indicating that the classification model will have great applications on human intestinal absorption (HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and RF models in identifying JFD00950 as a novel compound targeting against a colon cancer cell line, DLD-1, by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also used to predict flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus (DM), using ANN. Hence, in the era of big data, ML approaches have been evolved as a powerful and efficient way to deal with the huge amounts of generated data from modern drug discovery to model small-molecule drugs, gene biomarkers and identifying the novel drug targets for various diseases.

Most Promising Compounds for Treating COVID-19 and Recent Trends in Antimicrobial & Antifungal Agents

Multidrug resistance in microbes poses a major health crisis and demands for the discovery of novel antimicrobial agents. The recent pandemic of SARS-CoV-2 has raised a public health emergency in almost all the countries of the world. Unlike viruses, a bacterium plays a significant role in various environmental issues such as bioremediation. Furthermore, biosurfactants produced by various bacterial species have an edge over traditionally produced chemical surfactants for its biodegradability, low toxicity and better interfacial activity with various applications in agriculture and industry. This special issue focuses on the global perspective of drug discovery for various antimicrobial, antiviral, and antifungal agents for infectious diseases. The issue also emphasizes the ongoing developments and the role of microbes in environmental remediation. We wish the articles published in this issue will enhance the current understanding in microbiology among the readers, and serve as the "seed of an idea" for drug development for ongoing COVID-19 pandemic.

Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation

BACKGROUND

The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases have overwhelmed health and public health services. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have extended their major role in tracking disease patterns, and in identifying possible treatments.

OBJECTIVE

This study aims to identify potential COVID-19 protease inhibitors through shape-based Machine Learning assisted by Molecular Docking and Molecular Dynamics simulations.

METHODS

31 Repurposed compounds have been selected targeting the main coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from the 3D shape to the pharmacophoric features of their seed compound. Ligand-Receptor Docking was performed with Optimized Potential for Liquid Simulations (OPLS) algorithms to identify highaffinity compounds from the list of selected candidates for 6LU7, which were subjected to Molecular Dynamic Simulations followed by ADMET studies and other analyses.

RESULTS

Shape-based Machine learning reported remdesivir, valrubicin, aprepitant, and fulvestrant as the best therapeutic agents with the highest affinity for the target protein. Among the best shape-based compounds, a novel compound identified was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorv-EMBS'. Further, toxicity analysis showed nCorv-EMBS to be suitable for further consideration as the main protease inhibitor in COVID-19.

CONCLUSION

Effective ACE-II, GAK, AAK1, and protease 3C blockers can serve as a novel therapeutic approach to block the binding and attachment of the main COVID-19 protease (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 receptors in the lung. The novel compound nCorv- EMBS herein proposed stands as a promising inhibitor to be evaluated further for COVID-19 treatment.

Computer-Aided Structure Prediction of Bluetongue Virus Coat Protein VP2 Assisted by Optimized Potential for Liquid Simulations (OPLS)

Background:

The capsid coated protein of Bluetongue virus (BTV) VP2 is responsible for BTV transmission by the Culicoides vector to vertebrate hosts. Besides, VP2 is responsible for BTV entry into permissive cells and hence plays a major role in disease progression. However, its mechanism of action is still unknown.

Objective:

The present investigation aimed to predict the 3D structure of Viral Protein 2 of the bluetongue virus assisted by Optimized Potential for Liquid Simulations (OPLS), structure validation, and an active site prediction.

Methods:

The 3D structure of the VP2 protein was built using a Python-based Computational algorithm. The templates were identified using Smith waterman’s Local alignment. The VP2 protein structure validated using PROCHECK. Molecular Dynamics Simulation (MDS) studies were performed using an academic software Desmond, Schrodinger dynamics, for determining the stability of a model protein. The Ligand-Binding site was predicted by structure comparison using homology search and proteinprotein network analysis to reveal their stability and inhibition mechanism, followed by the active site identification.

Results:

The secondary structure of the VP2 reveals that the protein contains 220 alpha helix atoms, 40 310 helix, 151 beta sheets, 134 coils and 424 turns, whereas the 3D structure of Viral Protein 2 of BTV has been found to have 15774 total atoms in the structure. However, 961 amino acids were found in the final model. The dynamical cross-correlation matrix (DCCM) analysis tool identifies putative protein domains and also confirms the stability of the predicted model and their dynamical behavior difference with the correlative fluctuations in motion.

Conclusion:

The biological interpretation of the Viral Protein 2 was carried out. DCCM maps were calculated, using a different coordinate reference frame, through which, protein domain boundaries and protein domain residue constituents were identified. The obtained model shows good reliability. Moreover, we anticipated that this research should play a promising role in the identification of novel candidates with the target protein to inhibit their functional significance.