Identification of key biomarkers and associated pathways of pancreatic cancer using integrated transcriptomic and gene network analysis.

Pancreatic cancer shows malignancy around the world standing in 4th position for causing death globally. This cancer is majorly divided into exocrine and neuroendocrine where exocrine pancreatic ductal adenocarcinoma is observed to be nearly 85% of cases. The lack of diagnosis of pancreatic cancer is considered to be one of the major drawbacks to the prognosis and treatment of pancreatic cancer patients. The survival rate after diagnosis is very low, due to the higher incidence of drug resistance to cancer which leads to an increase in the mortality rate. The transcriptome analysis for pancreatic cancer involves dataset collection from the ENA database, incorporating them into quality control analysis to the quantification process to get the summarized read counts present in collected samples and used for further differential gene expression analysis using the DESeq2 package. Additionally, explore the enriched pathways using GSEA software and represented them by utilizing the enrichment map finally, the gene network has been constructed by Cytoscape software. Furthermore, explored the hub genes that are present in the particular pathways and how they are interconnected from one pathway to another has been analyzed. Finally, we identified the CDKN1A, IL6, and MYC genes and their associated pathways can be better biomarker for the clinical processes to increase the survival rate of of pancreatic cancer.

Computational formulation study of insulin on biodegradable polymers.

Insulin administered orally has a limited therapeutic profile due to factors such as digestion enzymes, pH, temperature, and acidic conditions in the gastrointestinal tract. Type 1 diabetes patients are typically restricted to use intradermal insulin injections to manage their blood sugar levels as oral administration is not available. Research has shown that polymers could enhance the oral bioavailability of therapeutic biologicals, but traditional methods for developing suitable polymers are time-consuming and resource-intensive. Although computational formulations can be used to identify the best polymers more quickly. The true potential of biological formulations has not been fully explored due to a lack of benchmarking studies. Therefore, molecular modelling techniques were used as a case study in this research to determine which polymer is most compatible among five natural biodegradable polymers to address insulin stability. Specially, molecular dynamics simulations were conducted in order to compare insulin-polymer mixtures at different pH levels and temperatures. Hormonal peptide morphological properties were analyzed in body and storage conditions to assess stability of insulin with and without polymers. According to our computational simulations and energetic analyses, polymer cyclodextrin and chitosan maintain insulin stability the most effectively, while alginate and pectin are less effective relatively. Overall, this study contributes valuable insight into the role of biopolymers in stabilizing hormonal peptides in biological and storage conditions. A study such as this could have a significant impact on the development of new drug delivery systems and encourage scientists to utilize them in the formulation of biologicals.

The impact of cycleanine in cancer research: a computational study.

Cancer is imposing a global health burden because of the steady increase in new cases. Moreover, current anticancer therapeutics are associated with many drawbacks, mainly the emergence of resistance and the severe adverse effects. Therefore, there is a continuous need for developing new anticancer agents with novel mechanisms of action and lower side effects. Natural products have been a rich source of anticancer medication. Cycleanine, a natural product, was reported to exert an antiproliferative effect on ovarian cancer cells by causing apoptosis through activation of caspases 3/7 and cleavage of poly (ADP-ribose) polymerase to form poly (ADP-ribose) polymerase-1 (PARP1). It is well-established that PARP1 is associated with carcinogenesis, and different PARP1 inhibitors are approved as anticancer drugs. In this study, the cytotoxic activity of cycleanine was computationally investigated to determine whether it is a PARP1 inhibitor or a caspase activator. Molecular docking and molecular dynamics (MD) simulations were utilized for this purpose. The results showed that cycleanine has a good binding affinity to PARP1; moreover, MD simulation showed that it forms a stable complex with the enzyme. Consequently, the results showed that cycleanine is a potential inhibitor of the PARP1 enzyme.

Screening of potent phytochemical inhibitors against SARS-CoV-2 protease and its two Asian mutants.

BACKGROUND: COVID-19, declared a pandemic in March 2020 by the World Health Organization is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The virus has already killed more than 2.3 million people worldwide. OBJECT: The principal intent of this work was to investigate lead compounds by screening natural product library (NPASS) for possible treatment of COVID-19. METHODS: Pharmacophore features were used to screen a large database to get a small dataset for structure-based virtual screening of natural product compounds. In the structure-based screening, molecular docking was performed to find a potent inhibitor molecule against the main protease (Mpro) of SARS-CoV-2 (PDB ID: 6Y7M). The predicted lead compound was further subjected to Molecular Dynamics (MD) simulation to check the stability of the leads compound with the evolution of time. RESULTS: In pharmacophore-based virtual screening, 2,361 compounds were retained out of 30,927. In the structure-based screening, the lead compounds were filtered based on their docking scores. Among the 2,360 compounds, 12 lead compounds were selected based on their docking score. Kazinol T with NPASS ID: NPC474104 showed the highest docking score of -14.355 and passed criteria of Lipinski's drug-like parameters. Monitoring ADMET properties, Kazinol T showed its safety for consumption. Docking of Kazinol T with two Asian mutants (R60C and I152V) showed variations in binding and energy parameters. Normal mode analysis for ligand-bound and unbound form of protease along with its mutants, revealed displacement and correlation parameters for C-alpha atoms. MD simulation results showed that all ligand-protein complexes remained intact and stable in a dynamic environment with negative Gibbs free energy. CONCLUSIONS: The natural product Kazinol T was a predicted lead compound against the main protease of SARS-CoV-2 and will be the possible treatment for COVID-19.

Structure based design, synthesis, and biological evaluation of imidazole derivatives targeting dihydropteroate synthase enzyme.

In this study, we have designed and synthesized 2-((5-acetyl-1-(phenyl)-4-methyl-1H-imidazol-2-yl)thio)-N-(4-((benzyl)oxy)phenyl) acetamide derivatives. Antimicrobial activities of all the imidazole derivatives have been examined against Gram-positive and Gram-negative bacteria and results showed that the conjugates have appreciable antibacterial activity. Besides, several analogous were evaluated for their in vitro antiresistant bacterial strains such as Extended-spectrum beta-lactamases (ESBL), Vancomycin-resistant Enterococcus (VRE), and Methicillin-resistant Staphylococcus aureus (MRSA). The SAR revealed that the 12l compound resulted in potency against all bacterial strains as well as ESBL, VRE, and MRSA strains. Lipinski's rule of five, and ADME studies were preformed for all the synthesized compounds with Staphylococcus aureus dihydropteroate synthase (saDHPS) protein (PDB ID: 6CLV) and were found standard drug-likeness properties of conjugates. Moreover, the binding mode of the ligands with the protein study has been examined by molecular docking and results are quite promising. Besides, all the analogous were tested for their in vitro antituberculosis, antimalarial, and antioxidant activity.

Computational insights into fluconazole resistance by the suspected mutations in lanosterol 14α-demethylase (Erg11p) of Candida albicans.

Mutations in the ergosterol biosynthesis gene 11 (ERG11) of Candida albicans have been frequently reported in fluconazole-resistant clinical isolates. Exploring the mutations and their effect could provide new insights into the underlying mechanism of fluconazole resistance. Erg11p_Threonine285Alanine (Erg11p_THR285ALA), Erg11p_Leucine321Phenylalanine (Erg11p_LEU321PHE) and Erg11p_Serine457Proline (Erg11p_SER457PRO) are three fluconazole-resistant suspected mutations reported in clinical isolates of C. albicans. Therefore, our study aims to investigate the role of these suspected mutations in fluconazole resistance using in-silico methods. Molecular dynamics simulation (MDS) analysis of apo-protein for 25ns (nanosecond) showed that suspected mutant proteins underwent slight conformational changes in the tertiary structure. Molecular docking with fluconazole followed by binding free energy analysis showed reduced non-bonded interactions with loss of heme interaction and the least binding affinity for Erg11p_SER457PRO mutation. MDS of suspected mutant proteins-fluconazole complexes for 50ns revealed that Erg11p_SER457PRO and Erg11p_LEU321PHE have clear differences in the interaction pattern and loss or reduced heme interaction compared to wild type Erg11p-fluconazole complex. MDS and binding free energy analysis of Erg11p_SER457PRO-fluconazole complex showed the least binding similar to verified mutation Erg11p_TYR447HIS-fluconazole complex. Taken together, our study concludes that suspected mutation Erg11p_THR285ALA may not have any role whereas Erg11p_LEU321PHE could have a moderate role. However, Erg11p_SER457PRO mutation has a strong possibility to play an active role in fluconazole resistance of C. albicans.

Screening approaches against claudin-4 focusing on therapeutics through molecular docking and the analysis of their relative dynamics: a theoretical approach.

Claudin-4 (CLDN4) is a class of transmembrane protein in the family of tight junction (TJ) proteins. Overexpression of CLDN4 is reported in the case of ovarian cancer and epithelial malignancies. The current study is focused on the identification of lead compounds for CLDN4 adopting the structure-based drug design method. The Schrodinger glide is used as a molecular docking tool for the initial docking of CLDN4 with Asinex Database by performing high throughput virtual screening, top hits were identified. Then, compounds BDF 33196188 and BDE 30874918 were identified by molecular docking based on binding energy in the active site of CLDN4. Subsequently, critical residues were identified such as Asp146 and Arg158 with the least binding energy from Extra Precision method. Further, molecular dynamics simulations of claudin-4 protein were used for the optimization of best ligands with claudin-4 in a dynamic system. Molecular docking and molecular dynamics simulations predicted critically important residues ASP146 and ARG158 involved in claudin-4 binding. The hits retrieved from screening were docked into protein by relevant procedures including HTVS, SP, and XP. Finally, two molecules were identified as potential claudin-4 inhibitors. The two ligands BDF 33196188 and BDE 30874918 are suggested as potential inhibitors for CLDN4. In summary, our computational strategy established novel leads against CLDN4 from Asinex Database and recommended as anti-cancer agents.

Understanding the role of galectin inhibitors as potential candidates for SARS-CoV-2 spike protein: in silico studies.

The Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) has been rapidly transmitting and leaving its footprints across the globe. Stringent measures like complete lockdown and extensive testing have been employed by many countries to slow it down in its tracks until a viable treatment is found. Therefore, in the current scenario, prompt solutions need to be uncovered to tackle the virus. In the present study, 330 galectin inhibitors were tested against SARS-CoV-2 spike (S) protein with the aid of molecular docking and molecular dynamics. Finally, the binding free energy and contributing energies were calculated for 2 top scoring ligands by using MM-GBSA method. Many of the galectin inhibitors displayed high binding score against the S protein. They were found to bind to the site of contact of S protein to ACE2. Thus, they show promise of disrupting the ACE2-S protein binding and prevent the virus from invading the host cell. Among the ligands screened, TD-139, a molecule currently in Phase IIb clinical trials, was found to be a potential hit. The present study paves the way for in vitro and in vivo testing of galectin inhibitors against SARS-CoV-2. In addition, it warrants a swift examination of TD-139 for treating COVID-19.

Potent and highly selective dual-targeting monoamine oxidase-B inhibitors: Fluorinated chalcones of morpholine versus imidazole.

Two series of fluorinated chalcones containing morpholine and imidazole-based compounds (f1-f8) were synthesized and evaluated for recombinant human monoamine oxidase (MAO)-A and -B as well as acetylcholinesterase inhibitory activities. Our results indicate that morpholine containing chalcones are highly selective MAO-B inhibitors having reversibility properties. All the imidazole-based fluorinated chalcones showed weak MAO inhibitions in both isoforms. Among the tested compounds, (2E)-3-(3-fluorophenyl)-1-[4-(morpholin-4-yl)phenyl]prop-2-en-1-one (f2) showed potent inhibitory activity for recombinant human MAO-B (IC50 = 0.087 µM) with a high selectivity index (SI) of 517.2. In the recovery experiments using dialysis, the residual activity of MAO-B inhibited by f2 was close to that with the reversible reference inhibitor. Inhibition assays revealed that the Ki values of f1 and f2 for MAO-B were 0.027 and 0.020 µM, respectively, with competitive patterns. All the morpholine-based compounds (f1-f4) showed moderate inhibition toward acetylcholinesterase with IC50 values ranging between 24 and 54 µM. All morpholine-containing compounds exhibit good blood-brain barrier permeation in the PAMPA method. The rational approach regarding the highly selective MAO-B inhibitor f2 was further ascertained by induced fit docking and molecular dynamics simulation studies.

Virtual screening of National Cancer Institute database for claudin-4 inhibitors: Synthesis, biological evaluation, and molecular dynamics studies.

Claudin-4 (CLDN4) is a vital member of tight-junction proteins that is often overexpressed in cancer and other malignancies. The three-dimensional structure of human CLDN4 was constructed based on homology modeling approach. A total of 265 242 molecules from the National Cancer Institute (NCI) database has been utilized as a dataset for this study. In the present work, structure-based virtual screening is performed with the NCI database using Glide. By molecular docking, 10 candidate molecules with high scoring functions, which binds to the active site of CLDN4 were identified. Subsequently, molecular dynamics simulations of membrane protein were used for optimization of the top-three lead compounds (NCI110039, NCI344682, and NCI661251) with CLDN4 in a dynamic system. The lead molecule from NCI database NCI11039 (purpurogallin carboxylic acid) was synthesized and cytotoxic properties were evaluated with A549, MCF7 cell lines. Our docking and dynamics simulations predicted that ARG31, ASN142, ASP146, and ARG158 as critically important residues involved in the CLDN4 activity. Finally, three lead candidates from the NCI database were identified as potent CLDN4 inhibitors. Cytotoxicity assays had proved that purpurogallin carboxylic acid had an inhibitory effect towards breast (MCF7) and lung (A549) cancer cell lines. Computational insights and in vitro (cytotoxicity) studies reported in this study are expected to be helpful for the development of novel anticancer agents.

Pharmacophore feature prediction and molecular docking approach to identify novel anti-HCV protease inhibitors.

Discovering a potential drug for HCV treatment is a challenging task in the field of drug research. This study initiates with computational screening and modeling of promising ligand molecules. The foremost modeling method involves the identification of novel compound and its molecular interaction based on pharmacophore features. A total of 197 HCV compounds for NS3/4A protein target were screened for our study. The pharmacophore models were generated using PHASE module implemented in Schrodinger suite. The pharmacophore features include one hydrogen bond acceptor, one hydrogen bond donor, and three hydrophobic sites. As a result, based on mentioned hypothesis the model ADHHH.159 corresponds to the CID 59533233. Furthermore, docking was performed using maestro for all the 197 compounds. Among these, the CID 59533313 and 59533233 possess the best binding energy of -11.75 and -10.40 kcal/mol, respectively. The interactions studies indicated that the CID complexed with the NS3/4A protein possess better binding affinity with the other compounds. Further the compounds were subjected to calculate the ADME properties. Therefore, it can be concluded that these two compounds could be a potential alternative drug for the development of HCV.