Inorganic nanoparticle-based treatment approaches for colorectal cancer: recent advancements and challenges.

Colorectal cancer, the third most prevalent cancer globally, contributes significantly to mortality rates, with over 1.9 million reported cases and nearly 935,000 fatalities annually. Surgical resection is a primary approach for localized colorectal tumors, with adjunct therapies like chemotherapy, radiotherapy, and targeted/immunotherapy considered depending on the tumor stage. However, despite preferences for targeted and immunotherapy post-surgery, chemotherapy remains commonly chosen due to its lower cost and high cancer-killing efficiency. Yet, chemotherapy faces issues such as tumor resistance and severe side effects. Nanotechnology has emerged in cancer therapy by alleviating the drawbacks of current treatment approaches. In the past few decades, inorganic nanoparticles have shown promise in combating colorectal cancer, offering advantages over conventional chemotherapy. Compared to organic nanoparticles, inorganic nanoparticles exhibit properties like photosensitivity, conductivity, magnetic allure, and thermal proficiency, allowing them to function as both drug carriers and therapeutic agents. Derived primarily from carbon, silica, metals, and metal oxides, they offer superior drug-loading capacity, heightened quantum yield, and participation in advanced photothermal and photodynamic therapies. This review provides a brief overview of the pathophysiology of colorectal cancer and the pivotal role of inorganic nanoparticles in photothermal therapy photodynamic therapy, and drug delivery. Additionally, it discusses numerous inorganic nanoparticles in colorectal cancer therapy based on recent literature.

An in silico approach to develop potential therapies against Middle East Respiratory Syndrome Coronavirus (MERS-CoV).

A deadly respiratory disease Middle East Respiratory Syndrome (MERS) is caused by a perilous virus known as MERS-CoV, which has a severe impact on human health. Currently, there is no approved vaccine, prophylaxis, or antiviral therapeutics for preventing MERS-CoV infection. Due to its inexorable and integral role in the maturation and replication of the MERS-CoV virus, the 3C-like protease is unavoidly a viable therapeutic target. In this study, 2369 phytoconstituents were enlisted from Japanese medicinal plants, and these compounds were screened against 3C-like protease to identify feasible inhibitors. The best three compounds were identified as Kihadanin B, Robustaflavone, and 3-beta-O- (trans-p-Coumaroyl) maslinic acid, with binding energies of -9.8, -9.4, and -9.2 kcal/mol, respectively. The top three potential candidates interacted with several active site residues in the targeted protein, including Cys145, Met168, Glu169, Ala171, and Gln192. The best three compounds were assessed by in silico technique to determine their drug-likeness properties, and they exhibited the least harmful features and the greatest drug-like qualities. Various descriptors, such as solvent-accessible surface area, root-mean-square fluctuation, root-mean-square deviation, hydrogen bond, and radius of gyration, validated the stability and firmness of the protein-ligand complexes throughout the 100ns molecular dynamics simulation. Moreover, the top three compounds exhibited better binding energy along with better stability and firmness than the inhibitor (Nafamostat), which was further confirmed by the binding free energy calculation. Therefore, this computational investigation could aid in the development of efficient therapeutics for life-threatening MERS-CoV infections.

RNA and Single-Stranded DNA Phages: Unveiling the Promise from the Underexplored World of Viruses.

RNA and single-stranded DNA (ssDNA) phages make up an understudied subset of bacteriophages that have been rapidly expanding in the last decade thanks to advancements in metaviromics. Since their discovery, applications of genetic engineering to ssDNA and RNA phages have revealed their immense potential for diverse applications in healthcare and biotechnology. In this review, we explore the past and present applications of this underexplored group of phages, particularly their current usage as therapeutic agents against multidrug-resistant bacteria. We also discuss engineering techniques such as recombinant expression, CRISPR/Cas-based genome editing, and synthetic rebooting of phage-like particles for their role in tailoring phages for disease treatment, imaging, biomaterial development, and delivery systems. Recent breakthroughs in RNA phage engineering techniques are especially highlighted. We conclude with a perspective on challenges and future prospects, emphasizing the untapped diversity of ssDNA and RNA phages and their potential to revolutionize biotechnology and medicine.

Biochemical and molecular docking-based strategies of Acalypha indica and Boerhavia diffusa extract by targeting bacterial strains and cancer proteins.

Antibiotic-resistant microbes have emerged around the world, presenting a risk to health. Plant-derived drugs have become a potential source for the production of antibiotic-resistant drugs and cancer therapies. In this study, we investigated the antibacterial, cytotoxic and antioxidant properties of Acalypha indica and Boerhavia diffusa, and conducted in silico molecular docking experiments against EGFR and VEGFR-2 proteins. The metabolic extract of A. indica inhibited Streptococcus iniae and Staphylococcus sciuri with inhibition zones of 21.66 ± 0.57 mm and 20.33 ± 0.57 mm, respectively. The B. diffusa leaf extract produced inhibition zones of 20.3333 ± 0.5773 mm and 20.33 ± 0.57 mm against Streptococcus iniae and Edwardsiella anguillarum, respectively. A. indica and B. diffusa extracts had toxicities of 162.01 µg/ml and 175.6 µg/ml, respectively. Moreover, B. diffusa (IC50 =154.42 µg/ml) leaf extract exhibited moderately higher antioxidant activity compared with the A. indica (IC50 = 218.97 µg/ml) leaf extract. Multiple interactions were observed at Leu694, Met769 and Leu820 sites for EGFR and at Asp1046 and Cys1045 sites for VEGFR during the molecular docking study. CID-235030, CID-70825 and CID-156619353 had binding energies of -7.6 kJ/mol, -7.5 kJ/mol and -7.6 kJ/mol, respectively, with EGFR protein. VEGFR-2 protein had docking energies of -7.5 kJ/mol, -7.6 kJ/mol and -7.3 kJ/mol, respectively, for CID-6420353, CID-156619353 and CID-70825 compounds. The MD simulation trajectories revealed the hit compound; CID-235030 and EGFR complex, CID-6420353 and VEGFR-2 exhibit stable profile in the root mean square deviation (RMSD), radius of gyration (Rg), solvent accessible surface area (SASA), hydrogen bond and root mean square fluctuation (RMSF) and the binding free energy by MM-PBSA method. This study indicates that methanol extracts of A. indica and B. diffusa may play a crucial role in developing antibiotic-resistant and cancer drugs.Communicated by Ramaswamy H. Sarma.

Screening of plasma IL-6 and IL-17 in Bangladeshi lung cancer patients.

Lung cancer is responsible for causing one of the highest numbers of cancer deaths. In Bangladesh, both men and women are affected by lung cancer, and environmental contaminants are believed to be one of the main risk factors apart from smoking. The diagnosis of lung cancer is difficult due to the delicate structure and complexity of the lungs. Diagnosis in later stages results in a poor prognosis of the disease. Tissue biopsy is the most reliable way of identifying lung cancer, but it is invasive and requires identification of the primary neoplasm within the lungs. As inflammation is involved in carcinogenesis, circulating levels of cytokines might be elevated in patients during the early stages of cancer. Increased IL-6 levels have been associated with the promotion of tumor growth, and IL-17 is believed to aid metastasis of lung cancer. In this study, the use of IL-6 and IL-17 was investigated as diagnostic markers for lung cancer. IL-6 and IL-17 levels were compared between 35 lung cancer patients and 19 healthy individuals. IL-6 levels were markedly elevated (7.417 pg/mL) in lung cancer cases compared to the controls (0.970 pg/mL), indicating a positive correlation (p < 0.05). IL-17 levels were only slightly higher in lung cancer patients (9.400 pg/mL) compared to healthy individuals (8.922 pg/mL). Both IL-6 and IL-17 levels were higher in patients with adenocarcinoma compared with other subtypes of lung cancer. Treatment with chemotherapy and radiotherapy did not significantly affect IL-6 levels. However, IL-17 levels were reduced due to cancer treatment. Further studies with larger sample sizes assessing the IL-6 and IL-17 in lung cancer patients are needed to establish the diagnostic role of the two cytokines.

Integrated gene expression profiling and functional enrichment analyses to discover biomarkers and pathways associated with Guillain-Barré syndrome and autism spectrum disorder to identify new therapeutic targets.

Guillain-Barré syndrome (GBS) is one of the most prominent and acute immune-mediated peripheral neuropathy, while autism spectrum disorders (ASD) are a group of heterogeneous neurodevelopmental disorders. The complete mechanism regarding the neuropathophysiology of these disorders is still ambiguous. Even after recent breakthroughs in molecular biology, the link between GBS and ASD remains a mystery. Therefore, we have implemented well-established bioinformatic techniques to identify potential biomarkers and drug candidates for GBS and ASD. 17 common differentially expressed genes (DEGs) were identified for these two disorders, which later guided the rest of the research. Common genes identified the protein-protein interaction (PPI) network and pathways associated with both disorders. Based on the PPI network, the constructed hub gene and module analysis network determined two common DEGs, namely CXCL9 and CXCL10, which are vital in predicting the top drug candidates. Furthermore, coregulatory networks of TF-gene and TF-miRNA were built to detect the regulatory biomolecules. Among drug candidates, imatinib had the highest docking and MM-GBSA score with the well-known chemokine receptor CXCR3 and remained stable during the 100 ns molecular dynamics simulation validated by the principal component analysis and the dynamic cross-correlation map. This study predicted the gene-based disease network for GBS and ASD and suggested prospective drug candidates. However, more in-depth research is required for clinical validation.Communicated by Ramaswamy H. Sarma.

17 common differentially expressed genes (DEGs) were identified from 693 DEGs of the GBS dataset (GSE72748) and 365 DEGs of the ASD dataset (GSE113834), which is the preliminary part of this investigation.From the PPI network analysis, a total of 10 hub genes were identified and two common DEGs named CXCL9 and CXCL10 were found in both the hub gene and essential module analysis.The identified leading pathways and GO pathways, TF-gene interaction, and TF-miRNAs network has made the process more relevant and appropriate for suggesting probable drug candidates.Among the drug candidates, imatinib was suggested as the main drug candidate due to its interaction with the hub gene CXCL9 and CXCL10 and lower p value than the other candidates. It showed the highest binding affinity score and remained stable with the CXCR3 chemokine receptor.

Molecular insights of anti-diabetic compounds and its hyaluronic acid conjugates against aldose reductase enzyme through molecular modeling and simulations study-a novel treatment option for inflammatory diabetes.

CONTEXT: Chronic inflammation is a risk factor for diabetes, but it can also be a complication of diabetes, leading to severe diabetes and causing many other clinical manifestations. Inflammation is a major emerging complication in both type I and type II diabetes, which causes increasing interest in targeting inflammation to improve and control diabetes. Diabetes with insulin resistance and impaired glucose utilization in humans and their underlying mechanism is not fully understood. But a growing understanding of the intricacy of the insulin signaling cascade in diabetic inflammatory cells reveals potential target genes and their proteins responsible for severe insulin resistance. With this baseline concept, the current project explores the binding affinities of the hyaluronic acid anti-diabetic compounds conjugates to such target proteins in diabetic inflammatory cells and their molecular geometries. A range of 48 anti-diabetic compounds was screened against aldose reductase binding pocket 3 protein target through in silico molecular docking, and results revealed that three compounds viz, metformin (CID:4091), phenformin (CID:8249), sitagliptin (CID:4,369,359), possess significant binding affinity out of 48 chosen drugs. Further, these three anti-diabetic compounds were conjugated with hyaluronic acid (HA), and their binding affinity and their molecular geometrics towards aldose reductase enzyme were screened compared with the free form of the drug. The molecular geometries of three shortlisted drugs (metformin, phenformin, sitagliptin) and their HA conjugates were also explored through density functional theory studies, and it proves their good molecular geometry towards pocket 3 of aldose reductase target. Further, MD simulation trajectories affirm that HA conjugates possess good binding affinity and simulation trajectories with protein target aldose reductase than a free form of the drug. Our current study unravels the new mechanism of drug targeting for diabetes through HA conjugation for inflammatory diabetes. HA conjugates act as novel drug candidates for treating inflammatory diabetes; however, it needs further human clinical trials. METHODS: For ligand structure, PubChem, ACD chem sketch, and online structure file generator platform are utilized for ligand preparation. Target protein aldose reductase obtained from protein database (PDB). For molecular docking analysis, AutoDock Vina (Version 4) was utilized. pKCSM online server used to predict ADMET properties of the above three shortlisted drugs from the docking study. Using mol-inspiration software (version 2011.06), three shortlisted compounds' bioactivity scores were predicted. DFT analysis for three shortlisted anti-diabetic drugs and their hyaluronic acid conjugates were calculated using a functional B3LYP set of Gaussian 09 software. Molecular dynamics simulation calculations for six chosen protein-ligand complexes were done through YASARA dynamics software and AMBER14 force field.

Plant-derived compounds effectively inhibit the main protease of SARS-CoV-2: An in silico approach.

The current coronavirus disease 2019 (COVID-19) pandemic, caused by the coronavirus 2 (SARS-CoV-2), involves severe acute respiratory syndrome and poses unprecedented challenges to global health. Structure-based drug design techniques have been developed targeting the main protease of the SARS-CoV-2, responsible for viral replication and transcription, to rapidly identify effective inhibitors and therapeutic targets. Herein, we constructed a phytochemical dataset of 1154 compounds using deep literature mining and explored their potential to bind with and inhibit the main protease of SARS-CoV-2. The three most effective phytochemicals Cosmosiine, Pelargonidin-3-O-glucoside, and Cleomiscosin A had binding energies of -8.4, -8.4, and -8.2 kcal/mol, respectively, in the docking analysis. These molecules could bind to Gln189, Glu166, Cys145, His41, and Met165 residues on the active site of the targeted protein, leading to specific inhibition. The pharmacological characteristics and toxicity of these compounds, examined using absorption, distribution, metabolism, excretion, and toxicity (ADMET) analyses, revealed no carcinogenicity or toxicity. Furthermore, the complexes were simulated with molecular dynamics for 100 ns to calculate the root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen profiles from the simulation trajectories. Our analysis validated the rigidity of the docked protein-ligand. Taken together, our computational study findings might help develop potential drugs to combat the main protease of the SARS-CoV-2 and help alleviate the severity of the pandemic.

Missense mutations in spike protein of SARS-CoV-2 delta variant contribute to the alteration in viral structure and interaction with hACE2 receptor.

INTRODUCTION: Many of the global pandemics threaten human existence over the decades among which coronavirus disease (COVID-19) is the newest exposure circulating worldwide. The RNA encoded severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is referred as the pivotal agent of this deadly disease that induces respiratory tract infection by interacting host ACE2 receptor with its spike glycoprotein. Rapidly evolving nature of this virus modified into new variants helps in perpetrating immune escape and protection against host defense mechanism. Consequently, a new isolate, delta variant originated from India is spreading perilously at a higher infection rate. METHODS: In this study, we focused to understand the conformational and functional significance of the missense mutations found in the spike glycoprotein of SARS-CoV-2 delta variant performing different computational analysis. RESULTS: From physiochemical analysis, we found that the acidic isoelectric point of the virus elevated to basic pH level due to the mutations. The targeted mutations were also found to change the interactive bonding pattern and conformational stability analyzed by the molecular dynamic's simulation. The molecular docking study also revealed that L452R and T478K mutations found in the RBD domain of delta variant spike protein contributed to alter interaction with the host ACE2 receptor. CONCLUSIONS: Overall, this study provided insightful evidence to understand the morphological and attributive impact of the mutations on SARS-CoV-2 delta variant.

Use of Next-Generation Sequencing for Identifying Mitochondrial Disorders.

Mitochondria are major contributors to ATP synthesis, generating more than 90% of the total cellular energy production through oxidative phosphorylation (OXPHOS): metabolite oxidation, such as the ß-oxidation of fatty acids, and the Krebs's cycle. OXPHOS inadequacy due to large genetic lesions in mitochondrial as well as nuclear genes and homo- or heteroplasmic point mutations in mitochondrially encoded genes is a characteristic of heterogeneous, maternally inherited genetic disorders known as mitochondrial disorders that affect multisystemic tissues and organs with high energy requirements, resulting in various signs and symptoms. Several traditional diagnostic approaches, including magnetic resonance imaging of the brain, cardiac testing, biochemical screening, variable heteroplasmy genetic testing, identifying clinical features, and skeletal muscle biopsies, are associated with increased risks, high costs, a high degree of false-positive or false-negative results, or a lack of precision, which limits their diagnostic abilities for mitochondrial disorders. Variable heteroplasmy levels, mtDNA depletion, and the identification of pathogenic variants can be detected through genetic sequencing, including the gold standard Sanger sequencing. However, sequencing can be time consuming, and Sanger sequencing can result in the missed recognition of larger structural variations such as CNVs or copy-number variations. Although each sequencing method has its own limitations, genetic sequencing can be an alternative to traditional diagnostic methods. The ever-growing roster of possible mutations has led to the development of next-generation sequencing (NGS). The enhancement of NGS methods can offer a precise diagnosis of the mitochondrial disorder within a short period at a reasonable expense for both research and clinical applications.

The Emergence of SARS-CoV-2 Variants With a Lower Antibody Response: A Genomic and Clinical Perspective.

The emergence of several novel SARS-CoV-2 variants regarded as variants of concern (VOCs) has exacerbated pathogenic and immunologic prominences, as well as reduced diagnostic sensitivity due to phenotype modification-capable mutations. Furthermore, latent and more virulent strains that have arisen as a result of unique mutations with increased evolutionary potential represent a threat to vaccine effectiveness in terms of incoming and existing variants. As a result, resisting natural immunity, which leads to higher reinfection rates, and avoiding vaccination-induced immunization, which leads to a lack of vaccine effectiveness, has become a crucial problem for public health around the world. This study attempts to review the genomic variation and pandemic impact of emerging variations of concern based on clinical characteristics management and immunization effectiveness. The goal of this study is to gain a better understanding of the link between genome level polymorphism, clinical symptom manifestation, and current vaccination in the instance of VOCs.

Rapid, Point-of-Care scFv-SERS Assay for Femtogram Level Detection of SARS-CoV-2.

Rapid, sensitive, on-site identification of SARS-CoV-2 infections is an important tool in the control and management of COVID-19. We have developed a surface-enhanced Raman scattering (SERS) immunoassay for highly sensitive detection of SARS-CoV-2. Single-chain Fv (scFv) recombinant antibody fragments that bind the SARS-CoV-2 spike protein were isolated by biopanning a human scFv library. ScFvs were conjugated to magnetic nanoparticles and SERS nanotags, followed by immunocomplex formation and detection of the SARS-CoV-2 spike protein with a limit of detection of 257 fg/mL in 30 min in viral transport medium. The assay also detected B.1.1.7 ("alpha"), B.1.351 ("beta"), and B.1.617.2 ("delta") spike proteins, while no cross-reactivity was observed with the common human coronavirus HKU1 spike protein. Inactivated whole SARS-CoV-2 virus was detected at 4.1 × 104 genomes/mL, which was 10-100-fold lower than virus loads typical of infectious individuals. The assay exhibited higher sensitivity for SARS-CoV-2 than commercial lateral flow assays, was compatible with viral transport media and saliva, enabled rapid pivoting to detect new virus variants, and facilitated highly sensitive, point-of-care diagnosis of COVID-19 in clinical and public health settings.

phytochemdb: a platform for virtual screening and computer-aided drug designing.

The phytochemicals of medicinal plants are regarded as a rich source of diverse chemical spaces that have been used as supplements and alternative medicines in the millennium. Even in this era of combinatorial chemical drugs, phytomedicines account for a large share of the statistics of newly approved drugs. In the field of computational aided and rational drug design, there is an urgent need to develop and build a useful phytochemical database management system with a user-friendly interface that allows proper data storage, retrieval and management. We showed 'phytochemdb', a manually managed database that compiles 525 plants and their corresponding 8093 phytochemicals, aiming to incorporate the activities of phytochemicals from medicinal plants. The database collects molecular formula, three-dimensional/two-dimensional structure, canonical SMILES, molecular weight, no. of heavy atoms, no. of aromatic heavy atoms, fraction Csp3, no. of rotatable bonds, no. of H-bond acceptors, no. of H-bond donors, molar refractivity, topological polar surface area, gastrointestinal absorption, Blood-Brain Barrier (BBB) permeant, P-gp substrate, CYP1A2 inhibitor, CYP2C19 inhibitor, CYP2C9 inhibitor, CYP2D6 inhibitor, CYP3A4 inhibitor, Log Kp, Ghose, Veber, Egan, Muegge, bioavailability scores, pan-assay interference compounds, Brenk, Leadlikeness, synthetic accessibility, iLOGP and Lipinski rule of five with the number of violations for each compound. It provides open contribution functions for the researchers who screen phytochemicals in the laboratory and have released their data. 'phytochemdb' is a comprehensive database that gathers most of the information about medicinal plants in one platform, which is considered to be very beneficial to the work of researchers on medicinal plants. 'phytochemdb' is available for free at https://phytochemdb.com/.

Phytochemical Compound Screening to Identify Novel Small Molecules against Dengue Virus: A Docking and Dynamics Study.

The spread of the Dengue virus over the world, as well as multiple outbreaks of different serotypes, has resulted in a large number of deaths and a medical emergency, as no viable medications to treat Dengue virus patients have yet been found. In this paper, we provide an in silico virtual screening and molecular dynamics-based analysis to uncover efficient Dengue infection inhibitors. Based on a Google search and literature mining, a large phytochemical library was generated and employed as ligand molecules. In this investigation, the protein target NS2B/NS3 from Dengue was employed, and around 27 compounds were evaluated in a docking study. Phellodendroside (-63 kcal/mole), quercimeritrin (-59.5 kcal/mole), and quercetin-7-O-rutinoside (-54.1 kcal/mole) were chosen based on their binding free energy in MM-GBSA. The tested compounds generated numerous interactions at Lys74, Asn152, and Gln167 residues in the active regions of NS2B/NS3, which is needed for the protein's inhibition. As a result, the stable mode of docked complexes is defined by various descriptors from molecular dynamics simulations, such as RMSD, SASA, Rg, RMSF, and hydrogen bond. The pharmacological properties of the compounds were also investigated, and no toxicity was found in computational ADMET properties calculations. As a result, this computational analysis may aid fellow researchers in developing innovative Dengue virus inhibitors.

Antiviral peptides against the main protease of SARS-CoV-2: A molecular docking and dynamics study.

The recent coronavirus outbreak has changed the world's economy and health sectors due to the high mortality and transmission rates. Because the development of new effective vaccines or treatments against the virus can take time, an urgent need exists for the rapid development and design of new drug candidates to combat this pathogen. Here, we obtained antiviral peptides obtained from the data repository of antimicrobial peptides (DRAMP) and screened their predicted tertiary structures for the ability to inhibit the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using multiple combinatorial docking programs, including PatchDock, FireDock, and ClusPro. The four best peptides, DRAMP00877, DRAMP02333, DRAMP02669, and DRAMP03804, had binding energies of -1125.3, -1084.5, -1005.2, and -924.2 Kcal/mol, respectively, as determined using ClusPro, and binding energies of -55.37, -50.96, -49.25, -54.81 Kcal/mol, respectively, as determined using FireDock, which were better binding energy values than observed for other peptide molecules. These peptides were found to bind with the active cavity of the SARS-CoV-2 main protease; at Glu166, Cys145, Asn142, Phe140, and Met165, in addition to the substrate-binding sites, Domain 2 and Domain 3, whereas fewer interactions were observed with Domain 1. The docking studies were further confirmed by a molecular dynamics simulation study, in which several descriptors, including the root-mean-square difference (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), radius of gyration (Rg), and hydrogen bond formation, confirmed the stable nature of the peptide-main protease complexes. Toxicity and allergenicity studies confirmed the non-allergenic nature of the peptides. This present study suggests that these identified antiviral peptide molecules might inhibit the main protease of SARS-CoV-2, although further wet-lab experiments remain necessary to verify these findings.

Molecular docking and dynamics study to explore phytochemical ligand molecules against the main protease of SARS-CoV-2 from extensive phytochemical datasets.

BACKGROUND: The high transmission and pathogenicity of SARS-CoV-2 has led to a pandemic that has halted the world's economy and health. The newly evolved strains and scarcity of vaccines has worsened the situation. The main protease (Mpro) of SARS-CoV-2 can act as a potential target due to its role in viral replication and conservation level. METHODS: In this study, we have enlisted more than 1100 phytochemicals from Asian plants based on deep literature mining. The compounds library was screened against the Mpro of SARS-CoV-2. RESULTS: The selected three ligands, Flemichin, Delta-Oleanolic acid, and Emodin 1-O-beta-D-glucoside had a binding energy of -8.9, -8.9, -8.7 KJ/mol respectively. The compounds bind to the active groove of the main protease at; Cys145, Glu166, His41, Met49, Pro168, Met165, Gln189. The multiple descriptors from the simulation study; root mean square deviation, root mean square fluctuation, radius of gyration, hydrogen bond, solvent accessible surface area confirms the stable nature of the protein-ligand complexes. Furthermore, post-md analysis confirms the rigidness in the docked poses over the simulation trajectories. CONCLUSIONS: Our combinatorial drug design approaches may help researchers to identify suitable drug candidates against SARS-CoV-2.

Designing a multi-epitope vaccine candidate to combat MERS-CoV by employing an immunoinformatics approach.

Currently, no approved vaccine is available against the Middle East respiratory syndrome coronavirus (MERS-CoV), which causes severe respiratory disease. The spike glycoprotein is typically considered a suitable target for MERS-CoV vaccine candidates. A computational strategy can be used to design an antigenic vaccine against a pathogen. Therefore, we used immunoinformatics and computational approaches to design a multi-epitope vaccine that targets the spike glycoprotein of MERS-CoV. After using numerous immunoinformatics tools and applying several immune filters, a poly-epitope vaccine was constructed comprising cytotoxic T-cell lymphocyte (CTL)-, helper T-cell lymphocyte (HTL)-, and interferon-gamma (IFN-γ)-inducing epitopes. In addition, various physicochemical, allergenic, and antigenic profiles were evaluated to confirm the immunogenicity and safety of the vaccine. Molecular interactions, binding affinities, and the thermodynamic stability of the vaccine were examined through molecular docking and dynamic simulation approaches, during which we identified a stable and strong interaction with Toll-like receptors (TLRs). In silico immune simulations were performed to assess the immune-response triggering capabilities of the vaccine. This computational analysis suggested that the proposed vaccine candidate would be structurally stable and capable of generating an effective immune response to combat viral infections; however, experimental evaluations remain necessary to verify the exact safety and immunogenicity profile of this vaccine.

Prospective Role of Peptide-Based Antiviral Therapy Against the Main Protease of SARS-CoV-2.

The recently emerged coronavirus (SARS-CoV-2) has created a crisis in world health, and economic sectors as an effective treatment or vaccine candidates are still developing. Besides, negative results in clinical trials and effective cheap solution against this deadly virus have brought new challenges. The viral protein, the main protease from SARS-CoV-2, can be effectively targeted due to its viral replication and pathogenesis role. In this study, we have enlisted 88 peptides from the AVPdb database. The peptide molecules were modeled to carry out the docking interactions. The four peptides molecules, P14, P39, P41, and P74, had more binding energy than the rest of the peptides in multiple docking programs. Interestingly, the active points of the main protease from SARS-CoV-2, Cys145, Leu141, Ser139, Phe140, Leu167, and Gln189, showed nonbonded interaction with the peptide molecules. The molecular dynamics simulation study was carried out for 200 ns to find out the docked complex's stability where their stability index was proved to be positive compared to the apo and control complex. Our computational works based on peptide molecules may aid the future development of therapeutic options against SARS-CoV-2.

Virtual screening and molecular dynamics simulation study of plant-derived compounds to identify potential inhibitors of main protease from SARS-CoV-2.

The new coronavirus (SARS-CoV-2) halts the world economy and caused unbearable medical emergency due to high transmission rate and also no effective vaccine and drugs has been developed which brought the world pandemic situations. The main protease (Mpro) of SARS-CoV-2 may act as an effective target for drug development due to the conservation level. Herein, we have employed a rigorous literature review pipeline to enlist 3063 compounds from more than 200 plants from the Asian region. Therefore, the virtual screening procedure helps us to shortlist the total compounds into 19 based on their better binding energy. Moreover, the Prime MM-GBSA procedure screened the compound dataset further where curcumin, gartanin and robinetin had a score of (-59.439, -52.421 and - 47.544) kcal/mol, respectively. The top three ligands based on binding energy and MM-GBSA scores have most of the binding in the catalytic groove Cys145, His41, Met165, required for the target protein inhibition. The molecular dynamics simulation study confirms the docked complex rigidity and stability by exploring root mean square deviations, root mean square fluctuations, solvent accessible surface area, radius of gyration and hydrogen bond analysis from simulation trajectories. The post-molecular dynamics analysis also confirms the interactions of the curcumin, gartanin and robinetin in the similar binding pockets. Our computational drug designing approach may contribute to the development of drugs against SARS-CoV-2.

Do knowledge and attitudes matter for preventive behavioral practices toward the COVID-19? A cross-sectional online survey among the adult population in Bangladesh.

The Government of Bangladesh has adopted several non-therapeutic measures to tackle the pandemic of SARS-CoV-2. However, the curve of COVID-19 positive cases has not significantly flattened yet, as the adoption of preventive measures by the general population is predominantly a behavioral phenomenon that is often influenced by people's knowledge and attitudes. This study aimed to assess the levels of knowledge, attitudes, and preventive behavioral practices toward COVID-19 and their interrelationships among the population of Bangladesh aged 18 years and above. This study adopted a web-based cross-sectional survey design and collected data from 1056 respondents using the online platform Google Form. We employed the independent sample t-test, one-way ANOVA, Pearson's product-moment correlation, and Spearman rank-order correlation to produce the bivariate level statistics. We also run multiple linear and logistic regression models to identify the factors affecting knowledge, attitudes, and preventive behavioral practices toward COVID-19. The respondents had an average knowledge score of 17.29 (Standard Deviation (SD) = 3.30). The average score for attitude scale toward COVID-19 was 13.6 (SD = 3.7). The respondents had excellent preventive behavioral practices toward COVID-19 (mean 7.7, SD = 0.72). However, this study found that knowledge and attitudes did not matter for preventive behavioral practices toward COVID-19. Instead, education appeared as a sole predictor for preventive behavioral practices toward COVID-19; that means preventive behavioral practices toward COVID-19 was lower among the less educated respondents. This study suggests increasing education as a long-term strategy and taking immediate action to increase knowledge and decrease negative attitudes toward COVID-19 through targeted health education initiatives as a short-term strategy.