In silico formulation of a next-generation multiepitope vaccine for use as a prophylactic candidate against Crimean-Congo hemorrhagic fever.

BACKGROUND: Crimean-Congo hemorrhagic fever (CCHF) is a widespread disease transmitted to humans and livestock animals through the bite of infected ticks or close contact with infected persons' blood, organs, or other bodily fluids. The virus is responsible for severe viral hemorrhagic fever outbreaks, with a case fatality rate of up to 40%. Despite having the highest fatality rate of the virus, a suitable treatment option or vaccination has not been developed yet. Therefore, this study aimed to formulate a multiepitope vaccine against CCHF through computational vaccine design approaches. METHODS: The glycoprotein, nucleoprotein, and RNA-dependent RNA polymerase of CCHF were utilized to determine immunodominant T- and B-cell epitopes. Subsequently, an integrative computational vaccinology approach was used to formulate a multi-epitopes vaccine candidate against the virus. RESULTS: After rigorous assessment, a multiepitope vaccine was constructed, which was antigenic, immunogenic, and non-allergenic with desired physicochemical properties. Molecular dynamics (MD) simulations of the vaccine-receptor complex show strong stability of the vaccine candidates to the targeted immune receptor. Additionally, the immune simulation of the vaccine candidates found that the vaccine could trigger real-life-like immune responses upon administration to humans. CONCLUSIONS: Finally, we concluded that the formulated multiepitope vaccine candidates would provide excellent prophylactic properties against CCHF.

Immune epitopes identification and designing of a multi-epitope vaccine against bovine leukemia virus: a molecular dynamics and immune simulation approaches.

BACKGROUND: Bovine leukemia virus (BLV) is an oncogenic delta-retrovirus causing bovine leucosis. Studies on BLV have shown the association with human breast cancer. However, the exact molecular mechanism is neither known nor their appropriate preventative measure to halt the disease initiation and progression. In this study, we designed a multi-epitope vaccine against BLV using a computational analyses. METHODS: Following a rigorous assessment, the vaccine was constructed using the T-cell epitopes from each BLV-derived protein with suitable adjuvant and linkers. Both physicochemistry and immunogenic potency as well as the safeness of the vaccine candidate were assessed. Population coverage was done to evaluate the vaccine probable efficiency in eliciting the immune response worldwide. After homology modeling, the three-dimensional structure was refined and validated to determine the quality of the designed vaccine. The vaccine protein was then subjected to molecular docking with Toll-like receptor 3 (TLR3) to evaluate the binding efficiency followed by dynamic simulation for stable interaction. RESULTS: Our vaccine construct has the potential immune response and good physicochemical properties. The vaccine is antigenic and immunogenic, and has no allergenic or toxic effect on the human body. This novel vaccine contains a significant interactions and binding affinity with the TLR3 receptor. CONCLUSIONS: The proposed vaccine candidate would be structurally stable and capable of generating an effective immune response to combat BLV infections. However, experimental evaluations are essential to validate the exact safety and immunogenic profiling of this vaccine.

Designing a multi-epitope vaccine against SARS-CoV-2: an immunoinformatics approach.

Ongoing COVID-19 outbreak has raised a drastic challenge to global public health security. Most of the patients with COVID-19 suffer from mild flu-like illnesses such as cold and fever; however, few percentages of the patients progress from severe illness to death, mostly in an immunocompromised individual. The causative agent of COVID-19 is an RNA virus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite these debilitating conditions, no medication to stop the disease progression or vaccination is available till now. Therefore, we aimed to formulate a multi-epitope vaccine against SARS-CoV-2 by utilizing an immunoinformatics approach. For this purpose, we used the SARS-CoV-2 spike glycoprotein to determine the immunodominant T- and B-cell epitopes. After rigorous assessment, we designed a vaccine construct using four potential epitopes from each of the three epitope classes such as cytotoxic T-lymphocytes, helper T-lymphocyte, and linear B-lymphocyte epitopes. The designed vaccine was antigenic, immunogenic, and non-allergenic with suitable physicochemical properties and has higher solubility. More importantly, the predicted vaccine structure was similar to the native protein. Further investigations indicated a strong and stable binding interaction between the vaccine and the toll-like receptor (TLR4). Strong binding stability and structural compactness were also evident in molecular dynamics simulation. Furthermore, the computer-generated immune simulation showed that the vaccine could trigger real-life-like immune responses upon administration into humans. Finally, codon optimization based on Escherichia coli K12 resulted in optimal GC content and higher CAI value followed by incorporating it into the cloning vector pET28+(a). Overall, these results suggest that the designed peptide vaccine can serve as an excellent prophylactic candidate against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Inhibition of biofilm formation, quorum sensing and other virulence factors in Pseudomonas aeruginosa by polyphenols of Gynura procumbens leaves.

Quorum sensing (QS) enables virulence factors in bacteria for biofilm formation and pathogenic invasion. Therefore, quorum quenching (QQ), disruption of QS circuit, becomes an alternative antimicrobial therapy. In this study, leaf extract of Gynura procumbens (GP) was used to inhibit biofilm and virulent factors in Pseudomonas aeruginosa. The extract inhibited the biofilm production (p ≤ 0.05) in P. aeruginosa strains MZ2F and MZ4A. The minimum biofilm eradication concentration (MBEC) was recorded at 250 and 500 µg/ml while total activity was found at 288 and 144 ml/g, respectively. Moreover, a significant reduction of virulence factors (p ≤ 0.05) at sub-MBEC without affecting the growth implies the QQ action of the extract. The bioactive fractions were rich in polyphenols and tentatively identified as quercetin and myricetin (Rf=0.53-0.60). Furthermore, we employed computational methods to validate our findings and their interactions with QS receptors (LasR and RhlR). Interestingly, docking studies have also shown that quercetin and myricetin are the promising anti-QS agents out of 31 GP compounds. Notably, their binding affinity ranged between -9.77 and -10.52 kcal/mol for both QS receptors, with controls ranging from -5.40 to -8.97 kcal/mol. Besides, ΔG of quercetin and myricetin with LasR was -71.56 and -74.88 kcal/mol, respectively. Moreover, compounds were suitable drug candidates with stable binding interactions. Therefore, the anti-QS activity of GP leaves and the identified polyphenols can be used in developing QQ-based therapeutics. Communicated by Ramaswamy H. Sarma.

Spike protein recognizer receptor ACE2 targeted identification of potential natural antiviral drug candidates against SARS-CoV-2.

Angiotensin-converting enzyme 2 (ACE2), also known as peptidyl-dipeptidase A, belongs to the dipeptidyl carboxydipeptidases family has emerged as a potential antiviral drug target against SARS-CoV-2. Most of the ACE2 inhibitors discovered till now are chemical synthesis; suffer from many limitations related to stability and adverse side effects. However, natural, and selective ACE2 inhibitors that possess strong stability and low side effects can be replaced instead of those chemicals' inhibitors. To envisage structurally diverse natural entities as an ACE2 inhibitor with better efficacy, a 3D structure-based-pharmacophore model (SBPM) has been developed and validated by 20 known selective inhibitors with their correspondence 1166 decoy compounds. The validated SBPM has excellent goodness of hit score and good predictive ability, which has been appointed as a query model for further screening of 11,295 natural compounds. The resultant 23 hits compounds with pharmacophore fit score 75.31 to 78.81 were optimized using in-silico ADMET and molecular docking analysis. Four potential natural inhibitory molecules namely D-DOPA (Amb17613565), L-Saccharopine (Amb6600091), D-Phenylalanine (Amb3940754), and L-Mimosine (Amb21855906) have been selected based on their binding affinity (-7.5, -7.1, -7.1, and -7.0 kcal/mol), respectively. Moreover, 250 ns molecular dynamics (MD) simulations confirmed the structural stability of the ligands within the protein. Additionally, MM/GBSA approach also used to support the stability of molecules to the binding site of the protein that also confirm the stability of the selected four natural compounds. The virtual screening strategy used in this study demonstrated four natural compounds that can be utilized for designing a future class of potential natural ACE2 inhibitor that will block the spike (S) protein dependent entry of SARS-CoV-2 into the host cell.

Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes.

The ongoing COVID-19 outbreak, caused by SARS-CoV-2, has posed a massive threat to global public health, especially to people with underlying health conditions. Type 2 diabetes (T2D) is lethal comorbidity of COVID-19. However, its pathogenetic link remains unclear. This research aims to determine the genetic factors and processes contributing to the synergistic severity of SARS-CoV-2 infection among T2D patients through bioinformatics approaches. We analyzed two sets of transcriptomic data of SARS-CoV-2 infection obtained from lung epithelium cells and PBMCs, and two sets of T2D data from pancreatic islet cells and PBMCs to identify the associated differentially expressed genes (DEGs) followed by their functional enrichment analyses in terms of protein-protein interaction (PPI) to detect hub-proteins and associated comorbidities, transcription factors (TFs), microRNAs (miRNAs) as well as the potential drug candidates. In PPI analysis, four potential hub-proteins (i.e., BIRC3, C3, MME, and IL1B) were identified among 25 DEGs shared between the disease pair. Enrichment analyses using the mutually overlapped DEGs revealed the most prevalent GO and cell signalling pathways, including TNF signalling, cytokine-cytokine receptor interaction, and IL-17 signalling, which are related to cytokine activities. Furthermore, as significant TFs, we identified IRF1, KLF11, FOSL1, and CREB3L1 while miRNAs including miR-1-3p, 34a-5p, 16-5p, 155-5p, 20a-5p, and let-7b-5p were found to be noteworthy. The findings illustrated the significant association between COVID-19 and T2D at the molecular level. These genetic determinants can further be explored for their specific roles in disease progression and therapeutic intervention, while significant pathways can also be studied as molecular checkpoints. Finally, the identified drug candidates may be evaluated for their potency to minimize the severity of COVID-19 patients with pre-existing T2D.

Transcriptomic studies revealed pathophysiological impact of COVID-19 to predominant health conditions.

Despite the association of prevalent health conditions with coronavirus disease 2019 (COVID-19) severity, the disease-modifying biomolecules and their pathogenetic mechanisms remain unclear. This study aimed to understand the influences of COVID-19 on different comorbidities and vice versa through network-based gene expression analyses. Using the shared dysregulated genes, we identified key genetic determinants and signaling pathways that may involve in their shared pathogenesis. The COVID-19 showed significant upregulation of 93 genes and downregulation of 15 genes. Interestingly, it shares 28, 17, 6 and 7 genes with diabetes mellitus (DM), lung cancer (LC), myocardial infarction and hypertension, respectively. Importantly, COVID-19 shared three upregulated genes (i.e. MX2, IRF7 and ADAM8) with DM and LC. Conversely, downregulation of two genes (i.e. PPARGC1A and METTL7A) was found in COVID-19 and LC. Besides, most of the shared pathways were related to inflammatory responses. Furthermore, we identified six potential biomarkers and several important regulatory factors, e.g. transcription factors and microRNAs, while notable drug candidates included captopril, rilonacept and canakinumab. Moreover, prognostic analysis suggests concomitant COVID-19 may result in poor outcome of LC patients. This study provides the molecular basis and routes of the COVID-19 progression due to comorbidities. We believe these findings might be useful to further understand the intricate association of these diseases as well as for the therapeutic development.

A Non-Linear Biostatistical Graphical Modeling of Preventive Actions and Healthcare Factors in Controlling COVID-19 Pandemic.

BACKGROUND: With the insurgence of the COVID-19 pandemic, many people died in the past several months, and the situation is ongoing with increasing health, social, and economic panic and vulnerability. As most of the countries relying on different preventive actions to control the outcomes of COVID-19, it is necessary to boost the knowledge about the effectiveness of such actions so that the policymakers take their country-based appropriate actions. This study generates evidence of taking the most impactful actions to combat COVID-19. OBJECTIVE: In order to generate community-based scientific evidence, this study analyzed the outcome of COVID-19 in response to different control measures, healthcare facilities, life expectancy, and prevalent diseases. METHODS: It used more than a hundred countries' data collected from different databases. We performed a comparative graphical analysis with non-linear correlation estimation using R. RESULTS: The reduction of COVID-19 cases is strongly correlated with the earliness of preventive initiation. The apathy of taking nationwide immediate precaution measures has been identified as one of the critical reasons to make the circumstances worse. There is significant non-linear relationship between COVID-19 case fatality and number of physicians (NCC = 0.22; p-value ≤ 0.001), nurses and midwives (NCC = 0.17; p-value ≤ 0.001), hospital beds (NCC = 0.20; p-value ≤ 0.001), life expectancy of both sexes (NCC = 0.22; p-value ≤ 0.001), life expectancy of female (NCC = 0.27; p-value ≤ 0.001), and life expectancy of male (NCC = 0.19; p-value ≤ 0.001). COVID-19 deaths were found to be reduced with increased medical personnel and hospital beds. Interestingly, no association between the comorbidities and severity of COVID-19 was found excluding asthma, cancer, Alzheimer's, and smoking. CONCLUSIONS: Enhancing healthcare facilities and early imposing the control measures could be valuable to prevent the COVID-19 pandemic. No association between COVID-19 and other comorbidities warranted further investigation at the pathobiological level.

A molecular modelling approach for identifying antiviral selenium-containing heterocyclic compounds that inhibit the main protease of SARS-CoV-2: an in silico investigation.

Coronavirus disease 2019 (COVID-19), an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been declared a global pandemic by the World Health Organization, and the situation worsens daily, associated with acute increases in case fatality rates. The main protease (Mpro) enzyme produced by SARS-CoV-2 was recently demonstrated to be responsible for not only viral reproduction but also impeding host immune responses. The element selenium (Se) plays a vital role in immune functions, both directly and indirectly. Thus, we hypothesised that Se-containing heterocyclic compounds might curb the activity of SARS-CoV-2 Mpro. We performed a molecular docking analysis and found that several of the selected selenocompounds showed potential binding affinities for SARS-CoV-2 Mpro, especially ethaselen (49), which exhibited a docking score of -6.7 kcal/mol compared with the -6.5 kcal/mol score for GC376 (positive control). Drug-likeness calculations suggested that these compounds are biologically active and possess the characteristics of ideal drug candidates. Based on the binding affinity and drug-likeness results, we selected the 16 most effective selenocompounds as potential anti-COVID-19 drug candidates. We also validated the structural integrity and stability of the drug candidate through molecular dynamics simulation. Using further in vitro and in vivo experiments, we believe that the targeted compound identified in this study (ethaselen) could pave the way for the development of prospective drugs to combat SARS-CoV-2 infections and trigger specific host immune responses.

Computational formulation and immune dynamics of a multi-peptide vaccine candidate against Crimean-Congo hemorrhagic fever virus.

The sole objective of this research is to devise an epitope-based vaccine candidate as prophylaxis for the Crimean-Congo hemorrhagic fever virus (CCHFV) using the knowledge of immunoinformatics and structural biology. Importantly, CCHFV outbreaks have increased in several countries resulting in increased mortality up to 40% due to the lack of prospective medication and an efficient vaccine. In this study, we have used several immunoinformatic tools and servers to anticipate potent B-cell and T-cell epitopes from the CCHFV glycoprotein with the highest antigenicity. After a comprehensive evaluation, a vaccine candidate was designed using 6 CD8+, 3 CD4+, and 7 B-cell epitopes with appropriate linkers. To enhance the vaccine's efficiency, we added Mycobacterium tuberculosis lipoprotein LprG (Rv1411c) to the vaccine as an adjuvant. The final construct was composed of a total of 468 amino acid residues. The epitope included in the construct showed 98% worldwide population coverage. Importantly, the construct appeared as antigenic, immunogenic, soluble, and non-allergenic in nature. To explore further, we modelled the three-dimensional (3D) structure of the constructed vaccine. Our chimeric vaccine showed stable and strong interactions for toll-like receptor 2 (TLR2) found on the cell surface. Moreover, the dynamics simulation of immune response showed elevated levels of cellular immune activity and faster clearance of antigen from the body upon repetitive exposure. Finally, the optimized codon (CAI≈1) ensured the marked translation efficiency of the vaccine protein in E. coli strain K12 bacterium followed by the insertion of construct DNA into the cloning vector pET28a (+). We believe that the designed vaccine chimera could be useful in vaccine development to fight CCHFV outbreaks.

Computational discovery of plant-based inhibitors against human carbonic anhydrase IX and molecular dynamics simulation.

Carbonic anhydrase IX (hCAIX) is a membrane-spanning metalloenzyme, encoded by CA9 gene, which can lead to various carcinomas if upregulated. Due to its overexpression in many cancer tissues, hCAIX has become a promising target for developing anticancer therapeutics. Furthermore, several classes of small-molecules have shown to inhibit the hCAIX expression. In this study, therefore, we screened (n = 42) plant-derived compounds to identify the most potent hCAIX inhibitors and to understand their interactions with hCAIX and drug candidacy through in silico approaches. Among all, only three compounds (i.e. fraxoside, scopolin, and xanthone,) provided higher binding affinity toward hCAIX protein as compared to the native ligand. In standard docking, scopolin showed -4.97 kcal/mol of binding energy with hCAIX while control ligand provided -4.45 kcal/mol. In precise docking, the highest binding affinity was found for fraxoside (-7.67 kcal/mol) as compared to -3.04 kcal/mol of the control. The Gibbs free energy (ΔG) of these potent leads was also consistent and in support of the docking studies. The binding interactions were also found to be stable in dynamics simulation. Furthermore, analysis of protein-protein interactions and co-expression revealed the possible association of CA9 gene with other tumorous genes, especially angiogenesis factor HIF1A which will most likely be affected by the identified inhibitors. With further experimental validation, therefore, these potential inhibitors could be effective against hCAIX protein, thereby, paving the way for prospective anticancer therapeutics.Communicated by Ramaswamy H. Sarma.

Pathogenetic profiling of COVID-19 and SARS-like viruses.

The novel coronavirus (2019-nCoV) has recently emerged, causing COVID-19 outbreaks and significant societal/global disruption. Importantly, COVID-19 infection resembles SARS-like complications. However, the lack of knowledge about the underlying genetic mechanisms of COVID-19 warrants the development of prospective control measures. In this study, we employed whole-genome alignment and digital DNA-DNA hybridization analyses to assess genomic linkage between 2019-nCoV and other coronaviruses. To understand the pathogenetic behavior of 2019-nCoV, we compared gene expression datasets of viral infections closest to 2019-nCoV with four COVID-19 clinical presentations followed by functional enrichment of shared dysregulated genes. Potential chemical antagonists were also identified using protein-chemical interaction analysis. Based on phylogram analysis, the 2019-nCoV was found genetically closest to SARS-CoVs. In addition, we identified 562 upregulated and 738 downregulated genes (adj. P ≤ 0.05) with SARS-CoV infection. Among the dysregulated genes, SARS-CoV shared ≤19 upregulated and ≤22 downregulated genes with each of different COVID-19 complications. Notably, upregulation of BCL6 and PFKFB3 genes was common to SARS-CoV, pneumonia and severe acute respiratory syndrome, while they shared CRIP2, NSG1 and TNFRSF21 genes in downregulation. Besides, 14 genes were common to different SARS-CoV comorbidities that might influence COVID-19 disease. We also observed similarities in pathways that can lead to COVID-19 and SARS-CoV diseases. Finally, protein-chemical interactions suggest cyclosporine, resveratrol and quercetin as promising drug candidates against COVID-19 as well as other SARS-like viral infections. The pathogenetic analyses, along with identified biomarkers, signaling pathways and chemical antagonists, could prove useful for novel drug development in the fight against the current global 2019-nCoV pandemic.

A comprehensive screening of the whole proteome of hantavirus and designing a multi-epitope subunit vaccine for cross-protection against hantavirus: Structural vaccinology and immunoinformatics study.

Hantaviruses are an emerging zoonotic group of rodent-borne viruses that are having serious implications on global public health due to the increase in outbreaks. Since there is no permanent cure, there is increasing interest in developing a vaccine against the hantavirus. This research aimed to design a robust cross-protective subunit vaccine using a novel immunoinformatics approach. After careful evaluation, the best predicted cytotoxic & helper T-cell and B-cell epitopes from nucleocapsid proteins, glycoproteins, RdRp proteins, and non-structural proteins were considered as potential vaccine candidates. Among the four generated vaccine models with different adjuvant, the model with toll-like receptor-4 (TLR-4) agonist adjuvant was selected because of its high antigenicity, non-allergenicity, and structural quality. The selected model was 654 amino acids long and had a molecular weight of 70.5 kDa, which characterizes the construct as a good antigenic vaccine candidate. The prediction of the conformational B-lymphocyte (CBL) epitope secured its ability to induce the humoral response. Thereafter, disulfide engineering improved vaccine stability. Afterwards, the molecular docking confirmed a good binding affinity of -1292 kj/mol with considered immune receptor TLR-4 and the dynamics simulation showed high stability of the vaccine-receptor complex. Later, the in silico cloning confirmed the better expression of the constructed vaccine protein in E. coli K12. Finally, in in silico immune simulation, significantly high levels of immunoglobulin M (IgM), immunoglobulin G1 (IgG1), cytotoxic & helper T lymphocyte (CTL & HTL) populations, and numerous cytokines such as interferon-γ (IFN-γ), interleukin-2 (IL-2) etc. were found as coherence with actual immune response and also showed faster antigen clearance for repeated exposures. Nonetheless, experimental validation can demonstrate the safety and cross-protective ability of the proposed vaccine to fight against hantavirus infection.

CRISPR-Cas9: A Promising Genome Editing Therapeutic Tool for Alzheimer's Disease-A Narrative Review.

Alzheimer's disease (AD) is a chronic and irreversible neurodegenerative disorder characterized by cognitive deficiency and development of amyloid-ß (Aß) plaques and neurofibrillary tangles, comprising hyperphosphorylated tau. The number of patients with AD is alarmingly increasing worldwide; currently, at least 50 million people are thought to be living with AD. The mutations or alterations in amyloid-ß precursor protein (APP), presenilin-1 (PSEN1), or presenilin-2 (PSEN2) genes are known to be associated with the pathophysiology of AD. Effective medication for AD is still elusive and many gene-targeted clinical trials have failed to meet the expected efficiency standards. The genome editing tool clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 has been emerging as a powerful technology to correct anomalous genetic functions and is now widely applied to the study of AD. This simple yet powerful tool for editing genes showed the huge potential to correct the unwanted mutations in AD-associated genes such as APP, PSEN1, and PSEN2. So, it has opened a new door for the development of empirical AD models, diagnostic approaches, and therapeutic lines in studying the complexity of the nervous system ranging from different cell types (in vitro) to animals (in vivo). This review was undertaken to study the related mechanisms and likely applications of CRISPR-Cas9 as an effective therapeutic tool in treating AD.

Computational assessment of MCM2 transcriptional expression and identification of the prognostic biomarker for human breast cancer.

Minichromosome maintenance protein 2 (MCM2) is a highly conserved protein from the MCM protein family that plays an important role in eukaryotic DNA replication as well as in cell cycle progression. In addition, it maintains the ploidy level consistency in eukaryotic cells, hence, mutations or alteration of this protein could result in the disintegration of the fine-tuned molecular machinery that can lead to uncontrolled cell proliferation. Moreover, MCM2 has been found to be an important marker for progression and prognosis in different cancers. Therefore, we aimed to analyze the MCM2 expression and the associated outcome in breast cancer (BC) patients based on the publicly available online databases. In this study, server-based gene expression analyses indicate the upregulation of MCM2 (p < 10-6; fold change>2.0) in various BC subtypes as compared to the respective normal tissues. Besides, the evaluation of histological sections from healthy and cancer tissues showed strong staining signals indicating higher expression of MCM2 protein. The overexpression of MCM2 was significantly correlated to promoter methylation and was related to patients' clinical features. Further, mutation analysis suggested missense as the predominant type of mutation (71.4%) with 18 copy-number alterations and 0.2% mutation frequency in the MCM2 gene. This study revealed a significant correlation (Cox p ≤ 0.05) between the higher MCM2 expression and lower patient survival. Finally, we identified the co-expressed genes with gene ontological features and signaling pathways associated in BC development. We believe that this study will provide a basis for MCM2 to be a significant biomarker for human BC prognosis.

Community-acquired pneumonia: aetiology, antibiotic resistance and prospects of phage therapy.

Bacteria are the most common aetiological agents of community-acquired pneumonia (CAP) and use a variety of mechanisms to evade the host immune system. With the emerging antibiotic resistance, CAP-causing bacteria have now become resistant to most antibiotics. Consequently, significant morbimortality is attributed to CAP despite their varying rates depending on the clinical setting in which the patients being treated. Therefore, there is a pressing need for a safe and effective alternative or supplement to conventional antibiotics. Bacteriophages could be a ray of hope as they are specific in killing their host bacteria. Several bacteriophages had been identified that can efficiently parasitize bacteria related to CAP infection and have shown a promising protective effect. Thus, bacteriophages have shown immense possibilities against CAP inflicted by multidrug-resistant bacteria. This review provides an overview of common antibiotic-resistant CAP bacteria with a comprehensive summarization of the promising bacteriophage candidates for prospective phage therapy.

Exploring Lassa Virus Proteome to Design a Multi-epitope Vaccine Through Immunoinformatics and Immune Simulation Analyses.

Lassa virus (LASV) is responsible for a type of acute viral haemorrhagic fever referred to as Lassa fever. Lack of adequate treatment and preventive measures against LASV resulted in a high mortality rate in its endemic regions. In this study, a multi-epitope vaccine was designed using immunoinformatics as a prophylactic agent against the virus. Following a rigorous assessment, the vaccine was built using T-cell (NCTL = 8 and NHTL = 6) and B-cell (NLBL = 4) epitopes from each LASV-derived protein in addition with suitable linkers and adjuvant. The physicochemistry, immunogenic potency and safeness of the designed vaccine (~ 68 kDa) were assessed. In addition, chosen CTL and HTL epitopes of our vaccine showed 97.37% worldwide population coverage. Besides, disulphide engineering also improved the stability of the chimeric vaccine. Molecular docking of our vaccine protein with toll-like receptor 2 (TLR2) showed binding efficiency followed by dynamics simulation for stable interaction. Furthermore, higher levels of cell-mediated immunity and rapid antigen clearance were suggested by immune simulation and repeated-exposure simulation, respectively. Finally, the optimized codons were used in in silico cloning to ensure higher expression within E. coli K12 bacterium. With further assessment both in vitro and in vivo, we believe that our proposed peptide-vaccine would be potential immunogen against Lassa fever.

Structural basis and designing of peptide vaccine using PE-PGRS family protein of Mycobacterium ulcerans-An integrated vaccinomics approach.

Buruli ulcer is an emerging tissue-necrosis infectious disease, caused by the pathogen Mycobacterium ulcerans, leading to permanent deformity if untreated. Despite this debilitating condition, no specific disease-modifying therapeutics or vaccination is available to date. Therefore, we aimed to design an effective multi-epitope vaccine against M. ulcerans using vaccinomics approach. Briefly, the highest antigenic PE-PGRS protein was selected from which the promiscuous T- and B-cell epitopes were predicted. After rigorous assessment, 15 promising T- and B-cell epitopes were selected. The identified T-cell epitopes showed marked interactions towards their HLA-binding alleles and provided 99.8 % world population coverage. Consequently, a vaccine chimera was designed by connecting these epitopes with suitable linkers and LprG adjuvant. The vaccine construct was highly antigenic, immunogenic and non-allergenic; hence, subjected to homology modelling. The molecular docking and dynamics simulation revealed a strong and stable interaction between vaccine and toll-like receptor 2. The binding energy and dissociation constant were -15.3 kcal/mol and 5.9 × 10-12 M, respectively. The computer-simulated immune responses showed abundance of immunoglobulins, increased interferon-γ production, and macrophages activation which are crucial for immune response against M. ulcerans. Furthermore, disulfide bridging and in silico cloning were also performed. These results suggest that the vaccine, if validated experimentally, will be a promising candidate against M. ulcerans and prevent Buruli ulcer disease.

Computational prediction of active sites and ligands in different AHL quorum quenching lactonases and acylases.

With the emergence of multidrug-resistant 'superbug', conventional treatments become obsolete. Quorum quenching (QQ), enzyme-dependent alteration of quorum sensing (QS), is now considered as a promising antimicrobial therapy because of its potentiality to impede virulence gene expression without resulting in growth inhibition and antibiotic resistance. In our study, we intended to compare between two major QQ enzyme groups (i.e., AHL lactonases and AHL acylases) in terms of their structural and functional aspects. The amino acid composition-based principal component analysis (PCA) suggested that probably there is no structural and functional overlapping between the two groups of enzymes as well as within the lactonase enzymes but the acylases may functionally be affected by one another. In subcellular localization analysis, we also found that most lactonases are cytoplasmic while acylases are periplasmic. Investigation on the secondary structural features showed random coil dominates over alpha-helix and beta-sheet in all evaluated enzymes. For structural comparison, the tertiary structures of the selected proteins were modelled and submitted to the PMDB database (Accession ID: PM0081007 to PM0081018). Interestingly, sequence alignment revealed the presence of several conserved domains important for functions in both protein groups. In addition, three amino acid residues, namely aspartic acid, histidine, and isoleucine, were common in the active sites of all protein models while most frequent ligands were found to be 3C7, FEO, and PAC. Importantly, binding interactions of predicted ligands were similar to that of native QS signal molecules. Furthermore, hydrogen bonds analysis suggested six proteins are more stable than others. We believe that the knowledge of this comparative study could be useful for further research in the development of QSbased universal antibacterial strategies.

Energy-optimized pharmacophore coupled virtual screening in the discovery of quorum sensing inhibitors of LasR protein of Pseudomonas aeruginosa.

Pseudomonas aeruginosa is an emerging opportunistic pathogen responsible for cystic fibrosis and nosocomial infections. In addition, empirical treatments are become inefficient due to their multiple-antibiotic resistance and extensive colonizing ability. Quorum sensing (QS) plays a vital role in the regulation of virulence factors in P. aeruginosa. Therefore, attenuation of virulence by QS inhibition could be an alternative and effective approach to control the infections. In this study, we sought to discover new QS inhibitors (QSIs) against LasR receptor in P. aeruginosa using chemoinformatics. Initially, a structure-based high-throughput virtual screening was performed using the LasR active site that identified 61404 relevant molecules. The e-pharmacophore (ADAHH) screening of these molecules rendered 72 QSI candidates. In standard-precision docking, only 7 compounds were found as potential QSIs based on their higher binding affinity to LasR receptor (-7.53 to -10.32 kcal/mol compared to -7.43 kcal/mol of native ligand). The ADMET properties of these compounds were suitable to be QSIs. Later, extra-precision docking and binding energy calculation suggested ZINC19765885 and ZINC72387263 as the most promising QSIs. The dynamic simulation of the docked complexes showed stable binding affinity and molecular interactions. The current study suggested that these two compounds could be used in P. aeruginosa QS inhibition to combat bacterial infections.Communicated by Ramaswamy H. Sarma.