Immunoinformatics aided designing of a next generation poly-epitope vaccine against uropathogenic Escherichia coli to combat urinary tract infections.

Urinary tract infections (UTIs) are the second most prevalent bacterial infections and uropathogenic Escherichia coli (UPEC) stands among the primary causative agents of UTIs. The usage of antibiotics is the routine therapy being used in various countries to treat UTIs but becoming ineffective because of increasing antibiotic resistance among UPEC strains. Thus, there must be the development of some alternative treatment strategies such as vaccine development against UPEC. In the following study, pan-genomics along with reverse vaccinology approaches is used under the framework of bioinformatics for the identification of core putative vaccine candidates, employing 307 UPEC genomes (complete and draft), available publicly. A total of nine T-cell epitopes (derived from B-cells) of both MHC classes (I and II), were prioritized among three potential protein candidates. These epitopes were then docked together by using linkers (GPGPG and AAY) and an adjuvant (Cholera Toxin B) to form a poly-valent vaccine construct. The chimeric vaccine construct was undergone by molecular modelling, further refinement and energy minimization. We predicted positive results of the vaccine construct in immune simulations with significantly high levels of immune cells. The protein-protein docking analysis of vaccine construct with toll-like receptors predicted efficient binding, which was further validated by molecular dynamics simulation of vaccine construct with TLR-2 and TLR-4 at 120 ns, resulting in stable complexes' conformation throughout the simulation run. Overall, the vaccine construct demonstrated positive antigenic response. In future, this chimeric vaccine construct or the identified epitopes could be experimentally validated for the development of UPEC vaccines against UTIs.Communicated by Ramaswamy H. Sarma.

Whole-genome sequencing of multidrug resistance Salmonella Typhi clinical strains isolated from Balochistan, Pakistan.

Introduction: Salmonella enterica serovar Typhi (S. Typhi) is a major cause of morbidity and mortality in developing countries, contributing significantly to the global disease burden. Methods: In this study, S. Typhi strains were isolated from 100 patients exhibiting symptoms of typhoid fever at a tertiary care hospital in Pakistan. Antimicrobial testing of all isolates was performed to determine the sensitivity and resistance pattern. Three MDR strains, namely QS194, QS430, and QS468, were subjected to whole genome sequencing for genomic characterization. Results and Discussion: MLST analysis showed that QS194, belonged to ST19, which is commonly associated with Salmonella enterica serovar typhimurium. In contrast, QS430 and QS468, belonged to ST1, a sequence type frequently associated with S. Typhi. PlasmidFinder identified the presence of IncFIB(S) and IncFII(S) plasmids in QS194, while IncQ1 was found in QS468. No plasmid was detected in QS430. CARD-based analysis showed that the strains were largely resistant to a variety of antibiotics and disinfecting agents/antiseptics, including fluoroquinolones, cephalosporins, monobactams, cephamycins, penams, phenicols, tetracyclines, rifamycins, aminoglycosides, etc. The S. Typhi strains possessed various virulence factors, such as Vi antigen, Agf/Csg, Bcf, Fim, Pef, etc. The sequencing data indicated that the strains had antibiotic resistance determinants and shared common virulence factors. Pangenome analysis of the selected S. Typhi strains identified 13,237 genes, with 3,611 being core genes, 2,093 shell genes, and 7,533 cloud genes. Genome-based typing and horizontal gene transfer analysis revealed that the strains had different evolutionary origins and may have adapted to distinct environments or host organisms. These findings provide important insights into the genetic characteristics of S. Typhi strains and their potential association with various ecological niches and host organisms.

Employing computational tools to design a multi-epitope vaccine targeting human immunodeficiency virus-1 (HIV-1).

BACKGROUND: Despite being in the 21st century, the world has still not been able to vanquish the global AIDS epidemic, and the only foreseeable solution seems to be a safe and effective vaccine. Unfortunately, vaccine trials so far have returned unfruitful results, possibly due to their inability to induce effective cellular, humoral and innate immune responses. The current study aims to tackle these limitations and propose the desired vaccine utilizing immunoinformatic approaches that have returned promising results in designing vaccines against various rapidly mutating organisms. For this, all polyprotein and protein sequences of HIV-1 were retrieved from the LANL (Los Alamos National Laboratory) database. The consensus sequence was generated after alignment and used to predict epitopes. Conserved, antigenic, non-allergenic, T-cell inducing, B-cell inducing, IFN-É£ inducing, non-human homologous epitopes were selected and combined to propose two vaccine constructs i.e., HIV-1a (without adjuvant) and HIV-1b (with adjuvant). RESULTS: HIV-1a and HIV-1b were subjected to antigenicity, allergenicity, structural quality analysis, immune simulations, and MD (molecular dynamics) simulations. Both proposed multi-epitope vaccines were found to be antigenic, non-allergenic, stable, and induce cellular, humoral, and innate immune responses. TLR-3 docking and in-silico cloning of both constructs were also performed. CONCLUSION: Our results indicate HIV-1b to be more promising than HIV-1a; experimental validations can confirm the efficacy and safety of both constructs and in-vivo efficacy in animal models.

An In Silico Deep Learning Approach to Multi-Epitope Vaccine Design: A Hepatitis E Virus Case Study.

Hepatitis E Virus (HEV) is a major cause of acute and chronic hepatitis. The severity of HEV infection increases manyfold in pregnant women and immunocompromised patients. Despite the extensive research on HEV in the last few decades, there is no widely available vaccine yet. In the current study, immunoinformatic analyses were applied to predict a multi-epitope vaccine candidate against HEV. From the ORF2 region, 41 conserved and immunogenic epitopes were prioritized. These epitopes were further analyzed for their probable antigenic and non-allergenic combinations with several linkers. The stability of the vaccine construct was confirmed by molecular dynamic simulations. The vaccine construct is potentially antigenic and docking analysis revealed stable interactions with TLR3. These results suggest that the proposed vaccine can efficiently stimulate both cellular and humoral immune responses. However, further studies are needed to determine the immunogenicity of the vaccine construct.

Recent advances in bioremediation of heavy metals and persistent organic pollutants: A review.

Heavy metals and persistent organic pollutants are causing detrimental effects on the environment. The seepage of heavy metals through untreated industrial waste destroys the crops and lands. Moreover, incineration and combustion of several products are responsible for primary and secondary emissions of pollutants. This review has gathered the remediation strategies, current bioremediation technologies, and their primary use in both in situ and ex situ methods, followed by a detailed explanation for bioremediation over other techniques. However, an amalgam of bioremediation techniques and nanotechnology could be a breakthrough in cleaning the environment by degrading heavy metals and persistant organic pollutants.

A Computational Approach to Modeling an Antagonistic Angiogenic VEGFR1-IL2 Fusion Protein for Cancer Therapy.

In cancer treatment, immunotherapy has great potential for improving the prognosis of patients with hematologic and solid malignancies. In this study, various bioinformatics tools and servers were used to design an antiangiogenic fusion protein. After comprehensive evaluation, an antiangiogenic fusion protein was designed using a soluble extracellular domain of human vascular endothelial growth factor receptor 1 (sVEGFR-1) and human interleukin-2 (IL-2) joined by a flexible linker. The final construct was composed of 875 amino acids. The secondary structure of the fusion protein, obtained by CFSSP, PSIPRED, and SOPMA tools, consisted of 14.17% helices, 29.71% extended strands, 4.69% beta turns and 51.43% random coils. Tertiary structure prediction by Raptor X showed that the fusion protein comprises 3 domains with 875 modeled amino acids, out of which 26 positions (2%) were considered disordered. The Ramachandran plot revealed 89.3%, 7.1%, and 3.6% amino acid residues in favored, allowed, and outlier regions, respectively. Physical features of the Molecular Dynamic (MD) simulated system such as root mean square deviation, root mean square fluctuation, solvent-on hand surface region, and radius of gyration identified the fusion construct as a stable and compact protein with few fluctuations in its overall structure. Docking of the fusion protein showed that interaction between sVEGFR-1/VEGFA and IL-2/IL-2R still exists. In silico analysis revealed that the fusion protein comprising IL-2 and sVEGFR-1 has stable structure and the selected linker can efficiently separate the two domains. These observations may be helpful in determining protein stability prior to protein expression.

Chimeric vaccine designs against Acinetobacter baumannii using pan genome and reverse vaccinology approaches.

Acinetobacter baumannii (A. baumannii), an opportunistic, gram-negative pathogen, has evoked the interest of the medical community throughout the world because of its ability to cause nosocomial infections, majorly infecting those in intensive care units. It has also drawn the attention of researchers due to its evolving immune evasion strategies and increased drug resistance. The emergence of multi-drug-resistant-strains has urged the need to explore novel therapeutic options as an alternative to antibiotics. Due to the upsurge in antibiotic resistance mechanisms exhibited by A. baumannii, the current therapeutic strategies are rendered less effective. The aim of this study is to explore novel therapeutic alternatives against A. baumannii to control the ailed infection. In this study, a computational framework is employed involving, pan genomics, subtractive proteomics and reverse vaccinology strategies to identify core promiscuous vaccine candidates. Two chimeric vaccine constructs having B-cell derived T-cell epitopes from prioritized vaccine candidates; APN, AdeK and AdeI have been designed and checked for their possible interactions with host BCR, TLRs and HLA Class I and II Superfamily alleles. These vaccine candidates can be experimentally validated and thus contribute to vaccine development against A. baumannii infections.

Pangenome Analysis of Mycobacterium tuberculosis Reveals Core-Drug Targets and Screening of Promising Lead Compounds for Drug Discovery.

Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), is one of the leading causes of human deaths globally according to the WHO TB 2019 report. The continuous rise in multi- and extensive-drug resistance in M. tuberculosis broadens the challenges to control tuberculosis. The availability of a large number of completely sequenced genomes of M. tuberculosis has provided an opportunity to explore the pangenome of the species along with the pan-phylogeny and to identify potential novel drug targets leading to drug discovery. We attempt to calculate the pangenome of M. tuberculosis that comprises a total of 150 complete genomes and performed the phylo-genomic classification and analysis. Further, the conserved core genome (1251 proteins) is subjected to various sequential filters (non-human homology, essentiality, virulence, physicochemical parameters, and pathway analysis) resulted in identification of eight putative broad-spectrum drug targets. Upon molecular docking analyses of these targets with ligands available at the DrugBank database shortlisted a total of five promising ligands with projected inhibitory potential; namely, 2'deoxy-thymidine-5'-diphospho-alpha-d-glucose, uridine diphosphate glucose, 2'-deoxy-thymidine-beta-l-rhamnose, thymidine-5'-triphosphate, and citicoline. We are confident that with further lead optimization and experimental validation, these lead compounds may provide a sound basis to develop safe and effective drugs against tuberculosis disease in humans.

Computer-aided drug design against spike glycoprotein of SARS-CoV-2 to aid COVID-19 treatment.

BACKGROUND: SARS-CoV-2 has the Spike glycoprotein (S) which is crucial in attachment with host receptor and cell entry leading to COVID-19 infection. The current study was conducted to explore drugs against Receptor Binding Domain (RBD) of SARS-CoV-2 using in silico pharmacophore modelling and virtual screening approach to combat COVID-19. METHODS: All the available sequences of RBD in NCBI were retrieved and multiple aligned to get insight into its diversity. The 3D structure of RBD was modelled and the conserved region was used as a template to design pharmacophore using LigandScout. Lead compounds were screened using Cambridge, Drugbank, ZINC and TIMBLE databases and these identified lead compounds were screened for their toxicity and Lipinski's rule of five. Molecular docking of shortlisted lead compounds was performed using AutoDock Vina and interacting residues were visualized. RESULTS: Active residues of Receptor Binding Motif (RBM) in S, involved in interaction with receptor, were found to be conserved in all 483 sequences. Using this RBM motif as a pharmacophore a total of 1327 lead compounds were predicted initially from all databases, however, only eight molecules fit the criteria for safe oral drugs. Conclusion: The RBM region of S interacts with Angiotensin Converting Enzyme 2 (ACE2) receptor and Glucose Regulated Protein 78 (GRP78) to mediate viral entry. Based on in silico analysis, the lead compounds scrutinized herewith interact with S, hence, can prevent its internalization in cell using ACE2 and GRP78 receptor.The compounds predicted in this study are based on rigorous computational analysis and the evaluation of predicted lead compounds can be promising in experimental studies.

Anti-COVID-19 multi-epitope vaccine designs employing global viral genome sequences.

BACKGROUND: The coronavirus SARS-CoV-2 is a member of the Coronaviridae family that has caused a global public health emergency. Currently, there is no approved treatment or vaccine available against it. The current study aimed to cover the diversity of SARS-CoV-2 strains reported from all over the world and to design a broad-spectrum multi-epitope vaccine using an immunoinformatics approach. METHODS: For this purpose, all available complete genomes were retrieved from GISAID and NGDC followed by genome multiple alignments to develop a global consensus sequence to compare with the reference genome. Fortunately, comparative genomics and phylogeny revealed a significantly high level of conservation between the viral strains. All the Open Reading Frames (ORFs) of the reference sequence NC_045512.2 were subjected to epitope mapping using CTLpred and HLApred, respectively. The predicted CTL epitopes were then screened for antigenicity, immunogenicity and strong binding affinity with HLA superfamily alleles. HTL predicted epitopes were screened for antigenicity, interferon induction potential, overlapping B cell epitopes and strong HLA DR binding potential. The shortlisted epitopes were arranged into two multi-epitope sequences, Cov-I-Vac and Cov-II-Vac, and molecular docking was performed with Toll-Like Receptor 8 (TLR8). RESULTS: The designed multi-epitopes were found to be antigenic and non-allergenic. Both multi-epitopes were stable and predicted to be soluble in an Escherichia coli expression system. The molecular docking with TLR8 also demonstrated that they have a strong binding affinity and immunogenic potential. These in silico analyses suggest that the proposed multi-epitope vaccine can effectively evoke an immune response.

Whole-genome sequencing of a new sequence type (ST5352) strain of community-acquired methicillin-resistant Staphylococcus aureus from a hospital in Pakistan.

OBJECTIVES: Methicillin-resistant Staphylococcus aureus (MRSA) is an important drug-resistant pathogen causing a number of diseases, resulting in increased mortality. Therefore, whole-genome sequencing of an MRSA strain isolated from a patient admitted to a hospital in Lahore, Pakistan, was performed to better characterise the strain and to understand the genetic components of antimicrobial resistance and virulence. METHODS: MRSA isolate Lr12 was sequenced on an Illumina HiSeq 2500 platform. The genome was assembled with SPAdes and was annotated using PGAP v.4.3. The strain was characterised using spaTyper 1.0, SCCmecFinder v.1.2 and MLST 2.0 server. Plasmids, antimicrobial resistance determinants and virulence factors were identified using PlasmidFinder v.2.0, CARD and VFDB, respectively. RESULTS: MRSA strain Lr12 has an estimated genome size of 2 769 144bp with a GC content of 32.7% and harbours 1 plasmid, 2 prophages, 11 antimicrobial resistance determinants and several virulence factors. The allelic profile of seven housekeeping genes was unique and the sequence type (ST) was classified as unknown, hence a novel sequence type (ST5352) was assigned. CONCLUSION: MRSA strain Lr12 has a novel sequence type (ST5352) and could be used as a reference strain for comparative genomic analysis of other MRSA strains belong to ST5352.

Immunoinformatics-Aided Design and Evaluation of a Potential Multi-Epitope Vaccine against Klebsiella Pneumoniae.

Klebsiella pneumoniae is an opportunistic gram-negative bacterium that causes nosocomial infection in healthcare settings. Despite the high morbidity and mortality rate associated with these bacterial infections, no effective vaccine is available to counter the pathogen. In this study, the pangenome of a total of 222 available complete genomes of K. pneumoniae was explored to obtain the core proteome. A reverse vaccinology strategy was applied to the core proteins to identify four antigenic proteins. These proteins were then subjected to epitope mapping and prioritization steps to shortlist nine B-cell derived T-cell epitopes which were linked together using GPGPG linkers. An adjuvant (Cholera Toxin B) was also added at the N-terminal of the vaccine construct to improve its immunogenicity and a stabilized multi-epitope protein structure was obtained using molecular dynamics simulation. The designed vaccine exhibited sustainable and strong bonding interactions with Toll-like receptor 2 and Toll-like receptor 4. In silico reverse translation and codon optimization also confirmed its high expression in E. coli K12 strain. The computer-aided analyses performed in this study imply that the designed multi-epitope vaccine can elicit specific immune responses against K. pneumoniae. However, wet lab validation is necessary to further verify the effectiveness of this proposed vaccine candidate.

Exploring NS3/4A, NS5A and NS5B proteins to design conserved subunit multi-epitope vaccine against HCV utilizing immunoinformatics approaches.

Hepatitis C virus (HCV) vaccines, designed to augment specific T-cell responses, have been designated as an important aspect of effective antiviral treatment. However, despite the current satisfactory progress of these vaccines, extensive past efforts largely remained unsuccessful in mediating clinically relevant anti-HCV activity in humans. In this study, we used a series of immunoinformatics approaches to propose a multiepitope vaccine against HCV by prioritizing 16 conserved epitopes from three viral proteins (i.e., NS34A, NS5A, and NS5B). The prioritised epitopes were tested for their possible antigenic combinations with each other along with linker AAY using structural modelling and epitope-epitope interactions analysis. An adjuvant (ß-defensin) at the N-terminal of the construct was added to enhance the immunogenicity of the vaccine construct. Molecular dynamics (MD) simulation revealed the most stable structure of the proposed vaccine. The designed vaccine is potentially antigenic in nature and can form stable and significant interactions with Toll-like receptor 3 and Toll-like receptor 8. The proposed vaccine was also subjected to an in silico cloning approach, which confirmed its expression efficiency. These analyses suggest that the proposed vaccine can elicit specific immune responses against HCV; however, experimental validation is required to confirm the safety and immunogenicity profile of the proposed vaccine construct.

Structural analysis and insight into Zika virus NS5 mediated interferon inhibition.

The Zika virus outbreak in 2015-2016 is the largest of its kind for which WHO declared a Public Health Emergency of International Concerns. No FDA approved drug is available for the treatment of the viral infection. The interaction of flavivirus NS5 protein with SIAH2 ubiquitin ligase has been previously known. NS5 of Zika virus has been implicated in the degradation of STAT2 protein, which activates interferon-stimulated antiviral activity. Based on our proposition that NS5 utilizes SIAH2-mediated proteasomal degradation of STAT2, an in-silico study was carried out to characterize the protein-protein interactions between NS5, SIAH2 and STAT2 proteins. The aim of our study was to identify the amino acid residues of NS5 involved in IFN antagonism as well as to find the association between NS5, SIAH2 and STAT2 to predict the interaction pattern of these proteins. Analysis proposed that NS5 recruits SIAH2 for the ubiquitination-dependent degradation of STAT2. NS5 residues involved in interaction with SIAH2 and/or STAT2 were found to be mostly conserved across related flaviviruses. These are novel findings regarding the Zika virus and require confirmation through experimental approaches.

Prediction of promiscuous T-cell epitopes in the Zika virus polyprotein: An in silico approach.

OBJECTIVE: To predict immunogenic promiscuous T cell epitopes from the polyprotein of the Zika virus using a range of bioinformatics tools. To date, no epitope data are available for the Zika virus in the IEDB database. METHODS: We retrieved nearly 54 full length polyprotein sequences of the Zika virus from the NCBI database belonging to different outbreaks. A consensus sequence was then used to predict the promiscuous T cell epitopes that bind MHC 1 and MHC II alleles using PorPred1 and ProPred immunoinformatic algorithms respectively. The antigenicity predicted score was also calculated for each predicted epitope using the VaxiJen 2.0 tool. RESULTS: By using ProPred1, 23 antigenic epitopes for HLA class I and 48 antigenic epitopes for HLA class II were predicted from the consensus polyprotein sequence of Zika virus. The greatest number of MHC class I binding epitopes were projected within the NS5 (21%), followed by Envelope (17%). For MHC class II, greatest number of predicted epitopes were in NS5 (19%) followed by the Envelope, NS1 and NS2 (17% each). A variety of epitopes with good binding affinity, promiscuity and antigenicity were predicted for both the HLA classes. CONCLUSION: The predicted conserved promiscuous T-cell epitopes examined in this study were reported for the first time and will contribute to the imminent design of Zika virus vaccine candidates, which will be able to induce a broad range of immune responses in a heterogeneous HLA population. However, our results can be verified and employed in future efficacious vaccine formulations only after successful experimental studies.