SPAAN: a software program for prediction of adhesins and adhesin-like proteins using neural networks

Computational strategies in Klebsiella pneumoniae vaccine design: navigating the landscape of in silico insights

The emergence of multidrug-resistant Klebsiella pneumoniae poses a grave threat to global public health, necessitating urgent strategies for vaccine development. In this context, computational tools have emerged as indispensable assets, offering unprecedented insights into klebsiellal biology and facilitating the design of effective vaccines. Here, a review of the application of computational methods in the development of K. pneumoniae vaccines is presented, elucidating the transformative impact of in silico approaches. Through a systematic exploration of bioinformatics, structural biology, and immunoinformatics techniques, the complex landscape of K. pneumoniae pathogenesis and antigenicity was unravelled. Key insights into virulence factors, antigen discovery, and immune response mechanisms are discussed, highlighting the pivotal role of computational tools in accelerating vaccine development efforts. Advancements in epitope prediction, antigen selection, and vaccine design optimisation are examined, highlighting the potential of in silico approaches to update vaccine development pipelines. Furthermore, challenges and future directions in leveraging computational tools to combat K. pneumoniae are discussed, emphasizing the importance of multidisciplinary collaboration and data integration. This review provides a comprehensive overview of the current state of computational contributions to K. pneumoniae vaccine development, offering insights into innovative strategies for addressing this urgent global health challenge.

A review on the development of bacterial multi-epitope recombinant protein vaccines via reverse vaccinology

Bacterial vaccines play a crucial role in combating bacterial infectious diseases. Apart from the prevention of disease, bacterial vaccines also help to reduce the mortality rates in infected populations. Advancements in vaccine development technologies have addressed the constraints of traditional vaccine design, providing novel approaches for the development of next-generation vaccines. Advancements in reverse vaccinology, bioinformatics, and comparative proteomics have opened horizons in vaccine development. Specifically, the use of protein structural data in crafting multi-epitope vaccines (MEVs) to target pathogens has become an important research focus in vaccinology. In this review, we focused on describing the methodologies and tools for epitope vaccine development, along with recent progress in this field. Moreover, this article also discusses the challenges in epitope vaccine development, providing insights for the future development of bacterial multi-epitope genetically engineered vaccines.

DTVF: A User-Friendly Tool for Virulence Factor Prediction Based on ProtT5 and Deep Transfer Learning Models

Virulencefactors (VFs) are key molecules that enable pathogens to evade the immune systems of the host. These factors are crucial for revealing the pathogenic processes of microbes and drug discovery. Identification of virulence factors in microbes become an important problem in the field of bioinformatics. To address this problem, this study proposes a novel model DTVF (Deep Transfer Learning for Virulence Factor Prediction), which integrates the ProtT5 protein sequence extraction model with a dual-channel deep learning model. In the dual-channel deep learning model, we innovatively integrate long short-term memory (LSTM) with convolutional neural networks (CNNs), creating a novel integrated architecture. Furthermore, by incorporating the attention mechanism, the accuracy of VF detection was significantly enhanced. We evaluated the DTVF model against other excellent-performing models in the field. DTVF demonstrates superior performance, achieving an accuracy rate of 84.55% and an AUROC of 92.08% on the benchmark dataset. DTVF shows state-of-the-art performance in this field, surpassing the existing models in nearly all metrics. To facilitate the use of biologists, we have also developed an interactive web-based user interface version of DTVF based on Gradio.

In silico and in vivo Investigations of the Immunoreactivity of Klebsiella pneumoniae OmpA Protein as a Vaccine Candidate

Background

The growing threat of antibiotic resistance and Klebsiella pneumoniae infection in healthcare settings highlights the urgent need for innovative solutions, such as vaccines, to address these challenges. This study sought to assess the potential of using K. pneumoniae outer membrane protein A (OmpA) as a vaccine candidate through both in silico and in vivo analyses.

Methods

The study examined the OmpA protein sequence for subcellular localization, antigenicity, allergenicity, similarity to the human proteome, physicochemical properties, B-cell epitopes, MHC binding sites, tertiary structure predictions, molecular docking, and immune response simulations. The ompA gene was cloned into the pET-28a (+) vector, expressed, purified and confirmed using Western blotting analysis. IgG levels in the serum of the immunized mice were measured using ELISA with dilutions ranging from 1:100 to 1:6400, targeting recombinant outer membrane protein A (rOmpA) and K. pneumoniae ATCC 13883. The sensitivity and specificity of the ELISA method were also assessed.

Results

The bioinformatics analysis identified rOmpA as a promising vaccine candidate. The immunized group demonstrated significant production of specific total IgG antibodies against rOmpA and K. pneumoniae ATCC1 13883, as compared to the control group (p < 0.0001). The titers of antibodies produced in response to bacterial exposure did not show any significant difference when compared to the anti-rOmpA antibodies (p > 0.05). The ELISA test sensitivity was 1:3200, and the antibodies in the serum could accurately recognize K. pneumoniae cells.

Conclusion

This study is a significant advancement in the development of a potential vaccine against K. pneumoniae that relies on OmpA. Nevertheless, additional experimental analyses are required.

Surface-Exposed Protein Moieties of Burkholderia cenocepacia J2315 in Microaerophilic and Aerobic Conditions

Burkholderia cepacia complex infections remain life-threatening to cystic fibrosis patients, and due to the limited eradication efficiency of current treatments, novel antimicrobial therapies are urgently needed. Surface proteins are among the best targets to develop new therapeutic strategies since they are exposed to the host's immune system. A surface-shaving approach was performed using Burkholderia cenocepacia J2315 to quantitatively compare the relative abundance of surface-exposed proteins (SEPs) expressed by the bacterium when grown under aerobic and microaerophilic conditions. After trypsin incubation of live bacteria and identification of resulting peptides by liquid chromatography coupled with mass spectrometry, a total of 461 proteins with ≥2 unique peptides were identified. Bioinformatics analyses revealed a total of 53 proteins predicted as localized at the outer membrane (OM) or extracellularly (E). Additionally, 37 proteins were predicted as moonlight proteins with OM or E secondary localization. B-cell linear epitope bioinformatics analysis of the proteins predicted to be OM and E-localized revealed 71 SEP moieties with predicted immunogenic epitopes. The protegenicity higher scores of proteins BCAM2761, BCAS0104, BCAL0151, and BCAL0849 point out these proteins as the best antigens for vaccine development. Additionally, 10 of the OM proteins also presented a high probability of playing important roles in adhesion to host cells, making them potential targets for passive immunotherapeutic approaches. The immunoreactivity of three of the OM proteins identified was experimentally demonstrated using serum samples from cystic fibrosis patients, validating our strategy for identifying immunoreactive moieties from surface-exposed proteins of potential interest for future immunotherapies development.

Characterisation of Putative Outer Membrane Proteins from Leptospira borgpetersenii Serovar Hardjo-Bovis Identifies Novel Adhesins and Diversity in Adhesion across Genomospecies Orthologs

Leptospirosis is a zoonotic bacterial disease affecting mammalian species worldwide. Cattle are a major susceptible host; infection with pathogenic Leptospira spp. represents a public health risk and results in reproductive failure and reduced milk yield, causing economic losses. The characterisation of outer membrane proteins (OMPs) from disease-causing bacteria dissects pathogenesis and underpins vaccine development. As most leptospire pathogenesis research has focused on Leptospira interrogans, this study aimed to characterise novel OMPs from another important genomospecies, Leptospira borgpetersenii, which has global distribution and is relevant to bovine and human diseases. Several putative L. borgpetersenii OMPs were recombinantly expressed, refolded and purified, and evaluated for function and immunogenicity. Two of these unique, putative OMPs (rLBL0972 and rLBL2618) bound to immobilised fibronectin, laminin and fibrinogen, which, together with structural and functional data, supports their classification as leptospiral adhesins. A third putative OMP (rLBL0375), did not exhibit saturable adhesion ability but, together with rLBL0972 and the included control, OmpL1, demonstrated significant cattle milk IgG antibody reactivity from infected cows. To dissect leptospire host-pathogen interactions further, we expressed alleles of OmpL1 and a novel multi-specific adhesin, rLBL2618, from a variety of genomospecies and surveyed their adhesion ability, with both proteins exhibiting divergences in extracellular matrix component binding specificity across synthesised orthologs. We also observed functional redundancy across different L. borgspetersenii OMPs which, together with diversity in function across genomospecies orthologs, delineates multiple levels of plasticity in adhesion that is potentially driven by immune selection and host adaptation. These data identify novel leptospiral proteins which should be further evaluated as vaccine and/or diagnostic candidates. Moreover, functional redundancy across leptospire surface proteins together with identified adhesion divergence across genomospecies further dissect the complex host-pathogen interactions of a genus responsible for substantial global disease burden.

VF-Pred: Predicting virulence factor using sequence alignment percentage and ensemble learning models

This study introduces VF-Pred, a novel framework developed for the purpose of detecting virulence factors (VFs) through the analysis of genomic data. VFs are crucial for pathogens to successfully infect host tissue and evade the immune system, leading to the onset of infectious diseases. Identifying VFs accurately is of utmost importance in the quest for developing potent drugs and vaccines to counter these diseases. To accomplish this, VF-Pred combines various feature engineering techniques to generate inputs for distinct machine learning classification models. The collective predictions of these models are then consolidated by a final downstream model using an innovative ensembling approach. One notable aspect of VF-Pred is the inclusion of a novel Seq-Alignment feature, which significantly enhances the accuracy of the employed machine learning algorithms. The framework was meticulously trained on 982 features obtained from extensive feature engineering, utilizing a comprehensive ensemble of 25 models. The new downstream ensembling technique adopted by VF-Pred surpasses existing stacking strategies and other ensembling methods, delivering superior performance in VF detection. There have been similar studies done earlier, VF-Pred stands out in comparison showing higher accuracy (83.5 %), higher sensitivity (87 %) towards identification of VFs. Accessible through a user-friendly web page, VF-Pred can be accessed by providing the identifier and protein sequence, enabling the prediction of high or low likelihoods of VFs. Overall, VF-Pred showcases a highly promising methodology for the identification of VFs, potentially paving the way for the development of more effective strategies in the battle against infectious diseases.

Vaxign-DL: A Deep Learning-based Method for Vaccine Design and its Evaluation

Reverse vaccinology (RV) provides a systematic approach to identifying potential vaccine candidates based on protein sequences. The integration of machine learning (ML) into this process has greatly enhanced our ability to predict viable vaccine candidates from these sequences. We have previously developed a Vaxign-ML program based on the eXtreme Gradient Boosting (XGBoost). In this study, we further extend our work to develop a Vaxign-DL program based on deep learning techniques. Deep neural networks assemble non-linear models and learn multilevel abstraction of data using hierarchically structured layers, offering a data-driven approach in computational design models. Vaxign-DL uses a three-layer fully connected neural network model. Using the same bacterial vaccine candidate training data as used in Vaxign-ML development, Vaxign-DL was able to achieve an Area Under the Receiver Operating Characteristic of 0.94, specificity of 0.99, sensitivity of 0.74, and accuracy of 0.96. Using the Leave-One-Pathogen-Out Validation (LOPOV) method, Vaxign-DL was able to predict vaccine candidates for 10 pathogens. Our benchmark study shows that Vaxign-DL achieved comparable results with Vaxign-ML in most cases, and our method outperforms Vaxi-DL in the accurate prediction of bacterial protective antigens.

Molecular structures mediating adhesion of Campylobacter jejuni to abiotic and biotic surfaces

Microaerophilic, Gram-negative Campylobacter jejuni is the causative agent of campylobacteriosis, the most common bacterial gastrointestinal infection worldwide. Adhesion is the crucial first step in both infection or interaction with the host and biofilm formation, and is a critical factor for bacterial persistence. Here we describe the proteins and other surface structures that promote adhesion to various surfaces, including abiotic surfaces, microorganisms, and animal and human hosts. In addition, we provide insight into the distribution of adhesion proteins among strains from different ecological niches and highlight unexplored proteins involved in C. jejuni adhesion. Protein-protein, protein-glycan, and glycan-glycan interactions are involved in C. jejuni adhesion, with different factors contributing to adhesion to varying degrees under different circumstances. As adhesion is essential for survival and persistence, it represents an interesting target for C. jejuni control. Knowledge of the adhesion process is incomplete, as different molecular and functional aspects have been studied for different structures involved in adhesion. Therefore, it is important to strive for an integration of different approaches to obtain a clearer picture of the adhesion process on different surfaces and to consider the involvement of proteins, glycoconjugates, and polysaccharides and their cooperation.

VirulentPred 2.0: An improved method for prediction of virulent proteins in bacterial pathogens

Virulence proteins in pathogens are essential for causing disease in a host. They enable the pathogen to invade, survive and multiply within the host, thus enhancing its potential to cause disease while also causing evasion of host defense mechanisms. Identifying these factors, especially potential vaccine candidates or drug targets, is critical for vaccine or drug development research. In this context, we present an improved version of VirulentPred 1.0 for rapidly identifying virulent proteins. The VirulentPred 2.0 is based on training machine learning models with experimentally validated virulent protein sequences. VirulentPred 2.0 achieved 84.71% accuracy with the validation dataset and 85.18% on an independent test dataset. The models are trained and evaluated with the latest sequence datasets of virulent proteins, which are three times greater in number than the proteins used in the earlier version of VirulentPred. Moreover, a significant improvement of 11% in the prediction accuracy over the earlier version is achieved with the best position-specific scoring matrix (PSSM)-based model for the latest test dataset. VirulentPred 2.0 is available as a user-friendly web interface at https://bioinfo.icgeb.res.in/virulent2/ and a standalone application suitable for bulk predictions. With higher efficiency and availability as a standalone tool, VirulentPred 2.0 holds immense potential for high throughput yet efficient identification of virulent proteins in bacterial pathogens.

Comprehensive probiogenomics analysis of the commensal Escherichia coli CEC15 as a potential probiotic strain

BACKGROUND

Probiotics have gained attention for their potential maintaining gut and immune homeostasis. They have been found to confer protection against pathogen colonization, possess immunomodulatory effects, enhance gut barrier functionality, and mitigate inflammation. However, a thorough understanding of the unique mechanisms of effects triggered by individual strains is necessary to optimize their therapeutic efficacy. Probiogenomics, involving high-throughput techniques, can help identify uncharacterized strains and aid in the rational selection of new probiotics. This study evaluates the potential of the Escherichia coli CEC15 strain as a probiotic through in silico, in vitro, and in vivo analyses, comparing it to the well-known probiotic reference E. coli Nissle 1917. Genomic analysis was conducted to identify traits with potential beneficial activity and to assess the safety of each strain (genomic islands, bacteriocin production, antibiotic resistance, production of proteins involved in host homeostasis, and proteins with adhesive properties). In vitro studies assessed survival in gastrointestinal simulated conditions and adhesion to cultured human intestinal cells. Safety was evaluated in BALB/c mice, monitoring the impact of E. coli consumption on clinical signs, intestinal architecture, intestinal permeability, and fecal microbiota. Additionally, the protective effects of both strains were assessed in a murine model of 5-FU-induced mucositis.

RESULTS

CEC15 mitigates inflammation, reinforces intestinal barrier, and modulates intestinal microbiota. In silico analysis revealed fewer pathogenicity-related traits in CEC15, when compared to Nissle 1917, with fewer toxin-associated genes and no gene suggesting the production of colibactin (a genotoxic agent). Most predicted antibiotic-resistance genes were neither associated with actual resistance, nor with transposable elements. The genome of CEC15 strain encodes proteins related to stress tolerance and to adhesion, in line with its better survival during digestion and higher adhesion to intestinal cells, when compared to Nissle 1917. Moreover, CEC15 exhibited beneficial effects on mice and their intestinal microbiota, both in healthy animals and against 5FU-induced intestinal mucositis.

CONCLUSIONS

These findings suggest that the CEC15 strain holds promise as a probiotic, as it could modulate the intestinal microbiota, providing immunomodulatory and anti-inflammatory effects, and reinforcing the intestinal barrier. These findings may have implications for the treatment of gastrointestinal disorders, particularly some forms of diarrhea.

A computational method for the prediction and functional analysis of potential Mycobacterium tuberculosis adhesin-related proteins

OBJECTIVES

Mycobacterial adherence plays a major role in the establishment of infection within the host. Adhesin-related proteins attach to host receptors and cell-surface components. The current study aimed to utilize in-silico strategies to determine the adhesin potential of conserved hypothetical (CH) proteins.

METHODS

Computational analysis was performed on the whole Mycobacterium tuberculosis H37Rv proteome using a software program for the prediction of adhesin and adhesin-like proteins using neural networks (SPAAN) to determine the adhesin potential of CH proteins. A robust pipeline of computational analysis tools: Phyre2 and pFam for homology prediction; Mycosub, PsortB, and Loctree3 for subcellular localization; SignalP-5.0 and SecretomeP-2.0 for secretory prediction, were utilized to identify adhesin candidates.

RESULTS

SPAAN revealed 776 potential adhesins within the whole MTB H37Rv proteome. Comprehensive analysis of the literature was cross-tabulated with SPAAN to verify the adhesin prediction potential of known adhesin (n = 34). However, approximately a third of known adhesins were below the probability of adhesin (Pad) threshold (Pad ≥0.51). Subsequently, 167 CH proteins of interest were categorized using essential in-silico tools.

CONCLUSION

The use of SPAAN with supporting in-silico tools should be fundamental when identifying novel adhesins. This study provides a pipeline to identify CH proteins as functional adhesin molecules.

Identification and Evaluation of Novel Antigen Candidates against Salmonella Pullorum Infection Using Reverse Vaccinology

Pullorum disease, caused by the Salmonella enterica serovar Gallinarum biovar Pullorum, is a highly contagious disease in the poultry industry, leading to significant economic losses in many developing countries. Due to the emergence of multidrug-resistant (MDR) strains, immediate attention is required to prevent their endemics and global spreading. To mitigate the prevalence of MDR Salmonella Pullorum infections in poultry farms, it is urgent to develop effective vaccines. Reverse vaccinology (RV) is a promising approach using expressed genomic sequences to find new vaccine targets. The present study used the RV approach to identify new antigen candidates against Pullorum disease. Initial epidemiological investigation and virulent assays were conducted to select strain R51 for presentative and general importance. An additional complete genome sequence (4.7 Mb) for R51 was resolved using the Pacbio RS II platform. The proteome of Salmonella Pullorum was analyzed to predict outer membrane and extracellular proteins, and was further selected for evaluating transmembrane domains, protein prevalence, antigenicity, and solubility. Twenty-two high-scored proteins were identified among 4713 proteins, with 18 recombinant proteins successfully expressed and purified. The chick embryo model was used to assess protection efficacy, in which vaccine candidates were injected into 18-day-old chick embryos for in vivo immunogenicity and protective effects. The results showed that the PstS, SinH, LpfB, and SthB vaccine candidates were able to elicit a significant immune response. Particularly, PstS confers a significant protective effect, with a 75% survival rate compared to 31.25% for the PBS control group, confirming that identified antigens can be promising targets against Salmonella Pullorum infection. Thus, we offer RV to discover novel effective antigens in an important veterinary infectious agent with high priority.

In silico prediction and expression analysis of vaccine candidate genes of Campylobacter jejuni

Campylobacter jejuni (C. jejuni) is the most common food-borne pathogen that causes human gastroenteritis in the United States. Consumption of contaminated poultry products is considered as the major source of human Campylobacter infection. An effective vaccine would be a promising alternative to antibiotic supplements to curb C. jejuni colonization in poultry gastrointestinal (GI) tract. However, the genetic diversity among the C. jejuni isolates makes vaccine production more challenging. Despite many attempts, an effective Campylobacter vaccine is not yet available. This study aimed to identify suitable candidates to develop a subunit vaccine against C. jejuni, which could reduce colonization in the GI tract of the poultry. In the current study, 4 C. jejuni strains were isolated from retail chicken meat and poultry litter samples and their genomes were sequenced utilizing next-generation sequencing technology. The genomic sequences of C. jejuni strains were screened to identify potential antigens utilizing the reverse vaccinology approach. In silico genome analysis predicted 3 conserved potential vaccine candidates (phospholipase A [PldA], TonB dependent vitamin B12 transporter [BtuB], and cytolethal distending toxin subunit B [CdtB]) suitable for the development of a vaccine. Furthermore, the expression of predicted genes during host-pathogen interaction was analyzed by an infection study using an avian macrophage-like immortalized cell line (HD11). The HD11 was infected with C. jejuni strains, and the RT-qPCR assay was performed to determine the expression of the predicted genes. The expression difference was analyzed using ΔΔCt methods. The results indicate that all 3 predicted genes, PldA, BtuB, and CdtB, were upregulated in 4 tested C. jejuni strains irrespective of their sources of isolation. In conclusion, in silico prediction and gene expression analysis during host-pathogen interactions identified 3 potential vaccine candidates for C. jejuni.

E. coli Common pili promote the fitness and virulence of a hybrid aEPEC/ExPEC strain within diverse host environments

Pathogenic subsets of Escherichia coli include diarrheagenic (DEC) strains that cause disease within the gut and extraintestinal pathogenic E. coli (ExPEC) strains that are linked with urinary tract infections, bacteremia, and other infections outside of intestinal tract. Among DEC strains is an emergent pathotype known as atypical enteropathogenic E. coli (aEPEC), which can cause severe diarrhea. Recent sequencing efforts revealed that some E. coli strains possess genetic features that are characteristic of both DEC and ExPEC isolates. BA1250 is a newly reclassified hybrid strain with characteristics of aEPEC and ExPEC. This strain was isolated from a child with diarrhea, but its genetic features indicate that it might have the capacity to cause disease at extraintestinal sites. The spectrum of adhesins encoded by hybrid strains like BA1250 are expected to be especially important in facilitating colonization of diverse niches. E. coli common pilus (ECP) is an adhesin expressed by many E. coli pathogens, but how it impacts hybrid strains has not been ascertained. Here, using zebrafish larvae as surrogate hosts to model both gut colonization and extraintestinal infections, we found that ECP can act as a multi-niche colonization and virulence factor for BA1250. Furthermore, our results indicate that ECP-related changes in activation of envelope stress response pathways may alter the fitness of BA1250. Using an in silico approach, we also delineated the broader repertoire of adhesins that are encoded by BA1250, and provide evidence that the expression of at least a few of these varies in the absence of functional ECP.

Streptococcus salivarius 24SMBc Genome Analysis Reveals New Biosynthetic Gene Clusters Involved in Antimicrobial Effects on Streptococcus pneumoniae and Streptococcus pyogenes

Streptococcus salivarius 24SMBc is an oral probiotic with antimicrobial activity against the otopathogens Streptococcus pyogenes and Streptococcus pneumoniae. Clinical studies have reinforced its role in reducing the recurrence of upper respiratory tract infections (URTIs) and rebalancing the nasal microbiota. In this study, for the first time, we characterized 24SMBc by whole genome sequencing and annotation; likewise, its antagonistic activity vs. Streptococcus pneumoniae and Streptococcus pyogenes was evaluated by in vitro co-aggregation and competitive adherence tests. The genome of 24SMBc comprises 2,131,204 bps with 1933 coding sequences (CDS), 44 tRNA, and six rRNA genes and it is categorized in 319 metabolic subsystems. Genome mining by BAGEL and antiSMASH tools predicted three novel biosynthetic gene clusters (BGCs): (i) a Blp class-IIc bacteriocin biosynthetic cluster, identifying two bacteriocins blpU and blpK; (ii) an ABC-type bacteriocin transporter; and (iii) a Type 3PKS (Polyketide synthase) involved in the mevalonate pathway for the isoprenoid biosynthetic process. Further analyses detected two additional genes for class-IIb bacteriocins and 24 putative adhesins and aggregation factors. Finally, in vitro assays of 24SMBc showed significant anti-adhesion and co-aggregation effects against Streptococcus pneumoniae strains, whereas it did not act as strongly against Streptococcus pyogenes. In conclusion, we identified a novel blpU-K bacteriocin-encoding BGC and two class-IIb bacteriocins involved in the activity against Streptococcus pneumoniae and Streptococcus pyogenes; likewise the type 3PKS pathway could have beneficial effects for the host including antimicrobial activity. Furthermore, the presence of adhesins and aggregation factors might be involved in the marked in vitro activity of co-aggregation with pathogens and competitive adherence, showing an additional antibacterial activity not solely related to metabolite production. These findings corroborate the antimicrobial activity of 24SMBc, especially against Streptococcus pneumoniae belonging to different serotypes, and further consolidate the use of this strain in URTIs in clinical settings.

Computational Based Designing of a Multi-Epitopes Vaccine against Burkholderia mallei

The emergence of antibiotic resistance in bacterial species is a major threat to public health and has resulted in high mortality as well as high health care costs. Burkholderia mallei is one of the etiological agents of health care-associated infections. As no licensed vaccine is available against the pathogen herein, using reverse vaccinology, bioinformatics, and immunoinformatics approaches, a multi-epitope-based vaccine against B. mallei was designed. In completely sequenced proteomes of B. mallei, 18,405 core, 3671 non-redundant, and 14,734 redundant proteins were predicted. Among the 3671 non-redundant proteins, 3 proteins were predicted in the extracellular matrix, 11 were predicted as outer membrane proteins, and 11 proteins were predicted in the periplasmic membrane. Only two proteins, type VI secretion system tube protein (Hcp) and type IV pilus secretin proteins, were selected for epitope prediction. Six epitopes, EAMPERMPAA, RSSPPAAGA, DNRPISINL, RQRFDAHAR, AERERQRFDA, and HARAAQLEPL, were shortlisted for multi-epitopes vaccine design. The predicted epitopes were linked to each other via a specific GPGPG linker and the epitopes peptide was then linked to an adjuvant molecule through an EAAAK linker to make the designed vaccine more immunologically potent. The designed vaccine was also found to have favorable physicochemical properties with a low molecular weight and fewer transmembrane helices. Molecular docking studies revealed vaccine construct stable binding with MHC-I, MHC-II, and TLR-4 with energy scores of −944.1 kcal/mol, −975.5 kcal/mol, and −1067.3 kcal/mol, respectively. Molecular dynamic simulation assay noticed stable dynamics of the docked vaccine-receptors complexes and no drastic changes were observed. Binding free energies estimation revealed a net value of −283.74 kcal/mol for the vaccine-MHC-I complex, −296.88 kcal/mol for the vaccine-MHC-II complex, and −586.38 kcal/mol for the vaccine-TLR-4 complex. These findings validate that the designed vaccine construct showed promising ability in terms of binding to immune receptors and may be capable of eliciting strong immune responses once administered to the host. Further evidence from experimentations in mice models is required to validate real immune protection of the designed vaccine construct against B. mallei.

Design of Multi-Epitope Vaccine for Staphylococcus saprophyticus: Pan-Genome and Reverse Vaccinology Approach

Staphylococcus saprophyticus is a Gram-positive coccus responsible for the occurrence of cystitis in sexually active, young females. While effective antibiotics against this organism exist, resistant strains are on the rise. Therefore, prevention via vaccines appears to be a viable solution to address this problem. In comparison to traditional techniques of vaccine design, computationally aided vaccine development demonstrates marked specificity, efficiency, stability, and safety. In the present study, a novel, multi-epitope vaccine construct was developed against S. saprophyticus by targeting fully sequenced proteomes of its five different strains, which were examined using a pangenome and subtractive proteomic strategy to characterize prospective vaccination targets. The three immunogenic vaccine targets which were utilized to map the probable immune epitopes were verified by annotating the entire proteome. The predicted epitopes were further screened on the basis of antigenicity, allergenicity, water solubility, toxicity, virulence, and binding affinity towards the DRB*0101 allele, resulting in 11 potential epitopes, i.e., DLKKQKEKL, NKDLKKQKE, QDKLKDKSD, NVMDNKDLE, TSGTPDSQA, NANSDGSSS, GSDSSSSNN, DSSSSNNDS, DSSSSDRNN, SSSDRNNGD, and SSDDKSKDS. All these epitopes have the efficacy to cover 99.74% of populations globally. Finally, shortlisted epitopes were joined together with linkers and three different adjuvants to find the most stable and immunogenic vaccine construct. The top-ranked vaccine construct was further scrutinized on the basis of its physicochemical characterization and immunological profile. The non-allergenic and antigenic features of modeled vaccine constructs were initially validated and then subjected to docking with immune receptor major histocompatibility complex I and II (MHC-I and II), resulting in strong contact. In silico cloning validations yielded a codon adaptation index (CAI) value of 1 and an ideal percentage of GC contents (46.717%), indicating a putative expression of the vaccine in E. coli. Furthermore, immune simulation demonstrated that, after injecting the proposed MEVC, powerful antibodies were produced, resulting in the sharpest peaks of IgM + IgG formation (>11,500) within 5 to 15 days. Experimental testing against S. saprophyticus can evaluate the safety and efficacy of these prophylactic vaccination designs.

Reverse Vaccinology and Immunoinformatic Assisted Designing of a Multi-Epitopes Based Vaccine Against Nosocomial Burkholderia cepacia

Burkholderia cepacia is a Gram-negative nosocomial pathogen and is considered as a troublesome bacterium due to its resistance to many common antibiotics. There is no licensed vaccine available to prevent the pathogen infections, thus making the condition more alarming and warrant the search for novel therapeutic and prophylactic approaches. In order to identify protective antigens from pathogen proteome, substantial efforts are put forth to prioritized potential vaccine targets and antigens that can be easily evaluated experimentally. In this vaccine design investigation, it was found that B. cepacia completely sequenced proteomes available in NCBI genome database has a total of 28,966 core proteins. Out of total, 25,282 proteins were found redundant while 3,684 were non-redundant. Subcellular localization revealed that 18 proteins were extracellular, 31 were part of the outer membrane, 75 proteins were localized in the periplasm, and 23 were virulent proteins. Five proteins namely flagellar hook protein (FlgE), fimbria biogenesis outer membrane usher protein, Type IV pilus secretin (PilQ), cytochrome c4, flagellar hook basal body complex protein (FliE) were tested for positive for antigenic, non-toxic, and soluble epitopes during predication of B-cell derived T-cell epitopes. A vaccine peptide of 14 epitopes (joined together via GPGPG linkers) and cholera toxin B subunit (CTBS) adjuvant (joined to epitopes peptide via EAAAK linker) was constructed. Binding interaction of the modeled vaccine with MHC-I, MHC-II, and Toll-like receptor 4 (TLR-4) immune receptors was studied using molecular docking studies and further analyzed in molecular dynamics simulations that affirms strong intermolecular binding and stable dynamics. The maximum root mean square deviation (RMSD) score of complexes in the simulation time touches to 2 Å. Additionally, complexes binding free energies were determined that concluded robust interaction energies dominated by van der Waals. The total energy of each complex is < -190 kcal/mol. In summary, the designed vaccine showed promising protective immunity against B. cepacia and needs to be examined in experiments.

Genomic Characterization of Lactobacillus delbrueckii Strains with Probiotics Properties

Probiotics are health-beneficial microorganisms with mainly immunomodulatory and anti-inflammatory properties. Lactobacillus delbrueckii species is a common bacteria used in the dairy industry, and their benefits to hosting health have been reported. This study analyzed the core genome of nine strains of L. delbrueckii species with documented probiotic properties, focusing on genes related to their host health benefits. For this, a combined methodology including several software and databases (BPGA, SPAAN, BAGEL4, BioCyc, KEEG, and InterSPPI) was used to predict the most important characteristics related to L. delbrueckii strains probiose. Comparative genomics analyses revealed that L. delbrueckii probiotic strains shared essential genes related to acid and bile stress response and antimicrobial activity. Other standard features shared by these strains are surface layer proteins and extracellular proteins-encoding genes, with high adhesion profiles that interacted with human proteins of the inflammatory signaling pathways (TLR2/4-MAPK, TLR2/4-NF-κB, and NOD-like receptors). Among these, the PrtB serine protease appears to be a strong candidate responsible for the anti-inflammatory properties reported for these strains. Furthermore, genes with high proteolytic and metabolic activity able to produce beneficial metabolites, such as acetate, bioactive peptides, and B-complex vitamins were also identified. These findings suggest that these proteins can be essential in biological mechanisms related to probiotics’ beneficial effects of these strains in the host.

Vaccinomics to Design a Multi-Epitopes Vaccine for Acinetobacter baumannii

Antibiotic resistance (AR) is the result of microbes’ natural evolution to withstand the action of antibiotics used against them. AR is rising to a high level across the globe, and novel resistant strains are emerging and spreading very fast. Acinetobacter baumannii is a multidrug resistant Gram-negative bacteria, responsible for causing severe nosocomial infections that are treated with several broad spectrum antibiotics: carbapenems, β-lactam, aminoglycosides, tetracycline, gentamicin, impanel, piperacillin, and amikacin. The A. baumannii genome is superplastic to acquire new resistant mechanisms and, as there is no vaccine in the development process for this pathogen, the situation is more worrisome. This study was conducted to identify protective antigens from the core genome of the pathogen. Genomic data of fully sequenced strains of A. baumannii were retrieved from the national center for biotechnological information (NCBI) database and subjected to various genomics, immunoinformatics, proteomics, and biophysical analyses to identify potential vaccine antigens against A. baumannii. By doing so, four outer membrane proteins were prioritized: TonB-dependent siderphore receptor, OmpA family protein, type IV pilus biogenesis stability protein, and OprD family outer membrane porin. Immuoinformatics predicted B-cell and T-cell epitopes from all four proteins. The antigenic epitopes were linked to design a multi-epitopes vaccine construct using GPGPG linkers and adjuvant cholera toxin B subunit to boost the immune responses. A 3D model of the vaccine construct was built, loop refined, and considered for extensive error examination. Disulfide engineering was performed for the stability of the vaccine construct. Blind docking of the vaccine was conducted with host MHC-I, MHC-II, and toll-like receptors 4 (TLR-4) molecules. Molecular dynamic simulation was carried out to understand the vaccine-receptors dynamics and binding stability, as well as to evaluate the presentation of epitopes to the host immune system. Binding energies estimation was achieved to understand intermolecular interaction energies and validate docking and simulation studies. The results suggested that the designed vaccine construct has high potential to induce protective host immune responses and can be a good vaccine candidate for experimental in vivo and in vitro studies.

Identification of Toxoplasma gondii adhesins through a machine learning approach

Toxoplasma gondii, as other apicomplexa, employs adhesins transmembrane proteins for binding and invasion to host cells. Search and characterization of adhesins is pivotal in understanding Apicomplexa invasion mechanisms and targeting new druggable candidates. This work developed a machine learning software called ApiPredictor UniQE V2.0, based on two approaches: support vector machines and multilayer perceptron, to predict adhesins proteins from amino acid sequences. By using ApiPredictor UniQE V2.0, five SAG-Related Sequences (SRSs) were identified within the Toxoplasma gondii proteome. One of those candidates, TgSRS12B, was cloned in plasmid pEXP5-CT/TOPO and expressed in E. coli BL21 DE3. The resulting recombinant protein was purified via affinity chromatography. Co-precipitation assays in CaCo and Muller cells showed interactions between TgSRS12B-His-tagged and the membrane fractions from both human cell lines. In conclusion, we demonstrated that ApiPredictor UniQE V2.0, a bioinformatic free software, was able to identify TgSRS12B as a new adhesin protein.

Identification of Putative Vaccine and Drug Targets against the Methicillin-Resistant Staphylococcus aureus by Reverse Vaccinology and Subtractive Genomics Approaches

Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen and responsible for causing life-threatening infections. The emergence of hypervirulent and multidrug-resistant (MDR) S. aureus strains led to challenging issues in antibiotic therapy. Consequently, the morbidity and mortality rates caused by S. aureus infections have a substantial impact on health concerns. The current worldwide prevalence of MRSA infections highlights the need for long-lasting preventive measures and strategies. Unfortunately, effective measures are limited. In this study, we focus on the identification of vaccine candidates and drug target proteins against the 16 strains of MRSA using reverse vaccinology and subtractive genomics approaches. Using the reverse vaccinology approach, 4 putative antigenic proteins were identified; among these, PrsA and EssA proteins were found to be more promising vaccine candidates. We applied a molecular docking approach of selected 8 drug target proteins with the drug-like molecules, revealing that the ZINC4235426 as potential drug molecule with favorable interactions with the target active site residues of 5 drug target proteins viz., biotin protein ligase, HPr kinase/phosphorylase, thymidylate kinase, UDP-N-acetylmuramoyl-L-alanyl-D-glutamate-L-lysine ligase, and pantothenate synthetase. Thus, the identified proteins can be used for further rational drug or vaccine design to identify novel therapeutic agents for the treatment of multidrug-resistant staphylococcal infection.

Plant-Based Vaccines: Antigen Design, Diversity, and Strategies for High Level Production

Vaccines for human use have conventionally been developed by the production of (1) microbial pathogens in eggs or mammalian cells that are then inactivated, or (2) by the production of pathogen proteins in mammalian and insect cells that are purified for vaccine formulation, as well as, more recently, (3) by using RNA or DNA fragments from pathogens. Another approach for recombinant antigen production in the last three decades has been the use of plants as biofactories. Only have few plant-produced vaccines been evaluated in clinical trials to fight against diseases, of which COVID-19 vaccines are the most recent to be FDA approved. In silico tools have accelerated vaccine design, which, combined with transitory antigen expression in plants, has led to the testing of promising prototypes in pre-clinical and clinical trials. Therefore, this review deals with a description of immunoinformatic tools and plant genetic engineering technologies used for antigen design (virus-like particles (VLP), subunit vaccines, VLP chimeras) and the main strategies for high antigen production levels. These key topics for plant-made vaccine development are discussed and perspectives are provided.

Adhesins in the virulence of opportunistic fungal pathogens of human

Aspergillosis, candidiasis, and cryptococcosis are the most common cause of mycoses-related disease and death among immune-compromised patients. Adhesins are cell-surface exposed proteins or glycoproteins of pathogens that bind to the extracellular matrix (ECM) constituents or mucosal epithelial surfaces of the host cells. The forces of interaction between fungal adhesins and host tissues are accompanied by ligand binding, hydrophobic interactions and protein-protein aggregation. Adherence is the primary and critical step involved in the pathogenesis; however, there is limited information on fungal adhesins compared to that on the bacterial adhesins. Except a few studies based on screening of proteome for adhesin identification, majority are based on characterization of individual adhesins. Recently, based on their characteristic signatures, many putative novel fungal adhesins have been predicted using bioinformatics algorithms. Some of these novel adhesin candidates have been validated by in-vitro studies; though, most of them are yet to be characterised experimentally. Morphotype specific adhesin expression as well as tissue tropism are the crucial determinants for a successful adhesion process. This review presents a comprehensive overview of various studies on fungal adhesins and discusses the targetability of the adhesins and adherence phenomenon, for combating the fungal infection in a preventive or therapeutic mode.

Application of Reverse Vaccinology and Immunoinformatic Strategies for the Identification of Vaccine Candidates Against Shigella flexneri

Reverse vaccinology (RV) was first introduced by Rappuoli for the development of an effective vaccine against serogroup B Neisseria meningitidis (MenB). With the advances in next generation sequencing technologies, the amount of genomic data has risen exponentially. Since then, the RV approach has widely been used to discover potential vaccine protein targets by screening whole genome sequences of pathogens using a combination of sophisticated computational algorithms and bioinformatic tools. In contrast to conventional vaccine development strategies, RV offers a novel method to facilitate rapid vaccine design and reduces reliance on the traditional, relatively tedious, and labor-intensive approach based on Pasteur"s principles of isolating, inactivating, and injecting the causative agent of an infectious disease. Advances in biocomputational techniques have remarkably increased the significance for the rapid identification of the proteins that are secreted or expressed on the surface of pathogens. Immunogenic proteins which are able to induce the immune response in the hosts can be predicted based on the immune epitopes present within the protein sequence. To date, RV has successfully been applied to develop vaccines against a variety of infectious pathogens. In this chapter, we apply a pipeline of bioinformatic programs for identification of Shigella flexneri potential vaccine candidates as an illustration immunoinformatic tools available for RV.

A Comprehensive Computer Aided Vaccine Design Approach to Propose a Multi-Epitopes Subunit Vaccine against Genus Klebsiella Using Pan-Genomics, Reverse Vaccinology, and Biophysical Techniques

Klebsiella is a genus of nosocomial bacterial pathogens and is placed in the most critical list of World Health Organization (WHO) for development of novel therapeutics. The pathogens of the genus are associated with high mortality and morbidity. Owing to their strong resistance profile against different classes of antibiotics and nonavailability of a licensed vaccine, urgent efforts are required to develop a novel vaccine candidate that can tackle all pathogenic species of the Klebsiella genus. The present study aims to design a broad-spectrum vaccine against all species of the Klebsiella genus with objectives to identify the core proteome of pathogen species, prioritize potential core vaccine proteins, analyze immunoinformatics of the vaccine proteins, construct a multi-epitopes vaccine, and provide its biophysical analysis. Herein, we investigated all reference species of the genus to reveal their core proteome. The core proteins were then subjected to multiple reverse vaccinology checks that are mandatory for the prioritization of potential vaccine candidates. Two proteins (TonB-dependent siderophore receptor and siderophore enterobactin receptor FepA) were found to fulfill all vaccine parameters. Both these proteins harbor several potent B-cell-derived T-cell epitopes that are antigenic, nonallergic, nontoxic, virulent, water soluble, IFN-γ producer, and efficient binder of DRB*0101 allele. The selected epitopes were modeled into a multi-epitope peptide comprising linkers and Cholera Toxin B adjuvant. For docking with innate immune and MHC receptors and afterward molecular dynamics simulations and binding free energy analysis, the vaccine structure was modeled for tertiary structure and refined for structural errors. To assess the binding affinity and presentation of the designed vaccine construct, binding mode and interactions analysis were performed using molecular docking and molecular dynamics simulation techniques. These biophysical approaches illustrated the vaccine as a good binder to the immune receptors and revealed robust interactions energies. The vaccine sequence was further translated to nucleotide sequence and cloned into an appropriate vector for expressing it at high rate in Escherichia coli K12 strain. In addition, the vaccine was illustrated to generate a good level of primary, secondary, and tertiary immune responses, proving good immunogenicity of the vaccine. Based on the reported results, the vaccine can be a good candidate to be evaluated for effectiveness in wet laboratory validation studies.

Identification of a Novel Antigen for Serological Diagnosis of Scrub Typhus

Scrub typhus is an acute infectious disease caused by the bacterium Orientia tsutsugamushi, which is widely distributed in northern, southern, and eastern Asia. Early diagnosis is essential because the average case fatality rate is usually >10% but can be as high as 45% if antimicrobial treatment is delayed. Although an O. tsutsugamushi 56-kDa type-specific antigen (TSA) is commonly used for serological diagnosis of scrub typhus, the 56-kDa TSA shows variations among O. tsutsugamushi strains, which may lead to poor diagnostic results. Therefore, the discovery of new antigenic proteins may improve diagnostic accuracy. In this study, we identified an O. tsutsugamushi 27 kDa antigen through an immunoinformatic approach and verified its diagnostic potential using patient samples. Compared with the O. tsutsugamushi 56-kDa antigen, the new 27-kDa antigen showed better diagnostic specificity with similar diagnostic sensitivity. Therefore, the O. tsutsugamushi 27-kDa antigen shows potential as a novel serological diagnostic antigen for scrub typhus, providing higher diagnostic accuracy for O. tsutsugamushi than the 56-kDa antigen.

Identification of anti-horn fly vaccine antigen candidates using a reverse vaccinology approach

Background

The horn fly, Haematobia irritans irritans, causes significant production losses to the cattle industry. Horn fly control relies on insecticides; however, alternative control methods such as vaccines are needed due to the fly's capacity to quickly develop resistance to insecticides, and the pressure for eco-friendly options.

Methods

We used a reverse vaccinology approach comprising three vaccine prediction and 11 annotation tools to evaluate and rank 79,542 translated open reading frames (ORFs) from the horn fly's transcriptome, and selected 10 transcript ORFs as vaccine candidates for expression in Pichia pastoris. The expression of the 10 selected transcripts and the proteins that they encoded were investigated in adult flies by reverse transcription polymerase chain reaction (RT-PCR) and mass spectrometry, respectively. Then, we evaluated the immunogenicity of a vaccine candidate in an immunization trial and the antigen’s effects on horn fly mortality and fecundity in an in vitro feeding assay.

Results

Six of the ten vaccine candidate antigens were successfully expressed in P. pastoris. RT-PCR confirmed the expression of all six ORFs in adult fly RNA. One of the vaccine candidate antigens, BI-HS009, was expressed in sufficient quantity for immunogenicity and efficacy trials. The IgG titers of animals vaccinated with BI-HS009 plus adjuvant were significantly higher than those of animals vaccinated with buffer plus adjuvant only from days 42 to 112, with a peak on day 56. Progeny of horn flies feeding upon blood from animals vaccinated with BI-HS009 plus adjuvant collected on day 56 had 63% lower pupariation rate and 57% lower adult emergence than the control group (ANOVA: F_{(1, 6)} = 8.221, P = 0.028 and F_{(1, 6)} = 8.299, P = 0.028, respectively).

Conclusions

The reverse vaccinology approach streamlined the discovery process by prioritizing possible vaccine antigen candidates. Through a thoughtful process of selection and in vivo and in vitro evaluations, we were able to identify a promising antigen for an anti-horn fly vaccine.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-04938-5.

Quest for Novel Preventive and Therapeutic Options Against Multidrug-Resistant Pseudomonas aeruginosa

Pseudomonas aeruginosa (P. aeruginosa) is a critical healthcare challenge due to its ability to cause persistent infections and the acquisition of antibiotic resistance mechanisms. Lack of preventive vaccines and rampant drug resistance phenomenon has rendered patients vulnerable. As new antimicrobials are in the preclinical stages of development, mining for the unexploited drug targets is also crucial. In the present study, we designed a B- and T-cell multi-epitope vaccine against P. aeruginosa using a subtractive proteomics and immunoinformatics approach. A total of five proteins were shortlisted based on essentiality, extracellular localization, virulence, antigenicity, pathway association, hydrophilicity, and low molecular weight. These include two outer membrane porins; OprF (P13794) and OprD (P32722), a protein activator precursor pra (G3XDA9), a probable outer membrane protein precursor PA1288 (Q9I456), and a conserved hypothetical protein PA4874 (Q9HUT9). These shortlisted proteins were further analyzed to identify immunogenic and antigenic B- and T-cell epitopes. The best scoring epitopes were then further subjected to the construction of a polypeptide multi-epitope vaccine and joined with cholera toxin B subunit adjuvant. The final chimeric construct was docked with TLR4 and confirmed by normal mode simulation studies. The designed B- and T-cell multi-epitope vaccine candidate is predicted immunogenic in nature and has shown strong interactions with TLR-4. Immune simulation predicted high-level production of B- and T-cell population and maximal expression was ensured in E. coli strain K12. The identified drug targets qualifying the screening criteria were: UDP-2-acetamido-2-deoxy-d-glucuronic acid 3-dehydrogenase WbpB (G3XD23), aspartate semialdehyde dehydrogenase (Q51344), 2-amino-4-hydroxy-6-hydroxymethyldihydropteridine pyrophosphokinase (Q9HV71), 3-deoxy-D-manno-octulosonic-acid transferase (Q9HUH7), glycyl-tRNA synthetase alpha chain (Q9I7B7), riboflavin kinase/FAD synthase (Q9HVM3), aconitate hydratase 2 (Q9I2V5), probable glycosyltransferase WbpH (G3XD85) and UDP-3-O-[3-hydroxylauroyl] glucosamine N-acyltransferase (Q9HXY6). For druggability and pocketome analysis crystal and homology structures of these proteins were retrieved and developed. A sequence-based search was performed in different databases (ChEMBL, Drug Bank, PubChem and Pseudomonas database) for the availability of reported ligands and tested drugs for the screened targets. These predicted targets may provide a basis for the development of reliable antibacterial preventive and therapeutic options against P. aeruginosa.

Pan-vaccinomics approach towards a universal vaccine candidate against WHO priority pathogens to address growing global antibiotic resistance

Antimicrobial resistance (AMR) in bacterial pathogens is a major global distress. Due to the slow progress of antibiotics development and the fast pace of resistance acquisition, there is an urgent need for effective vaccines against such bacterial pathogens. In-silico approaches including pan-genomics, subtractive proteomics, reverse vaccinology, immunoinformatics, molecular docking, and dynamics simulation studies were applied in the current study to identify a universal potential vaccine candidate against the 18 multi-drug resistance (MDRs) bacterial pathogenic species from a WHO priority list. Ten non-redundant, non-homologous, virulent, and antigenic vaccine candidates were filtered against all targeted species. Nine B-cell-derived T-cell antigen epitopes which show a great affinity to the dominant HLA allele (DRB1*0101) in the human population were screened from selected vaccine candidates using immunoinformatics approaches. Screened epitopes were then used to design a multi-epitope peptide vaccine construct (MEPVC) along with β-defensin adjuvant to improve the immunogenic properties of the proposed vaccine construct. Molecular docking and MD simulation were carried out to study the binding affinity and molecular interaction of MEPVC with human immune receptors (TLR2, TLR3, TLR4, and TLR6). The final MEPVC construct was reverse translated and in-silico cloned in the pET28a(+) vector to ensure its effectiveness. This in silico construct is expected to be helpful for vaccinologists to assess its immune protection effectiveness in vivo and in vitro to counter rising antibiotic resistance worldwide.

Immunoinformatic Approach for the Identification of Potential Epitopes Against Stenotrophomonas maltophilia: A Global Opportunistic Pathogen

Background:

Stenotrophomonas maltophilia is an aerobic, non-fermentative, gram negative, multidrug resistant and opportunistic nosocomial pathogen. It is associated with high morbidity and mortality in severely immunocompromised paediatric patients, including neonates. Immunoinformatic analysis paved a new way to design epitope-based vaccines which resulted in a potential immunogen with advantages such as lower cost, specific immunity, ease of production, devoid of side effects, and less time consumption than conventional vaccines. Till date, there is no development in the vaccines or antibody-based treatments for S. maltophilia-associated infections.

Introduction:

Currently, epitope-based peptide vaccines against pathogenic bacteria have grasped more attention. In our present study, we have utilized various immunoinformatic tools to find a prominent epitope that interacts with the maximum number of HLA alleles and also with the maximum population coverage for developing a vaccine against Stenotrophomonas maltophilia.

Methods:

This study has incorporated an immunoinformatic based screening approach to explore potential epitope-based vaccine candidates in Stenotrophomonas maltophilia proteome. In this study, 4365 proteins of the Stenotrophomonas maltophilia K279a proteome were screened to identify potential antigens that could be used as a good candidate for the vaccine. Various immunoinformatic tools were used to predict the binding of the promiscuous epitopes with Major Histocompatibility Complex (MHC) class I molecules. Other properties such as allergenicity, physiochemical properties, adhesion properties, antigenicity, population coverage, epitope conservancy and toxicity were analysed for the predicted epitope.

Results:

This study helps in finding the prominent epitope in Stenotrophomonas infections. Hence, the main objective in this research was to screen complete Stenotrophomonas maltophilia proteome to recognize putative epitope candidates for vaccine design. Using computational vaccinology and immunoinformatic tools approach, several aspects are obligatory to be fulfilled by an epitope to be considered as a vaccine candidate. Our findings were promising and showed that the predicted epitopes were non-allergenic and fulfilled other parameters required for being a suitable candidate based on certain physio-chemical, antigenic and adhesion properties.

Conclusion:

The epitopes LLFVLCWPL and KSGEGKCGA have shown the highest binding score of −103 and −78.1 kcal/mol with HLA-A*0201 and HLA-B*0702 MHC class I allele, respectively. They were also predicted to be immunogenic and non-allergenic. Further various immunological tests, both in vivo and in vitro methods, should be performed for finding the efficiency of the predicted epitope in the development of a targeted vaccine against Stenotrophomonas maltophilia infection.

Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning

Vaccination is one of the most significant inventions in medicine. Reverse vaccinology (RV) is a state-of-the-art technique to predict vaccine candidates from pathogen's genome(s). To promote vaccine development, we updated Vaxign2, the first web-based vaccine design program using reverse vaccinology with machine learning. Vaxign2 is a comprehensive web server for rational vaccine design, consisting of predictive and computational workflow components. The predictive part includes the original Vaxign filtering-based method and a new machine learning-based method, Vaxign-ML. The benchmarking results using a validation dataset showed that Vaxign-ML had superior prediction performance compared to other RV tools. Besides the prediction component, Vaxign2 implemented various post-prediction analyses to significantly enhance users' capability to refine the prediction results based on different vaccine design rationales and considerably reduce user time to analyze the Vaxign/Vaxign-ML prediction results. Users provide proteome sequences as input data, select candidates based on Vaxign outputs and Vaxign-ML scores, and perform post-prediction analysis. Vaxign2 also includes precomputed results from approximately 1 million proteins in 398 proteomes of 36 pathogens. As a demonstration, Vaxign2 was used to effectively analyse SARS-CoV-2, the coronavirus causing COVID-19. The comprehensive framework of Vaxign2 can support better and more rational vaccine design. Vaxign2 is publicly accessible at http://www.violinet.org/vaxign2.

Genomic Analysis of Pasteurella atlantica Provides Insight on Its Virulence Factors and Phylogeny and Highlights the Potential of Reverse Vaccinology in Aquaculture

Pasteurellosis in farmed lumpsuckers, Cyclopterus lumpus, has emerged as a serious disease in Norwegian aquaculture in recent years. Genomic characterization of the causative agent is essential in understanding the biology of the bacteria involved and in devising an efficient preventive strategy. The genomes of two clinical Pasteurella atlantica isolates were sequenced (≈2.3 Mbp), and phylogenetic analysis confirmed their position as a novel species within the Pasteurellaceae. In silico analyses revealed 11 genomic islands and 5 prophages, highlighting the potential of mobile elements as driving forces in the evolution of this species. The previously documented pathogenicity of P. atlantica is strongly supported by the current study, and 17 target genes were recognized as putative primary drivers of pathogenicity. The expression level of a predicted vaccine target, an uncharacterized adhesin protein, was significantly increased in both broth culture and following the exposure of P. atlantica to lumpsucker head kidney leucocytes. Based on in silico and functional analyses, the strongest gene target candidates will be prioritized in future vaccine development efforts to prevent future pasteurellosis outbreaks.

An integrative docking and simulation-based approach towards the development of epitope-based vaccine against enterotoxigenic Escherichia coli

Enterotoxigenic E.coli is causing diarrheal illness in children as well as adults with the majority of the cases occurring in developing countries. To reduce the number of cases occurring worldwide, the development of an effectual vaccine against these bacteria can be the only prevention. This conjectural work was performed using modern bioinformatics tools for investigation of proteome of ETEC strain E24377A. Different computational vaccinology approaches were deployed to assess several parameters including antigenicity, allergenicity, stability, localization, molecular weight and toxicity of the predicted epitopes required for good vaccine candidate to elicit immune response against diarrhea. We estimated two known control antigens, epitope ¹⁴¹STLPETTVV¹⁴⁹ of Hepatitis B virus and epitope ²⁶⁵ILRGSVAHK²⁷³ of H1N1 Nucleoprotein in an attempt to corroborate our research work. Furthermore molecular docking was performed to evaluate the interaction between HLA allele and peptide, the peptide QYGGGNSAL and peptide LPYFELRWL were considered to be the most promiscuous T cell epitopes with the highest binding energy value of −2.09 kcal/mol and −1.84 kcal/mol, respectively. In addition, dynamic simulation revealed good stability of the vaccine construct as well as population coverage analysis exhibits the highest population coverage in the regions of East Asia, India, Northeast Asia, South Asia and North America. Therefore, these two epitopes can be further synthesized for wet lab analysis and could be considered as a promising vaccine against diarrhea.

Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors

MOTIVATION

Identification of virulence factors (VFs) is critical to the elucidation of bacterial pathogenesis and prevention of related infectious diseases. Current computational methods for VF prediction focus on binary classification or involve only several class(es) of VFs with sufficient samples. However, thousands of VF classes are present in real-world scenarios, and many of them only have a very limited number of samples available.

RESULTS

We first construct a large VF dataset, covering 3446 VF classes with 160 495 sequences, and then propose deep convolutional neural network models for VF classification. We show that (i) for common VF classes with sufficient samples, our models can achieve state-of-the-art performance with an overall accuracy of 0.9831 and an F1-score of 0.9803; (ii) for uncommon VF classes with limited samples, our models can learn transferable features from auxiliary data and achieve good performance with accuracy ranging from 0.9277 to 0.9512 and F1-score ranging from 0.9168 to 0.9446 when combined with different predefined features, outperforming traditional classifiers by 1-13% in accuracy and by 1-16% in F1-score.

AVAILABILITY AND IMPLEMENTATION

All of our datasets are made publicly available at http://www.mgc.ac.cn/VFNet/, and the source code of our models is publicly available at https://github.com/zhengdd0422/VFNet.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Design of a Novel Multi Epitope-Based Vaccine for Pandemic Coronavirus Disease (COVID-19) by Vaccinomics and Probable Prevention Strategy against Avenging Zoonotics

The emergence and rapid expansion of the coronavirus disease (COVID-19) require the development of effective countermeasures especially a vaccine to provide active acquired immunity against the virus. This study presented a comprehensive vaccinomics approach applied to the complete protein data published so far in the National Center for Biotechnological Information (NCBI) coronavirus data hub. We identified non-structural protein 8 (Nsp8), 3C-like proteinase, and spike glycoprotein as potential targets for immune responses to COVID-19. Epitopes prediction illustrated both B-cell and T-cell epitopes associated with the mentioned proteins. The shared B and T-cell epitopes: DRDAAMQRK and QARSEDKRA of Nsp8, EDMLNPNYEDL and EFTPFDVVR of 3C-like proteinase, and VNNSYECDIPI of the spike glycoprotein are regions of high potential interest and have a high likelihood of being recognized by the human immune system. The vaccine construct of the epitopes shows stimulation of robust primary immune responses and high level of interferon gamma. Also, the construct has the best conformation with respect to the tested innate immune receptors involving vigorous molecular mechanics and solvation energy. Designing of vaccination strategies that target immune response focusing on these conserved epitopes could generate immunity that not only provide cross protection across Betacoronaviruses but additionally resistant to virus evolution.

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning

To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein, have been tested for vaccine development against SARS and MERS. However, these vaccine candidates might lack the induction of complete protection and have safety concerns. We then applied the Vaxign and the newly developed machine learning-based Vaxign-ML reverse vaccinology tools to predict COVID-19 vaccine candidates. Our Vaxign analysis found that the SARS-CoV-2 N protein sequence is conserved with SARS-CoV and MERS-CoV but not from the other four human coronaviruses causing mild symptoms. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10), were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and the predicted linear B-cell epitopes were found to be localized on the surface of the protein. Our predicted vaccine targets have the potential for effective and safe COVID-19 vaccine development. We also propose that an "Sp/Nsp cocktail vaccine" containing a structural protein(s) (Sp) and a non-structural protein(s) (Nsp) would stimulate effective complementary immune responses.

DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy

Virulence factors (VFs) enable pathogens to infect their hosts. A wealth of individual, disease-focused studies has identified a wide variety of VFs, and the growing mass of bacterial genome sequence data provides an opportunity for computational methods aimed at predicting VFs. Despite their attractive advantages and performance improvements, the existing methods have some limitations and drawbacks. Firstly, as the characteristics and mechanisms of VFs are continually evolving with the emergence of antibiotic resistance, it is more and more difficult to identify novel VFs using existing tools that were previously developed based on the outdated data sets; secondly, few systematic feature engineering efforts have been made to examine the utility of different types of features for model performances, as the majority of tools only focused on extracting very few types of features. By addressing the aforementioned issues, the accuracy of VF predictors can likely be significantly improved. This, in turn, would be particularly useful in the context of genome wide predictions of VFs. In this work, we present a deep learning (DL)-based hybrid framework (termed DeepVF) that is utilizing the stacking strategy to achieve more accurate identification of VFs. Using an enlarged, up-to-date dataset, DeepVF comprehensively explores a wide range of heterogeneous features with popular machine learning algorithms. Specifically, four classical algorithms, including random forest, support vector machines, extreme gradient boosting and multilayer perceptron, and three DL algorithms, including convolutional neural networks, long short-term memory networks and deep neural networks are employed to train 62 baseline models using these features. In order to integrate their individual strengths, DeepVF effectively combines these baseline models to construct the final meta model using the stacking strategy. Extensive benchmarking experiments demonstrate the effectiveness of DeepVF: it achieves a more accurate and stable performance compared with baseline models on the benchmark dataset and clearly outperforms state-of-the-art VF predictors on the independent test. Using the proposed hybrid ensemble model, a user-friendly online predictor of DeepVF (http://deepvf.erc.monash.edu/) is implemented. Furthermore, its utility, from the user's viewpoint, is compared with that of existing toolkits. We believe that DeepVF will be exploited as a useful tool for screening and identifying potential VFs from protein-coding gene sequences in bacterial genomes.

A systematic strategy for the investigation of vaccines and drugs targeting bacteria

Infectious and epidemic diseases induced by bacteria have historically caused great distress to people, and have even resulted in a large number of deaths worldwide. At present, many researchers are working on the discovery of viable drug and vaccine targets for bacteria through multiple methods, including the analyses of comparative subtractive genome, core genome, replication-related proteins, transcriptomics and riboswitches, which plays a significant part in the treatment of infectious and pandemic diseases. The 3D structures of the desired target proteins, drugs and epitopes can be predicted and modeled through target analysis. Meanwhile, molecular dynamics (MD) analysis of the constructed drug/epitope-protein complexes is an important standard for testing the suitability of these screened drugs and vaccines. Currently, target discovery, target analysis and MD analysis are integrated into a systematic set of drug and vaccine analysis strategy for bacteria. We hope that this comprehensive strategy will help in the design of high-performance vaccines and drugs.

Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens

MOTIVATION

Reverse vaccinology (RV) is a milestone in rational vaccine design, and machine learning (ML) has been applied to enhance the accuracy of RV prediction. However, ML-based RV still faces challenges in prediction accuracy and program accessibility.

RESULTS

This study presents Vaxign-ML, a supervised ML classification to predict bacterial protective antigens (BPAgs). To identify the best ML method with optimized conditions, five ML methods were tested with biological and physiochemical features extracted from well-defined training data. Nested 5-fold cross-validation and leave-one-pathogen-out validation were used to ensure unbiased performance assessment and the capability to predict vaccine candidates against a new emerging pathogen. The best performing model (eXtreme Gradient Boosting) was compared to three publicly available programs (Vaxign, VaxiJen, and Antigenic), one SVM-based method, and one epitope-based method using a high-quality benchmark dataset. Vaxign-ML showed superior performance in predicting BPAgs. Vaxign-ML is hosted in a publicly accessible web server and a standalone version is also available.

AVAILABILITY AND IMPLEMENTATION

Vaxign-ML website at http://www.violinet.org/vaxign/vaxign-ml, Docker standalone Vaxign-ML available at https://hub.docker.com/r/e4ong1031/vaxign-ml and source code is available at https://github.com/VIOLINet/Vaxign-ML-docker.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Putative β-Barrel Outer Membrane Proteins of the Bovine Digital Dermatitis-Associated Treponemes: Identification, Functional Characterization, and Immunogenicity

Bovine digital dermatitis (BDD), an infectious disease of the bovine foot with a predominant treponemal etiology, is a leading cause of lameness in dairy and beef herds worldwide. BDD is poorly responsive to antimicrobial therapy and exhibits a relapsing clinical course; an effective vaccine is therefore urgently sought. Using a reverse vaccinology approach, the present study surveyed the genomes of the three BDD-associated Treponema phylogroups for putative β-barrel outer membrane proteins and considered their potential as vaccine candidates. Selection criteria included the presence of a signal peptidase I cleavage site, a predicted β-barrel fold, and cross-phylogroup homology. Four candidate genes were overexpressed in Escherichia coli BL21(DE3), refolded, and purified. Consistent with their classification as β-barrel OMPs, circular-dichroism spectroscopy revealed the adoption of a predominantly β-sheet secondary structure. These recombinant proteins, when screened for their ability to adhere to immobilized extracellular matrix (ECM) components, exhibited a diverse range of ligand specificities. All four proteins specifically and dose dependently adhered to bovine fibrinogen. One recombinant protein was identified as a candidate diagnostic antigen (disease specificity, 75%). Finally, when adjuvanted with aluminum hydroxide and administered to BDD-naive calves using a prime-boost vaccination protocol, these proteins were immunogenic, eliciting specific IgG antibodies. In summary, we present the description of four putative treponemal β-barrel OMPs that exhibit the characteristics of multispecific adhesins. The observed interactions with fibrinogen may be critical to host colonization and it is hypothesized that vaccination-induced antibody blockade of these interactions will impede treponemal virulence and thus be of therapeutic value.

Identification of putative adhesins and carbohydrate ligands of Lactobacillus paracasei using a combinatorial in silico and glycomics microarray profiling approach

Commensal bacteria must colonize host mucosal surfaces to exert health-promoting properties, and bind to gastrointestinal tract (GIT) mucins via their cell surface adhesins. Considerable effort has been directed towards discovery of pathogen adhesins and their ligands to develop anti-infective strategies; however, little is known about the lectin-like adhesins and associated carbohydrate ligands in commensals. In this study, an in silico approach was used to detect surface exposed adhesins in the human commensal Lactobacillus paracasei subsp. paracasei, a promising probiotic commonly used in dairy product fermentation that presents anti-microbial activity. Of the 13 adhesin candidates, 3 sortase-dependent pili clusters were identified in this strain and expression of the adhesin candidate genes was confirmed in vitro. Mass spectrometry analysis confirmed the presence of surface adhesin elongation factor Tu and the chaperonin GroEL, but not pili expression. Whole cells were subsequently incubated on microarrays featuring a panel of GIT mucins from nine different mammalian species and two human-derived cell lines and a library of carbohydrate structures. Binding profiles were compared to those of two known pili-producing lactobacilli, L. johnsonii and L. rhamnosus and all Lactobacillus species displayed overlapping but distinct signatures, which may indicate different abilities for regiospecific GIT colonization. In addition, L. paracasei whole cells favoured binding to α-(2 → 3)-linked sialic acid and α-(1 → 2)-linked fucose-containing carbohydrate structures including blood groups A, B and O and Lewis antigens x, y and b. This study furthers our understanding of host-commensal cross-talk by identifying potential adhesins and specific GIT mucin and carbohydrate ligands and provides insight into the selection of colonization sites by commensals in the GIT.

COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning

To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein, have been tested for vaccine development against SARS and MERS. However, these vaccine candidates might lack the induction of complete protection and have safety concerns. We then applied the Vaxign reverse vaccinology tool and the newly developed Vaxign-ML machine learning tool to predict COVID-19 vaccine candidates. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10), were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and linear B-cell epitopes localized in specific locations and functional domains of the protein. By applying reverse vaccinology and machine learning, we predicted potential vaccine targets for effective and safe COVID-19 vaccine development. We then propose that an "Sp/Nsp cocktail vaccine" containing a structural protein(s) (Sp) and a non-structural protein(s) (Nsp) would stimulate effective complementary immune responses.

Reverse vaccinology approach for the identification and characterization of outer membrane proteins of Shigella flexneri as potential cellular- and antibody-dependent vaccine candidates

Purpose

In the developing world, bacillary dysentery is one of the most common communicable diarrheal infections. There are approximately 169 million cases of shigellosis reported worldwide. The disease is transmitted by a group of Gram-negative intracellular enterobacteria known as Shigella flexneri, S. sonnei, S. dysenteriae, and S. boydii. Conventional treatment regimens for Shigella have been less effective due to the development of resistant strains against antibiotics. Therefore, an effective vaccine for the long term control of Shigella transmission is urgently needed.

Materials and Methods

In this study, a reverse vaccinology approach was employed to identify most conserved and immunogenic outer membrane proteins (OMPs) of S. flexneri 2a.

Results

Five OMPs including fepA, ompC, nlpD_1, tolC, and nlpD_2 were identified as potential vaccine candidates. Protein-protein interactions analysis using STRING software (https://string-db.org/) revealed that five of these OMPs may potentially interact with other intracellular proteins which are involved in beta-lactam resistance pathway. B- and T-cell epitopes of the selected OMPs were predicted using BCPred as well as Propred I and Propred (http://crdd.osdd.net/raghava/propred/), respectively. Each of these OMPs contains regions which are capable to induce B- and T-cell immune responses.

Conclusion

Analysis acquired from this study showed that five selected OMPs have great potential for vaccine development against S. flexneri infection. The predicted immunogenic epitopes can also be used for development of peptide vaccines or multi-epitope vaccines against human shigellosis. Reverse vaccinology is a promising strategy for the discovery of potential vaccine candidates which can be used for future vaccine development against global persistent infections.

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning

To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein, have been tested for vaccine development against SARS and MERS. However, these vaccine candidates might lack the induction of complete protection and have safety concerns. We then applied the Vaxign and the newly developed machine learning-based Vaxign-ML reverse vaccinology tools to predict COVID-19 vaccine candidates. Our Vaxign analysis found that the SARS-CoV-2 N protein sequence is conserved with SARS-CoV and MERS-CoV but not from the other four human coronaviruses causing mild symptoms. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10), were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and the predicted linear B-cell epitopes were found to be localized on the surface of the protein. Our predicted vaccine targets have the potential for effective and safe COVID-19 vaccine development. We also propose that an "Sp/Nsp cocktail vaccine" containing a structural protein(s) (Sp) and a non-structural protein(s) (Nsp) would stimulate effective complementary immune responses.

ReVac: a reverse vaccinology computational pipeline for prioritization of prokaryotic protein vaccine candidates

Background

Reverse vaccinology accelerates the discovery of potential vaccine candidates (PVCs) prior to experimental validation. Current programs typically use one bacterial proteome to identify PVCs through a filtering architecture using feature prediction programs or a machine learning approach. Filtering approaches may eliminate potential antigens based on limitations in the accuracy of prediction tools used. Machine learning approaches are heavily dependent on the selection of training datasets with experimentally validated antigens (positive control) and non-protective-antigens (negative control). The use of one or few bacterial proteomes does not assess PVC conservation among strains, an important feature of vaccine antigens.

Results

We present ReVac, which implements both a panoply of feature prediction programs without filtering out proteins, and scoring of candidates based on predictions made on curated positive and negative control PVCs datasets. ReVac surveys several genomes assessing protein conservation, as well as DNA and protein repeats, which may result in variable expression of PVCs. ReVac’s orthologous clustering of conserved genes, identifies core and dispensable genome components. This is useful for determining the degree of conservation of PVCs among the population of isolates for a given pathogen. Potential vaccine candidates are then prioritized based on conservation and overall feature-based scoring. We present the application of ReVac, applied to 69 Moraxella catarrhalis and 270 non-typeable Haemophilus influenzae genomes, prioritizing 64 and 29 proteins as PVCs, respectively.

Conclusion

ReVac’s use of a scoring scheme ranks PVCs for subsequent experimental testing. It employs a redundancy-based approach in its predictions of features using several prediction tools. The protein’s features are collated, and each protein is ranked based on the scoring scheme. Multi-genome analyses performed in ReVac allow for a comprehensive overview of PVCs from a pan-genome perspective, as an essential pre-requisite for any bacterial subunit vaccine design. ReVac prioritized PVCs of two human respiratory pathogens, identifying both novel and previously validated PVCs.

Compositional model based on factorial evolution for realizing multi-task learning in bacterial virulent protein prediction

The ability of multitask learning promulgated its sovereignty in the machine learning field with the diversified application including but not limited to bioinformatics and pattern recognition. Bioinformatics provides a wide range of applications for Multitask Learning (MTL) methods. Identification of Bacterial virulent protein is one such application that helps in understanding the virulence mechanism for the design of drug and vaccine. However, the limiting factor in a reliable prediction model is the scarcity of the experimentally verified training data. To deal with, casting the problem in a Multitask Learning scenario, could be beneficial. Reusability of auxiliary data from related multiple domains in the prediction of target domain with limited labeled data is the primary objective of multitask learning model. Due to the amalgamation of multiple related data, it is possible that the probability distribution between the features tends to vary. Therefore, to deal with change amongst the feature distribution, this paper proposes a composite model for multitask learning framework which is based on two principles: discovering the shared parameters for identifying the relationships between tasks and common underlying representation of features amongst the related tasks. Through multi-kernel and factorial evolution, the proposed framework able to discover the shared kernel parameters and latent feature representation that is common amongst the tasks. To examine the benefits of the proposed model, an extensive experiment is performed on the freely available dataset at VirulentPred web server. Based on the results, we found that multitask learning model performs better than the conventional single task model. Additionally, our findings state that if the distribution between the tasks is high, then training the multiple models yield slightly better prediction. However, if the data distribution difference is low, multitask learning significantly outperforms the individual learning.