Identification and evaluation of immunogenic MHC-I and MHC-II binding peptides from Mycobacterium tuberculosis

In silico and in vitro evaluation of the immunogenic potential of Leishmania donovani ascorbate peroxidase and its derived peptides

The control and eradication of any infectious disease is only possible with a potential vaccine, which has not been accomplished for human visceral leishmaniasis (VL). The lack of vaccines may increase the risk of VL outbreaks periodically in endemic zones. Identifying a reliable vaccine candidate for Leishmania is a major challenge. Here, we considered Leishmania donovani ascorbate peroxidase (LdAPx) for its in vitro evaluation with the hope of future vaccine candidates for VL. LdAPx was selected based on its unique presence in Leishmania and virulence in VL pathogenesis. Initially, we found antibodies against recombinant LdAPx (rLdAPx) in the serum of VL patients. Therefore, using bioinformatics, we predicted and selected ten (MHC class I and II) peptides. These peptides, evaluated in vitro with PBMCs from healthy, active VL, and treated VL individuals induced PBMC proliferation, IFN-γ secretion, and Nitric Oxide (NO) production, indicating host-protective immune responses. Among them, three peptides (PEP6, PEP8, and PEP9) consistently elicited a Th1-type immune response in PBMCs. Treated VL individuals showed a stronger Th1 response compared to active VL patients and healthy subjects, highlighting these peptides' potential as vaccine candidates. Further studies are on the way toward evaluating the LdAPx-derived peptides or sub-unit vaccine in animal models against the L. donovani challenge.

Artificial intelligence-driven reverse vaccinology for Neisseria gonorrhoeae vaccine: Prioritizing epitope-based candidates

Neisseria gonorrhoeae is the causative agent of the sexually transmitted disease gonorrhea. The increasing prevalence of this disease worldwide, the rise of antibiotic-resistant strains, and the difficulties in treatment necessitate the development of a vaccine, highlighting the significance of preventative measures to control and eradicate the infection. Currently, there is no widely available vaccine, partly due to the bacterium's ability to evade natural immunity and the limited research investment in gonorrhea compared to other diseases. To identify distinct vaccine candidates, we chose to focus on the uncharacterized, hypothetical proteins (HPs) as our initial approach. Using the in silico method, we first carried out a comprehensive assessment of hypothetical proteins of Neisseria gonorrhoeae, encompassing assessments of physicochemical properties, cellular localization, secretary pathways, transmembrane regions, antigenicity, toxicity, and prediction of B-cell and T-cell epitopes, among other analyses. Detailed analysis of all HPs resulted in the functional annotation of twenty proteins with a great degree of confidence. Further, using the immuno-informatics approach, the prediction pipeline identified one CD8+ restricted T-cell epitope, seven linear B-cell epitopes, and seven conformational B-cell epitopes as putative epitope-based peptide vaccine candidates which certainly require further validation in laboratory settings. The study accentuates the promise of functional annotation and immuno-informatics in the systematic design of epitope-based peptide vaccines targeting Neisseria gonorrhoeae.

Immunoinformatics-guided approach for designing a pan-proteome multi-epitope subunit vaccine against African swine fever virus

Despite being identified over a hundred years ago, there is still no commercially available vaccine for the highly contagious and deadly African swine fever virus (ASFV). This study used immunoinformatics for the rapid and inexpensive designing of a safe and effective multi-epitope subunit vaccine for ASFV. A total of 18,858 proteins from 100 well-annotated ASFV proteomes were screened using various computational tools to identify potential epitopes, or peptides capable of triggering an immune response in swine. Proteins from genotypes I and II were prioritized for their involvement in the recent global ASFV outbreaks. The screened epitopes exhibited promising qualities that positioned them as effective components of the ASFV vaccine. They demonstrated antigenicity, immunogenicity, and cytokine-inducing properties indicating their ability to induce potent immune responses. They have strong binding affinities to multiple swine allele receptors suggesting a high likelihood of yielding more amplified responses. Moreover, they were non-allergenic and non-toxic, a crucial prerequisite for ensuring safety and minimizing any potential adverse effects when the vaccine is processed within the host. Integrated with an immunogenic 50S ribosomal protein adjuvant and linkers, the epitopes formed a 364-amino acid multi-epitope subunit vaccine. The ASFV vaccine construct exhibited notable immunogenicity in immune simulation and molecular docking analyses, and stable profiles in secondary and tertiary structure assessments. Moreover, this study designed an optimized codon for efficient translation of the ASFV vaccine construct into the Escherichia coli K-12 expression system using the pET28a(+) vector. Overall, both sequence and structural evaluations suggested the potential of the ASFV vaccine construct as a candidate for controlling and eradicating outbreaks caused by the pathogen.

Mycobacterium smegmatis, a Promising Vaccine Vector for Preventing TB and Other Diseases: Vaccinomics Insights and Applications

Mycobacterium smegmatis (M.sm) is frequently used as an alternative model organism in Mycobacterium tuberculosis (M.tb) studies. While containing high sequence homology with M.tb, it is considered non-pathogenic in humans. As such it has been used to study M.tb and other infections in vivo and more recently been explored for potential therapeutic applications. A body of previous research has highlighted the potential of using genetically modified M.sm displaying rapid growth and unique immunostimulatory characteristics as an effective vaccine vector. Novel systems biology techniques can further serve to optimize these delivery constructs. In this article, we review recent advancements in vaccinomics tools that support the efficacy of a M.sm-based vaccine vector. Moreover, the integration of systems biology and molecular omics techniques in these pioneering studies heralds a potential accelerated pipeline for the development of next-generation recombinant vaccines against rapidly developing diseases.

Design of a Multi-Epitope Vaccine against Tuberculosis from Mycobacterium tuberculosis PE_PGRS49 and PE_PGRS56 Proteins by Reverse Vaccinology

Tuberculosis is a disease caused by Mycobacterium tuberculosis, representing the second leading cause of death by an infectious agent worldwide. The available vaccine against this disease has insufficient coverage and variable efficacy, accounting for a high number of cases worldwide. In fact, an estimated third of the world's population has a latent infection. Therefore, developing new vaccines is crucial to preventing it. In this study, the highly antigenic PE_PGRS49 and PE_PGRS56 proteins were analyzed. These proteins were used for predicting T- and B-cell epitopes and for human leukocyte antigen (HLA) protein binding efficiency. Epitopes GGAGGNGSLSS, FAGAGGQGGLGG, GIGGGTQSATGLG (PE_PGRS49), and GTGWNGGKGDTG (PE_PGRS56) were selected based on their best physicochemical, antigenic, non-allergenic, and non-toxic properties and coupled to HLA I and HLA II structures for in silico assays. A construct with an adjuvant (RS09) plus each epitope joined by GPGPG linkers was designed, and the stability of the HLA-coupled construct was further evaluated by molecular dynamics simulations. Although experimental and in vivo studies are still necessary to ensure its protective effect against the disease, this study shows that the vaccine construct is dynamically stable and potentially effective against tuberculosis.

Epigenetic orchestration of host immune defences by Mycobacterium tuberculosis

Being among the top 10 causes of adult deaths, tuberculosis (TB) disease is considered a major global public health concern to address. The human tuberculosis pathogen, Mycobacterium tuberculosis (Mtb), is an extremely competent and well-versed pathogen that promotes pathogenesis by evading the host immune systems through numerous tactics. Investigations revealed that Mtb could evade the host defense mechanisms by reconfiguring the host gene transcription and causing epigenetic changes. Although results indicate the link between epigenetics and disease manifestation in other bacterial infections, little is known regarding the kinetics of the epigenetic alterations in mycobacterial infection. This literature review discusses the studies in Mtb-induced epigenetic alterations inside the host and its contribution in the host immune evasion strategies. It also discusses how the Mtb-induced alterations could be used as 'epibiomarkers' to diagnose TB. Additionally, this review also discusses therapeutic interventions to be enhanced through remodification by 'epidrugs'.

Advances in computational frameworks in the fight against TB: The way forward

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

Bitter-RF: A random forest machine model for recognizing bitter peptides

Introduction

Bitter peptides are short peptides with potential medical applications. The huge potential behind its bitter taste remains to be tapped. To better explore the value of bitter peptides in practice, we need a more effective classification method for identifying bitter peptides.

Methods

In this study, we developed a Random forest (RF)-based model, called Bitter-RF, using sequence information of the bitter peptide. Bitter-RF covers more comprehensive and extensive information by integrating 10 features extracted from the bitter peptides and achieves better results than the latest generation model on independent validation set.

Results

The proposed model can improve the accurate classification of bitter peptides (AUROC = 0.98 on independent set test) and enrich the practical application of RF method in protein classification tasks which has not been used to build a prediction model for bitter peptides.

Discussion

We hope the Bitter-RF could provide more conveniences to scholars for bitter peptide research.