AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes

Alg-MFDL: A multi-feature deep learning framework for allergenic proteins prediction

The escalating global incidence of allergy patients illustrates the growing impact of allergic issues on global health. Allergens are small molecule antigens that trigger allergic reactions. A widely recognized strategy for allergy prevention involves identifying allergens and avoiding re-exposure. However, the laboratory methods to identify allergenic proteins are often time-consuming and resource-intensive. There is a crucial need to establish efficient and reliable computational approaches for the identification of allergenic proteins. In this study, we developed a novel allergenic proteins predictor named Alg-MFDL, which integrates pre-trained protein language models (PLMs) and traditional handcrafted features to achieve a more complete protein representation. First, we compared the performance of eight pre-trained PLMs from ProtTrans and ESM-2 and selected the best-performing one from each of the two groups. In addition, we evaluated the performance of three handcrafted features and different combinations of them to select the optimal feature or feature combination. Then, these three protein representations were fused and used as inputs to train the convolutional neural network (CNN). Finally, the independent validation was performed on benchmark datasets to evaluate the performance of Alg-MFDL. As a result, Alg-MFDL achieved an accuracy of 0.973, a precision of 0.996, a sensitivity of 0.951, and an F1 value of 0.973, outperforming the most of current state-of-the-art (SOTA) methods across all key metrics. We anticipated that the proposed model could be considered a useful tool for predicting allergen proteins.

Brevibacterium enzymes as biological tools for ochratoxin A detoxification in dairy foods

The origin of ochratoxin A (OTA) in cheeses is mainly due to mould growth during the ripening process, and to a lesser extent, to the use of OTA-contaminated milk in cheese production. Bacterial smear-ripened cheeses developed a smear microbiota on their rind during ripening that greatly contributes to its typical aroma and texture. Bacteria from the Brevibacterium genus belong to the typical smear microbiota of cheeses. Type strains from Brevibacterium species frequently isolated from cheese and milk products were able to transform OTA into much less toxic ochratoxin α (OTα) and L-phenylalanine. Protein searches allowed the identification of a protein annotated as amidohydrolase in these OTA-degrader Brevibacterium strains. The OTA-hydrolytic activity of the identified amidohydrolase was demonstrated by the heterologous production of this protein from B. linens DSM 20425T (BlOTA). In vitro assays revealed that BlOTA transformed OTA into less toxic OTα, as well as ochratoxin B. When compared with other previously described OTA-degrading amidohydrolases, BlOTA exhibited optimal activity at a higher pH (8.0), while showing similar high temperature for optimal activity (55 °C) and thermostability; in addition, a clear preference for substrates with Phe, Tyr or Leu amino acid residues at the C-terminal position was clearly observed. BlOTA efficiently detoxifies OTA-contaminated bovine milk, without provoking changes on its free amino acid composition. Moreover, in silico predictions revealed that BlOTA is a non-allergenic, non-antigenic, and poorly immunogenic protein. Therefore, the QPS status possessed by cheese-Brevibacterium species, as well as the characteristics exhibited by BlOTA, make them suitable tools for the biological detoxification of OTA in dairy and food products.

A comprehensive review of effects of ultrasound pretreatment on processing technologies for food allergens: Allergenicity, nutritional value, and technofunctional properties and safety assessment

Many proteins are essential food components but also major allergens. Reducing protein allergenicity while preserving its nutritional value and technofunctional properties has always been the goal of the food industry. Ultrasound (US) is a green processing method for modifying proteins. In addition, US pretreatment combined with other processing techniques (USPCT) has been increasingly used in the food industry. Therefore, this review presents an overview of recent advances in the impact of US and USPCT (US-combined enzymatic hydrolysis [USCE], US-combined glycation [USCG], and US-combined polyphenol conjugation [USCP]) on the allergenicity, nutritional value, and technofunctional properties of food allergens. We discuss the potential mechanisms, advantages, and limitations of these technologies for improving the properties of proteins and analyze their safety, challenges, and corresponding solutions. It was found that USPCT can improve the efficiency and effectiveness of different methods, which in turn can be more effective in reducing protein allergenicity and improving the nutritional value and functional properties of processed products. Future research should start with new processing methods, optimization of process conditions, industrial production, and the use of new research techniques to promote technical progress. This paper is expected to provide reference for the development of high-quality hypoallergenic protein raw materials.

Immunogenicity of a multivalent protein subunit vaccine based on non-glycosylated RBD antigens of SARS-cov-2 and its variants

COVID-19 infections continue due to accessibility barriers to vaccines and the emergence of SARS-CoV-2 variants. An effective, safe, accessible, and broad-spectrum vaccine is still needed to control the disease. We developed a multivalent protein subunit vaccine comprising antigens designed from a non-N-glycosylated region of the receptor-binding domain of the spike protein of SARS-CoV-2. We combined a previously developed antigen based on the Wuhan original viral strain, and a site-mutated antigen based on several variants including Alpha, Beta, Gamma, Eta, Iota, Theta, Zeta, Mu and Omicron. The recombinant antigens were expressed in a prokaryotic system and the immunogenicity of the multivalent vaccine was tested in a mouse model. The evaluation of the subunit vaccine candidate, incorporating different variant-based multivalent recombinant antigens from non-glycosylated regions of the RBD, demonstrated a favorable safety profile, significant immunogenicity, and potent neutralizing activity, collectively supporting its potential efficacy and safety for further development.

Strategies for the design of biomimetic cell-penetrating peptides using AI-driven in silico tools for drug delivery

Cell-penetrating peptides (CPP) have gained rapid attention over the last 25 years; this is attributed to their versatility, customisation, and 'Trojan horse' delivery that evades the immune system. However, the current CPP rational design process is limited, as it requires several rounds of peptide synthesis, prediction and wet-lab validation, which is expensive, time-consuming and requires extensive knowledge in peptide chemistry. Artificial intelligence (AI) has emerged as a promising alternative which can augment the design process, for example by determining physiochemical characteristics, secondary structure, solvent accessibility, disorder and flexibility, as well as predicting in vivo behaviour such as toxicity and peptidase degradation. Other more recent tools utilise supervised machine learning (ML) to predict the penetrative ability of an amino acid sequence. The use of AI in the CPP design process has the potential to reduce development costs and increase the chances of success with respect to delivery. This review provides a survey of in silico tools and AI platforms which can be utilised in the design process, and the key features that should be taken into consideration when designing next generation CPPs.

Developing a universal multi-epitope protein vaccine candidate for enhanced borna virus pandemic preparedness

Introduction

Borna disease virus 1 (BoDV-1) is an emerging zoonotic RNA virus that can cause severe acute encephalitis with high mortality. Currently, there are no effective countermeasures, and the potential risk of a future outbreak requires urgent attention. To address this challenge, the complete genome sequence of BoDV-1 was utilized, and immunoinformatics was applied to identify antigenic peptides suitable for vaccine development.

Methods

Immunoinformatics and antigenicity-focused protein screening were employed to predict B-cell linear epitopes, B-cell conformational epitopes, and cytotoxic T lymphocyte (CTL) epitopes. Only overlapping epitopes with antigenicity greater than 1 and non-toxic, non-allergenic properties were selected for subsequent vaccine construction. The epitopes were linked using GPGPG linkers, incorporating β-defensins at the N-terminus to enhance immune response, and incorporating Hit-6 at the C-terminus to improve protein solubility and aid in protein purification. Computational tools were used to predict the immunogenicity, physicochemical properties, and structural stability of the vaccine. Molecular docking was performed to predict the stability and dynamics of the vaccine in complex with Toll-like receptor 4 (TLR-4) and major histocompatibility complex I (MHC I) receptors. The vaccine construct was cloned through in silico restriction to create a plasmid for expression in a suitable host.

Results

Among the six BoDV-1 proteins analyzed, five exhibited high antigenicity scores. From these, eight non-toxic, non-allergenic overlapping epitopes with antigenicity scores greater than 1 were selected for vaccine development. Computational predictions indicated favorable immunogenicity, physicochemical properties, and structural stability. Molecular docking analysis showed that the vaccine remained stable in complex with TLR-4 and MHC I receptors, suggesting strong potential for immune recognition. A plasmid construct was successfully generated, providing a foundation for the experimental validation of vaccines in future pandemic scenarios.

Discussion

These findings demonstrate the potential of the immunoinformatics-designed multi-epitope vaccines for the prevention and treatment of BoDV-1. Relevant preparations were made in advance for possible future outbreaks and could be quickly utilized for experimental verification.

Evaluation and characterization of lytic phages and their recombinant endolysins for control of Staphylococcus aureus aiming to mitigate bovine mastitis

As a natural alternative to conventional antimicrobials, bacteriophages are emerging as highly effective biocontrol agents against Staphylococcus aureus and other pathogenic bacteria. Due to the wide diversity of S. aureus types and the emergence of antibiotic-resistant strains, the search for highly lytic and prevalent bacteriophages against S. aureus is justified. In this study, we sought to characterized the lytic Phage 2 and Phage 4 biologically and morphologically and their recombinant endolysins EndF2 and EndF4. Transmission electron microscopy analysis revealed that these phages exhibited a structure with a polyhedral head and non-contractile tail, typical characteristics of the Siphoviridae family. Host spectrum identification showed that Phage 2 lysed 62.2 % (N = 90) of S. aureus strains and Phage 4 lysed 51.1 % (N = 90). In vitro tests with extracorporeal cow teats indicated that Phage 2 reduced the S. aureus load by up to 78.7 %. Furthermore, recombinant endolysins EndF2 and EndF4 have catalytic and recognition/binding domains in their structures related to lytic activity, and both endolysins do not present critical aspects of allergenicity. Furthermore, EndF2 lysed 71.4 % (N = 42) and EndF4 lysed 76.2 % (N = 42) of the S. aureus strains tested. These findings indicate that Phages 2 and 4 and their recombinant endolysins EndF2 and EndF4 could potentially be used as tools for the prevention and control of S. aureus, suggesting they are potentially valuable biocontrol agents to mitigate the spread of S. aureus in the dairy industries and production chain.

Computational modeling of cyclotides as antimicrobial agents against Neisseria gonorrhoeae PorB porin protein: integration of docking, immune, and molecular dynamics simulations

Background

Neisseria gonorrhoeae is the bacterium responsible for gonorrhoea, one of the most common sexually transmitted infections (STIs) globally. In 2020, the World Health Organization (WHO) estimated 82.4 million new cases of Neisseria gonorrhoeae infections. Current treatments rely on antibiotics, but the emergence of multi drug resistance (MDR) strains poses a significant threat to public health. This research aims to use computational modeling of cyclotides as antimicrobial agents targeting the Neisseria gonorrhoeae PorB Porin protein to inhibit its pathogenicity.

Methodology

The PorB Porin protein was retrieved from the Protein Data Bank (PDB ID: 4AUI), cleaned, and visualized using Discovery Visual Studio. Physicochemical properties were predicted using ProtParam. Cyclotides were obtained from the CyBase database, with 3D models generated and refined via the Swiss Model for docking studies. HDOCK was used for molecular docking. Toxicity and allergenicity predictions were performed with ToxinPred and AlgPred. A heatmap of the peptide was created using Protein-Sol. Molecular dynamics (MD) simulations were conducted for 100,000 picoseconds using Desmond from Schrödinger LLC, while binding energy was analyzed using MMGBSA. Immune response simulations were done with C-ImmSim 10.1, and peptide simulation in water was performed via WebGro.

Results

The protein's GRAVY value is -0.539, indicating moderate hydrophilicity, and its isoelectric point is 9.14, suggesting a fundamental nature. Globa D had the highest docking score (-270.04 kcal/mol) and was deemed non-toxic and non-allergenic. MD simulations showed stable protein-ligand interactions, and MMGBSA revealed a low binding energy of -36.737 kcal/mol. Immune simulations indicated an effective immune response and peptide simulations demonstrated Globa D's stability in water, making it a potential candidate for pharmaceutical applications.

Conclusion

Globa D proved the best drug candidate against Neisseria gonorrhoeae by inhibiting PorB Porin protein chain A. Further in vitro and in vivo studies are recommended to validate these findings and explore clinical applications.

Prediction of Plant Resistance Proteins Using Alignment-Based and Alignment-Free Approaches

Plant disease resistance (PDR) proteins are critical in identifying plant pathogens. Predicting PDR protein is essential for understanding plant-pathogen interactions and developing strategies for crop protection. This study proposes a hybrid model for predicting and designing PDR proteins against plant-invading pathogens. Initially, we tried alignment-based approaches, such as Basic Local Alignment Search Tool (BLAST) for similarity search and MERCI for motif search. These alignment-based approaches exhibit very poor coverage or sensitivity. To overcome these limitations, we developed alignment-free or machine learning (ML)-based methods using compositional features of proteins. Our ML-based model, developed using compositional features of proteins, achieved a maximum performance area under the receiver operating characteristic curve (AUROC) of 0.91. The performance of our model improved significantly from AUROC of 0.91-0.95 when we used evolutionary information instead of protein sequence. Finally, we developed a hybrid or ensemble model that combined our best ML model with BLAST and obtained the highest AUROC of 0.98 on the validation dataset. We trained and tested our models on a training dataset and evaluated them on a validation dataset. None of the proteins in our validation dataset are more than 40% similar to proteins in the training dataset. One of the objectives of this study is to facilitate the scientific community working in plant biology. Thus, we developed an online platform for predicting and designing plant resistance proteins, "PlantDRPpred" (https://webs.iiitd.edu.in/raghava/plantdrppred).

A hybrid method for discovering interferon-gamma inducing peptides in human and mouse

Interferon-gamma (IFN-γ) is a versatile pleiotropic cytokine essential for both innate and adaptive immune responses. It exhibits both pro-inflammatory and anti-inflammatory properties, making it a promising therapeutic candidate for treating various infectious diseases and cancers. We present IFNepitope2, a host-specific technique to annotate IFN-γ inducing peptides, it is an updated version of IFNepitope introduced by Dhanda et al. In this study, dataset used for developing prediction method contain experimentally validated 25,492 and 7983 IFN-γ inducing peptides in human and mouse host, respectively. In initial phase, machine learning techniques have been exploited to develop classification model using wide range of peptide features. Further, to improve machine learning based models or alignment free models, we explore potential of similarity-based technique BLAST. Finally, a hybrid model has been developed that combine best machine learning based model with BLAST. In most of the case, models based on extra tree perform better than other machine learning techniques. In case of peptide features, compositional feature particularly dipeptide composition performs better than one-hot encoding or binary profile. Our best machine learning based models achieved AUROC 0.89 and 0.83 for human and mouse host, respectively. The hybrid model achieved the AUROC 0.90 and 0.85 for human and mouse host, respectively. All models have been evaluated on an independent/validation dataset not used for training or testing these models. Newly developed method performs better than existing method on independent dataset. The major objective of this study is to predict, design and scan IFN-γ inducing peptides, thus server/software have been developed ( https://webs.iiitd.edu.in/raghava/ifnepitope2/ ). This method is also available as standalone at https://github.com/raghavagps/ifnepitope2 and python package index at https://pypi.org/project/ifnepitope2/ .

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.

Design and development of dual targeting CAR protein for the development of CAR T-cell therapy against KRAS mutated pancreatic ductal adenocarcinoma using computational approaches

Mutant KRAS promotes the proliferation, metastasis, and aggressiveness of various cancers including pancreatic ductal adenocarcinoma (PDAC), non-small cell lung cancer (NSCLC), and colorectal adenocarcinoma (CRC) respectively. Mutant KRAS therapeutics are limited, while Sotorasib and Adagrasib were the only FDA-approved drugs for the treatment of KRASG12C mutated NSCLC. Chimeric antigen receptor (CAR) T-cell therapy has been emerged as an effective strategy against hematological malignancies and being extended towards solid cancers including PDAC. mesothelin (MSLN) and Carcinoembryonic Antigen (CEA) were reported to be highly overexpressed in KRAS-mutated PDAC. Meanwhile, in clinical trials, several CAR T-cell therapy studies are mainly focused towards these two cancer antigens in PDAC, however, the dual targeting of these two neoantigens is not reported. In the present study, we have designed and developed a novel dual-targeting CAR protein by employing various bioinformatics approaches such as functional analysis (antigenicity, allergenicity, antigen binding sites & signalling cascades), qualitative analysis (physicochemical, prediction, refinement & validation of 2D and 3D structures), molecular docking, and in silico cloning. Our results revealed that the designed CAR protein specifically binds with both MSLN & CEA with significant binding affinities, and was predicted to be stable & non-allergenic. Additionally, the protein-protein interaction network reveals the T-cell mediated antitumor responses of each domain in the designed CAR. Conclusively, we have designed and developed a dual targeting (MSLN & CEA) CAR protein towards KRAS-mutated PDAC using computational approaches. Alongside, we further recommend to engineer this designed CAR in T-cells and evaluating their therapeutic efficiency in in vitro and in vivo studies in the near future.

T-Cell Epitope-Based Vaccines: A Promising Strategy for Prevention of Infectious Diseases

With the development of novel vaccine strategies, T-cell epitope-based vaccines have become promising prophylactic and therapeutic tools against infectious diseases that cannot be controlled via traditional vaccines. T-cell epitope-based vaccines leverage specific immunogenic peptides to elicit protective T-cell responses against infectious pathogens. Compared to traditional vaccines, they provide superior efficacy and safety, minimizing the risk of adverse side effects. In this review, we summarized and compared the prediction and identification methods of T-cell epitopes. By integrating bioinformatic prediction and experimental validation, efficient and precise screening of T-cell epitopes can be achieved. Importantly, we delved into the development approaches to diverse T-cell epitope-based vaccines, comparing their merits and demerits, as well as discussing the prevalent challenges and perspectives in their applications. This review offers fresh perspectives for the formulation of safe and efficacious epitope-based vaccines for the devastating diseases against which no vaccines are currently available.

Prediction of peptide hormones using an ensemble of machine learning and similarity-based methods

Peptide hormones serve as genome-encoded signal transduction molecules that play essential roles in multicellular organisms, and their dysregulation can lead to various health problems. In this study, we propose a method for predicting hormonal peptides with high accuracy. The dataset used for training, testing, and evaluating our models consisted of 1174 hormonal and 1174 non-hormonal peptide sequences. Initially, we developed similarity-based methods utilizing BLAST and MERCI software. Although these similarity-based methods provided a high probability of correct prediction, they had limitations, such as no hits or prediction of limited sequences. To overcome these limitations, we further developed machine and deep learning-based models. Our logistic regression-based model achieved a maximum AUROC of 0.93 with an accuracy of 86% on an independent/validation dataset. To harness the power of similarity-based and machine learning-based models, we developed an ensemble method that achieved an AUROC of 0.96 with an accuracy of 89.79% and a Matthews correlation coefficient (MCC) of 0.8 on the validation set. To facilitate researchers in predicting and designing hormone peptides, we developed a web-based server called HOPPred. This server offers a unique feature that allows the identification of hormone-associated motifs within hormone peptides. The server can be accessed at: https://webs.iiitd.edu.in/raghava/hoppred/.

Delineating multi-epitopes vaccine designing from membrane protein CL5 against all monkeypox strains: a pangenome reverse vaccinology approach

The recently identified monkeypox virus (MPXV or mpox) is a zoonotic orthopox virus that infects humans and causes diseases with traits like smallpox. The world health organization (WHO) estimates that 3-6% of MPXV cases result in death. As it might impact everyone globally, like COVID, and become the next pandemic, the cure for this disease is important for global public health. The high incidence and disease ratio of MPXV necessitates immediate efforts to design a unique vaccine candidate capable of addressing MPXV diseases. Here, we used a computational pan-genome-based vaccine design strategy for all currently reported 19 MPXV strains acquired from different regions of the world. Thus, this study's objective was to develop a new and safe vaccine candidate against MPXV by targeting the membrane CL5 protein; identified after the pangenome analysis. Proteomics and reverse vaccinology have covered up all of the MPXV epitopes that would usually stimulate robust host immune responses. Following this, only two mapped (MHC-I, MHC-II, and B-cell) epitopes were observed to be extremely effective that can be used in the construction of CL5 protein vaccine candidates. The suggested vaccine (V5) candidate from eight vaccine models was shown to be antigenic, non-allergenic, and stable (with 213 amino acids). The vaccine's candidate efficacy was evaluated by using many in silico methods to predict, improve, and validate its 3D structure. Molecular docking and molecular dynamics simulations further reveal that the proposed vaccine candidate ensemble has a high interaction energy with the HLAs and TRL2/4 immunological receptors under study. Later, the vaccine sequence was used to generate an expression vector for the E. coli K12 strain. Further study uncovers that V5 was highly immunogenic because it produced robust primary, secondary, and tertiary immune responses. Eventually, the use of computer-aided vaccine designing may significantly reduce costs and speed up the process of developing vaccines. Although, the results of this research are promising, however, more research (experimental; in vivo, and in vitro studies) is needed to verify the biological efficacy of the proposed vaccine against MPXV.Communicated by Ramaswamy H. Sarma.

Cross-reactivity of hantavirus antibodies after immunization with PUUV antigens

Nephropathia epidemica (NE), caused by Puumala (PUUV) orthohantavirus, is endemic in the Republic of Tatarstan (RT). There are limited options for NE prevention in RT. Currently, available vaccines are made using Haantan (HNTV) orthohantavirus antigens. In this study, the efficacy of microvesicles (MVs) loaded with PUUV antigens to induce the humoral immune response in small mammals was analyzed. Additionally, the cross-reactivity of serum from immunized small mammals and NE patients with HNTV, Dobrava, and Andes orthohantaviruses was investigated using nucleocapsid (N) protein peptide libraries. Finally, the selected peptides were analyzed for allergenicity, their ability to induce an autoimmune response, and their interaction with Class II HLA. Several N protein peptides were found to be cross-reactive with serum from MVs immunized small mammals. These cross-reactive epitopes were located in oligomerization perinuclear targeting and Daxx-interacting domains. Most cross-reactive peptides lack allergenic and autoimmune reactivity. Molecular docking revealed two cross-reacting peptides, N6 and N19, to have good binding with three Class II HLA alleles. These peptides could be candidates for developing vaccines and therapeutics for NE.

Designing of neoepitopes based vaccine against breast cancer using integrated immuno and bioinformatics approach

Cancer is characterized by genetic instability due to accumulation of somatic mutations in the genes which generate neoepitopes (mutated epitopes) for targeting by Cytotoxic T lymphocytes (CTL). Breast cancer has a high transformation rate with unique composition of mutational burden and neoepitopes load that open a platform to designing a neoepitopes-based vaccine. Neoepitopes-based therapeutic cancer vaccines designed by neoantigens have shown to be feasible, nontoxic, and immunogenic in cancer patients. Stimulation of CTL by neoepitope-based vaccine of self-antigenic proteins plays a key role in distinguishing cancer cells from normal cells and selectively targets only malignant cells. A neoepitopes-based vaccine to combat breast cancer was designed by combining immunology and bioinformatics approaches. The vaccine construct was assembled by the fusion of CTL neoepitopes, helper sequences (used for better separation of the epitopes), and adjuvant together with linkers. The neoepitopes were identified from somatic mutations in the MUC16, TP53, RYR2, F5, DNAH17, ASPM, and ABCA13 self-antigenic proteins. The vaccine construct was undertaken to study the immune simulations (IS), physiochemical characteristics (PP), molecular docking (MD) and simulations, and cloning in appropriate vector. Together, these parameters establish safety, stability, and a strong binding affinity against class I MHC molecules capable of inducing a complete immune response against breast cancer cells.Communicated by Ramaswamy H. Sarma.

A therapeutic epitopes-based vaccine engineering against Salmonella enterica XDR strains for typhoid fever: a Pan-vaccinomics approach

A prevalent food-borne pathogen, Salmonella enterica serotypes Typhi, is responsible for gastrointestinal and systemic infections globally. Salmonella vaccines are the most effective, however, producing a broad-spectrum vaccine remains challenging due to Salmonella's many serotypes. Efforts are urgently required to develop a novel vaccine candidate that can tackle all S. Typhi strains because of their high resistance to multiple kinds of antibiotics (particularly the XDR H58 strain). In this work, we used a computational pangenome-based vaccine design technique on all available (n = 119) S. Typhi reference genomes and identified one TonB-dependent siderophore receptor (WP_001034967.1) as highly conserved and prospective vaccine candidates from the predicted core genome (n = 3,351). The applied pan-proteomics and Immunoinformatic approaches help in the identification of four epitopes that may trigger adequate host body immune responses. Furthermore, the proposed vaccine ensemble demonstrates a stable binding conformation with the examined immunological receptor (HLAs and TRL2/4) and has large interaction energy determined via molecular docking and molecular dynamics simulation techniques. Eventually, an expression vector for the Escherichia. coli K12 strain was constructed from the vaccine sequence. Additional analysis revealed that the vaccine may help to elicit strong immune responses for typhoid infections, however, experimental analysis is required to verify the vaccine's effectiveness based on these results. Moreover, the applied computer-assisted vaccine design may considerably decrease vaccine development costs and speed up the process. The study's findings are intriguing, but they must be evaluated in the experimental labs to confirm the developed vaccine's biological efficiency against XDR S. Typhi.Communicated by Ramaswamy H. Sarma.

PreAlgPro: Prediction of allergenic proteins with pre-trained protein language model and efficient neutral network

Allergy is a prevalent phenomenon, involving allergens such as nuts and milk. Avoiding exposure to allergens is the most effective preventive measure against allergic reactions. However, current homology-based methods for identifying allergenic proteins encounter challenges when dealing with non-homologous data. Traditional machine learning approaches rely on manually extracted features, which lack important protein functional characteristics, including evolutionary information. Consequently, there is still considerable room for improvement in existing methods. In this study, we present PreAlgPro, a method for identifying allergenic proteins based on pre-trained protein language models and deep learning techniques. Specifically, we employed the ProtT5 model to extract protein embedding features, replacing the manual feature extraction step. Furthermore, we devised an Attention-CNN neural network architecture to identify potential features that contribute to the classification of allergenic proteins. The performance of our model was evaluated on four independent test sets, and the experimental results demonstrate that PreAlgPro surpasses existing state-of-the-art methods. Additionally, we collected allergenic protein samples to validate the robustness of the model and conducted an analysis of model interpretability.

Salmonella pathogenesis-based In-silico design and immunoinformatic analysis of multi-epitope vaccine constructs in broiler veterinary medicine

Salmonellosis, a zoonotic gastrointestinal disease, presents a significant global health burden with a high incidence rate. Transmission primarily occurs through the consumption of contaminated poultry products, although water and contact with asymptomatic animals are also vectors. The disease's pervasiveness has prompted international health organizations to advocate for robust prevention and control strategies. This study focuses on the in-silico design of a multi-epitope vaccine targeting Salmonella enterica serovar Typhimurium's fimH protein, a fimbriae component crucial for bacterial adhesion and pathogenicity. The vaccine construct was developed by identifying and synthesizing non-allergenic, antigenic, and non-toxic epitopes for both Cytotoxic T Lymphocytes and Helper T Lymphocytes. Adjuvants were incorporated to enhance immunogenicity, and the vaccine's structure was modeled using advanced bioinformatics tools. The proposed vaccine demonstrated promising antigenicity and immunogenicity profiles, with a favorable physical-chemical property analysis. The vaccine's structures, designed by computational analysis, suggests high likelihood to native protein configurations. Antigenicity and allergenicity assessments validate the vaccine's immunogenic potential and hypoallergenic nature. Physicochemical evaluations indicate favorable stability and solubility profiles, essential for vaccine efficacy. This comprehensive approach to vaccine design expressed in Chlorella vulgaris holds promises for effective salmonellosis control. The multi-epitope vaccine, designed through meticulous in-silico methods, emerges as a promising candidate for controlling salmonellosis. Its strategic construction based on the fimH protein epitopes offers a targeted approach to elicit a robust immune response, potentially curbing the spread of this disease in poultry.

An immunoinformatics approach for design and validation of multi-subunit vaccine against Plasmodium falciparum from essential hypothetical proteins

Malaria, caused by Plasmodium falciparum, remains a pressing global health concern. Advancements in combating this parasite involve the development of a protein vaccine. This study employs immunoinformatics to identify potential vaccine candidates within the repertoire of 218 P. falciparum exported essential proteins identified through saturaturation mutagenesis study. Our screening approach narrows down to 65 Plasmodium-exported proteins with uncharacterized functions while exhibiting non-mutability in CDS (coding sequences). The transmembrane helix, antigenicity, allergenicity of the shortlisted proteins was assessed through diverse prediction algorithm, culminating in the identification of five promising vaccination contenders, based on probability scores. We discerned B-cell, helper T-lymphocyte, and cytotoxic T-lymphocyte epitopes. Two proteins with the most favorable epitope were harnessed to construct a multi-subunit vaccine, through judicious linker integration. Employing the I-TASSER software, three-dimensional models of the constituent proteins was obtained and was validated using diverse tools like ProSA, VERIFY3D, and ERRAT. The modelled proteins underwent Molecular Dynamics (MD) simulation in a solvent environment to evaluate the stability of the multi-subunit vaccine. Furthermore, we conducted molecular docking through the ClusPro web server to elucidate potential interactions with Toll-like receptors (TLR2 and TLR4). Docking scores revealed a pronounced affinity of the multi-subunit vaccine for TLR2. Significantly, 100 ns MD simulation of the protein-receptor complex unveiled a persistent hydrogen bond linkage between the ARG63 residue of the sub-unit vaccine and the GLU32 residue of the TLR2 receptor. These findings collectively advocate the potential efficacy of the first multi-subunit vaccine from the potential hypothetical proteins of P. falciparum.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12639-024-01696-w.

Structural proteomics guided annotation of vaccine targets and designing of multi-epitopes vaccine to instigate adaptive immune response against Francisella tularensis

Francisella tularensis can cause severe disease in humans via the respiratory or cutaneous routes and a case fatality ratio of up to 10 % is reported due to lack of proper antibiotic treatment, while F. novicida causes disease in severely immunocompromised individuals. Efforts are needed to develop effective vaccine candidates against Francisella species. Thus, in this study, a systematic computational work frame was used to deeply investigate the whole proteome of Francisella novicida containing 1728 proteins to develop vaccine against F. tularensis and related species. Whole-proteome analysis revealed that four proteins including (A0Q492) (A0Q7Y4), (A0Q4N4), and (A0Q5D9) are the suitable vaccine targets after the removal of homologous, paralogous and prediction of subcellular localization. These proteins were used to predict the T cell, B cell, and HTL epitopes which were joined together through suitable linkers to construct a multi-epitopes vaccine (MEVC). The MEVC was found to be highly immunogenic and non-allergenic while the physiochemical properties revealed the feasible expression and purification. Moreover, the molecular interaction of MEVC with TLR2, molecular simulation, and binding free energy analyses further validated the immune potential of the construct. According to Jcat analysis, the refined sequence demonstrates GC contents of 41.48 % and a CAI value of 1. The in-silico cloning and optimization process ensured compatibility with host codon usage, thereby facilitating efficient expression. Computational immune simulation studies underscored the capacity of MEVC to induce both primary and secondary immune responses. The conservation analysis further revealed that the selected epitopes exhibit 100 % conservation across different species and thus provides wider protection against Francisella.

Assessing the predictive ability of computational epitope prediction methods on Fel d 1 and other allergens

While computational epitope prediction methods have found broad application, their use, specifically in allergy-related contexts, remains relatively less explored. This study benchmarks several publicly available epitope prediction tools, focusing on the allergenic IgE and T-cell epitopes of Fel d 1, an extensively studied allergen. Using a variety of tools accessible via the Immune Epitope Database (IEDB) and other resources, we evaluate their ability to identify the known linear IgE and T-cell epitopes of Fel d 1. Our results show a limited effectiveness for B-cell epitope prediction methods, with most performing only marginally better than random selection. We also explored the general predictive abilities on other allergens, and the results were largely random. When predicting T-cell epitopes, ProPred successfully identified all known Fel d 1 T-cell epitopes, whereas the IEDB approach missed two known epitopes and demonstrated a tendency to over-predict. However, when applied to a larger test set, both methods performed only slightly better than random selection. Our findings show the limitations of current computational epitope prediction methods in accurately identifying allergenic epitopes, emphasizing the need for methodological advancements in allergen research.

Aspergillus niger Ochratoxinase Is a Highly Specific, Metal-Dependent Amidohydrolase Suitable for OTA Biodetoxification in Food and Feed

Microbial enzymes can be used as processing aids or additives in food and feed industries. Enzymatic detoxification of ochratoxin A (OTA) is a promising method to reduce OTA content. Here, we characterize the full-length enzyme ochratoxinase (AnOTA), an amidohydrolase from Aspergillus niger. AnOTA hydrolyzes OTA and ochratoxin B (OTB) mycotoxins efficiently and also other substrates containing phenylalanine, alanine, or leucine residues at their C-terminal position, revealing a narrow specificity profile. AnOTA lacks endopeptidase or aminoacylase activities. The structural basis of the molecular recognition by AnOTA of OTA, OTB, and a wide array of model substrates has been investigated by molecular docking simulation. AnOTA shows maximal hydrolytic activity at neutral pH and high temperature (65 °C) and retained high activity after prolonged incubation at 45 °C. The reduction of OTA levels in food products by AnOTA has been investigated using several commercial plant-based beverages. The results showed complete degradation of OTA with no detectable modification of beverage proteins. Therefore, the addition of AnOTA seems to be a useful procedure to eliminate OTA in plant-based beverages. Moreover, computational predictions of in vivo characteristics indicated that AnOTA is neither an allergenic nor antigenic protein. All characteristics found for AnOTA supported the suitability of its use for OTA detoxification in food and feed.

An integrated in silico approach for the identification of novel potential drug target and chimeric vaccine against Neisseria meningitides strain 331401 serogroup X by subtractive genomics and reverse vaccinology

Neisseria meningitidis, commonly known as the meningococcus, leads to substantial illness and death among children and young adults globally, revealing as either epidemic or sporadic meningitis and/or septicemia. In this study, we have designed a novel peptide-based chimeric vaccine candidate against the N. meningitidis strain 331,401 serogroup X. Through rigorous analysis of subtractive genomics, two essential cytoplasmic proteins, namely UPI000012E8E0(UDP-3-O-acyl-GlcNAc deacetylase) and UPI0000ECF4A9(UDP-N-acetylglucosamine acyltransferase) emerged as potential drug targets. Additionally, using reverse vaccinology, the outer membrane protein UPI0001F4D537 (Membrane fusion protein MtrC) identified by subcellular localization and recognized for its known indispensable role in bacterial survival was identified as a novel chimeric vaccine target. Following a careful comparison of MHC-I, MHC-II, T-cell, and B-cell epitopes, three epitopes derived from UPI0001F4D537 were linked with three types of linkers-GGGS, EAAAK, and the essential PADRE-for vaccine construction. This resulted in eight distinct vaccine models (V1-V8). Among them V1 model was selected as the final vaccine construct. It exhibits exceptional immunogenicity, safety, and enhanced antigenicity, with 97.7 % of its residues in the Ramachandran plot's most favored region. Subsequently, the vaccine structure was docked with the TLR4/MD2 complex and six different HLA allele receptors using the HADDOCK server. The docking resulted in the lowest HADDOCK score of 39.3 ± 9.0 for TLR/MD2. Immune stimulation showed a strong immune response, including antibodies creation and the activation of B-cells, T Cytotoxic cells, T Helper cells, Natural Killer cells, and interleukins. Furthermore, the vaccine construct was successfully expressed in the Escherichia coli system by reverse transcription, optimization, and ligation in the pET-28a (+) vector for the expression study. The current study proposes V1 construct has the potential to elicit both cellular and humoral responses, crucial for the developing an epitope-based vaccine against N. meningitidis strain 331,401 serogroup X.

Designing multi-epitope vaccine against human cytomegalovirus integrating pan-genome and reverse vaccinology pipelines

Human cytomegalovirus (HCMV) is accountable for high morbidity in neonates and immunosuppressed individuals. Due to the high genetic variability of HCMV, current prophylactic measures are insufficient. In this study, we employed a pan-genome and reverse vaccinology approach to screen the target for efficient vaccine candidates. Four proteins, envelope glycoprotein M, UL41A, US23, and US28, were shortlisted based on cellular localization, high solubility, antigenicity, and immunogenicity. A total of 29 B-cell and 44 T-cell highly immunogenic and antigenic epitopes with high global population coverage were finalized using immunoinformatics tools and algorithms. Further, the epitopes that were overlapping among the finalized B-cell and T-cell epitopes were linked with suitable linkers to form various combinations of multi-epitopic vaccine constructs. Among 16 vaccine constructs, Vc12 was selected based on physicochemical and structural properties. The docking and molecular simulations of VC12 were performed, which showed its high binding affinity (-23.35 kcal/mol) towards TLR4 due to intermolecular hydrogen bonds, salt bridges, and hydrophobic interactions, and there were only minimal fluctuations. Furthermore, Vc12 eliciting a good response was checked for its expression in Escherichia coli through in silico cloning and codon optimization, suggesting it to be a potent vaccine candidate.

Computational approach for drug discovery against Gardnerella vaginalis in quest for safer and effective treatments for bacterial vaginosis

Bacterial vaginosis (BV), primarily attributed to Gardnerella vaginalis, poses significant challenges due to antibiotic resistance and suboptimal treatment outcomes. This study presents an integrated approach to identify potential drug targets and screen compounds against this bacterium by leveraging a computational methodology. Subtractive proteomics of the reference strain ASM286196v1/UMB0386 (assembly accession: GCA_002861965.1) facilitated the prioritization of proteins with essential bacterial functions and pathways as potential drug targets. We selected 3-deoxy-7-phosphoheptulonate synthase (aroG gene product; also known as DAHP synthase) for downstream analysis. Molecular docking was employed in PyRx (AutoDock Vina) to predict binding affinities between aroG inhibitors from the ZINC database and 3-deoxy-7-phosphoheptulonate synthase. Molecular dynamics simulations of 100 ns, using GROMACS, validated the stability of drug-target interactions. Additionally, ADMET profiling aided in the selection of compounds with favorable pharmacokinetic properties and safety profile for human hosts. PBPK profiling showed that ZINC98088375 had the highest bioavailability and efficient systemic circulation. Conversely, ZINC5113880 demonstrated the lowest absorption rate (39.661%). Moreover, cirrhosis, steatosis, and renal impairment appeared to influence blood concentration of the drug, impacting bioavailability. The integrative -omics approach utilized in this study underscores the potential of computer-aided drug design and offers a rational strategy for targeted inhibitor discovery against G. vaginalis. The strategy is an attempt to address the limitations of current BV treatments, including antibiotic resistance, and pave way for the development of safer and more effective therapeutics.

Computational method for designing vaccines applied to virus-like particles (VLPs) as epitope carriers

Nonenveloped virus-like particles (VLPs) are self-assembled oligomeric structures composed of one or more proteins that originate from diverse viruses. Because these VLPs have similar antigenicity to the parental virus, they are successfully used as vaccines against cognate virus infection. Furthermore, after foreign antigenic sequences are inserted in their protein components (chimVLPs), some VLPs are also amenable to producing vaccines against pathogens other than the virus it originates from (these VLPs are named platform or epitope carrier). Designing chimVLP vaccines is challenging because the immunogenic response must be oriented against a given antigen without altering stimulant properties inherent to the VLP. An important step in this process is choosing the location of the sequence modifications because this must be performed without compromising the assembly and stability of the original VLP. Currently, many immunogenic data and computational tools can help guide the design of chimVLPs, thus reducing experimental costs and work. In this study, we analyze the structure of a novel VLP that originate from an insect virus and describe the putative regions of its three structural proteins amenable to insertion. For this purpose, we employed molecular dynamics (MD) simulations to assess chimVLP stability by comparing mutated and wild-type (WT) VLP protein trajectories. We applied this procedure to design a chimVLP that can serve as a prophylactic vaccine against the SARS-CoV-2 virus. The methodology described in this work is generally applicable for VLP-based vaccine development.

Current treatment strategies for targeting virulence factors and biofilm formation in Acinetobacter baumannii

A higher prevalence of Acinetobacter baumannii infections and mortality rate has been reported recently in hospital-acquired infections (HAI). The biofilm-forming capability of A. baumannii makes it an extremely dangerous pathogen, especially in device-associated hospital-acquired infections (DA-HAI), thereby it resists the penetration of antibiotics. Further, the transmission of the SARS-CoV-2 virus was exacerbated in DA-HAI during the epidemic. This review specifically examines the complex interconnections between several components and genes that play a role in the biofilm formation and the development of infections. The current review provides insights into innovative treatments and therapeutic approaches to combat A. baumannii biofilm-related infections, thereby ultimately improving patient outcomes and reducing the burden of HAI.

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.

SEP-AlgPro: An efficient allergen prediction tool utilizing traditional machine learning and deep learning techniques with protein language model features

Allergy is a hypersensitive condition in which individuals develop objective symptoms when exposed to harmless substances at a dose that would cause no harm to a "normal" person. Most current computational methods for allergen identification rely on homology or conventional machine learning using limited set of feature descriptors or validation on specific datasets, making them inefficient and inaccurate. Here, we propose SEP-AlgPro for the accurate identification of allergen protein from sequence information. We analyzed 10 conventional protein-based features and 14 different features derived from protein language models to gauge their effectiveness in differentiating allergens from non-allergens using 15 different classifiers. However, the final optimized model employs top 10 feature descriptors with top seven machine learning classifiers. Results show that the features derived from protein language models exhibit superior discriminative capabilities compared to traditional feature sets. This enabled us to select the most discriminatory baseline models, whose predicted outputs were aggregated and used as input to a deep neural network for the final allergen prediction. Extensive case studies showed that SEP-AlgPro outperforms state-of-the-art predictors in accurately identifying allergens. A user-friendly web server was developed and made freely available at https://balalab-skku.org/SEP-AlgPro/, making it a powerful tool for identifying potential allergens.

AllergenAI: a deep learning model predicting allergenicity based on protein sequence

Innovations in protein engineering can help redesign allergenic proteins to reduce adverse reactions in sensitive individuals. To accomplish this aim, a better knowledge of the molecular properties of allergenic proteins and the molecular features that make a protein allergenic is needed. We present a novel AI-based tool, AllergenAI, to quantify the allergenic potential of a given protein. Our approach is solely based on protein sequences, differentiating it from previous tools that use some knowledge of the allergens' physicochemical and other properties in addition to sequence homology. We used the collected data on protein sequences of allergenic proteins as archived in the three well-established databases, SDAP 2.0, COMPARE, and AlgPred 2, to train a convolutional neural network and assessed its prediction performance by cross-validation. We then used Allergen AI to find novel potential proteins of the cupin family in date palm, spinach, maize, and red clover plants with a high allergenicity score that might have an adverse allergenic effect on sensitive individuals. By analyzing the feature importance scores (FIS) of vicilins, we identified a proline-alanine-rich (P-A) motif in the top 50% of FIS regions that overlapped with known IgE epitope regions of vicilin allergens. Furthermore, using∼ 1600 allergen structures in our SDAP database, we showed the potential to incorporate 3D information in a CNN model. Future, incorporating 3D information in training data should enhance the accuracy. AllergenAI is a novel foundation for identifying the critical features that distinguish allergenic proteins.

Designing a Conserved Immunogenic Peptide Construct from the Nucleocapsid Protein of Puumala orthohantavirus

Puumala orthohantavirus (PUUV) is an emerging zoonotic virus endemic to Europe and Russia that causes nephropathia epidemica, a mild form of hemorrhagic fever with renal syndrome (HFRS). There are limited options for treatment and diagnosis of orthohantavirus infection, making the search for potential immunogenic candidates crucial. In the present work, various bioinformatics tools were employed to design conserved immunogenic peptides containing multiple epitopes of PUUV nucleocapsid protein. Eleven conserved peptides (90% conservancy) of the PUUV nucleocapsid protein were identified. Three conserved peptides containing multiple T and B cell epitopes were selected using a consensus epitope prediction algorithm. Molecular docking using the HPEP dock server demonstrated strong binding interactions between the epitopes and HLA molecules (ten alleles for each class I and II HLA). Moreover, an analysis of population coverage using the IEDB database revealed that the identified peptides have over 90% average population coverage across six continents. Molecular docking and simulation analysis reveal a stable interaction with peptide constructs of chosen immunogenic peptides and Toll-like receptor-4. These computational analyses demonstrate selected peptides' immunogenic potential, which needs to be validated in different experimental systems.

Anti-HIV Humoral Response Induced by Different Anti-Idiotype Antibody Formats: An In Silico and In Vivo Approach

Despite advancements in vaccinology, there is currently no effective anti-HIV vaccine. One strategy under investigation is based on the identification of epitopes recognized by broadly neutralizing antibodies to include in vaccine preparation. Taking into account the benefits of anti-idiotype molecules and the diverse biological attributes of different antibody formats, our aim was to identify the most immunogenic antibody format. This format could serve as a foundational element for the development of an oligo-polyclonal anti-idiotype vaccine against HIV-1. For our investigation, we anchored our study on an established b12 anti-idiotype, referred to as P1, and proposed four distinct formats: two single chains and two minibodies, both in two different orientations. For a deeper characterization of these molecules, we used immunoinformatic tools and tested them on rabbits. Our studies have revealed that a particular minibody conformation, MbVHVL, emerges as the most promising candidate. It demonstrates a significant binding affinity with b12 and elicits a humoral anti-HIV-1 response in rabbits similar to the Fab format. This study marks the first instance where the minibody format has been shown to provoke a humoral response against a pathogen. Furthermore, this format presents biological advantages over the Fab format, including bivalency and being encoded by a monocistronic gene, making it better suited for the development of RNA-based vaccines.

Design of a multi-epitope-based vaccine candidate against Bovine Genital Campylobacteriosis using a reverse vaccinology approach

BACKGROUND

Bovine Genital Campylobacteriosis (BGC), a worldwide distributed venereal disease caused by Campylobacter fetus subsp. venerealis (Cfv), has a relevant negative economic impact in cattle herds. The control of BGC is hampered by the inexistence of globally available effective vaccines. The present in silico study aimed to develop a multi-epitope vaccine candidate against Cfv through reverse vaccinology.

RESULTS

The analysis of Cfv strain NCTC 10354 proteome allowed the identification of 9 proteins suitable for vaccine development. From these, an outer membrane protein, OmpA, and a flagellar protein, FliK, were selected for prediction of B-cell and T-cell epitopes. The top-ranked epitopes conservancy was assessed in 31 Cfv strains. The selected epitopes were integrated to form a multi-epitope fragment of 241 amino acids, which included 2 epitopes from OmpA and 13 epitopes from FliK linked by GPGPG linkers and connected to the cholera toxin subunit B by an EAAAK linker. The vaccine candidate was predicted to be antigenic, non-toxic, non-allergenic, and soluble upon overexpression. The protein structure was predicted and optimized, and the sequence was successfully cloned in silico into a plasmid vector. Additionally, immunological simulations demonstrated the vaccine candidate's ability to stimulate an immune response.

CONCLUSIONS

This study developed a novel vaccine candidate suitable for further in vitro and in vivo experimental validation, which may become a useful tool for the control of BGC.

Structural vaccinology, molecular simulation and immune simulation approaches to design multi-epitopes vaccine against John Cunningham virus

The JCV (John Cunningham Virus) is known to cause progressive multifocal leukoencephalopathy, a condition that results in the formation of tumors. Symptoms of this condition such as sensory defects, cognitive dysfunction, muscle weakness, homonosapobia, difficulties with coordination, and aphasia. To date, there is no specific and effective treatment to completely cure or prevent John Cunningham polyomavirus infections. Since the best way to control the disease is vaccination. In this study, the immunoinformatic tools were used to predict the high immunogenic and non-allergenic B cells, helper T cells (HTL), and cytotoxic T cells (CTL) epitopes from capsid, major capsid, and T antigen proteins of JC virus to design the highly efficient subunit vaccines. The specific immunogenic linkers were used to link together the predicted epitopes and subjected to 3D modeling by using the Robetta server. MD simulation was used to confirm that the newly constructed vaccines are stable and properly fold. Additionally, the molecular docking approach revealed that the vaccines have a strong binding affinity with human TLR-7. The codon adaptation index (CAI) and GC content values verified that the constructed vaccines would be highly expressed in E. coli pET28a (+) plasmid. The immune simulation analysis indicated that the human immune system would have a strong response to the vaccines, with a high titer of IgM and IgG antibodies being produced. In conclusion, this study will provide a pre-clinical concept to construct an effective, highly antigenic, non-allergenic, and thermostable vaccine to combat the infection of the John Cunningham virus.

AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens

Previous epitope-based cancer vaccines have focused on analyzing a limited number of mutated epitopes and clinical variables preliminarily to experimental trials. As a result, relatively few positive clinical outcomes have been observed in epitope-based cancer vaccines. Further efforts are required to diversify the selection of mutated epitopes tailored to cancers with different genetic signatures. To address this, we developed the first version of AutoEpiCollect, a user-friendly GUI software, capable of generating safe and immunogenic epitopes from missense mutations in any oncogene of interest. This software incorporates a novel, machine learning-driven epitope ranking method, leveraging a probabilistic logistic regression model that is trained on experimental T-cell assay data. Users can freely download AutoEpiCollectGUI with its user guide for installing and running the software on GitHub. We used AutoEpiCollect to design a pan-cancer vaccine targeting missense mutations found in the proto-oncogene PIK3CA, which encodes the p110ɑ catalytic subunit of the PI3K kinase protein. We selected PIK3CA as our gene target due to its widespread prevalence as an oncokinase across various cancer types and its lack of presence as a gene target in clinical trials. After entering 49 distinct point mutations into AutoEpiCollect, we acquired 361 MHC Class I epitope/HLA pairs and 219 MHC Class II epitope/HLA pairs. From the 49 input point mutations, we identified MHC Class I epitopes targeting 34 of these mutations and MHC Class II epitopes targeting 11 mutations. Furthermore, to assess the potential impact of our pan-cancer vaccine, we employed PCOptim and PCOptim-CD to streamline our epitope list and attain optimized vaccine population coverage. We achieved a world population coverage of 98.09% for MHC Class I data and 81.81% for MHC Class II data. We used three of our predicted immunogenic epitopes to further construct 3D models of peptide-HLA and peptide-HLA-TCR complexes to analyze the epitope binding potential and TCR interactions. Future studies could aim to validate AutoEpiCollect's vaccine design in murine models affected by PIK3CA-mutated or other mutated tumor cells located in various tissue types. AutoEpiCollect streamlines the preclinical vaccine development process, saving time for thorough testing of vaccinations in experimental trials.

A method for predicting linear and conformational B-cell epitopes in an antigen from its primary sequence

B-cell is an essential component of the immune system that plays a vital role in providing the immune response against any pathogenic infection by producing antibodies. Existing methods either predict linear or conformational B-cell epitopes in an antigen. In this study, a single method was developed for predicting both types (linear/conformational) of B-cell epitopes. The dataset used in this study contains 3875 B-cell epitopes and 3996 non-B-cell epitopes, where B-cell epitopes consist of both linear and conformational B-cell epitopes. Our primary analysis indicates that certain residues (like Asp, Glu, Lys, and Asn) are more prominent in B-cell epitopes. We developed machine-learning based methods using different types of sequence composition and achieved the highest AUROC of 0.80 using dipeptide composition. In addition, models were developed on selected features, but no further improvement was observed. Our similarity-based method implemented using BLAST shows a high probability of correct prediction with poor sensitivity. Finally, we developed a hybrid model that combines alignment-free (dipeptide based random forest model) and alignment-based (BLAST-based similarity) models. Our hybrid model attained a maximum AUROC of 0.83 with an MCC of 0.49 on the independent dataset. Our hybrid model performs better than existing methods on an independent dataset used in this study. All models were trained and tested on 80 % of the data using a cross-validation technique, and the final model was evaluated on 20 % of the data, called an independent or validation dataset. A webserver and standalone package named "CLBTope" has been developed for predicting, designing, and scanning B-cell epitopes in an antigen sequence available at (https://webs.iiitd.edu.in/raghava/clbtope/).

A random forest model for predicting exosomal proteins using evolutionary information and motifs

Non-invasive diagnostics and therapies are crucial to prevent patients from undergoing painful procedures. Exosomal proteins can serve as important biomarkers for such advancements. In this study, we attempted to build a model to predict exosomal proteins. All models are trained, tested, and evaluated on a non-redundant dataset comprising 2831 exosomal and 2831 non-exosomal proteins, where no two proteins have more than 40% similarity. Initially, the standard similarity-based method Basic Local Alignment Search Tool (BLAST) was used to predict exosomal proteins, which failed due to low-level similarity in the dataset. To overcome this challenge, machine learning (ML) based models were developed using compositional and evolutionary features of proteins achieving an area under the receiver operating characteristics (AUROC) of 0.73. Our analysis also indicated that exosomal proteins have a variety of sequence-based motifs which can be used to predict exosomal proteins. Hence, we developed a hybrid method combining motif-based and ML-based approaches for predicting exosomal proteins, achieving a maximum AUROC of 0.85 and MCC of 0.56 on an independent dataset. This hybrid model performs better than presently available methods when assessed on an independent dataset. A web server and a standalone software ExoProPred (https://webs.iiitd.edu.in/raghava/exopropred/) have been created to help scientists predict and discover exosomal proteins and find functional motifs present in them.

Immunoinformatics-driven In silico vaccine design for Nipah virus (NPV): Integrating machine learning and computational epitope prediction

The Nipah virus (NPV) is a highly lethal virus, known for its significant fatality rate. The virus initially originated in Malaysia in 1998 and later led to outbreaks in nearby countries such as Bangladesh, Singapore, and India. Currently, there are no specific vaccines available for this virus. The current work employed the reverse vaccinology method to conduct a comprehensive analysis of the entire proteome of the NPV virus. The aim was to identify and choose the most promising antigenic proteins that could serve as potential candidates for vaccine development. We have also designed B and T cell epitopes-based vaccine candidate using immunoinformatics approach. We have identified a total of 5 novel Cytotoxic T Lymphocytes (CTL), 5 Helper T Lymphocytes (HTL), and 6 linear B-cell potential antigenic epitopes which are novel and can be used for further vaccine development against Nipah virus. Then we performed the physicochemical properties, antigenic, immunogenic and allergenicity prediction of the designed vaccine candidate against NPV. Further, Computational analysis indicated that these epitopes possessed highly antigenic properties and were capable of interacting with immune receptors. The designed vaccine were then docked with the human immune receptors, namely TLR-2 and TLR-4 showed robust interaction with the immune receptor. Molecular dynamics simulations demonstrated robust binding and good dynamics. After numerous dosages at varied intervals, computational immune response modeling showed that the immunogenic construct might elicit a significant immune response. In conclusion, the immunogenic construct shows promise in providing protection against NPV, However, further experimental validation is required before moving to clinical trials.

The role of Aedes aegypti in inducing/aggravating IgE-mediated allergic airway disease: extensive computational studies for identification of allergenic proteins

Respiratory allergies have become a major public health concern and affect one-third of the world's population. Several factors like environmental changes, industrialization, and immunologic interactions are reported to contribute to allergic respiratory diseases. Immunological reactions because of mosquito bite (allergic proteins) have been reported to have a high contribution to IgE-mediated allergic airway disease but they are largely ignored. In this study, we aim to predict the potential allergens (proteins) from Aedes aegypti that might play a role in the reactions of IgE-mediated allergic airway diseases. The allergens are identified from an extensive literature search and the 3D structures were prepared using the SwissDock server. Computational studies were performed to identify the potential allergens that might be responsible for IgE-mediated allergies. Our docking and molecular dynamics (MD) simulation results suggest that ADE-3, an allergen from Aedes aegypti, has the highest docking score and is predicted to be responsible for IgE-mediated allergic reaction(s). Overall, this study highlights the importance of immunoinformatics, and the obtained information can be used for designing prophylactic peptide vaccine candidates and inhibitors for controlling IgE-mediated inflammations.Communicated by Ramaswamy H. Sarma.

Designing a T-cell epitope-based vaccine using in silico approaches against the Sal k 1 allergen of Salsola kali plant

Allergens originated from Salsola kali (Russian thistle) pollen grains are one of the most important sources of aeroallergens causing pollinosis in desert and semi-desert regions. T-cell epitope-based vaccines (TEV) are more effective among different therapeutic approaches developed to alleviate allergic diseases. The physicochemical properties, and B as well as T cell epitopes of Sal k 1 (a major allergen of S. kali) were predicted using immunoinformatic tools. A TEV was constructed using the linkers EAAAK, GPGPG and the most suitable CD4+ T cell epitopes. RS04 adjuvant was added as a TLR4 agonist to the amino (N) and carboxyl (C) terminus of the TEV protein. The secondary and tertiary structures, solubility, allergenicity, toxicity, stability, physicochemical properties, docking with immune receptors, BLASTp against the human and microbiota proteomes, and in silico cloning of the designed TEV were assessed using immunoinformatic analyses. Two CD4+ T cell epitopes of Sal k1 that had high affinity with different alleles of MHC-II were selected and used in the TEV. The molecular docking of the TEV with HLADRB1, and TLR4 showed TEV strong interactions and stable binding pose to these receptors. Moreover, the codon optimized TEV sequence was cloned between NcoI and XhoI restriction sites of pET-28a(+) expression plasmid. The designed TEV can be used as a promising candidate in allergen-specific immunotherapy against S. kali. Nonetheless, effectiveness of this vaccine should be validated through immunological bioassays.

Accelerating the identification of the allergenic potential of plant proteins using a stacked ensemble-learning framework

Plant-allergenic proteins (PAPs) have the potential to induce allergic reactions in certain individuals. While these proteins are generally innocuous for the majority of people, they can elicit an immune response in those with particular sensitivities. Thus, screening and prioritizing the allergenic potential of plant proteins is indispensable for the development of diagnostic tools, therapeutic interventions or medications to treat allergic reactions. However, investigating the allergenic potential of plant proteins based on experimental methods is costly and labour-intensive. Therefore, we develop StackPAP, a three-layer stacking ensemble framework for accurate large-scale identification of PAPs. In StackPAP, at the first layer, we conducted a comprehensive analysis of an extensive set of feature descriptors. Subsequently, we selected and fused five potential sequence-based feature descriptors, including amphiphilic pseudo-amino acid composition, dipeptide deviation from expected mean, amino acid composition, pseudo amino acid composition and dipeptide composition. Additionally, we applied an efficient genetic algorithm (GA-SAR) to determine informative feature sets. In the second layer, 12 powerful machine learning (ML) methods, in combination with all the informative feature sets, were employed to construct a pool of base classifiers. Finally, 13 potential base classifiers were selected using the GA-SAR method and combined to develop the final meta-classifier. Our experimental results revealed the promising prediction performance of StackPAP, with an accuracy, Matthew's correlation coefficient and AUC of 0.984, 0.969 and 0.993, respectively, as judged by the independent test dataset. In conclusion, both cross-validation and independent test results indicated the superior performance of StackPAP compared with several ML-based classifiers. To accelerate the identification of the allergenicity of plant proteins, we developed a user-friendly web server for StackPAP (https://pmlabqsar.pythonanywhere.com/StackPAP). We anticipate that StackPAP will be an efficient and useful tool for rapidly screening PAPs from a vast number of plant proteins.Communicated by Ramaswamy H. Sarma.

Research gaps and future needs for allergen prediction in food safety

The allergenicity and protein risk assessments in food safety are facing new challenges. Demands for healthier and more sustainable food systems have led to significant advances in biotechnology, the development of more complex foods, and the search for alternative protein sources. All this has increased the pressure on the safety assessment prediction approaches anchored into requirements defined in the late 90's. In 2022, the EFSA's Panel on Genetically Modified Organisms published a scientific opinion focusing on the developments needed for allergenicity and protein safety assessments of new products derived from biotechnology. Here, we further elaborate on the main elements described in this scientific opinion and prioritize those development needs requiring critical attention. The starting point of any new recommendation would require a focus on clinical relevance and the development of a fit-for-purpose database targeted for specific risk assessment goals. Furthermore, it is imperative to review and clarify the main purpose of the allergenicity risk assessment. An internationally agreed consensus on the overall purpose of allergenicity risk assessment will accelerate the development of fit-for-purpose methodologies, where the role of exposure should be better clarified. Considering the experience gained over the last 25 years and recent scientific developments in the fields of biotechnology, allergy, and risk assessment, it is time to revise and improve the allergenicity safety assessment to ensure the reliability of allergenicity assessments for food of the future.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Rational design of a multivalent vaccine targeting arthropod-borne viruses using reverse vaccinology strategies

Viruses transmitted by arthropods, such as Dengue, Zika, and Chikungunya, represent substantial worldwide health threats, particularly in countries like India. The lack of approved vaccines and effective antiviral therapies calls for developing innovative strategies to tackle these arboviruses. In this study, we employed immunoinformatics methodologies, incorporating reverse vaccinology, to design a multivalent vaccine targeting the predominant arboviruses. Epitopes of B and T cells were recognized within the non-structural proteins of Dengue, Zika, and Chikungunya viruses. The predicted epitopes were enhanced with adjuvants β-defensin and RS-09 to boost the vaccine's immunogenicity. Sixteen distinct vaccine candidates were constructed, each incorporating epitopes from all three viruses. FUVAC-11 emerged as the most promising vaccine candidate through molecular docking and molecular dynamics simulations, demonstrating favorable binding interactions and stability. Its effectiveness was further evaluated using computational immunological studies confirming strong immune responses. The in silico cloning performed using the pET-28a(+) plasmid facilitates the future experimental implementation of this vaccine candidate, paving the way for potential advancements in combating these significant arboviral threats. However, further in vitro and in vivo studies are warranted to confirm the results obtained in this computational study, which highlights the effectiveness of immunoinformatics and reverse vaccinology in creating vaccines against major Arboviruses, offering a promising model for developing vaccines for other vector-borne diseases and enhancing global health security.

Chromosome-level genome of Ambrosia trifida provides insights into adaptation and the evolution of pollen allergens

Ambrosia trifida (giant ragweed) is an invasive plant that can cause serious damage to natural ecosystems and severe respiratory allergies. However, the genomic basis of invasive adaptation and pollen allergens in Ambrosia species remain largely unknown. Here, we present a 1.66 Gb chromosome-scale reference genome for giant ragweed and identified multiple types of genome duplications, which are responsible for its rapid environmental adaptation and pollen development. The largest copies number and species-specific expansions of resistance-related gene families compared to Heliantheae alliance might contribute to resist stresses, pathogens and rapid adaptation. To extend the knowledge of evolutionary process of allergic pollen proteins, we predicted 26 and 168 potential pollen allergen candidates for giant ragweed and other Asteraceae plant species by combining machine learning and identity screening. Interestingly, we observed a specific tandemly repeated array for potential allergenic pectate lyases among Ambrosia species. Rapid evolutionary rates on putative pectate lyase allergens may imply a crucial role of nonsynonymous mutations on amino acid residues for plant biological function and allergenicity. Altogether, this study provides insight into the molecular ecological adaptation and putative pollen allergens prediction that will be helpful in promoting invasion genomic research and evolution of putative pollen allergy in giant ragweed.

Invited review: Camel milk-derived bioactive peptides and diabetes-Molecular view and perspectives

In dairy science, camel milk (CM) constitutes a center of interest for scientists due to its known beneficial effect on diabetes as demonstrated in many in vitro, in vivo, and clinical studies and trials. Overall, CM had positive effects on various parameters related to glucose transport and metabolism as well as the structural and functional properties of the pancreatic β-cells and insulin secretion. Thus, CM consumption may help manage diabetes; however, such a recommendation will become rationale and clinically conceivable only if the exact molecular mechanisms and pathways involved at the cellular levels are well understood. Moreover, the application of CM as an alternative antidiabetic tool may first require the identification of the exact bioactive molecules behind such antidiabetic properties. In this review, we describe the advances in our knowledge of the molecular mechanisms reported to be involved in the beneficial effects of CM in managing diabetes using different in vitro and in vivo models. This mainly includes the effects of CM on the different molecular pathways controlling (1) insulin receptor signaling and glucose uptake, (2) the pancreatic β-cell structure and function, and (3) the activity of key metabolic enzymes in glucose metabolism. Moreover, we described the current status of the identification of CM-derived bioactive peptides and their structure-activity relationship study and characterization in the context of molecular markers related to diabetes. Such an overview will not only enrich our scientific knowledge of the plausible mode of action of CM in diabetes but should ultimately rationalize the claim of the potential application of CM against diabetes. This will pave the way toward new directions and ideas for developing a new generation of antidiabetic products taking benefits from the chemical composition of CM.

pLM4Alg: Protein Language Model-Based Predictors for Allergenic Proteins and Peptides

The rising prevalence of allergy demands efficient and accurate bioinformatic tools to expedite allergen identification and risk assessment while also reducing wet experiment expenses and time. Recently, pretrained protein language models (pLMs) have successfully predicted protein structure and function. However, to our best knowledge, they have not been used for predicting allergenic proteins/peptides. Therefore, this study aims to develop robust models for allergenic protein/peptide prediction using five pLMs of varying sizes and systematically assess their performance through fine-tuning with a convolutional neural network. The developed pLM4Alg models have achieved state-of-the-art performance with accuracy, Matthews correlation coefficient, and area under the curve scoring 93.4-95.1%, 0.869-0.902, and 0.981-0.990, respectively. Moreover, pLM4Alg is the first model capable of handling prediction tasks involving residue-missed sequences and sequences containing nonstandard amino acid residues. To facilitate easy access, a user-friendly web server (https://f6wxpfd3sh.us-east-1.awsapprunner.com) has been established. pLM4Alg is expected to become the leading machine learning-based prediction model for allergenic peptides and proteins. Its collaboration with other predictors holds great promise for accelerating allergy research.

Immunoinformatic Analysis of Leishmania Major gp46 Protein and Potential Targets for Vaccination against Leishmaniasis

BACKGROUND

Cutaneous leishmaniasis (CL) is a parasitic disease with a significant burden in the Old World countries.

OBJECTIVE

In the current study, some of the primary biochemical properties and IFN-γ inducing epitopes with specific binding capacity to human and mouse MHC alleles were predicted for Leishmania major gp46 antigenic protein.

METHODS

Several online servers were used to predict physico-chemical traits, allergenicity, antigenicity, transmembrane domain and signal peptide, subcellular localization, post-translational modifications (PTMs), secondary and tertiary structures, tertiary model refining with validations. Also, IEDB web server was used to predict mouse/human cytotoxic T-lymphocyte (CTL) and helper T-lymphocyte (HTL) epitopes.

RESULTS

The 33.25 kDa protein was stable, hydrophilic, antigenic, while non-allergenic, with enhanced thermotolerance and 45 PTM sites. The secondary structure encompassed a random coil, followed by extended strands and helices. Ramachandran-based analysis of the refined model showed 73.1%, 21.6%, 3.4% and 1.9% of residues in the most favored, additional allowed, generously-allowed and disallowed regions, respectively. Epitope screening demonstrated 4 HTL epitopes against seemingly protective HLA alleles, 5 HTL epitopes against the HLA reference set, 3 human CTL epitopes and a number of mouse MHC-restricted epitopes.

CONCLUSION

This paper provides insights into the bioinformatics characteristics of the L. major gp46 protein as a promising vaccine candidate.