Antibody Class(es) Predictor for Epitopes (AbCPE): A Multi-Label Classification Algorithm

One Health Approach to the Computational Design of a Lipoprotein-Based Multi-Epitope Vaccine Against Human and Livestock Tuberculosis

Tuberculosis (TB) remains a major cause of ill health and one of the leading causes of death worldwide, with about 1.25 million deaths estimated in 2023. Control measures have focused principally on early diagnosis, the treatment of active TB, and vaccination. However, the widespread emergence of anti-tuberculosis drug resistance remains the major public health threat to progress made in global TB care and control. Moreover, the Bacillus Calmette-Guérin (BCG) vaccine, the only licensed vaccine against TB in children, has been in use for over a century, and there have been considerable debates concerning its effectiveness in TB control. A multi-epitope vaccine against TB would be an invaluable tool to attain the Global Plan to End TB 2023-2030 target. A rational approach that combines several B-cell and T-cell epitopes from key lipoproteins was adopted to design a novel multi-epitope vaccine candidate. In addition, interactions with TLR4 were implemented to assess its ability to elicit an innate immune response. The conservation of the selected proteins suggests the possibility of cross-protection in line with the One Health approach to disease control. The vaccine candidate was predicted to be both antigenic and immunogenic, and immune simulation analyses demonstrated its ability to elicit both humoral and cellular immune responses. Protein-protein docking and normal-mode analyses of the vaccine candidate with TLR4 predicted efficient binding and stable interaction. This study provides a promising One Health approach for the design of multi-epitope vaccines against human and livestock tuberculosis. Overall, the designed vaccine candidate demonstrated immunogenicity and safety features that warrant further experimental validation in vitro and in vivo.

Advances of computational methods enhance the development of multi-epitope vaccines

Vaccine development is one of the most promising fields, and multi-epitope vaccine, which does not need laborious culture processes, is an attractive alternative to classical vaccines with the advantage of safety, and efficiency. The rapid development of algorithms and the accumulation of immune data have facilitated the advancement of computer-aided vaccine design. Here we systemically reviewed the in silico data and algorithms resource, for different steps of computational vaccine design, including immunogen selection, epitope prediction, vaccine construction, optimization, and evaluation. The performance of different available tools on epitope prediction and immunogenicity evaluation was tested and compared on benchmark datasets. Finally, we discuss the future research direction for the construction of a multiepitope vaccine.

Multi-perspectives and challenges in identifying B-cell epitopes

The identification of B-cell epitopes (BCEs) in antigens is a crucial step in developing recombinant vaccines or immunotherapies for various diseases. Over the past four decades, numerous in silico methods have been developed for predicting BCEs. However, existing reviews have only covered specific aspects, such as the progress in predicting conformational or linear BCEs. Therefore, in this paper, we have undertaken a systematic approach to provide a comprehensive review covering all aspects associated with the identification of BCEs. First, we have covered the experimental techniques developed over the years for identifying linear and conformational epitopes, including the limitations and challenges associated with these techniques. Second, we have briefly described the historical perspectives and resources that maintain experimentally validated information on BCEs. Third, we have extensively reviewed the computational methods developed for predicting conformational BCEs from the structure of the antigen, as well as the methods for predicting conformational epitopes from the sequence. Fourth, we have systematically reviewed the in silico methods developed in the last four decades for predicting linear or continuous BCEs. Finally, we have discussed the overall challenge of identifying continuous or conformational BCEs. In this review, we only listed major computational resources; a complete list with the URL is available from the BCinfo website (https://webs.iiitd.edu.in/raghava/bcinfo/).

In silico design of a polypeptide as a vaccine candidate against ascariasis

Ascariasis is the most prevalent zoonotic helminthic disease worldwide, and is responsible for nutritional deficiencies, particularly hindering the physical and neurological development of children. The appearance of anthelmintic resistance in Ascaris is a risk for the target of eliminating ascariasis as a public health problem by 2030 set by the World Health Organisation. The development of a vaccine could be key to achieving this target. Here we have applied an in silico approach to design a multi-epitope polypeptide that contains T-cell and B-cell epitopes of reported novel potential vaccination targets, alongside epitopes from established vaccination candidates. An artificial toll-like receptor-4 (TLR4) adjuvant (RS09) was added to improve immunogenicity. The constructed peptide was found to be non-allergic, non-toxic, with adequate antigenic and physicochemical characteristics, such as solubility and potential expression in Escherichia coli. A tertiary structure of the polypeptide was used to predict the presence of discontinuous B-cell epitopes and to confirm the molecular binding stability with TLR2 and TLR4 molecules. Immune simulations predicted an increase in B-cell and T-cell immune response after injection. This polypeptide can now be validated experimentally and compared to other vaccine candidates to assess its possible impact in human health.

AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information

Introduction

Antibody-mediated immunity is an essential part of the immune system in vertebrates. The ability to specifically bind to antigens allows antibodies to be widely used in the therapy of cancers and other critical diseases. A key step in antibody therapeutics is the experimental identification of antibody-antigen interactions, which is generally time-consuming, costly, and laborious. Although some computational methods have been proposed to screen potential antibodies, the dependence on 3D structures still limits the application of these methods.

Methods

Here, we developed a deep learning-assisted prediction method (i.e., AbAgIntPre) for fast identification of antibody-antigen interactions that only relies on amino acid sequences. A Siamese-like convolutional neural network architecture was established with the amino acid composition encoding scheme for both antigens and antibodies.

Results and Discussion

The generic model of AbAgIntPre achieved satisfactory performance with the Area Under Curve (AUC) of 0.82 on a high-quality generic independent test dataset. Besides, this approach also showed competitive performance on the more specific SARS-CoV dataset. We expect that AbAgIntPre can serve as an important complement to traditional experimental methods for antibody screening and effectively reduce the workload of antibody design. The web server of AbAgIntPre is freely available at http://www.zzdlab.com/AbAgIntPre.

Rational design of a novel multi-epitope peptide-based vaccine against Onchocerca volvulus using transmembrane proteins

Almost a decade ago, it was recognized that the global elimination of onchocerciasis by 2030 will not be feasible without, at least, an effective prophylactic and/or therapeutic vaccine to complement chemotherapy and vector control strategies. Recent advances in computational immunology (immunoinformatics) have seen the design of novel multi-epitope onchocerciasis vaccine candidates which are however yet to be evaluated in clinical settings. Still, continued research to increase the pool of vaccine candidates, and therefore the chance of success in a clinical trial remains imperative. Here, we designed a multi-epitope vaccine candidate by assembling peptides from 14 O. volvulus (Ov) proteins using an immunoinformatics approach. An initial 126 Ov proteins, retrieved from the Wormbase database, and at least 90% similar to orthologs in related nematode species of economic importance, were screened for localization, presence of transmembrane domain, and antigenicity using different web servers. From the 14 proteins retained after the screening, 26 MHC-1 and MHC-II (T-cell) epitopes, and linear B-lymphocytes epitopes were predicted and merged using suitable linkers. The Mycobacterium tuberculosis Resuscitation-promoting factor E (RPFE_MYCTU), which is an agonist of TLR4, was then added to the N-terminal of the vaccine candidate as a built-in adjuvant. Immune simulation analyses predicted strong B-cell and IFN-γ based immune responses which are necessary for protection against O. volvulus infection. Protein-protein docking and molecular dynamic simulation predicted stable interactions between the 3D structure of the vaccine candidate and human TLR4. These results show that the designed vaccine candidate has the potential to stimulate both humoral and cellular immune responses and should therefore be subject to further laboratory investigation.

Women in the European Virus Bioinformatics Center

Viruses are the cause of a considerable burden to human, animal and plant health, while on the other hand playing an important role in regulating entire ecosystems. The power of new sequencing technologies combined with new tools for processing "Big Data" offers unprecedented opportunities to answer fundamental questions in virology. Virologists have an urgent need for virus-specific bioinformatics tools. These developments have led to the formation of the European Virus Bioinformatics Center, a network of experts in virology and bioinformatics who are joining forces to enable extensive exchange and collaboration between these research areas. The EVBC strives to provide talented researchers with a supportive environment free of gender bias, but the gender gap in science, especially in math-intensive fields such as computer science, persists. To bring more talented women into research and keep them there, we need to highlight role models to spark their interest, and we need to ensure that female scientists are not kept at lower levels but are given the opportunity to lead the field. Here we showcase the work of the EVBC and highlight the achievements of some outstanding women experts in virology and viral bioinformatics.