Prediction of B cell epitopes in proteins using a novel sequence similarity-based method

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.

An Immunoinformatic-Based In Silico Identification on the Creation of a Multiepitope-Based Vaccination Against the Nipah Virus

The zoonotic viruses pose significant threats to public health. Nipah virus (NiV) is an emerging virus transmitted from bats to humans. The NiV causes severe encephalitis and acute respiratory distress syndrome, leading to high mortality rates, with fatality rates ranging from 40% to 75%. The first emergence of the disease was found in Malaysia in 1998-1999 and later in Bangladesh, Cambodia, Timor-Leste, Indonesia, Singapore, Papua New Guinea, Vietnam, Thailand, India, and other South and Southeast Asian nations. Currently, no specific vaccines or antiviral drugs are available. The potential advantages of epitope-based vaccines include their ability to elicit specific immune responses while minimizing potential side effects. The epitopes have been identified from the conserved region of viral proteins obtained from the UniProt database. The selection of conserved epitopes involves analyzing the genetic sequences of various viral strains. The present study identified two B cell epitopes, seven cytotoxic T lymphocyte (CTL) epitopes, and seven helper T lymphocyte (HTL) epitope interactions from the NiV proteomic inventory. The antigenic and physiological properties of retrieved protein were analyzed using online servers ToxinPred, VaxiJen v2.0, and AllerTOP. The final vaccine candidate has a total combined coverage range of 80.53%. The tertiary structure of the constructed vaccine was optimized, and its stability was confirmed with the help of molecular simulation. Molecular docking was performed to check the binding affinity and binding energy of the constructed vaccine with TLR-3 and TLR-5. Codon optimization was performed in the constructed vaccine within the Escherichia coli K12 strain, to eliminate the danger of codon bias. However, these findings must require further validation to assess their effectiveness and safety. The development of vaccines and therapeutic approaches for virus infection is an ongoing area of research, and it may take time before effective interventions are available for clinical use.

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/).

Recent Progress in Antibody Epitope Prediction

Recent progress in epitope prediction has shown promising results in the development of vaccines and therapeutics against various diseases. However, the overall accuracy and success rate need to be improved greatly to gain practical application significance, especially conformational epitope prediction. In this review, we examined the general features of antibody-antigen recognition, highlighting the conformation selection mechanism in flexible antibody-antigen binding. We recently highlighted the success and warning signs of antibody epitope predictions, including linear and conformation epitope predictions. While deep learning-based models gradually outperform traditional feature-based machine learning, sequence and structure features still provide insight into antibody-antigen recognition problems.

Analysis of sequence diversity in Plasmodium falciparum glutamic acid-rich protein (PfGARP), an asexual blood stage vaccine candidate

Glutamic acid-rich protein of Plasmodium falciparum (PfGARP) binds to erythrocyte band 3 and may enhance cytoadherence of infected erythrocytes. Naturally acquired anti-PfGARP antibodies could confer protection against high parasitemia and severe symptoms. While whole genome sequencing analysis has suggested high conservation in this locus, little is known about repeat polymorphism in this vaccine candidate antigen. Direct sequencing was performed from the PCR-amplified complete PfGARP gene of 80 clinical isolates from four malaria endemic provinces in Thailand and an isolate from a Guinean patient. Publicly available complete coding sequences of this locus were included for comparative analysis. Six complex repeat (RI-RVI) and two homopolymeric glutamic acid repeat (E1 and E2) domains were identified in PfGARP. The erythrocyte band 3-binding ligand in domain RIV and the epitope for mAB7899 antibody eliciting in vitro parasite killing property were perfectly conserved across isolates. Repeat lengths in domains RIII and E1-RVI-E2 seemed to be correlated with parasite density of the patients. Sequence variation in PfGARP exhibited genetic differentiation across most endemic areas of Thailand. Phylogenetic tree inferred from this locus has shown that most Thai isolates formed closely related lineages, suggesting local expansion/contractions of repeat-encoding regions. Positive selection was observed in non-repeat region preceding domain RII which corresponded to a helper T cell epitope predicted to be recognized by a common HLA class II among Thai population. Predicted linear B cell epitopes were identified in both repeat and non-repeat domains. Besides length variation in some repeat domains, sequence conservation in non-repeat regions and almost all predicted immunogenic epitopes have suggested that PfGARP-derived vaccine may largely elicit strain-transcending immunity.

Evaluation of immunomodulatory potential of recombinant histidyl-tRNA synthetase (rLdHisRS) protein of Leishmania donovani as a vaccine candidate against visceral leishmaniasis

Visceral leishmaniasis is neglected tropical protozoan disease caused by Leishmania donovani and are associated with high fatality rate in developing countries since prophylactic vaccines are not available. In the present study, we evaluated the immunomodulatory potential of L. donovani histidyl-tRNA synthetase (LdHisRS) and predicted the epitopes using immunoinformatic tools. Histidyl-tRNA synthetase (HisRS) is a class IIa aminoacyl t-RNA synthetase enzyme (aaRS) required for histidine incorporation into proteins during protein synthesis. The recombinant LdHisRS protein (rLdHisRS) was expressed in E coli BL-21cells, and its immunomodulatory role was assessed in J774A.1 murine macrophage and in BALB/c mice, respectively. LdHisRS specifically stimulated and triggered enhance cell proliferation, nitric oxide release and IFN-γ (70%; P < 0.001), and IL-12 (55.37%; P < 0.05) cytokine release in vitro, whereas BALB/c mice immunized with rLdHisRS show higher NO release (80.95%; P<0.001), higher levels of Th1 cytokines IFN-γ (14%; P < 0.05), TNF-α (34.93%; P < 0.001), and IL-12 (28.49%; P < 0.001) and robust IgG (p<0.001) and IgG2a (p<0.001) production. We also identified 20 Helper T-lymphocytes (HTLs), 30 cytotoxic T lymphocytes (CTLs), and 18 B-cell epitopes from HisRS protein of L. donovani. All these epitopes can be further used to make a multiepitope vaccine against L. donovani.

Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction

Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.

Prediction of Linear B Cell Epitopes in Proteins

The accurate prediction of B cell epitopes is crucial for the design and development of vaccines, especially of those preventive for emerging pathogenic diseases. Preventive vaccines are mainly based on the induction of highly specific neutralizing antibodies. This chapter deals with some prediction methods, which are currently available as user-friendly online servers, to predict B cell epitopes in proteins. A final assessment to validate these predictions is done by recurring to the Immune Epitope Database (IEDB).