Immunoinformatics and modeling perspective of T cell epitope-based cancer immunotherapy: a holistic picture

Advancements and hurdles in the development of a vaccine for triple-negative breast cancer: A comprehensive review of multi-omics and immunomics strategies

Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.

Computational Structural and Functional Analyses of ORF10 in Novel Coronavirus SARS-CoV-2 Variants to Understand Evolutionary Dynamics

Introduction:

In an effort to combat SARS-CoV-2 through multi-subunit vaccine design, during studies using whole genome and immunome, ORF10, located at the 3′ end of the genome, displayed unique features. It showed no homology to any known protein in other organisms, including SARS-CoV. It was observed that its nucleotide sequence is 100% identical in the SARS-CoV-2 genomes sourced worldwide, even in the recent-most VoCs and VoIs of B.1.1.529 (Omicron), B.1.617 (Delta), B.1.1.7 (Alpha), B.1.351 (Beta), and P.1 (Gamma) lineages, implicating its constant nature throughout the evolution of deadly variants.

Aim:

The structure and function of SARS-CoV-2 ORF10 and the role it may play in the viral evolution is yet to be understood clearly. The aim of this study is to predict its structure, function, and understand evolutionary dynamics on the basis of mutations and likely heightened immune responses in the immunopathogenesis of this deadly virus.

Methods:

Sequence analysis, ab-initio structure modeling and an understanding of the impact of likely substitutions in key regions of protein was carried out. Analyses of viral T cell epitopes and primary anchor residue mutations was done to understand the role it may play in the evolution as a molecule with likely enhanced immune response and consequent immunopathogenesis.

Results:

Few amino acid substitution mutations are observed, most probably due to the ribosomal frameshifting, and these mutations may not be detrimental to its functioning. As ORF10 is observed to be an expressed protein, ab-initio structure modeling shows that it comprises mainly an α-helical region and maybe an ER-targeted membrane mini-protein. Analyzing the whole proteome, it is observed that ORF10 presents amongst the highest number of likely promiscuous and immunogenic CTL epitopes, specifically 11 out of 30 promiscuous ones and 9 out of these 11, immunogenic CTL epitopes. Reactive T cells to these epitopes have been uncovered in independent studies. Majority of these epitopes are located on the α-helix region of its structure, and the substitution mutations of primary anchor residues in these epitopes do not affect immunogenicity. Its conserved nucleotide sequence throughout the evolution and diversification of virus into several variants is a puzzle yet to be solved.

Conclusions:

On the basis of its sequence, structure, and epitope mapping, it is concluded that it may function like those mini-proteins used to boost immune responses in medical applications. Due to the complete nucleotide sequence conservation even a few years after SARS-CoV-2 genome was first sequenced, it poses a unique puzzle to be solved, in view of the evolutionary dynamics of variants emerging in the populations worldwide.

Identification of Potential Vaccine Candidates Against SARS-CoV-2 to Fight COVID-19: Reverse Vaccinology Approach

Background

The recent emergence of COVID-19 has caused an immense global public health crisis. The etiological agent of COVID-19 is the novel coronavirus SARS-CoV-2. More research in the field of developing effective vaccines against this emergent viral disease is indeed a need of the hour.

Objective

The aim of this study was to identify effective vaccine candidates that can offer a new milestone in the battle against COVID-19.

Methods

We used a reverse vaccinology approach to explore the SARS-CoV-2 genome among strains prominent in India. Epitopes were predicted and then molecular docking and simulation were used to verify the molecular interaction of the candidate antigenic peptide with corresponding amino acid residues of the host protein.

Results

A promising antigenic peptide, GVYFASTEK, from the surface glycoprotein of SARS-CoV-2 (protein accession number QIA98583.1) was predicted to interact with the human major histocompatibility complex (MHC) class I human leukocyte antigen (HLA)-A*11-01 allele, showing up to 90% conservancy and a high antigenicity value. After vigorous analysis, this peptide was predicted to be a suitable epitope capable of inducing a strong cell-mediated immune response against SARS-CoV-2.

Conclusions

These results could facilitate selecting SARS-CoV-2 epitopes for vaccine production pipelines in the immediate future. This novel research will certainly pave the way for a fast, reliable, and effective platform to provide a timely countermeasure against this dangerous virus responsible for the COVID-19 pandemic.

Immunoinformatics based designing a multi-epitope vaccine against pathogenic Chandipura vesiculovirus

Chandipura vesiculovirus (CHPV) is a rapidly emerging pathogen responsible for causing acute encephalitis. Due to its widespread occurrence in Asian and African countries, this has become a global threat, and there is an urgent need to design an effective and nonallergenic vaccine against this pathogen. The present study aimed to develop a multi-epitope vaccine using an immunoinformatics approach. The conventional method of vaccine design involves large proteins or whole organism which leads to unnecessary antigenic load with increased chances of allergenic reactions. In addition, the process is also very time-consuming and labor-intensive. These limitations can be overcome by peptide-based vaccines comprising short immunogenic peptide fragments that can elicit highly targeted immune responses, avoiding the chances of allergenic reactions, in a relatively shorter time span. The multi-epitope vaccine constructed using CTL, HTL, and IFN-γ epitopes was able to elicit specific immune responses when exposed to the pathogen, in silico. Not only that, molecular docking and molecular dynamics simulation studies confirmed a stable interaction of the vaccine with the immune receptors. Several physicochemical analyses of the designed vaccine candidate confirmed it to be highly immunogenic and nonallergic. The computer-aided analysis performed in this study suggests that the designed multi-epitope vaccine can elicit specific immune responses and can be a potential candidate against CHPV.

Designing a multi-epitope vaccine candidate to combat MERS-CoV by employing an immunoinformatics approach

Currently, no approved vaccine is available against the Middle East respiratory syndrome coronavirus (MERS-CoV), which causes severe respiratory disease. The spike glycoprotein is typically considered a suitable target for MERS-CoV vaccine candidates. A computational strategy can be used to design an antigenic vaccine against a pathogen. Therefore, we used immunoinformatics and computational approaches to design a multi-epitope vaccine that targets the spike glycoprotein of MERS-CoV. After using numerous immunoinformatics tools and applying several immune filters, a poly-epitope vaccine was constructed comprising cytotoxic T-cell lymphocyte (CTL)-, helper T-cell lymphocyte (HTL)-, and interferon-gamma (IFN-γ)-inducing epitopes. In addition, various physicochemical, allergenic, and antigenic profiles were evaluated to confirm the immunogenicity and safety of the vaccine. Molecular interactions, binding affinities, and the thermodynamic stability of the vaccine were examined through molecular docking and dynamic simulation approaches, during which we identified a stable and strong interaction with Toll-like receptors (TLRs). In silico immune simulations were performed to assess the immune-response triggering capabilities of the vaccine. This computational analysis suggested that the proposed vaccine candidate would be structurally stable and capable of generating an effective immune response to combat viral infections; however, experimental evaluations remain necessary to verify the exact safety and immunogenicity profile of this vaccine.

In silico designing of vaccine candidate against Clostridium difficile

Clostridium difficile is a spore-forming gram-positive bacterium, recognized as the primary cause of antibiotic-associated nosocomial diarrhoea. Clostridium difficile infection (CDI) has emerged as a major health-associated infection with increased incidence and hospitalization over the years with high mortality rates. Contamination and infection occur after ingestion of vegetative spores, which germinate in the gastro-intestinal tract. The surface layer protein and flagellar proteins are responsible for the bacterial colonization while the spore coat protein, is associated with spore colonization. Both these factors are the main concern of the recurrence of CDI in hospitalized patients. In this study, the CotE, SlpA and FliC proteins are chosen to form a multivalent, multi-epitopic, chimeric vaccine candidate using the immunoinformatics approach. The overall reliability of the candidate vaccine was validated in silico and the molecular dynamics simulation verified the stability of the vaccine designed. Docking studies showed stable vaccine interactions with Toll‐Like Receptors of innate immune cells and MHC receptors. In silico codon optimization of the vaccine and its insertion in the cloning vector indicates a competent expression of the modelled vaccine in E. coli expression system. An in silico immune simulation system evaluated the effectiveness of the candidate vaccine to trigger a protective immune response.

Development of an in silico multi-epitope vaccine against SARS-COV-2 by précised immune-informatics approaches

The coronavirus family has been infecting the human population for the past two decades, but the ongoing coronavirus called SARS-CoV-2 has posed an enigmatic challenge to global public health security. Since last year, the mutagenic quality of this virus is causing changes to its genetic material. To prevent those situations, the FDA approved some emergency vaccines but there is no assurance that these will function properly in the complex human body system. In point of view, a short but efficient effort has made in this study to develop an immune epitope-based therapy for the rapid exploitation of SARS-CoV-2 by applying in silico structural biology and advancing immune information strategies. The antigenic epitopes were screened from the Surface, Membrane, Envelope proteins of SARS-CoV-2 and passed through several immunological filters to determine the best possible one. According to this, 7CD4+, 10CD8+ and 5 B-cell epitopes were found to be prominent, antigenic, immunogenic, and most importantly, highly conserved among 128 Bangladeshi and 110 other infected countries SARS-CoV-2 variants. After that, the selected epitopes and adjuvant were linked to finalize the multi-epitope vaccine by appropriate linkers. The immune simulation disclosed that the engineered vaccine could activate both humoral and innate immune responses. For the prediction of an effective binding, molecular docking was carried out between the vaccine and immunological receptors (TLRs). Strong binding affinity and good docking scores clarified the stringency of the vaccines. Furthermore, MD simulation was performed within the highest binding affinity complex to observe the stability. Codon optimization and other physicochemical properties revealed that the vaccine would be suitable for a higher expression at cloning level. So, monitoring the overall in silico assessment, we anticipated that our engineered vaccine would be a plausible prevention against COVID-19.

Designing of cytotoxic and helper T cell epitope map provides insights into the highly contagious nature of the pandemic novel coronavirus SARS-CoV-2

Novel coronavirus, SARS-CoV-2, has emerged as one of the deadliest pathogens of this century, creating an unprecedented pandemic. Belonging to the betacoronavirus family, it primarily spreads through human contact via symptomatic and asymptomatic transmission. Despite several attempts since it emerged, there is no known treatment in the form of drugs or vaccines. Hence, work on developing a potential multi-subunit vaccine is the need of the hour. In this study, attempts have been made to find globally conserved epitopes from the entire set of SARS-CoV-2 proteins as there is as yet, no clear information on the immunogenicity of these proteins. Using diverse computational tools, a ranked list of probable immunogenic, promiscuous epitopes generated through all the three main stages of antigen processing and presentation pathways has been prioritized. Moreover, several useful insights were gleaned during these analyses. One of the most important insights is that all of the proteins in this pathogen present unique epitopes, so that the targeting of a few specific viral proteins is not likely to result in an effective immune response in humans. Due to the presence of these unique epitopes in all of the SARS-CoV-2 proteins, stronger immune responses generated by T cell hyperactivation may lead to cytokine storm and immunopathology and consequently, remote chances of human survival. These epitopes, after due validation in vitro, may thus need to be presented to the human body in that form of multi-subunit epitope-based vaccine that avoids such immunopathologies.

A candidate multi-epitope vaccine against SARS-CoV-2

In the past two decades, 7 coronaviruses have infected the human population, with two major outbreaks caused by SARS-CoV and MERS-CoV in the year 2002 and 2012, respectively. Currently, the entire world is facing a pandemic of another coronavirus, SARS-CoV-2, with a high fatality rate. The spike glycoprotein of SARS-CoV-2 mediates entry of virus into the host cell and is one of the most important antigenic determinants, making it a potential candidate for a vaccine. In this study, we have computationally designed a multi-epitope vaccine using spike glycoprotein of SARS-CoV-2. The overall quality of the candidate vaccine was validated in silico and Molecular Dynamics Simulation confirmed the stability of the designed vaccine. Docking studies revealed stable interactions of the vaccine with Toll-Like Receptors and MHC Receptors. The in silico cloning and codon optimization supported the proficient expression of the designed vaccine in E. coli expression system. The efficiency of the candidate vaccine to trigger an effective immune response was assessed by an in silico immune simulation. The computational analyses suggest that the designed multi-epitope vaccine is structurally stable which can induce specific immune responses and thus, can be a potential vaccine candidate against SARS-CoV-2.

Current status and future directions of cancer immunotherapy

In the past decades, our knowledge about the relationship between cancer and the immune system has increased considerably. Recent years' success of cancer immunotherapy including monoclonal antibodies (mAbs), cancer vaccines, adoptive cancer therapy and the immune checkpoint therapy has revolutionized traditional cancer treatment. However, challenges still exist in this field. Personalized combination therapies via new techniques will be the next promising strategies for the future cancer treatment direction.

A multi-subunit based, thermodynamically stable model vaccine using combined immunoinformatics and protein structure based approach

Immunizations with the conventional vaccines have failed to effectively inhibit the incidences and further dissemination of the infections. To address it, we have implemented protein structure based strategies to design an efficient multi-epitope subunit vaccine against Mycobacterium avium subsp. paratuberculosis (MAP). Previously reported immunodominant peptide epitope sequences from MAP1611 protein were conjugated together with a stretch of conserved amino acid residues of heparin-binding hemagglutinin, reported as a TLR4 agonist and was employed as an adjuvant to polarize the cellular responses toward host protective Th1 responses. These three types of component peptides were combined with the help of relevant linkers for efficient separation to improve and intensify the antigen processing and presentation. The primary structures of these multi peptides were 3-dimensional homology modeled to yield the final chimeric vaccine. Further, its conformational correctness and stability enhancement was assessed using molecular dynamics (MD) simulations. Finally, disulfide engineering in the most flexible regions of the molecule yielded three potential mutants, Y593C-E610C, Q631C-A634C and a double mutant Q631C-A634C/Y593C-E610C. The double mutant represents thermodynamically most stable version among them. It is potentially highly antigenic, soluble and non-allergen molecule interacting with the TLR receptor expressed on the immune cells. This vaccine contains both T-cell and several B-cell epitopes and an adjuvant which potentially possess protective cellular and humoral immune responses triggering properties. The presented vaccine strategy will be proven a promising pathogen specific candidate with wide therapeutic application against MAP which may be extended to other prevalent infections in future.

Identification of human leukemia antigen A*0201-restricted epitopes derived from epidermal growth factor pathway substrate number 8

T-cell-mediated immunotherapy of hematological malignancies requires selection of targeted tumor-associated antigens and T-cell epitopes contained in these tumor proteins. Epidermal growth factor receptor pathway substrate 8 (EPS8), whose function is pivotal for tumor proliferation, progression and metastasis, has been found to be overexpressed in most human tumor types, while its expression in normal tissue is low. The aim of the present study was to identify human leukemia antigen (HLA)-A*0201-restricted epitopes of EPS8 by using a reverse immunology approach. To achieve this, computer algorithms were used to predict HLA-A*0201 molecular binding, proteasome cleavage patterns as well as translocation of transporters associated with antigen processing. Candidate peptides were experimentally validated by T2 binding affinity assay and brefeldin-A decay assay. The functional avidity of peptide-specific cytotoxic T lymphocytes (CTLs) induced from peripheral blood mononuclear cells of healthy volunteers were evaluated by using an enzyme-linked immunosorbent spot assay and a cytotoxicity assay. Four peptides, designated as P455, P92, P276 and P360, had high affinity and stability of binding towards the HLA-A*0201 molecule, and specific CTLs induced by them significantly responded to the corresponding peptides and secreted IFN-γ. At the same time, the CTLs were able to specifically lyse EPS8-expressing cell lines in an HLA-A*0201-restricted manner. The present study demon-strated that P455, P92, P276 and P360 were CTL epitopes of EPS8, and were able to be used for epitope-defined adoptive T-cell transfer and multi-epitope-based vaccine design.

Current trends in cancer vaccines--a bioinformatics perspective

Cancer vaccine development is in the process of becoming reality in future, due to successful phase II/III clinical trials. However, there are still problems due to the specificity of tumor antigens and weakness of tumor associated antigens in eliciting an effective immune response. Computational models to assess the vaccine efficacy have helped to improve and understand what is necessary for personalized treatment. Further research is needed to elucidate the mechanisms of activation of antigen specific cytotoxic T lymphocytes, decreased TREG number functionality and antigen cascade, so that overall improvement in vaccine efficacy and disease free survival can be attained. T cell epitomic based in sillico approaches might be very effective for the design and development of novel cancer vaccines.

A novel multi-epitope peptide vaccine against cancer: an in silico approach

Cancer immunotherapy has an outstanding position in cancer prevention and treatment. In this kind of therapy, the immune system is activated to eliminate cancerous cells. Multi-epitope peptide cancer vaccines are manifesting as the next generation of cancer immunotherapy. In the present study, we have implemented various strategies to design an efficient multi-epitope vaccine. CD8+ cytolytic T lymphocytes (CTLs) epitopes, which have a pivotal role in cellular immune responses, helper epitopes and adjuvant, are three crucial components of peptide vaccine. CTL epitopes were determined from two high immunogenic protein Wilms tumor-1 (WT1) and human papillomavirus (HPV) E7 by various servers, which apply different algorithms. CTL epitopes were linked together by AAY and HEYGAEALERAG motifs to enhance epitope presentation. Pan HLA DR-binding epitope (PADRE) peptide sequence and helper epitopes, which have defined from Tetanus toxin fragment C (TTFrC) by various servers, were used to induce CD4+ helper T lymphocytes (HTLs) responses. Additionally, helper epitopes were conjugated together via GPGPG motifs that stimulate HTL immunity. Heparin-Binding Hemagglutinin (HBHA), a novel TLR4 agonist was employed as an adjuvant to polarize CD4+ T cells toward T-helper 1 to induce strong CTL responses. Moreover, the EAAAK linker was introduced to N and C terminals of HBHA for efficient separation. 3D model of protein was generated and predicted B cell epitopes were determined from the surface of built structure. Our protein contains several linear and conformational B cell epitopes, which suggests the antibody triggering property of this novel vaccine. Hence, our final protein can be used for prophylactic or therapeutic usages, because it can potentially stimulate both cellular and humoral immune responses.

Immunoinformatics, molecular modeling, and cancer vaccines

Cancer vaccines are a natural way of fighting the development and progression of cancer as they harness the power of immune system to tweak it into killing cancerous cells. One of the most important agents in an immune system, the cytotoxic T cells (CTL), play a major role and the CTL epitopes in the form of an immunotherapeutic product have been shown to help mount an immune response towards tumor cell destruction. Immunoinformatics and molecular modeling tools have proven powerful towards the prediction of plausible CTL epitopes as well as other epitopes, cutting short the time and cost. We focus on the sequential methodology using these tools as well as some databases to generate a succinct list of enterprising subtype-specific or promiscuous peptide epitopes.

Exhaustive proteome mining for functional MHC-I ligands

We present the development and application of a new machine-learning approach to exhaustively and reliably identify major histocompatibility complex class I (MHC-I) ligands among all 20(8) octapeptides and in genome-derived proteomes of Mus musculus , influenza A H3N8, and vesicular stomatitis virus (VSV). Focusing on murine H-2K(b), we identified potent octapeptides exhibiting direct MHC-I binding and stabilization on the surface of TAP-deficient RMA-S cells. Computationally identified VSV-derived peptides induced CD8(+) T-cell proliferation after VSV-infection of mice. The study demonstrates that high-level machine-learning models provide a unique access to rationally designed peptides and a promising approach toward "reverse vaccinology".

Early relaxation dynamics in the LC 13 T cell receptor in reaction to 172 altered peptide ligands: a molecular dynamics simulation study

The interaction between the T cell receptor and the major histocompatibility complex is one of the most important events in adaptive immunology. Although several different models for the activation process of the T cell via the T cell receptor have been proposed, it could not be shown that a structural mechanism, which discriminates between peptides of different immunogenicity levels, exists within the T cell receptor. In this study, we performed systematic molecular dynamics simulations of 172 closely related altered peptide ligands in the same T cell receptor/major histocompatibility complex system. Statistical evaluations yielded significant differences in the initial relaxation process between sets of peptides at four different immunogenicity levels.

Cancer vaccines: state of the art of the computational modeling approaches

Cancer vaccines are a real application of the extensive knowledge of immunology to the field of oncology. Tumors are dynamic complex systems in which several entities, events, and conditions interact among them resulting in growth, invasion, and metastases. The immune system includes many cells and molecules that cooperatively act to protect the host organism from foreign agents. Interactions between the immune system and the tumor mass include a huge number of biological factors. Testing of some cancer vaccine features, such as the best conditions for vaccine administration or the identification of candidate antigenic stimuli, can be very difficult or even impossible only through experiments with biological models simply because a high number of variables need to be considered at the same time. This is where computational models, and, to this extent, immunoinformatics, can prove handy as they have shown to be able to reproduce enough biological complexity to be of use in suggesting new experiments. Indeed, computational models can be used in addition to biological models. We now experience that biologists and medical doctors are progressively convinced that modeling can be of great help in understanding experimental results and planning new experiments. This will boost this research in the future.

On the selectivity and efficacy of defense peptides with respect to cancer cells

Here, we review potential determinants of the anticancer efficacy of innate immune peptides (ACPs) for cancer cells. These determinants include membrane-based factors, such as receptors, phosphatidylserine, sialic acid residues, and sulfated glycans, and peptide-based factors, such as residue composition, sequence length, net charge, hydrophobic arc size, hydrophobicity, and amphiphilicity. Each of these factors may contribute to the anticancer action of ACPs, but no single factor(s) makes an overriding contribution to their overall selectivity and toxicity. Differences between the anticancer actions of ACPs seem to relate to different levels of interplay between these peptide and membrane-based factors.

PeptX: using genetic algorithms to optimize peptides for MHC binding

Background

The binding between the major histocompatibility complex and the presented peptide is an indispensable prerequisite for the adaptive immune response. There is a plethora of different in silico techniques for the prediction of the peptide binding affinity to major histocompatibility complexes. Most studies screen a set of peptides for promising candidates to predict possible T cell epitopes. In this study we ask the question vice versa: Which peptides do have highest binding affinities to a given major histocompatibility complex according to certain in silico scoring functions?

Results

Since a full screening of all possible peptides is not feasible in reasonable runtime, we introduce a heuristic approach. We developed a framework for Genetic Algorithms to optimize peptides for the binding to major histocompatibility complexes. In an extensive benchmark we tested various operator combinations. We found that (1) selection operators have a strong influence on the convergence of the population while recombination operators have minor influence and (2) that five different binding prediction methods lead to five different sets of "optimal" peptides for the same major histocompatibility complex. The consensus peptides were experimentally verified as high affinity binders.

Conclusion

We provide a generalized framework to calculate sets of high affinity binders based on different previously published scoring functions in reasonable runtime. Furthermore we give insight into the different behaviours of operators and scoring functions of the Genetic Algorithm.

T-cell epitope prediction and immune complex simulation using molecular dynamics: state of the art and persisting challenges

Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems. Specifically, it provides a means by which we can measure theoretically that which cannot be measured experimentally: the dynamic time-evolution of complex systems comprising atoms and molecules. It is particularly suitable for the simulation and analysis of the otherwise inaccessible details of MHC-peptide interaction and, on a larger scale, the simulation of the immune synapse. Progress has been relatively tentative yet the emergence of truly high-performance computing and the development of coarse-grained simulation now offers us the hope of accurately predicting thermodynamic parameters and of simulating not merely a handful of proteins but larger, longer simulations comprising thousands of protein molecules and the cellular scale structures they form. We exemplify this within the context of immunoinformatics.