| Literature DB >> 34613880 |
Maria da Conceição Viana Invenção1, Alanne Rayssa da Silva Melo1, Larissa Silva de Macêdo1, Thaís Souto Paula da Costa Neves1, Cristiane Moutinho Lagos de Melo2, Marcelo Nazário Cordeiro1, Marcus Vinicius de Aragão Batista3, Antonio Carlos de Freitas1.
Abstract
The current pandemic called COVID-19 caused by the SARS-CoV-2 virus brought the need for the search for fast alternatives to both control and fight the SARS-CoV-2 infection. Therefore, a race for a vaccine against COVID-19 took place, and some vaccines have been approved for emergency use in several countries in a record time. Ongoing prophylactic research has sought faster, safer, and precise alternatives by redirecting knowledge of other vaccines, and/or the development of new strategies using available tools, mainly in the areas of genomics and bioinformatics. The current review highlights the development of synthetic antigen vaccines, focusing on the usage of bioinformatics tools for the selection and construction of antigens on the different vaccine constructions under development, as well as strategies to optimize vaccines for COVID-19.Entities:
Keywords: SARS-CoV-2; adjuvants; immunoinformatics; in silico; nucleic acid vaccines
Mesh:
Substances:
Year: 2021 PMID: 34613880 PMCID: PMC8506811 DOI: 10.1080/21645515.2021.1974288
Source DB: PubMed Journal: Hum Vaccin Immunother ISSN: 2164-5515 Impact factor: 3.452
Main vaccine candidates that are in phases 2/3 of clinical trials or have been approved for emergency use to date
| Platform | Description | Advantages | Disadvantages | References |
|---|---|---|---|---|
| Inactivated | Viral pathogens inactivated by chemical agents or radiation | Easy to prepare | Variable Efficacy | |
| Vaccine Candidates: | Sinovac (CoronaVac) | [ | ||
| Inactivated novel coronavirus vaccine | [ | |||
| Inactivated novel coronavirus vaccine (BBIBP-CorV) | [ | |||
| Non-replicant recombining viral vector | Unrelated virus, designed to encode the target gene of the pathogen. Viral vectors can be replicating or non-replicating | Induces high cell and humoral immune responses | Possible preexisting immunity against vector | |
| Vaccine Candidates: | AZD1222 (ChAdOx1-S, Vaxzevria, or Covishield in India) | [ | ||
| Ad5-nCoV (or Convidecia) | [ | |||
| Gam-COVID-Vac (or Sputnik V) | [ | |||
| Ad26.COV2.S (or JNJ-78436735) | [ | |||
| Subunit vaccines | Antigen components of the target protein produced in the laboratory | High-scale production | Low immunogenicity and may require the use of adjuvants or repeated doses | |
| Vaccine Candidates: | SARS-CoV-2 rs/NVX-CoV2373 | [ | ||
| DNA vaccines | DNA encoding the target antigen | Rapid large-scale vaccine construction and production | It naturally has low immunogenicity | |
| Vaccine Candidates: | INO-4800 | [ | ||
| AG0301-COVID19 | ||||
| mRNA vaccines | The mRNA encoding the target antigen. It is usually complexed with lipids or polymer-based nanoparticles | It is easy, fast, scalable, and economical to produce | It naturally has low immunogenicity and presents high instability | |
| Vaccine Candidates: | mRNA-1273 | [ | ||
| BNT162b1/BNT162b2 (or Comirnaty, and tonizameran) | [ | |||
| CVnCoV | ||||
IM = intramuscular; EP = electroporation; ID = intradermic; GMT = Geometric Means Titer from detected antibodies.
Figure 1.Mechanism of action of DNA and mRNA vaccines and the pathways for activating the cellular and humoral response. DNA vaccines are commonly delivered by electroporation through transient pores formed in the membrane (1). Thus, the DNA reaches the cell cytoplasm and then the nucleus, where it will be transcribed (2). Then the mRNA goes to the cytoplasm, where it is translated in the vaccine peptide (3). Another strategy is the direct delivery of the mRNA (mRNA vaccine) encapsulated in lipid nanoparticles in the cell cytoplasm (4). After the endosome escape, the mRNA is translated in the cytoplasm, followed by the vaccine antigen processing in the proteasomes (5), where they are cleaved into smaller peptides. Next, the peptides are transported by the TAP transporter (not shown) into the endoplasmic reticulum, where they are linked to the MHC-I (6) for TCD8 lymphocyte presentation at the cell surface (7), activating the cytotoxic response and generating effective and memory cells. While the cytotoxic response is triggered through the processing of intracellular antigens, the helper response, as a general rule, is triggered through the exogenous pathway, in which transfected somatic cells – such as myocytes at the injection site – produce the vaccine peptide (8). The peptides can be released outside the cell and be directly engulfed by DCs, or they can be internalized by the apoptotic or necrotic bodies, provoked by an inflammatory environment caused by the electroporation. Thus, the fusion of endocytic vesicles – containing the peptides processed by the lysosomal pathway – with vesicles containing MHC-II molecules of DCs (9), allows the presentation of epitopes to the TCD4 lymphocytes at the cell surface (10), with the activation of helper response and generation of memory cells. The TCD4+ lymphocytes, in turn, play a fundamental role in the activation (11) and maturation of B cell affinity inside the germinal centers (12) for the activation of the humoral response (T cell-dependent B cell activation) generating plasmatic cells that can produce high-affinity neutralizing antibodies, as well as memory cells. Another possible activation pathway for humoral response, but with the induction of a weaker immune response, is the direct linkage to the vaccine antigen with B cell receptors (BCRs) (T-cell independent B cell activation).
Usage of linker sequences in different studies with the aim to ensure the correct processing/directing of peptides in multiepitope vaccines
| Linker | Description | Reference |
|---|---|---|
| Ubiquitin | The introduction of the coding sequence of the ubiquitin gene at one extremity of the vaccine construction aims to favor the peptide degradation by proteasomes during the epitope-specific CTL response | [ |
| GPGPG | The introduction of this spacer between MHC-II binding epitopes in multiepitope vaccine construction promotes the disruption of junctional epitopes in these vaccines, restoring immunogenicity against the target epitopes during helper response | [ |
| EAAAK | It consists of a helical linker to control the distance and reduce the interference between the domains of functional proteins with other protein regions in the vaccine construction. Thus, it is ideally incorporated into N and C-terminal of B cell conformational epitopes | [ |
| ALL and SSL | These linkers are expected to direct the cleavage to the C-terminus of the preceding peptide and to the N-terminus of the next peptide | [ |
| RKSYL and RKSY | Similar to the previous sequence, these motifs are expected to direct the cleavage to the C-terminus of the proceeding peptide, but enable a more flexible cleavage at the N-terminus of the next peptide with multiple potential cleavage sites, optimizing binding to TAP transporter | [ |
| KFLRQY; ADRIW; ADKQW; ADRQW; ADNQY; AKRW; ADNIW. | The initial amino acids of each of these flanking sequences aim to optimize the processing and release of epitopes by the proteasome, and, after cleavage, the following amino acids provide binding sites to TAP transporter | [ |
| ARY | This sequence is a high-affinity motif for TAP recognition based on the preferences of human TAP for flanking of epitopes in the polyepitope construct | [ |
| R/K-R/K | The introduction of a dibasic motif flanking MHC-II binding epitopes in a polyepitope construct enhances its processing, since these motifs represent cleavage sites for lysosomal cathepsins B and L, thus optimizing helper response activation | [ |
| RKRSHAGYQTI; YQTI | This sequence represents the C-terminal tyrosine-based motif of LAMP-1 (lysosome-associated membrane protein-1) glycoprotein and its function is to direct the immunogen from the secretory pathway to lysosomes for degradation, where the peptide fragments bind to MHC class II molecules. Thus, this strategy allows the redirecting of gene vaccines activation route for the activation of the helper response as well | [ |
Figure 2.Structure of a hypothetical synthetic multiepitope vaccine construct containing adjuvant and linkers sequences. In this example, the construct contains sequences that act as adjuvants, which are capable of increasing the immunogenicity of nucleic acid vaccines. Moreover, linker sequences were added between each epitope in order to provide proteasomal and lysosomal processing sites, and TAP transporter binding sites. Concerning the epitopes, in this construction MHC-I, MHC-II ligands, and linear B cell epitopes were added in order to induce both cellular and humoral responses. The epitopes shown in purple are intended for binding to MHC-I molecules and must have between 8 and 11 amino acids. In light blue, the MHC-II ligands are found, these must feature more than 11 amino acids. Meanwhile, the epitopes for B cell activation are shown in gray and contain larger-sized epitopes, up to about 16 aa. LK: Linker, ADJ: Adjuvant.
Figure 3.Summary showing, step by step, the criteria for the development of a COVID-19 vaccine through the construction of synthetic antigens.
In silico methods to predict T cells epitopes
| Method | Description | Reference |
|---|---|---|
| Artificial Neural Network (ANN) | Corresponds to a system similar to the brain neural connection, where each cell receives a signal and sends it to another cell. The union between these cells works as a network | [ |
| NetMHCpan 4.0 | Uses an ANN method to predict epitopes using peptide sequences as entry information, and the exit information is generated from the binding affinity data and elution of linkers with mass spectrometer. This method structure is pan because it analyzes just one model, HLA data (Human MHC), and the peptide length | [ |
| Stabilized Matrix Method (SMM) | It is a method that does specificity modeling of sequences of biological processes that can be quantified. When it comes to epitope prediction, it can be used to predict information regarding the peptide capacity to bind to MHC, TAP transport, and proteasome cleavage The entry data corresponds to amino acid or nucleotide sequences, where the coding is done binarily (0 or 1). To each nucleotide sequence, the weight of each residue that can occur in each position of the sequence will be multiplied. The result of this product is the value of prediction y. To measure the efficacy of the process, an experimental average y value will be generated | [ |
| Support Vectors Machine (SVM) | Through machine learning and statistic learning theory, a model capable of recognizing linear and nonlinear data patterns is created. The data is classified by Kernel functions, linear, radial basis, string, and others | [ |
| NetCTL | It is a prediction method by ANN that uses information about binding affinity, TAP efficacy, and peptide cleavage via proteasomes | [ |
| NetCTLpan | Epitope prediction in different vertebrate species (pan-specific), amongst which is the human species | [ |
| NetChop | Allows the choice of prediction methods named NetChop C-term 3.0 and NetChop 20S-3.0 and allows the alteration of limit score that might interfere with specificity and sensitivity | [ |
| Consensus | Gather different epitope prediction methods in a single open approach, with the aim of obtaining the best performance of the peptide selection process to those considered epitopes | [ |