Loading dynamics of one SARS-CoV-2-derived peptide into MHC-II revealed by kinetic models.

Major histocompatibility complex class II (MHC-II) plays an indispensable role in activating CD4+ T cell immune responses by presenting antigenic peptides on the cell surface for recognition by T cell receptors. The assembly of MHC-II and antigenic peptide is therefore a prerequisite for the antigen presentation. To date, however, the atomic-level mechanism underlying the peptide-loading dynamics for MHC-II is still elusive. Here, by constructing Markov state models based on extensive all-atom molecular dynamics simulations, we reveal the complete peptide-loading dynamics into MHC-II for one SARS-CoV-2 S-protein-derived antigenic peptide (235ITRFQTLLALHRSYL249). Our Markov state model identifies six metastable states (S1-S6) during the peptide-loading process and determines two dominant loading pathways. The peptide could potentially approach the antigen-binding groove via either its N- or C-terminus. Then, the consecutive insertion of several anchor residues into the binding pockets profoundly dictates the peptide-loading dynamics. Notably, the MHC-II αA52-E55 motif could guide the peptide loading into the antigen-binding groove via forming ß-sheets conformation with the incoming peptide. The rate-limiting step, namely S5→S6, is mainly attributed to a considerable desolvation penalty triggered by the binding of the peptide C-terminus. Moreover, we further examined the conformational changes associated with the peptide exchange process catalyzed by the chaperon protein HLA-DM. A flipped-out conformation of MHC-II αW43 captured in S1-S3 is considered a critical anchor point for HLA-DM to modulate the structural dynamics. Our work provides deep structural insights into the key regulatory factors in MHC-II responsible for peptide recognition and guides future design for peptide vaccines against SARS-CoV-2.

CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins.

The development of autoimmune diseases following SARS-CoV-2 infection, including multisystem inflammatory syndrome, has been reported, and several mechanisms have been suggested, including molecular mimicry. We developed a scalable, comparative immunoinformatics pipeline called cross-reactive-epitope-search-using-structural-properties-of-proteins (CRESSP) to identify cross-reactive epitopes between a collection of SARS-CoV-2 proteomes and the human proteome using the structural properties of the proteins. Overall, by searching 4 911 245 proteins from 196 352 SARS-CoV-2 genomes, we identified 133 and 648 human proteins harboring potential cross-reactive B-cell and CD8+ T-cell epitopes, respectively. To demonstrate the robustness of our pipeline, we predicted the cross-reactive epitopes of coronavirus spike proteins, which were recognized by known cross-neutralizing antibodies. Using single-cell expression data, we identified PARP14 as a potential target of intermolecular epitope spreading between the virus and human proteins. Finally, we developed a web application (https://ahs2202.github.io/3M/) to interactively visualize our results. We also made our pipeline available as an open-source CRESSP package (https://pypi.org/project/cressp/), which can analyze any two proteomes of interest to identify potentially cross-reactive epitopes between the proteomes. Overall, our immunoinformatic resources provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune and chronic inflammatory diseases following COVID-19.

Can molecular mimicry explain the cytokine storm of SARS-CoV-2?: An in silico approach.

PARP14 and PARP9 play a key role in macrophage immune regulation. SARS-CoV-2 is an emerging viral disease that triggers hyper-inflammation known as a cytokine storm. In this study, using in silico tools, we hypothesize about the immunological phenomena of molecular mimicry between SARS-CoV-2 Nsp3 and the human PARP14 and PARP9. The results showed an epitope of SARS-CoV-2 Nsp3 protein that contains consensus sequences for both human PARP14 and PARP9 that are antigens for MHC Classes 1 and 2, which can potentially induce an immune response against human PARP14 and PARP9; while its depletion causes a hyper-inflammatory state in SARS-CoV-2 patients.

Landscape and selection of vaccine epitopes in SARS-CoV-2.

BACKGROUND: Early in the pandemic, we designed a SARS-CoV-2 peptide vaccine containing epitope regions optimized for concurrent B cell, CD4+ T cell, and CD8+ T cell stimulation. The rationale for this design was to drive both humoral and cellular immunity with high specificity while avoiding undesired effects such as antibody-dependent enhancement (ADE). METHODS: We explored the set of computationally predicted SARS-CoV-2 HLA-I and HLA-II ligands, examining protein source, concurrent human/murine coverage, and population coverage. Beyond MHC affinity, T cell vaccine candidates were further refined by predicted immunogenicity, sequence conservation, source protein abundance, and coverage of high frequency HLA alleles. B cell epitope regions were chosen from linear epitope mapping studies of convalescent patient serum, followed by filtering for surface accessibility, sequence conservation, spatial localization near functional domains of the spike glycoprotein, and avoidance of glycosylation sites. RESULTS: From 58 initial candidates, three B cell epitope regions were identified. From 3730 (MHC-I) and 5045 (MHC-II) candidate ligands, 292 CD8+ and 284 CD4+ T cell epitopes were identified. By combining these B cell and T cell analyses, as well as a manufacturability heuristic, we proposed a set of 22 SARS-CoV-2 vaccine peptides for use in subsequent murine studies. We curated a dataset of ~ 1000 observed T cell epitopes from convalescent COVID-19 patients across eight studies, showing 8/15 recurrent epitope regions to overlap with at least one of our candidate peptides. Of the 22 candidate vaccine peptides, 16 (n = 10 T cell epitope optimized; n = 6 B cell epitope optimized) were manually selected to decrease their degree of sequence overlap and then synthesized. The immunogenicity of the synthesized vaccine peptides was validated using ELISpot and ELISA following murine vaccination. Strong T cell responses were observed in 7/10 T cell epitope optimized peptides following vaccination. Humoral responses were deficient, likely due to the unrestricted conformational space inhabited by linear vaccine peptides. CONCLUSIONS: Overall, we find our selection process and vaccine formulation to be appropriate for identifying T cell epitopes and eliciting T cell responses against those epitopes. Further studies are needed to optimize prediction and induction of B cell responses, as well as study the protective capacity of predicted T and B cell epitopes.

The Human Leukocyte Antigen Class II Immunopeptidome of the SARS-CoV-2 Spike Glycoprotein.

Precise elucidation of the antigen sequences for T cell immunosurveillance greatly enhances our ability to understand and modulate humoral responses to viral infection or active immunization. Mass spectrometry is used to identify 526 unique sequences from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein extracellular domain in a complex with human leukocyte antigen class II molecules on antigen-presenting cells from a panel of healthy donors selected to represent a majority of allele usage from this highly polymorphic molecule. The identified sequences span the entire spike protein, and several sequences are isolated from a majority of the sampled donors, indicating promiscuous binding. Importantly, many peptides derived from the receptor binding domain used for cell entry are identified. This work represents a precise and comprehensive immunopeptidomic investigation with the SARS-CoV-2 spike glycoprotein and allows detailed analysis of features that may aid vaccine development to end the current coronavirus disease 2019 (COVID-19) pandemic.

Exploring the out of sight antigens of SARS-CoV-2 to design a candidate multi-epitope vaccine by utilizing immunoinformatics approaches.

SARS-CoV-2 causes a severe respiratory disease called COVID-19. Currently, global health is facing its devastating outbreak. However, there is no vaccine available against this virus up to now. In this study, a novel multi-epitope vaccine against SARS-CoV-2 was designed to provoke both innate and adaptive immune responses. The immunodominant regions of six non-structural proteins (nsp7, nsp8, nsp9, nsp10, nsp12 and nsp14) of SARS-CoV-2 were selected by multiple immunoinformatic tools to provoke T cell immune response. Also, immunodominant fragment of the functional region of SARS-CoV-2 spike (400-510 residues) protein was selected for inducing neutralizing antibodies production. The selected regions' sequences were connected to each other by furin-sensitive linker (RVRR). Moreover, the functional region of ß-defensin as a well-known agonist for the TLR-4/MD complex was added at the N-terminus of the vaccine using (EAAAK)3 linker. Also, a CD4 + T-helper epitope, PADRE, was used at the C-terminal of the vaccine by GPGPG and A(EAAAK)2A linkers to form the final vaccine construct. The physicochemical properties, allergenicity, antigenicity, functionality and population coverage of the final vaccine construct were analyzed. The final vaccine construct was an immunogenic, non-allergen and unfunctional protein which contained multiple CD8 + and CD4 + overlapping epitopes, IFN-γ inducing epitopes, linear and conformational B cell epitopes. It could form stable and significant interactions with TLR-4/MD according to molecular docking and dynamics simulations. Global population coverage of the vaccine for HLA-I and II were estimated 96.2% and 97.1%, respectively. At last, the final vaccine construct was reverse translated to design the DNA vaccine. Although the designed vaccine exhibited high efficacy in silico, further experimental validation is necessary.

Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions.

We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations for SARS-CoV-2. Our approach optimizes the presentation likelihood of a diverse set of vaccine peptides conditioned on a target human-population HLA haplotype distribution and expected epitope drift. Our proposed SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA average hits per person (≥ 1 peptide: 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our proposed MHC class II vaccine formulations provide 97.21% predicted coverage with at least five vaccine peptide-HLA average hits per person with all peptides having an observed mutation probability of ≤ 0.001. We provide an open-source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts here: https://github.com/gifford-lab/optivax.

Bioinformatic prediction of potential T cell epitopes for SARS-Cov-2.

To control and prevent the current COVID-19 pandemic, the development of novel vaccines is an emergent issue. In addition, we need to develop tools that can measure/monitor T-cell and B-cell responses to know how our immune system is responding to this deleterious virus. However, little information is currently available about the immune target epitopes of novel coronavirus (SARS-CoV-2) to induce host immune responses. Through a comprehensive bioinformatic screening of potential epitopes derived from the SARS-CoV-2 sequences for HLAs commonly present in the Japanese population, we identified 2013 and 1399 possible peptide epitopes that are likely to have the high affinity (<0.5%- and 2%-rank, respectively) to HLA class I and II molecules, respectively, that may induce CD8+ and CD4+ T-cell responses. These epitopes distributed across the structural (spike, envelope, membrane, and nucleocapsid proteins) and the nonstructural proteins (proteins corresponding to six open reading frames); however, we found several regions where high-affinity epitopes were significantly enriched. By comparing the sequences of these predicted T cell epitopes to the other coronaviruses, we identified 781 HLA-class I and 418 HLA-class II epitopes that have high homologies to SARS-CoV. To further select commonly-available epitopes that would be applicable to larger populations, we calculated population coverages based on the allele frequencies of HLA molecules, and found 2 HLA-class I epitopes covering 83.8% of the Japanese population. The findings in the current study provide us valuable information to design widely-available vaccine epitopes against SARS-CoV-2 and also provide the useful information for monitoring T-cell responses.

Development of epitope-based peptide vaccine against novel coronavirus 2019 (SARS-COV-2): Immunoinformatics approach.

Recently, a novel coronavirus (SARS-COV-2) emerged which is responsible for the recent outbreak in Wuhan, China. Genetically, it is closely related to SARS-CoV and MERS-CoV. The situation is getting worse and worse, therefore, there is an urgent need for designing a suitable peptide vaccine component against the SARS-COV-2. Here, we characterized spike glycoprotein to obtain immunogenic epitopes. Next, we chose 13 Major Histocompatibility Complex-(MHC) I and 3 MHC-II epitopes, having antigenic properties. These epitopes are usually linked to specific linkers to build vaccine components and molecularly dock on toll-like receptor-5 to get binding affinity. Therefore, to provide a fast immunogenic profile of these epitopes, we performed immunoinformatics analysis so that the rapid development of the vaccine might bring this disastrous situation to the end earlier.