A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines.

The growing incidences of new viral diseases and increasingly frequent viral epidemics have strained therapeutic and preventive measures; the high mutability of viral genes puts additional strains on developmental efforts. Given the high cost and time requirements for new drugs development, vaccines remain as a viable alternative, but there too traditional techniques of live-attenuated or inactivated vaccines have the danger of allergenic reactions and others. Peptide vaccines have, over the last several years, begun to be looked on as more appropriate alternatives, which are economically affordable, require less time for development and hold the promise of multi-valent dosages. The developments in bioinformatics, proteomics, immunogenomics, structural biology and other sciences have spurred the growth of vaccinomics where computer assisted approaches serve to identify suitable peptide targets for eventual development of vaccines. In this mini-review we give a brief overview of some of the recent trends in computer assisted vaccine development with emphasis on the primary selection procedures of probable peptide candidates for vaccine development.

AutoPepVax, a Novel Machine-Learning-Based Program for Vaccine Design: Application to a Pan-Cancer Vaccine Targeting EGFR Missense Mutations.

The current epitope selection methods for peptide vaccines often rely on epitope binding affinity predictions, prompting the need for the development of more sophisticated in silico methods to determine immunologically relevant epitopes. Here, we developed AutoPepVax to expedite and improve the in silico epitope selection for peptide vaccine design. AutoPepVax is a novel program that automatically identifies non-toxic and non-allergenic epitopes capable of inducing tumor-infiltrating lymphocytes by considering various epitope characteristics. AutoPepVax employs random forest classification and linear regression machine-learning-based models, which are trained with datasets derived from tumor samples. AutoPepVax, along with documentation on how to run the program, is freely available on GitHub. We used AutoPepVax to design a pan-cancer peptide vaccine targeting epidermal growth factor receptor (EGFR) missense mutations commonly found in lung adenocarcinoma (LUAD), colorectal adenocarcinoma (CRAD), glioblastoma multiforme (GBM), and head and neck squamous cell carcinoma (HNSCC). These mutations have been previously targeted in clinical trials for EGFR-specific peptide vaccines in GBM and LUAD, and they show promise but lack demonstrated clinical efficacy. Using AutoPepVax, our analysis of 96 EGFR mutations identified 368 potential MHC-I-restricted epitope-HLA pairs from 49,113 candidates and 430 potential MHC-II-restricted pairs from 168,669 candidates. Notably, 19 mutations presented viable epitopes for MHC I and II restrictions. To evaluate the potential impact of a pan-cancer vaccine composed of these epitopes, we used our program, PCOptim, to curate a minimal list of epitopes with optimal population coverage. The world population coverage of our list ranged from 81.8% to 98.5% for MHC Class II and Class I epitopes, respectively. From our list of epitopes, we constructed 3D epitope-MHC models for six MHC-I-restricted and four MHC-II-restricted epitopes, demonstrating their epitope binding potential and interaction with T-cell receptors. AutoPepVax's comprehensive approach to in silico epitope selection addresses vaccine safety, efficacy, and broad applicability. Future studies aim to validate the AutoPepVax-designed vaccines with murine tumor models that harbor the studied mutations.

HOGVAX: Exploiting epitope overlaps to maximize population coverage in vaccine design with application to SARS-CoV-2.

The efficacy of epitope vaccines depends on the included epitopes as well as the probability that the selected epitopes are presented by the major histocompatibility complex (MHC) proteins of a vaccinated individual. Designing vaccines that effectively immunize a high proportion of the population is challenging because of high MHC polymorphism, diverging MHC-peptide binding affinities, and physical constraints on epitope vaccine constructs. Here, we present HOGVAX, a combinatorial optimization approach for epitope vaccine design. To optimize population coverage within the constraint of limited vaccine construct space, HOGVAX employs a hierarchical overlap graph (HOG) to identify and exploit overlaps between selected peptides and explicitly models the structure of linkage disequilibrium in the MHC. In a SARS-CoV-2 case study, we demonstrate that HOGVAX-designed vaccines contain substantially more epitopes than vaccines built from concatenated peptides and predict vaccine efficacy in over 98% of the population with high numbers of presented peptides in vaccinated individuals.

Ranking-Based Convolutional Neural Network Models for Peptide-MHC Class I Binding Prediction.

T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identifying peptides that can bind to MHC class-I molecules plays a vital role in the design of peptide vaccines. Many computational methods, for example, the state-of-the-art allele-specific method MHCflurry , have been developed to predict the binding affinities between peptides and MHC molecules. In this manuscript, we develop two allele-specific Convolutional Neural Network-based methods named ConvM and SpConvM to tackle the binding prediction problem. Specifically, we formulate the problem as to optimize the rankings of peptide-MHC bindings via ranking-based learning objectives. Such optimization is more robust and tolerant to the measurement inaccuracy of binding affinities, and therefore enables more accurate prioritization of binding peptides. In addition, we develop a new position encoding method in ConvM and SpConvM to better identify the most important amino acids for the binding events. We conduct a comprehensive set of experiments using the latest Immune Epitope Database (IEDB) datasets. Our experimental results demonstrate that our models significantly outperform the state-of-the-art methods including MHCflurry with an average percentage improvement of 6.70% on AUC and 17.10% on ROC5 across 128 alleles.

An insight into the epitope-based peptide vaccine design strategy and studies against COVID-19.

SARS-CoV-2 is a new member of the coronavirus family and caused the pandemic of coronavirus disease 2019 (COVID-19) in 2020. It is crucial to design and produce an effective vaccine for the prevention of rapid transmission and possible deaths wcaused by the disease. Although intensive work and research are being carried out all over the world to develop a vaccine, an effective and approved formulation that can prevent the infection and limit the outbreak has not been announced yet. Among all types of vaccines, epitope-based peptide vaccines outshine with their low-cost production, easy modification in the structure, and safety. In this review, vaccine studies against COVID-19 have been summarized and detailed information about the epitope-based peptide vaccines against COVID-19 has been provided. We have not only compared the peptide vaccine with other types of vaccines but also presented comprehensive literature information about development steps for an effective and protective formulation to give an insight into on-going peptide vaccine studies against SARS-CoV-2.

Computer-Assisted and Data Driven Approaches for Surveillance, Drug Discovery, and Vaccine Design for the Zika Virus.

Human life has been at the edge of catastrophe for millennia due diseases which emerge and reemerge at random. The recent outbreak of the Zika virus (ZIKV) is one such menace that shook the global public health community abruptly. Modern technologies, including computational tools as well as experimental approaches, need to be harnessed fast and effectively in a coordinated manner in order to properly address such challenges. In this paper, based on our earlier research, we have proposed a four-pronged approach to tackle the emerging pathogens like ZIKV: (a) Epidemiological modelling of spread mechanisms of ZIKV; (b) assessment of the public health risk of newly emerging strains of the pathogens by comparing them with existing strains/pathogens using fast computational sequence comparison methods; (c) implementation of vaccine design methods in order to produce a set of probable peptide vaccine candidates for quick synthesis/production and testing in the laboratory; and (d) designing of novel therapeutic molecules and their laboratory testing as well as validation of new drugs or repurposing of drugs for use against ZIKV. For each of these stages, we provide an extensive review of the technical challenges and current state-of-the-art. Further, we outline the future areas of research and discuss how they can work together to proactively combat ZIKV or future emerging pathogens.

Computational redesign of human respiratory syncytial virus epitope as therapeutic peptide vaccines against pediatric pneumonia.

Human respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infections in infants and young children. Here, the RSV fusion (F) glycoprotein epitope FFL was redesigned based on its complex crystal structure with motavizumab, an mAb drug in development for the prevention of RSV infections, aiming to obtain therapeutic peptide vaccines with high affinity to induce RSV-specific neutralizing antibodies. Computational modeling and analysis found that only a small region covering the helix-turn-helix (HTH) motif of FFL can directly interact with motavizumab and confer stability and specificity to the complex system, while the rest of the epitope primarily serves as a structural scaffold that stabilizes the HTH conformation of motavizumab-binding site. Molecular dynamics simulations revealed a large flexibility and intrinsic disorder for the isolated linear HTH peptide, which would incur a considerable entropy penalty upon binding to motavizumab. In this respect, the FFL epitope was redesigned by truncation, mutation, and cyclization to derive a number of small cyclic peptide immunogens. We also employed in vitro fluorescence-based assays to demonstrate that the linear epitope peptide has no observable affinity to motavizumab, whereas redesigned versions of the peptide can bind with a moderate or high potency. Graphical abstract Computationally modeled complex structure of RSV F glycoprotein with motavizumab and zoom up of the complex binding site.

Preclinical development of peptide vaccination combined with oncolytic MG1-E6E7 for HPV-associated cancer.

Human papilloma virus (HPV)-associated cancer is a significant global health burden and despite the presence of viral transforming antigens within neoplastic cells, therapeutic vaccinations are ineffective for advanced disease. HPV positive TC1 cells are susceptible to viral oncolysis by MG1-E6E7, a custom designed oncolytic Maraba virus. Epitope mapping of mice vaccinated with MG1-E6E7 enabled the rational design of synthetic long peptide (SLP) vaccines against HPV16 and HPV18 antigens. SLPs were able to induce specific CD8+ immune responses and the magnitude of these responses significantly increased when boosted by MG1-E6E7. Logically designed vaccination induced multi-functional CD8+ T cells and provided complete sterilising immunity of mice challenged with TC1 cells. In mice bearing large HPV-positive tumours, SLP vaccination combined with MG1-E6E7 was able to clear tumours in 60% of mice and these mice were completely protected against a long term aggressive re-challenge with the TC1 tumour model. Combining conventional SLPs with the multi-functional oncolytic MG1-E6E7 represents a promising approach against advanced HPV positive neoplasia.

Development of an epitope-based vaccine inhibiting immune cells rolling and migration against atherosclerosis using in silico approaches.

Atherosclerosis is a chronic inflammatory disease characterized by formation of pro-oxidative lipids in large and medium-sized vessels. Over the years, many treatments and drugs have entered the market to improve atherosclerosis and autoantigen-mediated active immunization is currently considered as a beneficial method. Therefore, this study was conducted to design a novel epitope-based vaccine against atherosclerosis employing CD99, CD81 and CD99L2 antigens. In this way, structural vaccinology approaches were used to design a novel multi-epitope vaccine against atherosclerosis. Six epitopes were predicted from CD99, CD81 and CD99L2 proteins. In addition, helper epitopes selected from Tetanus toxin fragment C (TTFrC)ion were applied to induce CD4+ helper T lymphocytes (HTLs) responses. Moreover, cholera toxin B (CTB) was employed as an adjuvant. Finally, EAAAK AND GPGPG sequences as linkers were considered to make a linkage between favorite peptide sequences. A multi-epitope construction was designed based on the predicted epitopes which was 270 residues in length. Further immunoinformatic analyses were carried out to assess physicochemical properties, secondary and tertiary structures, stability, intrinsic protein disorder, solubility, and allergenicity of this chimeric protein. Based on the obtained results, a soluble, and non-allergic protein with a molecular weight of 28.7kDa was designed. Further analyses revealed that the chimeric protein is a stable protein and the predicted epitopes indicated strong potential to induce B-cell and T-cell mediated immune response. Our immunoinformatic analyses revealed that the modeled multi-epitope vaccine had appropriate properties,which can properly stimulate the immune responses of both T and B cells.