VirVACPRED: A Web Server for Prediction of Protective Viral Antigens

Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool

Viral proteins that evade the host's innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potential therapeutic strategies. Identifying these proteins through traditional methods is challenging and time-consuming due to the complexity of virus-host interactions. Leveraging advancements in computational biology, we present VirusHound-II, a novel tool that utilizes machine learning techniques to predict viral proteins evading the innate immune response with high accuracy. We evaluated a comprehensive range of machine learning models, including ensemble methods, neural networks, and support vector machines. Using a dataset of 1337 viral proteins known to evade the innate immune response (VPEINRs) and an equal number of non-VPEINRs, we employed pseudo amino acid composition as the molecular descriptor. Our methodology involved a tenfold cross-validation strategy on 80% of the data for training, followed by testing on an independent dataset comprising the remaining 20%. The random forest model demonstrated superior performance metrics, achieving 0.9290 accuracy, 0.9283 F1 score, 0.9354 precision, and 0.9213 sensitivity in the independent testing phase. These results establish VirusHound-II as an advancement in computational virology, accessible via a user-friendly web application. We anticipate that VirusHound-II will be a crucial resource for researchers, enabling the rapid and reliable prediction of viral proteins evading the innate immune response. This tool has the potential to accelerate the identification of therapeutic targets and enhance our understanding of viral evasion mechanisms, contributing to the development of more effective antiviral strategies and advancing our knowledge of virus-host interactions.

Prediction of viral oncoproteins through the combination of generative adversarial networks and machine learning techniques

Viral oncoproteins play crucial roles in transforming normal cells into cancer cells, representing a significant factor in the etiology of various cancers. Traditionally, identifying these oncoproteins is both time-consuming and costly. With advancements in computational biology, bioinformatics tools based on machine learning have emerged as effective methods for predicting biological activities. Here, for the first time, we propose an innovative approach that combines Generative Adversarial Networks (GANs) with supervised learning methods to enhance the accuracy and generalizability of viral oncoprotein prediction. Our methodology evaluated multiple machine learning models, including Random Forest, Multilayer Perceptron, Light Gradient Boosting Machine, eXtreme Gradient Boosting, and Support Vector Machine. In ten-fold cross-validation on our training dataset, the GAN-enhanced Random Forest model demonstrated superior performance metrics: 0.976 accuracy, 0.976 F1 score, 0.977 precision, 0.976 sensitivity, and 1.0 AUC. During independent testing, this model achieved 0.982 accuracy, 0.982 F1 score, 0.982 precision, 0.982 sensitivity, and 1.0 AUC. These results establish our new tool, VirOncoTarget, accessible via a web application. We anticipate that VirOncoTarget will be a valuable resource for researchers, enabling rapid and reliable viral oncoprotein prediction and advancing our understanding of their role in cancer biology.

Exploring the nuclear proteins, viral capsid protein, and early antigen protein using immunoinformatic and molecular modeling approaches to design a vaccine candidate against Epstein Barr virus

The available Epstein Barr virus vaccine has tirelessly harnessed the gp350 glycoprotein as its target epitope, but the result has not been preventive. Right here, we designed a global multi-epitope vaccine for EBV; with special attention to making sure all strains and preventive antigens are covered. Using a robust computational vaccine design approach, our proposed vaccine is armed with 6-16 mers linear B-cell epitopes, 4-9 mer CTL epitopes, and 8-15 mer HTL epitopes which are verified to induce interleukin 4, 10 & IFN-gamma. We employed deep computational mining coupled with expert intelligence in designing the vaccine, using human Beta defensin-3-which has been reported to induce the same TLRs as EBV-as the adjuvant. The tendency of the vaccine to cause autoimmune disorder is quenched by the assurance that the construct contains no EBNA-1 homolog. The protein vaccine construct exhibited excellent physicochemical attributes such as Aliphatic index 59.55 and GRAVY - 0.710; and a ProsaWeb Z score of - 3.04. Further computational analysis revealed the vaccine docked favorably with EBV indicted TLR 1, 2, 4 & 9 with satisfactory interaction patterns. With global coverage of 85.75% and the stable molecular dynamics result obtained for the best two interactions, we are optimistic that our nontoxic, non-allergenic multi-epitope vaccine will help to ameliorate the EBV-associated diseases-which include various malignancies, tumors, and cancers-preventively.

Development and use of machine learning algorithms in vaccine target selection

Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.

VirusHound-I: prediction of viral proteins involved in the evasion of host adaptive immune response using the random forest algorithm and generative adversarial network for data augmentation

Throughout evolution, pathogenic viruses have developed different strategies to evade the response of the adaptive immune system. To carry out successful replication, some pathogenic viruses encode different proteins that manipulate the molecular mechanisms of host cells. Currently, there are different bioinformatics tools for virus research; however, none of them focus on predicting viral proteins that evade the adaptive system. In this work, we have developed a novel tool based on machine and deep learning for predicting this type of viral protein named VirusHound-I. This tool is based on a model developed with the multilayer perceptron algorithm using the dipeptide composition molecular descriptor. In this study, we have also demonstrated the robustness of our strategy for data augmentation of the positive dataset based on generative antagonistic networks. During the 10-fold cross-validation step in the training dataset, the predictive model showed 0.947 accuracy, 0.994 precision, 0.943 F1 score, 0.995 specificity, 0.896 sensitivity, 0.894 kappa, 0.898 Matthew's correlation coefficient and 0.989 AUC. On the other hand, during the testing step, the model showed 0.964 accuracy, 1.0 precision, 0.967 F1 score, 1.0 specificity, 0.936 sensitivity, 0.929 kappa, 0.931 Matthew's correlation coefficient and 1.0 AUC. Taking this model into account, we have developed a tool called VirusHound-I that makes it possible to predict viral proteins that evade the host's adaptive immune system. We believe that VirusHound-I can be very useful in accelerating studies on the molecular mechanisms of evasion of pathogenic viruses, as well as in the discovery of therapeutic targets.

Proteomics-based vaccine targets annotation and design of subunit and mRNA-based vaccines for Monkeypox virus (MPXV) against the recent outbreak

Monkeypox Virus (MPXV) is a growing public health threat with increasing cases and fatalities globally. To date, no specific vaccine or small molecule therapeutic choices are available for the treatment of MPXV disease. In this work, we employed proteomics and structural vaccinology approaches to design mRNA and multi-epitopes-based vaccines (MVC) against MPXV. We first identified ten proteins from the whole proteome of MPXV as potential vaccine targets. We then employed structural vaccinology approaches to map potential epitopes of these proteins for B cell, cytotoxic T lymphocytes (CTL), and Helper T lymphocytes (HTL). Finally, 9 CTL, 6 B cell, and 5 HTL epitopes were joined together through suitable linkers to construct MVC (multi-epitope vaccine) and mRNA-based vaccines. Molecular docking, binding free energy calculation, and in silico cloning revealed robust interaction of the designed MVC with toll-like receptor 2 (TLR2) and efficient expression in E. Coli K12 strain. The immune simulation results revealed that the antigen titer after the injection reached to the maximum level on the 5th day and an abrupt decline in the antigen titer was observed upon the production of IgM, IgG and IgM + IgG, dendritic cells, IFN-gamma, and IL (interleukins), which suggested the potential of our designed vaccine candidate for inducing an immune response against MPXV.

Viral T-cell epitopes - Identification, characterization and clinical application

T-cell immunity, mediated by CD4⁺ and CD8⁺ T cells, represents a cornerstone in the control of viral infections. Virus-derived T-cell epitopes are represented by human leukocyte antigen (HLA)-presented viral peptides on the surface of virus-infected cells. They are the prerequisite for the recognition of infected cells by T cells. Knowledge of viral T-cell epitopes provides on the one hand a diagnostic tool to decipher protective T-cell immune responses in the human population and on the other hand various prophylactic and therapeutic options including vaccination approaches and the transfer of virus-specific T cells. Such approaches have already been proven to be effective against various viral infections, particularly in immunocompromised patients lacking sufficient humoral, antibody-based immune response. This review provides an overview on the state of the art as well as current studies regarding the identification and characterization of viral T-cell epitopes and approaches of clinical application. In the first chapter in silico prediction tools and direct, mass spectrometry-based identification of viral T-cell epitopes is compared. The second chapter provides an overview of commonly used assays for further characterization of T-cell responses and phenotypes. The final chapter presents an overview of clinical application of viral T-cell epitopes with a focus on human immunodeficiency virus (HIV), human cytomegalovirus (HCMV) and severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), being representatives of relevant viruses.

A Genome of Temperate Enterococcus Bacteriophage Placed in a Space of Pooled Viral Dark Matter Sequences

In the human gut, temperate bacteriophages interact with bacteria through predation and horizontal gene transfer. Relying on taxonomic data, metagenomic studies have associated shifts in phage abundance with a number of human diseases. The temperate bacteriophage VEsP-1 with siphovirus morphology was isolated from a sample of river water using Enterococcus faecalis as a host. Starting from the whole genome sequence of VEsP-1, we retrieved related phage genomes in blastp searches of the tail protein and large terminase sequences, and blastn searches of the whole genome sequences, with matches compiled from several different databases, and visualized a part of viral dark matter sequence space. The genome network and phylogenomic analyses resulted in the proposal of a novel genus "Vespunovirus", consisting of temperate, mainly metagenomic phages infecting Enterococcus spp.

Design of Linear B Cell Epitopes and Evaluation of Their Antigenicity, Allergenicity, and Toxicity: An Immunoinformatics Approach

Immunoinformatics is a modern branch of science formed as a result of the intersection between immunology and computer science. One of the important steps in the design of multi-epitope vaccines is the prediction of B cell epitopes. B cell epitopes are of two types, linear and discontinuous. Linear epitope residues lie next to each other in the primary structure of a protein. The amino acids that constitute discontinuous epitopes lie close to each other in the three-dimensional structure of the protein. Recognition of B cell epitopes by antibodies on an antigen constitutes an important event in the immune responses toward the antigenic challenge and also forms the basis for several immunological applications. Prediction of B cell epitopes in an antigen constitutes one of the important steps in the design of multi-epitope-based vaccines. This chapter explains the prediction of linear B cell epitopes in an antigen as well as their allergenicity, antigenicity, and toxicity by using online tools.