Current methods for the prediction of T-cell epitopes

Rational computational design and development of an immunogenic multiepitope vaccine incorporating transmembrane proteins of Staphylococcus lugdunensis

Staphylococcus lugdunensis has emerged as a significant human pathogen, responsible for a range of infections from skin and soft tissue infections to endocarditis and bacteremia. Notably, abscess formation is a common manifestation, reflecting its potential shift from a benign skin commensal to a serious pathogen, akin to infective endocarditis. With the rising prevalence of antibiotic resistance, there is a pressing need for novel therapeutic strategies. This study addresses this need by exploring the development of an effective S. lugdunensis vaccine. Multiepitope vaccines, which incorporate various antigenic fragments from S. lugdunensis proteins, offer a promising approach to elicit a robust immune response. Computational tools are instrumental in selecting epitopes based on their predicted immunogenicity and non-toxicity. Molecular docking and molecular dynamics (MD) simulations further elucidate the interactions between vaccine constructs and immune system molecules, such as B-cell and T-cell receptors, providing detailed insights into binding affinity, specificity, and stability. This study highlights the potential of integrating multiepitope vaccine design with advanced computational methods to expedite and enhance vaccine development, addressing a critical gap amid escalating antibiotic resistance.

FASTMAP-a flexible and scalable immunopeptidomics pipeline for HLA- and antigen-specific T-cell epitope mapping based on artificial antigen-presenting cells

The study of peptide repertoires presented by major histocompatibility complex (MHC) molecules and the identification of potential T-cell epitopes contribute to a multitude of immunopeptidome-based treatment approaches. Epitope mapping is essential for the development of promising epitope-based approaches in vaccination as well as for innovative therapeutics for autoimmune diseases, infectious diseases, and cancer. It also plays a critical role in the immunogenicity assessment of protein therapeutics with regard to safety and efficacy concerns. The main challenge emerges from the highly polymorphic nature of the human leukocyte antigen (HLA) molecules leading to the requirement of a peptide mapping strategy for a single HLA allele. As many autoimmune diseases are linked to at least one specific antigen, we established FASTMAP, an innovative strategy to transiently co-transfect a single HLA allele combined with a disease-specific antigen into a human cell line. This approach allows the specific identification of HLA-bound peptides using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Using FASTMAP, we found a comparable spectrum of endogenous peptides presented by the most frequently expressed HLA alleles in the world's population compared to what has been described in literature. To ensure a reliable peptide mapping workflow, we combined the HLA alleles with well-known human model antigens like coagulation factor VIII, acetylcholine receptor subunit alpha, protein structures of the SARS-CoV-2 virus, and myelin basic protein. Using these model antigens, we have been able to identify a broad range of peptides that are in line with already published and in silico predicted T-cell epitopes of the specific HLA/model antigen combination. The transient co-expression of a single affinity-tagged MHC molecule combined with a disease-specific antigen in a human cell line in our FASTMAP pipeline provides the opportunity to identify potential T-cell epitopes/endogenously processed MHC-bound peptides in a very cost-effective, fast, and customizable system with high-throughput potential.

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.

Riding the wave of innovation: immunoinformatics in fish disease control

The spread of infectious illnesses has been a significant factor restricting aquaculture production. To maximise aquatic animal health, vaccination tactics are very successful and cost-efficient for protecting fish and aquaculture animals against many disease pathogens. However, due to the increasing number of immunological cases and their complexity, it is impossible to manage, analyse, visualise, and interpret such data without the assistance of advanced computational techniques. Hence, the use of immunoinformatics tools is crucial, as they not only facilitate the management of massive amounts of data but also greatly contribute to the creation of fresh hypotheses regarding immune responses. In recent years, advances in biotechnology and immunoinformatics have opened up new research avenues for generating novel vaccines and enhancing existing vaccinations against outbreaks of infectious illnesses, thereby reducing aquaculture losses. This review focuses on understanding in silico epitope-based vaccine design, the creation of multi-epitope vaccines, the molecular interaction of immunogenic vaccines, and the application of immunoinformatics in fish disease based on the frequency of their application and reliable results. It is believed that it can bridge the gap between experimental and computational approaches and reduce the need for experimental research, so that only wet laboratory testing integrated with in silico techniques may yield highly promising results and be useful for the development of vaccines for fish.

Fungal Vaccine Development: State of the Art and Perspectives Using Immunoinformatics

Fungal infections represent a serious global health problem, causing damage to health and the economy on the scale of millions. Although vaccines are the most effective therapeutic approach used to combat infectious agents, at the moment, no fungal vaccine has been approved for use in humans. However, the scientific community has been working hard to overcome this challenge. In this sense, we aim to describe here an update on the development of fungal vaccines and the progress of methodological and experimental immunotherapies against fungal infections. In addition, advances in immunoinformatic tools are described as an important aid by which to overcome the difficulty of achieving success in fungal vaccine development. In silico approaches are great options for the most important and difficult questions regarding the attainment of an efficient fungal vaccine. Here, we suggest how bioinformatic tools could contribute, considering the main challenges, to an effective fungal vaccine.

COVID-19 Vaccines: Computational tools and Development

The 2019 coronavirus outbreak, also known as COVID-19, poses a serious threat to global health and has already had widespread, devastating effects around the world. Scientists have been working tirelessly to develop vaccines to stop the virus from spreading as much as possible, as its cure has not yet been found. As of December 2022, 651,918,402 cases and 6,656,601 deaths had been reported. Globally, over 13 billion doses of vaccine have been administered, representing 64.45% of the world's population that has received the vaccine. To expedite the vaccine development process, computational tools have been utilized. This paper aims to analyze some computational tools that aid vaccine development by presenting positive evidence for proving the efficacy of these vaccines to suppress the spread of the virus and for the use of computational tools in the development of vaccines for emerging diseases.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Computationally-Aided Modeling of Hsp70-Client Interactions: Past, Present, and Future

Hsp70 molecular chaperones play central roles in maintaining a healthy cellular proteome. Hsp70s function by binding to short peptide sequences in incompletely folded client proteins, thus preventing them from misfolding and/or aggregating, and in many cases holding them in a state that is competent for subsequent processes like translocation across membranes. There is considerable interest in predicting the sites where Hsp70s may bind their clients, as the ability to do so sheds light on the cellular functions of the chaperone. In addition, the capacity of the Hsp70 chaperone family to bind to a broad array of clients and to identify accessible sequences that enable discrimination of those that are folded from those that are not fully folded, which is essential to their cellular roles, is a fascinating puzzle in molecular recognition. In this article we discuss efforts to harness computational modeling with input from experimental data to develop a predictive understanding of the promiscuous yet selective binding of Hsp70 molecular chaperones to accessible sequences within their client proteins. We trace how an increasing understanding of the complexities of Hsp70-client interactions has led computational modeling to new underlying assumptions and design features. We describe the trend from purely data-driven analysis toward increased reliance on physics-based modeling that deeply integrates structural information and sequence-based functional data with physics-based binding energies. Notably, new experimental insights are adding to our understanding of the molecular origins of "selective promiscuity" in substrate binding by Hsp70 chaperones and challenging the underlying assumptions and design used in earlier predictive models. Taking the new experimental findings together with exciting progress in computational modeling of protein structures leads us to foresee a bright future for a predictive understanding of selective-yet-promiscuous binding exploited by Hsp70 molecular chaperones; the resulting new insights will also apply to substrate binding by other chaperones and by signaling proteins.

An overview of immunoinformatics approaches and databases linking T cell receptor repertoires to their antigen specificity

Recent advances in molecular and bioinformatic methods have greatly improved our ability to study the formation of an adaptive immune response towards foreign pathogens, self-antigens, and cancer neoantigens. T cell receptors (TCR) are the key players in this process that recognize peptides presented by major histocompatibility complex (MHC). Owing to the huge diversity of both TCR sequence variants and peptides they recognize, accumulation and complex analysis of large amounts of TCR-antigen specificity data is required for understanding the structure and features of adaptive immune responses towards pathogens, vaccines, cancer, as well as autoimmune responses. In the present review, we summarize recent efforts on gathering and interpreting TCR-antigen specificity data and outline the critical role of tighter integration with other immunoinformatics data sources that include epitope MHC restriction, TCR repertoire structure models, and TCR/peptide/MHC structural data. We suggest that such integration can lead to the ability to accurately annotate individual TCR repertoires, efficiently estimate epitope and neoantigen immunogenicity, and ultimately, in silico identify TCRs specific to yet unstudied antigens and predict self-peptides related to autoimmunity.

Simulation-assisted machine learning

MOTIVATION

In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel.

RESULTS

We demonstrate and explore the simulation-based kernel (SimKern) concept using four synthetic complex systems-three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available.

AVAILABILITY AND IMPLEMENTATION

The Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Third generation antibody discovery methods: in silico rational design

Owing to their outstanding performances in molecular recognition, antibodies are extensively used in research and applications in molecular biology, biotechnology and medicine. Recent advances in experimental and computational methods are making it possible to complement well-established in vivo (first generation) and in vitro (second generation) methods of antibody discovery with novel in silico (third generation) approaches. Here we describe the principles of computational antibody design and review the state of the art in this field. We then present Modular, a method that implements the rational design of antibodies in a modular manner, and describe the opportunities offered by this approach.