Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

Application of in-silico approaches in subunit vaccines: Overcoming the challenges of antigen and adjuvant development

Subunit vaccines are crucial in preventing modern diseases due to their safety, stability, and ability to elicit targeted immune responses. However, challenges in antigen and adjuvant design hinder their development. Recent advancements in in-silico approaches, including reverse vaccinology, structural vaccinology, and machine learning, have revolutionized vaccine development from empirical practices to rational design approaches. This review summarizes the transformative impact of in-silico approaches on subunit vaccine development. We address the challenges of antigen identification and designation, highlighting how advanced computational techniques are employed to accelerate antigen acquisition. We also examine the challenges in adjuvant discovery and illustrate how machine learning helps overcome these barriers. Finally, we explore potential future directions for subunit vaccines, highlighting the importance of combining computational methods with other technologies to tackle the challenges associated with subunit vaccine development.

Probing homogalacturonan in situ: A comprehensive review of available molecular recognition tools

Among the major plant cell wall components, pectic-type homogalacturonan emerges as a chemically and spatiotemporally dynamic matrix-forming agent embedded within the cell wall through various inter- and intramolecular interactions. Its abundance, localization, and chemistry profoundly influence cell wall biomechanics and all facets of plant physiology. Precise tracking of homogalacturonan in a native context is crucial for understanding cell wall organization, particularly the relation between molecular structure and function. It also enables the detailed characterization of plant-based resources for industrial, food, and biomedical applications. This review offers a comprehensive and focused survey of the state-of-the-art molecular recognition tools being employed to visualize homogalacturonan in diverse plant samples. We particularly highlight homogalacturonan-specific monoclonal antibodies, which represent the most used and well-established probes. However, we also discuss less common reagents, such as fluorophores, oligosaccharide-based probes, carbohydrate-binding modules, and whole enzymes, as well as emerging chemical biology approaches exemplified by click chemistry. We critically evaluate their strengths, limitations, and suitability for given research objectives and provide the most notable examples of their usage. Lastly, we outline the anticipated future expansion of an advanced, improved range of new molecular tools, which holds the potential to overcome some of the current experimental hurdles.

Design of immunogens to present a tumor-specific cryptic epitope

In many cancers, the epidermal growth factor receptor (EGFR) gene is amplified, mutated, or both. The monoclonal antibody mAb806 binds selectively to cancer cells that overexpress EGFR or express the truncated mutant EGFRvIII, but not to normal cells. This suggests that a promising avenue for developing cancer vaccines may be to design immunogens that elicit mAb806-like antibodies. In this study, we designed immunogens that present the mAb806-binding epitope in the same conformation as in overexpressed or truncated EGFR. We first used molecular dynamics simulations to identify conformations of EGFR in which the residues of the mAb806-binding epitope are accessible. We then designed immunogens by substituting that epitope in place of a structurally similar loop in a different protein and generating mutants that could potentially stabilize the mAb806-binding conformation in this new context. Two mutants in which the epitope remained stable in subsequent simulations were chosen for evaluation in vitro. Binding kinetics experiments with these designed immunogens provided strong evidence that the epitope was successfully stabilized in the mAb806-binding conformation, suggesting that they could potentially form the basis of vaccines that elicit cancer-selective antibodies.

Antibody immunotherapies for personalized opioid addiction treatment

Approved therapies for managing opioid addiction involve intensive treatment regimens which remain both costly and ineffective. As pharmaceutical interventions have achieved variable success treating substance use disorders (SUD), alternative therapeutics must be considered. Antidrug antibodies induced by vaccination or introduced as monoclonal antibody formulations can neutralize or destroy opioids in circulation before they reach their central nervous system targets or act as enzymes to deactivate opioid receptors, preventing the physiologic and psychoactive effects of the substance. A lack of "reward" for those suffering from SUD has been shown to result in cessation of use and promote long-term abstinence. Decreased antibody production costs and the advent of novel gene therapies that stimulate in vivo production of monoclonal antibodies have renewed interest in this strategy. Furthermore, advances in understanding of SUD immunopathogenesis have revealed distinct mechanisms of neuroimmune dysregulation underlying the disorder. Beyond assisting with cessation of drug use, antibody therapies could treat or reverse pathophysiologic hallmarks that contribute to addiction and which could be the cause of chronic cognitive defects resulting from drug use. In this review, we synthesize key current literature regarding the efficacy of immunotherapies in managing opioid addiction and SUD. We will explore the neuropharmacology underlying these treatments by relating evidence from studies on the use of antibody therapeutics to counteract various drug behaviors and by drawing parallels to the similar immunopathology observed in neurodegenerative disorders. Finally, we will discuss the implications of novel immunization technologies and the application of computational methods in developing personalized addiction treatments. SIGNIFICANCE STATEMENT: Significant new evidence contributing to our understanding of substance use disorders has recently emerged leading to a paradigm shift concerning the role of immunology in the neuropathogenesis of opioid use disorder. Concurrently, immunotherapeutic technologies such as antibody therapeutics have advanced the capabilities regarding applications that take advantage of these key principles. This article reviews key antibody-based treatments being studied and highlights directions for further research that may contribute to the management of opioid use disorder.

Biophysics of SARS-CoV-2 spike protein's receptor-binding domain interaction with ACE2 and neutralizing antibodies: from computation to functional insights

The spike protein encoded by the SARS-CoV-2 has become one of the most studied macromolecules in recent years due to its central role in COVID-19 pathogenesis. The spike protein's receptor-binding domain (RBD) directly interacts with the host-encoded receptor protein, ACE2. This review critically examines computational insights into RBD's interaction with ACE2 and with therapeutic antibodies designed to interfere with this interaction. We begin by summarizing insights from early computational studies on pre-pandemic SARS-CoV-1 RBD interactions and how these early studies shaped the understanding of SARS-CoV-2. Next, we highlight key theoretical contributions that revealed the molecular mechanisms behind the binding affinity of SARS-CoV-2 RBD against ACE2, and the structural changes that have enhanced the infectivity of emerging variants. Special attention is given to the "RBD charge rule", a predictive framework for determining variant infectivity based on the electrostatic properties of the RBD. Towards applying the computational insights to therapy, we discuss a multiscale computational protocol for optimizing monoclonal antibodies to improve binding affinity across multiple spike protein variants, including representatives from the Omicron family. Finally, we explore how these insights can inform the development of future vaccines and therapeutic interventions for combating future coronavirus diseases.

Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools

Antibodies play a crucial role in defending the human body against diseases, including life-threatening conditions like cancer. They mediate immune responses against foreign antigens and, in some cases, self-antigens. Over time, antibody-based technologies have evolved from monoclonal antibodies (mAbs) to chimeric antigen receptor T cells (CAR-T cells), significantly impacting biotechnology, diagnostics, and therapeutics. Although these advancements have enhanced therapeutic interventions, the integration of artificial intelligence (AI) is revolutionizing antibody design and optimization. This review explores recent AI advancements, including large language models (LLMs), diffusion models, and generative AI-based applications, which have transformed antibody discovery by accelerating de novo generation, enhancing immune response precision, and optimizing therapeutic efficacy. Through advanced data analysis, AI enables the prediction and design of antibody sequences, 3D structures, complementarity-determining regions (CDRs), paratopes, epitopes, and antigen-antibody interactions. These AI-powered innovations address longstanding challenges in antibody development, significantly improving speed, specificity, and accuracy in therapeutic design. By integrating computational advancements with biomedical applications, AI is driving next-generation cancer therapies, transforming precision medicine, and enhancing patient outcomes.

Advances in Chicken Infectious Anemia Vaccines

Chicken infectious anemia (CIA) is caused by the CIA virus (CIAV) and is a globally distributed immunosuppressive disease, resulting in substantial economic losses for the poultry industry. Vaccination is the most cost-effective and efficient strategy for preventing and controlling infectious diseases. The most common CIA vaccines used internationally are attenuated vaccines. Although inactivated vaccines, subunit vaccines, immune complex vaccines, recombinant live viral vector vaccines, and DNA vaccines used for preventing CIAV infection have been developed and exhibited relatively satisfactory immune responses, they have not yet achieved large-scale market applications. Therefore, accelerating the introduction of safe and effective CIA vaccines to the market and developing novel vaccines are crucial for the control of CIA in the poultry industry. This article reviews the etiological characteristics of CIAV, the epidemic features, and the research progress of CIA vaccines, and proposes future research directions, with the aim of providing innovative ideas and scientific references for the research and development of new, safe, and efficient CIA vaccines.

Advancements in the conservation of the conformational epitope of membrane protein immunogens

Generating antibodies targeting native membrane proteins presents various challenges because these proteins are often embedded in the lipid bilayer, possess various extracellular and intracellular domains, and undergo post-translational modifications. These properties of MPs make it challenging to preserve their stable native conformations for immunization or antibody generation outside of the membranes. In addition, MPs are often hydrophobic due to their membrane-spanning regions, making them difficult to solubilize and purify in their native form. Therefore, employing purified MPs for immunogen preparation may result in denaturation or the loss of native structure, rendering them inadequate for producing antibodies recognizing native conformations. Despite these obstacles, various new approaches have emerged to address these problems. We outline recent advancements in designing and preparing immunogens to produce antibodies targeting MPs. Strategies outlined here are relevant for producing antibodies for research, diagnostics, and therapies and designing immunogens for vaccination purposes.

Advancing vaccine development in genomic era: a paradigm shift in vaccine discovery

The issue of antibiotic resistance is increasing with time because of the quick rise of microbial strains. Overuse of antibiotics has led to multidrug-resistant, pan-drug-resistant, and extensively drug-resistant bacterial strains, which have worsened the situation. Different techniques have been considered and applied to combat this issue, such as developing new antibiotics, practicing antibiotic stewardship, improving hygiene levels, and controlling antibiotic overuse. Vaccine development made a substantial contribution to overcoming this issue, although it has been underestimated. In the recent era, reverse vaccinology has contributed to developing different kinds of vaccines against pathogens, revolutionizing the vaccine development process. Reverse vaccinology helps to prioritize better vaccine candidates by using various tools to filter the pathogen's complete genome. In this review, we will shed light on computational vaccine designing, immunoinformatic tools, genomic and proteomic data, and the challenges and success stories of computational vaccine designing.

Predicting Antibody Affinity Changes upon Mutation Based on Unbound Protein Structures

Antibodies are key proteins in the immune system that can reversibly and non-covalently bind specifically to their corresponding antigens, forming antigen-antibody complexes. They play a crucial role in recognizing foreign or self-antigens during the adaptive immune response. Monoclonal antibodies have emerged as a promising class of biological macromolecule therapeutics with broad market prospects. In the process of antibody drug development, a key engineering challenge is to improve the affinity of candidate antibodies, without experimentally resolved structures of the antigen-antibody complexes as input for computer-aided predictive methods. In this work, we present an approach for predicting the effect of residue mutations on antibody affinity without the structures of the antigen-antibody complexes. The method involves the graph representation of proteins and utilizes a pre-trained encoder. The encoder captures the residue-level microenvironment of the target residue on the antibody along with the antigen context pre- and post-mutation. The encoder inherently possesses the potential to identify paratope residues. In addition, we curated a benchmark dataset specifically for mutations of the antibody. Compared to baseline methods based on complex structures and sequences, our approach achieves superior or comparable average accuracy on benchmark datasets. Additionally, we validate its advantage of not requiring antigen-antibody complex structures as input for predicting the effects of mutations in antibodies against SARS-CoV-2, influenza, and human cytomegalovirus. Our method shows its potential for identifying mutations that improve antibody affinity in practical antibody engineering applications.

Identification and Validation of B-Cell Epitopes on the VP1 Protein of Parvovirus B19 through Molecular Docking and Dynamics Simulation

This study aimed to identify B-cell epitope candidates using multiple epitope identification software and in silico analysis of the modeled B19 V protein against specific antibodies using molecular docking and dynamics simulation. Materials and Methods : Full-length amino acid sequences of the VP1 protein of B19 V were retrieved from NCBI. A consensus sequence was generated using CLC sequence viewer. Linear B cell epitopes were identified using Bepipred 2.0, ABCpred, and LBTope. The linear epitope was synthesized and validated against B19 V-specific antibodies. A 3D model of the B19 V VP1 consensus protein was generated using the ITASSER server. Discontinuous B cell epitopes were identified using Discotope 2.0 and Ellipro. Molecular docking and molecular dynamics simulation was performed to investigate the interaction between the modeled B19 V protein and specific anti-B19 V antibody. Results : The identified epitope was 100% conserved and similarly identified through ABCpred and LBTope. The HADDOCK score and MDS analysis, such as hydrogen bond interactions and MMPBSA analysis, revealed that the VP1 and mAb H chains formed a significantly stable complex. The MDS demonstrated that the VP1-mAb H chain complexes had lower RMSF values around 130 to 200 residues, a region responsible for the catalytic network for enzyme activity; as a result, the flexibility of the antibody-bound VP1 decreased when compared to Apo-VP1. Conclusion: A viable epitope identified through this process was synthesized and validated using ELISA, which highlighted the role of the epitope identification process in diagnostics. This study also sheds light on the complex interplay between VP1 and the mAb H chain and highlights key binding specificity and stability determinants.

PAbFold: Linear Antibody Epitope Prediction using AlphaFold2

Defining the binding epitopes of antibodies is essential for understanding how they bind to their antigens and perform their molecular functions. However, while determining linear epitopes of monoclonal antibodies can be accomplished utilizing well-established empirical procedures, these approaches are generally labor- and time-intensive and costly. To take advantage of the recent advances in protein structure prediction algorithms available to the scientific community, we developed a calculation pipeline based on the localColabFold implementation of AlphaFold2 that can predict linear antibody epitopes by predicting the structure of the complex between antibody heavy and light chains and target peptide sequences derived from antigens. We found that this AlphaFold2 pipeline, which we call PAbFold, was able to accurately flag known epitope sequences for several well-known antibody targets (HA / Myc) when the target sequence was broken into small overlapping linear peptides and antibody complementarity determining regions (CDRs) were grafted onto several different antibody framework regions in the single-chain antibody fragment (scFv) format. To determine if this pipeline was able to identify the epitope of a novel antibody with no structural information publicly available, we determined the epitope of a novel anti-SARS-CoV-2 nucleocapsid targeted antibody using our method and then experimentally validated our computational results using peptide competition ELISA assays. These results indicate that the AlphaFold2-based PAbFold pipeline we developed is capable of accurately identifying linear antibody epitopes in a short time using just antibody and target protein sequences. This emergent capability of the method is sensitive to methodological details such as peptide length, AlphaFold2 neural network versions, and multiple-sequence alignment database. PAbFold is available at https://github.com/jbderoo/PAbFold.

Revisiting the dimensions of universal vaccine with special focus on COVID-19: Efficacy versus methods of designing

Even though the use of SARS-CoV-2 vaccines during the COVID-19 pandemic showed unprecedented success in a short time, it also exposed a flaw in the current vaccine design strategy to offer broad protection against emerging variants of concern. However, developing broad-spectrum vaccines is still a challenge for immunologists. The development of universal vaccines against emerging pathogens and their variants appears to be a practical solution to mitigate the economic and physical effects of the pandemic on society. Very few reports are available to explain the basic concept of universal vaccine design and development. This review provides an overview of the innate and adaptive immune responses generated against vaccination and essential insight into immune mechanisms helpful in designing universal vaccines targeting influenza viruses and coronaviruses. In addition, the characteristics, safety, and factors affecting the efficacy of universal vaccines have been discussed. Furthermore, several advancements in methods worthy of designing universal vaccines are described, including chimeric immunogens, heterologous prime-boost vaccines, reverse vaccinology, structure-based antigen design, pan-reactive antibody vaccines, conserved neutralizing epitope-based vaccines, mosaic nanoparticle-based vaccines, etc. In addition to the several advantages, significant potential constraints, such as defocusing the immune response and subdominance, are also discussed.

Design of Cryptococcus neoformans multi-epitope vaccine based on immunoinformatics method

Cryptococcus neoformans is a widely distributed opportunistic pathogenic fungus. While C. neoformans commonly infects immunocompromised individuals, it can also affect those who are immunocompetent. Transmission of C. neoformans primarily occurs through the respiratory tract, leading to the development of meningitis. The mortality rate of Cryptococcal meningitis is high, and treatment options are limited. Cryptococcus neoformans infections pose a significant public health threat and currently lack targeted and effective response strategies. This study aimed to screen T lymphocyte (cytotoxic T lymphocyte and helper T lymphocyte) and B lymphocyte epitopes derived from four C. neoformans antigens and develop two multi-epitope vaccines by combining them with various adjuvants. Molecular docking results demonstrated that the vaccines bind stably to Toll-like receptor 4 ( and induce innate immunity. The credibility of the molecular docking results was validated through subsequent molecular dynamics simulations. Furthermore, the results of immune simulation analyses underscored the multi-epitope vaccine's capability to effectively induce robust humoral and cellular immune responses within the host organism. These two vaccines have demonstrated theoretical efficacy against C. neoformans infection as indicated by computer analysis. Nevertheless, additional experimental validation is essential to substantiate the protective efficacy of the vaccines.

A new era of antibody discovery: an in-depth review of AI-driven approaches

Given their high affinity and specificity for a range of macromolecules, antibodies are widely used in the treatment of autoimmune diseases, cancers, inflammatory diseases, and Alzheimer's disease (AD). Traditional experimental methods are time-consuming, expensive, and labor-intensive. Recent advances in artificial intelligence (AI) technologies provide complementary methods that can reduce the time and costs required for antibody design by minimizing failures and increasing the success rate of experimental tests. In this review, we scrutinize the plethora of AI-driven methodologies that have been deployed over the past 4 years for modeling antibody structures, predicting antibody-antigen interactions, optimizing antibody affinity, and generating novel antibody candidates. We also briefly address the challenges faced in integrating AI-based models with traditional antibody discovery pipelines and highlight the potential future directions in this burgeoning field.

AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences

The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods have been actively developed to accelerate and improve the development of therapeutic antibodies. In this study, we developed an end-to-end sequence-based deep learning model, termed AttABseq, for the predictions of the antigen-antibody binding affinity changes connected with antibody mutations. AttABseq is a highly efficient and generic attention-based model by utilizing diverse antigen-antibody complex sequences as the input to predict the binding affinity changes of residue mutations. The assessment on the three benchmark datasets illustrates that AttABseq is 120% more accurate than other sequence-based models in terms of the Pearson correlation coefficient between the predicted and experimental binding affinity changes. Moreover, AttABseq also either outperforms or competes favorably with the structure-based approaches. Furthermore, AttABseq consistently demonstrates robust predictive capabilities across a diverse array of conditions, underscoring its remarkable capacity for generalization across a wide spectrum of antigen-antibody complexes. It imposes no constraints on the quantity of altered residues, rendering it particularly applicable in scenarios where crystallographic structures remain unavailable. The attention-based interpretability analysis indicates that the causal effects of point mutations on antibody-antigen binding affinity changes can be visualized at the residue level, which might assist automated antibody sequence optimization. We believe that AttABseq provides a fiercely competitive answer to therapeutic antibody optimization.

Utilizing Extraepitopic Amino Acid Substitutions to Define Changes in the Accessibility of Conformational Epitopes of the Bacillus cereus HlyII C-Terminal Domain

Hemolysin II (HlyII)-one of the pathogenic factors of Bacillus cereus, a pore-forming β-barrel toxin-possesses a C-terminal extension of 94 amino acid residues, designated as the C-terminal domain of HlyII (HlyIICTD), which plays an important role in the functioning of the toxin. Our previous work described a monoclonal antibody (HlyIIC-20), capable of strain-specific inhibition of hemolysis caused by HlyII, and demonstrated the dependence of the efficiency of hemolysis on the presence of proline at position 324 in HlyII outside the conformational antigenic determinant. In this work, we studied 16 mutant forms of HlyIICTD. Each of the mutations, obtained via multiple site-directed mutagenesis leading to the replacement of amino acid residues lying on the surface of the 3D structure of HlyIICTD, led to a decrease in the interaction of HlyIIC-20 with the mutant form of the protein. Changes in epitope structure confirm the high conformational mobility of HlyIICTD required for the functioning of HlyII. Comparison of the effect of the introduced mutations on the effectiveness of interactions between HlyIICTD and HlyIIC-20 and a control antibody recognizing a non-overlapping epitope enabled the identification of the amino acid residues N339 and K340, included in the conformational antigenic determinant recognized by HlyIIC-20.