Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design

The road toward AAV-mediated gene therapy of Duchenne muscular dystrophy

Forty years after the dystrophin gene was cloned, significant progress has been made in developing gene therapy approaches for Duchenne muscular dystrophy (DMD). The disorder has presented numerous challenges, including the enormous size of the gene (2.2 MB), the need to target muscles body wide, and immunogenic issues against both vectors and dystrophin. Among human genetic disorders, DMD is relatively common, and the genetics are complicated since one-third of all cases arise from a spontaneous new mutation, resulting in thousands of independent lesions throughout the locus. Many approaches have been pursued in the goal of finding an effective therapy, including exon skipping, nonsense codon suppression, upregulation of surrogate genes, gene replacement, and gene editing. Here, we focus specifically on methods using AAV vectors, as these approaches have been tested in numerous clinical trials and are able to target muscles systemically. We discuss early advances to understand the structure of dystrophin, which are crucial for the design of effective DMD gene therapies. Included is a summary of efforts to deliver micro-, mini-, and full-length dystrophins to muscles. Finally, we review current approaches to adapt gene editing to the enormous DMD gene with prospects for improved therapies using all these methods.

Design of Mimetic Antibodies Targeting the SARS-CoV-2 Spike Glycoprotein Based on the GB1 Domain: A Molecular Simulation and Experimental Study

In the context of fast and significant technological transformations, it is natural for innovative artificial intelligence (AI) methods to emerge for the design of bioactive molecules. In this study, we demonstrated that the design of mimetic antibodies (MA) can be achieved using a combination of software and algorithms traditionally employed in molecular simulation. This combination, organized as a genetic algorithm (GA), has the potential to address one of the main challenges in the design of bioactive molecules: GA convergence occurs rapidly due to the careful selection of initial populations based on intermolecular interactions at antigenic surfaces. Experimental immunoenzymatic tests prove that the GA successfully optimized the molecular recognition capacity of one of the MA. One of the significant results of this study is the discovery of new structural motifs, which can be designed in an original and innovative way based on the MA structure itself, eliminating the need for preexisting databases. Through the GA developed in this study, we demonstrated the application of a new protocol capable of guiding experimental methods in the development of new bioactive molecules.

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.

RosettaHDX: Predicting antibody-antigen interaction from hydrogen-deuterium exchange mass spectrometry data

High-throughput characterization of antibody-antigen complexes at the atomic level is critical for understanding antibody function and enabling therapeutic development. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) enables rapid epitope mapping, but its data are too sparse for independent structure determination. In this study, we introduce RosettaHDX, a hybrid method that combines computational docking with differential HDX-MS data to enhance the accuracy of antibody-antigen complex models beyond what either method can achieve individually. By incorporating HDX data as both distance restraints and a scoring term in the RosettaDock algorithm, RosettaHDX successfully generated near-native models (interface root-mean square deviation ≤ 4 Å) for all 9 benchmark complexes examined, averaging 3.6 times more near-native models than Rosetta alone. Near-native models among the top 10 scoring were identified in 3/9 cases, compared to 1/9 with Rosetta alone. Additionally, we developed a predictive metric based on docking results with HDX restraints to identify allosteric peptides in HDX datasets.

Rapid restoration of potent neutralization activity against the latest Omicron variant JN.1 via AI rational design and antibody engineering

The rapid evolution of the viral genome has led to the continual generation of new variants of SARS-CoV-2. Developing antibody drugs with broad-spectrum and high efficiency is a long-term task. It is promising but challenging to develop therapeutic neutralizing antibodies (nAbs) through in vitro evolution based on antigen-antibody binding interactions. From an early B cell antibody repertoire, we isolated antibody 8G3 that retains its nonregressive neutralizing activity against Omicron BA.1 and various other strains in vitro. 8G3 protected ACE2 transgenic mice from BA.1 and WA1/2020 virus infection without adverse clinical manifestations and completely cleared viral load in the lungs. Similar to most IGHV3-53 antibodies, the binding sites of 8G3 and ACE2 largely overlap, enabling competition with ACE2 for binding to RBD. By comprehensively considering the binding free energy changes of the antigen-antibody complexes, the biological environment of their interactions, and the evolutionary direction of the antibodies, we were able to select 50 mutants. Among them, 11 were validated by experiments showing better neutralizing activities. Further, a combination of four mutations were identified in 8G3 that increased its neutralization potency against JN.1, the latest Omicron mutant, by approximately 1,500-fold, and one of the mutations led to an improvement in activity against multiple variants to a certain extent. Together, we established a procedure of rapid selection of neutralizing antibodies with potent SARS-CoV-2 neutralization activity. Our results provide a reference for engineering neutralizing antibodies against future SARS-CoV-2 variants and even other pandemic viruses.

Nanobody engineering: computational modelling and design for biomedical and therapeutic applications

Nanobodies, the smallest functional antibody fragment derived from camelid heavy-chain-only antibodies, have emerged as powerful tools for diverse biomedical applications. In this comprehensive review, we discuss the structural characteristics, functional properties, and computational approaches driving the design and optimisation of synthetic nanobodies. We explore their unique antigen-binding domains, highlighting the critical role of complementarity-determining regions in target recognition and specificity. This review further underscores the advantages of nanobodies over conventional antibodies from a biosynthesis perspective, including their small size, stability, and solubility, which make them ideal candidates for economical antigen capture in diagnostics, therapeutics, and biosensing. We discuss the recent advancements in computational methods for nanobody modelling, epitope prediction, and affinity maturation, shedding light on their intricate antigen-binding mechanisms and conformational dynamics. Finally, we examine a direct example of how computational design strategies were implemented for improving a nanobody-based immunosensor, known as a Quenchbody. Through combining experimental findings and computational insights, this review elucidates the transformative impact of nanobodies in biotechnology and biomedical research, offering a roadmap for future advancements and applications in healthcare and diagnostics.

An outlook on structural biology after AlphaFold: tools, limits and perspectives

AlphaFold and similar groundbreaking, AI-based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab-initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. Here, we present the current landscape of structural bioinformatics shaped by AlphaFold, and discuss how the field is dynamically responding to this revolution, with new software, methods, and pipelines. While the excitement around AI-based tools led to their widespread application, it is essential to acknowledge that their practical success hinges on their integration into established protocols within structural bioinformatics, often neglected in the context of AI-driven advancements. Indeed, user-driven intervention is still as pivotal in the structure prediction process as in complementing state-of-the-art algorithms with functional and biological knowledge.

Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy

Messenger RNA (mRNA) vaccines offer an adaptable and scalable platform for cancer immunotherapy, requiring optimal design to elicit a robust and targeted immune response. Recent advancements in bioinformatics and artificial intelligence (AI) have significantly enhanced the design, prediction, and optimization of mRNA vaccines. This paper reviews technologies that streamline mRNA vaccine development, from genomic sequencing to lipid nanoparticle (LNP) formulation. We discuss how accurate predictions of neoantigen structures guide the design of mRNA sequences that effectively target immune and cancer cells. Furthermore, we examine AI-driven approaches that optimize mRNA-LNP formulations, enhancing delivery and stability. These technological innovations not only improve vaccine design but also enhance pharmacokinetics and pharmacodynamics, offering promising avenues for personalized cancer immunotherapy.

NIAID Workshop Report: Systematic Approaches for ESKAPE Bacteria Antigen Discovery

On 14-15 November 2023, the National Institute of Allergy and Infectious Diseases (NIAID) organized a workshop entitled "Systematic Approaches for ESKAPE Bacteria Antigen Discovery". The goal of the workshop was to engage scientists from diverse relevant backgrounds to explore novel technologies that can be harnessed to identify and address current roadblocks impeding advances in antigen and vaccine discoveries for the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species). The workshop consisted of four sessions that addressed ESKAPE infections, antigen discovery and vaccine efforts, and new technologies including systems immunology and vaccinology approaches. Each session was followed by a panel discussion. In total, there were over 260 in-person and virtual attendees, with high levels of engagement. This report provides a summary of the event and highlights challenges and opportunities in the field of ESKAPE vaccine discovery.

Epitope mapping via in vitro deep mutational scanning methods and its applications

Epitope mapping is a technique employed to define the region of an antigen that elicits an immune response, providing crucial insight into the structural architecture of the antigen as well as epitope-paratope interactions. With this breadth of knowledge, immunotherapies, diagnostics, and vaccines are being developed with a rational and data-supported design. Traditional epitope mapping methods are laborious, time-intensive, and often lack the ability to screen proteins in a high-throughput manner or provide high resolution. Deep mutational scanning (DMS), however, is revolutionizing the field as it can screen all possible single amino acid mutations and provide an efficient and high-throughput way to infer the structures of both linear and three-dimensional epitopes with high resolution. Currently, more than 50 publications take this approach to efficiently identify enhancing or escaping mutations, with many then employing this information to rapidly develop broadly neutralizing antibodies, T-cell immunotherapies, vaccine platforms, or diagnostics. We provide a comprehensive review of the approaches to accomplish epitope mapping while also providing a summation of the development of DMS technology and its impactful applications.

Rational structure-guided design of a blood stage malaria vaccine immunogen presenting a single epitope from PfRH5

There is an urgent need for improved malaria vaccine immunogens. Invasion of erythrocytes by Plasmodium falciparum is essential for its life cycle, preceding symptoms of disease and parasite transmission. Antibodies which target PfRH5 are highly effective at preventing erythrocyte invasion and the most potent growth-inhibitory antibodies bind a single epitope. Here we use structure-guided approaches to design a small synthetic immunogen, RH5-34EM which recapitulates this epitope. Structural biology and biophysics demonstrate that RH5-34EM is correctly folded and binds neutralising monoclonal antibodies with nanomolar affinity. In immunised rats, RH5-34EM induces PfRH5-targeting antibodies that inhibit parasite growth. While PfRH5-specific antibodies were induced at a lower concentration by RH5-34EM than by PfRH5, RH5-34EM induced antibodies that were a thousand-fold more growth-inhibitory as a factor of PfRH5-specific antibody concentration. Finally, we show that priming with RH5-34EM and boosting with PfRH5 achieves the best balance between antibody quality and quantity and induces the most effective growth-inhibitory response. This rationally designed vaccine immunogen is now available for use as part of future malaria vaccines, alone or in combination with other immunogens.

Characterization of a Novel Monoclonal Antibody with High Affinity and Specificity against Aflatoxins: A Discovery from Rosetta Antibody-Ligand Computational Simulation

Aflatoxin B1 (AFB1) accumulates in crops, where it poses a threat to human health. To detect AFB1, anti-AFB1 monoclonal antibodies have been developed and are widely used. While the sensitivity and specificity of these antibodies have been extensively studied, information regarding the atomic-level docking of AFB1 (and its derivatives) with these antibodies is limited. Such information is crucial for understanding the key interactions that are required for high affinity and specificity in aflatoxin binding. First, a 3D comparative model of anti-AFB1 antibody (Ab-4B5G6) was predicted from the sequence using RosettaAntibody. We then utilized RosettaLigand to dock AFB1 onto ten homology models, producing a total of 10,000 binding modes. Interestingly, the best-scoring mode predicted strong interactions involving four sites within the heavy chain: ALA33, ASN52, HIS95, and TRP99. Importantly, these strong binding interactions exclusively involve the variable domain of the heavy chain. The best-scoring mode with AFB1 was also obtained through AF multimer combined with RosettaLigand, and two interactions at TRP and HIS were consistent with those found by Rosetta antibody-ligand computational simulation. The role of tryptophan in π interactions in antibodies was confirmed through mutation experiments, and the resulting mutant (W99A) exhibited a >1000-fold reduction in binding affinity for AFB1 and analogs, indicating the effect of tryptophan on the stability of CDR-H3 region. Additionally, we evaluated the binding of two glycolic acid-derived molecular derivatives (with impaired hydrogen bonding potential), and these derivatives (AFB2-GA and AFG2-GA) demonstrated a very weak binding affinity for Ab-4B5G6. The heavy chain was successfully isolated, and its sensitivity and specificity were consistent with those of the intact antibody. The homology models of variable heavy (VH) single-domain antibodies were established by RosettaAntibody, and the docking analysis revealed the same residues, including Ala, His, and Trp. Compared to the potential binding mode of fragment variable (FV) region, the results from a model of VH indicated that there are seven models involved in hydrophobic interaction with TYR32, which is usually referred to as polar amino acid and has both hydrophobic and hydrophilic features depending on the circumstances. Our work encompasses the entire process of Rosetta antibody-ligand computational simulation, highlighting the significance of variable heavy domain structural design in enhancing molecular interactions.

Strategies of rational and structure-driven vaccine design for Arenaviruses

The COVID-19 outbreak has highlighted the importance of pandemic preparedness for the prevention of future health crises. One virus family with high pandemic potential are Arenaviruses, which have been detected almost worldwide, particularly in Africa and the Americas. These viruses are highly understudied and many questions regarding their structure, replication and tropism remain unanswered, making the design of an efficacious and molecularly-defined vaccine challenging. We propose that structure-driven computational vaccine design will contribute to overcome these challenges. Computational methods for stabilization of viral glycoproteins or epitope focusing have made progress during the last decades and particularly during the COVID-19 pandemic, and have proven useful for rational vaccine design and the establishment of novel diagnostic tools. In this review, we summarize gaps in our understanding of Arenavirus molecular biology, highlight challenges in vaccine design and discuss how structure-driven and computationally informed strategies will aid in overcoming these obstacles.

Molecular Modeling Methods in the Development of Affine and Specific Protein-Binding Agents

High-affinity and specific agents are widely applied in various areas, including diagnostics, scientific research, and disease therapy (as drugs and drug delivery systems). It takes significant time to develop them. For this reason, development of high-affinity agents extensively utilizes computer methods at various stages for the analysis and modeling of these molecules. The review describes the main affinity and specific agents, such as monoclonal antibodies and their fragments, antibody mimetics, aptamers, and molecularly imprinted polymers. The methods of their obtaining as well as their main advantages and disadvantages are briefly described, with special attention focused on the molecular modeling methods used for their analysis and development.

An in silico investigation on the binding site preference of PD-1 and PD-L1 for designing antibodies for targeted cancer therapy

Cancer control and treatment remain a significant challenge in cancer therapy and recently immune checkpoints has considered as a novel treatment strategy to develop anti-cancer drugs. Many cancer types use the immune checkpoints and its ligand, PD-1/PD-L1 pathway, to evade detection and destruction by the immune system, which is associated with altered effector function of PD-1 and PD-L1 overexpression on cancer cells to deactivate T cells. In recent years, mAbs have been employed to block immune checkpoints, therefore normalization of the anti-tumor response has enabled the scientists to develop novel biopharmaceuticals. In vivo affinity maturation of antibodies in targeted therapy has sometimes failed, and current experimental methods cannot accommodate the accurate structural details of protein-protein interactions. Therefore, determining favorable binding sites on the protein surface for modulator design of these interactions is a major challenge. In this study, we used the in silico methods to identify favorable binding sites on the PD-1 and PD-L1 and to optimize mAb variants on a large scale. At first, all the binding areas on PD-1 and PD-L1 have been identified. Then, using the RosettaDesign protocol, thousands of antibodies have been generated for 11 different regions on PD-1 and PD-L1 and then the designs with higher stability, affinity, and shape complementarity were selected. Next, molecular dynamics simulations and MM-PBSA analysis were employed to understand the dynamic, structural features of the complexes and measure the binding affinity of the final designs. Our results suggest that binding sites 1, 3 and 6 on PD-1 and binding sites 9 and 11 on PD-L1 can be regarded as the most appropriate sites for the inhibition of PD-1-PD-L1 interaction by the designed antibodies. This study provides comprehensive information regarding the potential binding epitopes on PD-1 which could be considered as hotspots for designing potential biopharmaceuticals. We also showed that mutations in the CDRs regions will rearrange the interaction pattern between the designed antibodies and targets (PD-1 and PD-L1) with improved affinity to effectively inhibit protein-protein interaction and block the immune checkpoint.

[Bioinformatics analysis and validation of the interaction between PML protein and TAB1 protein]

OBJECTIVE

To analyze the interaction between PML protein and TAB1 protein using bioinformatic approaches and experimentally verify the results.

METHODS

Using Rosetta software, a 3D model of TAB1 protein was constructed through a comparative modeling approach; the secondary structure of PML protein was retrieved in the PDB database and its crystal structure and 3D structure were resolved. Zdock 3.0.2 software was used to perform protein-protein docking of PML and TAB1, and the best conformation was extracted for molecular structure analysis of the docking model. The interaction between the two proteins was detected using immunoprecipitation in α-MMC-treated M1 inflammatory macrophages.

RESULTS

When 6IMQ of PML was used as the docking site, PML protein formed 3 salt bridges, 6 hydrogen bonds and 6 hydrophobic interactions with TAB1 proteins; when 5YUF of PML was used as the docking site, PML protein formed 1 hydrogen bond, 3 electrostatic interactions and 9 hydrophobic interactions with TAB1 proteins, and both of the docking modes formed good molecular docking and interactions. In the M1 inflammatory macrophages treated with α-MMC for 4 h, positive protein bands of PML and TAB1 were detected in the cell lysates in PML-IP group.

CONCLUSION

PML protein can interact strongly with TAB1 protein.

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.

Split-Protein Therapeutic Platforms: Identifying Binder Pairs

Therapeutic proteins, including enzymes, interferons, interleukins, and growth factors, are emerging as important modalities to treat many diseases that elude management by small molecule drugs. One challenge of protein treatment is the propensity for off-target or systemic activity. A promising approach to overcome such toxicity is to create conditionally active constructs by splitting the therapeutic protein into two, or more, inactive fragments and by fusing these fragments to binders (e.g., antibodies) that target distinct epitopes on a cell surface. When these antibodies bind to their respective targets, the protein fragments are brought into proximity and then reconstitute into the active form of the therapeutic protein. In this chapter, we describe approaches to determine antibody pairs that enable the reconstitution of the active protein. General computational and empirical methods are provided to facilitate the identification of pairs starting only from protein sequence data.

Simultaneous selection of nanobodies for accessible epitopes on immune cells in the tumor microenvironment

In the rapidly advancing field of synthetic biology, there exists a critical need for technology to discover targeting moieties for therapeutic biologics. Here we present INSPIRE-seq, an approach that utilizes a nanobody library and next-generation sequencing to identify nanobodies selected for complex environments. INSPIRE-seq enables the parallel enrichment of immune cell-binding nanobodies that penetrate the tumor microenvironment. Clone enrichment and specificity vary across immune cell subtypes in the tumor, lymph node, and spleen. INSPIRE-seq identifies a dendritic cell binding clone that binds PHB2. Single-cell RNA sequencing reveals a connection with cDC1s, and immunofluorescence confirms nanobody-PHB2 colocalization along cell membranes. Structural modeling and docking studies assist binding predictions and will guide nanobody selection. In this work, we demonstrate that INSPIRE-seq offers an unbiased approach to examine complex microenvironments and assist in the development of nanobodies, which could serve as active drugs, modified to become drugs, or used as targeting moieties.

Hybrid structural modeling of alloantibody binding to human leukocyte antigen with rapid and reproducible cross-linking mass spectrometry

Alloantibody recognition of donor human leukocyte antigen (HLA) is associated with poor clinical transplantation outcomes. However, the molecular and structural basis for the alloantibody-HLA interaction is not well understood. Here, we used a hybrid structural modeling approach on a previously studied alloantibody-HLA interacting pair with inputs from ab initio, in silico, and in vitro data. Highly reproducible cross-linking mass spectrometry data were obtained with both discovery- and targeted mass spectrometry-based approaches approaches. The cross-link information was then used together with predicted antibody Fv structure, predicted antibody paratope, and in silico-predicted interacting surface to model the antibody-HLA interaction. This hybrid structural modeling approach closely recapitulates the key interacting residues from a previously solved crystal structure of an alloantibody-HLA-A∗11:01 pair. These results suggest that a predictive-based hybrid structural modeling approach supplemented with cross-linking mass spectrometry data can provide functionally relevant structural models to understand the structural basis of antibody-HLA mismatch in transplantation.

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

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Molecular engineering tools for the development of vaccines against infectious diseases: current status and future directions

INTRODUCTION

The escalating global changes have fostered conditions for the expansion and transmission of diverse biological factors, leading to the rise of emerging and reemerging infectious diseases. Complex viral infections, such as COVID-19, influenza, HIV, and Ebola, continue to surface, necessitating the development of effective vaccine technologies.

AREAS COVERED

This review article highlights recent advancements in molecular biology, virology, and genomics that have propelled the design and development of innovative molecular tools. These tools have promoted new vaccine research platforms and directly improved vaccine efficacy. The review summarizes the cutting-edge molecular engineering tools used in creating novel vaccines and explores the rapidly expanding molecular tools landscape and potential directions for future vaccine development.

EXPERT OPINION

The strategic application of advanced molecular engineering tools can address conventional vaccine limitations, enhance the overall efficacy of vaccine products, promote diversification in vaccine platforms, and form the foundation for future vaccine development. Prioritizing safety considerations of these novel molecular tools during vaccine development is crucial.

Untangling immunoglobulin genotype-function associations

Immunoglobulin (IG) genes, encoding B cell receptors (BCRs), are fundamental components of the mammalian immune system, which evolved to recognize the diverse antigenic universe present in nature. To handle these myriad inputs, BCRs are generated through combinatorial recombination of a set of highly polymorphic germline genes, resulting in a vast repertoire of antigen receptors that initiate responses to pathogens and regulate commensals. Following antigen recognition and B cell activation, memory B cells and plasma cells form, allowing for the development of anamnestic antibody (Ab) responses. How inherited variation in IG genes impacts host traits, disease susceptibility, and Ab recall responses is a topic of great interest. Here, we consider approaches to translate emerging knowledge about IG genetic diversity and expressed repertoires to inform our understanding of Ab function in health and disease etiology. As our understanding of IG genetics grows, so will our need for tools to decipher preferences for IG gene or allele usage in different contexts, to better understand antibody responses at the population level.

Accurate Prediction of Enzyme Thermostabilization with Rosetta Using AlphaFold Ensembles

Thermostability enhancement is a fundamental aspect of protein engineering as a biocatalyst's half-life is key for its industrial and biotechnological application, particularly at high temperatures and under harsh conditions. Thermostability changes upon mutation originate from modifications of the free energy of unfolding (ΔG_u), making thermostabilization extremely challenging to predict with computational methods. In this contribution, we combine global conformational sampling with energy prediction using AlphaFold and Rosetta to develop a new computational protocol for the quantitative prediction of thermostability changes upon laboratory evolution of acyltransferase LovD and lipase LipA. We highlight how using an ensemble of protein conformations rather than a single three-dimensional model is mandatory for accurate thermostability predictions. By comparing our approaches with existing ones, we show that ensembles based on AlphaFold models provide more accurate and robust calculated thermostability trends than ensembles based solely on crystallographic structures as the latter introduce a strong distortion (scaffold bias) in computed thermostabilities. Eliminating this bias is critical for computer-guided enzyme design and evaluating the effect of multiple mutations on protein stability.

Single-Cell B-Cell Sequencing to Generate Natively Paired scFab Yeast Surface Display Libraries

The immune cell profiling capabilities of single-cell RNA sequencing (scRNA-seq) are powerful tools that can be applied to the design of theranostic monoclonal antibodies (mAbs). Using scRNA-seq to determine natively paired B-cell receptor (BCR) sequences of immunized mice as a starting point for design, this method outlines a simplified workflow to express single-chain antibody fragments (scFabs) on the surface of yeast for high-throughput characterization and further refinement with directed evolution experiments. While not extensively detailed in this chapter, this method easily accommodates the implementation of a growing body of in silico tools that improve affinity and stability among a range of other developability criteria (e.g., solubility and immunogenicity).

Biothermodynamics of Viruses from Absolute Zero (1950) to Virothermodynamics (2022)

Biothermodynamics of viruses is among the youngest but most rapidly developing scientific disciplines. During the COVID-19 pandemic, it closely followed the results published by molecular biologists. Empirical formulas were published for 50 viruses and thermodynamic properties for multiple viruses and virus variants, including all variants of concern of SARS-CoV-2, SARS-CoV, MERS-CoV, Ebola virus, Vaccinia and Monkeypox virus. A review of the development of biothermodynamics of viruses during the last several decades and intense development during the last 3 years is described in this paper.

Computational Structure Prediction for Antibody-Antigen Complexes From Hydrogen-Deuterium Exchange Mass Spectrometry: Challenges and Outlook

Although computational structure prediction has had great successes in recent years, it regularly fails to predict the interactions of large protein complexes with residue-level accuracy, or even the correct orientation of the protein partners. The performance of computational docking can be notably enhanced by incorporating experimental data from structural biology techniques. A rapid method to probe protein-protein interactions is hydrogen-deuterium exchange mass spectrometry (HDX-MS). HDX-MS has been increasingly used for epitope-mapping of antibodies (Abs) to their respective antigens (Ags) in the past few years. In this paper, we review the current state of HDX-MS in studying protein interactions, specifically Ab-Ag interactions, and how it has been used to inform computational structure prediction calculations. Particularly, we address the limitations of HDX-MS in epitope mapping and techniques and protocols applied to overcome these barriers. Furthermore, we explore computational methods that leverage HDX-MS to aid structure prediction, including the computational simulation of HDX-MS data and the combination of HDX-MS and protein docking. We point out challenges in interpreting and incorporating HDX-MS data into Ab-Ag complex docking and highlight the opportunities they provide to build towards a more optimized hybrid method, allowing for more reliable, high throughput epitope identification.

Epitope-focused immunogen design based on the ebolavirus glycoprotein HR2-MPER region

The three human pathogenic ebolaviruses: Zaire (EBOV), Bundibugyo (BDBV), and Sudan (SUDV) virus, cause severe disease with high fatality rates. Epitopes of ebolavirus glycoprotein (GP) recognized by antibodies with binding breadth for all three ebolaviruses are of major interest for rational vaccine design. In particular, the heptad repeat 2 -membrane-proximal external region (HR2-MPER) epitope is relatively conserved between EBOV, BDBV, and SUDV GP and targeted by human broadly-neutralizing antibodies. To study whether this epitope can serve as an immunogen for the elicitation of broadly-reactive antibody responses, protein design in Rosetta was employed to transplant the HR2-MPER epitope identified from a co-crystal structure with the known broadly-reactive monoclonal antibody (mAb) BDBV223 onto smaller scaffold proteins. From computational analysis, selected immunogen designs were produced as recombinant proteins and functionally validated, leading to the identification of a sterile alpha motif (SAM) domain displaying the BDBV-HR2-MPER epitope near its C terminus as a promising candidate. The immunogen was fused to one component of a self-assembling, two-component nanoparticle and tested for immunogenicity in rabbits. Robust titers of cross-reactive serum antibodies to BDBV and EBOV GPs and moderate titers to SUDV GP were induced following immunization. To confirm the structural composition of the immunogens, solution NMR studies were conducted and revealed structural flexibility in the C-terminal residues of the epitope. Overall, our study represents the first report on an epitope-focused immunogen design based on the structurally challenging BDBV-HR2-MPER epitope.

Charged Residue Implantation Improves the Affinity of a Cross-Reactive Dengue Virus Antibody

Dengue virus (DENV) has four serotypes that complicate vaccine development. Envelope protein domain III (EDIII) of DENV is a promising target for therapeutic antibody development. One EDIII-specific antibody, dubbed 1A1D-2, cross-reacts with DENV 1, 2, and 3 but not 4. To improve the affinity of 1A1D-2, in this study, we analyzed the previously solved structure of 1A1D-2-DENV2 EDIII complex. Mutations were designed, including A54E and Y105R in the heavy chain, with charges complementary to the epitope. Molecular dynamics simulation was then used to validate the formation of predicted salt bridges. Interestingly, a surface plasmon resonance experiment showed that both mutations increased affinities of 1A1D-2 toward EDIII of DENV1, 2, and 3 regardless of their sequence variation. Results also revealed that A54E improved affinities through both a faster association and slower dissociation, whereas Y105R improved affinities through a slower dissociation. Further simulation suggested that the same mutants interacted with different residues in different serotypes. Remarkably, combination of the two mutations additively improved 1A1D-2 affinity by 8, 36, and 13-fold toward DENV1, 2, and 3, respectively. In summary, this study demonstrated the utility of tweaking antibody-antigen charge complementarity for affinity maturation and emphasized the complexity of improving antibody affinity toward multiple antigens.

VHH Structural Modelling Approaches: A Critical Review

VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VHH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VHH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence.

How much metagenome data is needed for protein structure prediction: The advantages of targeted approach from the ecological and evolutionary perspectives

It has been proven that three-dimensional protein structures could be modeled by supplementing homologous sequences with metagenome sequences. Even though a large volume of metagenome data is utilized for such purposes, a significant proportion of proteins remain unsolved. In this review, we focus on identifying ecological and evolutionary patterns in metagenome data, decoding the complicated relationships of these patterns with protein structures, and investigating how these patterns can be effectively used to improve protein structure prediction. First, we proposed the metagenome utilization efficiency and marginal effect model to quantify the divergent distribution of homologous sequences for the protein family. Second, we proposed that the targeted approach effectively identifies homologous sequences from specified biomes compared with the untargeted approach's blind search. Finally, we determined the lower bound for metagenome data required for predicting all the protein structures in the Pfam database and showed that the present metagenome data is insufficient for this purpose. In summary, we discovered ecological and evolutionary patterns in the metagenome data that may be used to predict protein structures effectively. The targeted approach is promising in terms of effectively extracting homologous sequences and predicting protein structures using these patterns.

Human Antibodies for Viral Infections

Antibodies have been used to prevent or treat viral infections since the nineteenth century, but the full potential to use passive immunization for infectious diseases has yet to be realized. The advent of efficient methods for isolating broad and potently neutralizing human monoclonal antibodies is enabling us to develop antibodies with unprecedented activities. The discovery of IgG Fc region modifications that extend antibody half-life in humans to three months or more suggests that antibodies could become the principal tool with which we manage future viral epidemics. Antibodies for members of most virus families that cause severe disease in humans have been isolated, and many of them are in clinical development, an area that has accelerated during the effort to prevent or treat COVID-19 (coronavirus disease 2019). Broad and potently neutralizing antibodies are also important research reagents for identification of protective epitopes that can be engineered into active vaccines through structure-based reverse vaccinology. Expected final online publication date for the Annual Review of Immunology, Volume 40 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Prediction of the RNA Tertiary Structure Based on a Random Sampling Strategy and Parallel Mechanism

Background: Macromolecule structure prediction remains a fundamental challenge of bioinformatics. Over the past several decades, the Rosetta framework has provided solutions to diverse challenges in computational biology. However, it is challenging to model RNA tertiary structures effectively when the de novo modeling of RNA involves solving a well-defined small puzzle. Methods: In this study, we introduce a stepwise Monte Carlo parallelization (SMCP) algorithm for RNA tertiary structure prediction. Millions of conformations were randomly searched using the Monte Carlo algorithm and stepwise ansatz hypothesis, and SMCP uses a parallel mechanism for efficient sampling. Moreover, to achieve better prediction accuracy and completeness, we judged and processed the modeling results. Results: A benchmark of nine single-stranded RNA loops drawn from riboswitches establishes the general ability of the algorithm to model RNA with high accuracy and integrity, including six motifs that cannot be solved by knowledge mining-based modeling algorithms. Experimental results show that the modeling accuracy of the SMCP algorithm is up to 0.14 Å, and the modeling integrity on this benchmark is extremely high. Conclusion: SMCP is an ab initio modeling algorithm that substantially outperforms previous algorithms in the Rosetta framework, especially in improving the accuracy and completeness of the model. It is expected that the work will provide new research ideas for macromolecular structure prediction in the future. In addition, this work will provide theoretical basis for the development of the biomedical field.

Deep mutational scanning for therapeutic antibody engineering

The biophysical and functional properties of monoclonal antibody (mAb) drug candidates are often improved by protein engineering methods to increase the probability of clinical efficacy. One emerging method is deep mutational scanning (DMS) which combines the power of exhaustive protein mutagenesis and functional screening with deep sequencing and bioinformatics. The application of DMS has yielded significant improvements to the affinity, specificity, and stability of several preclinical antibodies alongside novel applications such as introducing multi-specific binding properties. DMS has also been applied directly on target antigens to precisely map antibody-binding epitopes and notably to profile the mutational escape potential of viral targets (e.g., SARS-CoV-2 variants). Finally, DMS combined with machine learning is enabling advances in the computational screening and engineering of therapeutic antibodies.